negatively impacted, there is a tendency to move resources to nonagricultural land
uses; this may explain the positive impact of extreme temperatures on nonagricultural output.
Human capital (measured by literacy rate for 2001) has the expected positive
impact on the district’s output. A one percentage point increase in the literacy rate
of the district increases its non-primary output per capita by `60,630. This is reasonable to expect. Human resources with specific skills are required to utilize
natural resources to produce output, both agricultural and nonagricultural.
Finally, the ratio of manufacturing to services employment has a negative impact
on nonagricultural output, demonstrating the silent services revolution that has
taken over India’s cities.
A specification reported in Table 2 excludes rainfall, since there was limited
number of observations for this variable. Hence, removal of rainfall explains the
larger sample size for the specification. This specification continues to exhibit the
same robust signs for most variables as in the model with rainfall, except the human
capital variable which becomes insignificant. The impact of average temperature
differences on nonagricultural income becomes somewhat decreased, once rainfall
is removed. The one variable which now has the expected impact on nonagricultural output is the road length per 1000 population which has a positive impact on
nonagricultural output per capita. This is highly likely since roads represent access
to markets, jobs, and public services; hence, they facilitate greatly the production of
goods and services. Specifically, for every 1 km of extra (both pucca and katcha)
road per 1000 population, there is increase in the non-primary output to the extent
of nearly Rs. 2336 per capita.
A considerable number of studies discuss the impact of climate change in coastal
or riverine locations). As discussed earlier, the impacts of climate change are
usually more pronounced in coastal than they are in inland areas. In coastal
districts/cities, the land available for productive economic activity (such as buildings used to house manufacturing or service firms) would be limited compared to
that for inland districts. Hence, while coastal locations would lead to lower agricultural output, we nonetheless estimate the impact of coastal locations on nonagricultural output. For this reason, Table 3 summarizes the impact of climate change
on district output per capita, by including a dummy for coastal districts of the
country.
We estimate two specifications when we control for the effects of a district’s
coastal location, as before, one with rainfall included, and a second specification
with rainfall excluded, to benefit from the larger number of observations that would
result. In the first specification, with rainfall included, we find that the human
capital measured by the literacy rate has a positive impact on nonagricultural
output, which is highly plausible to expect, since human capital is the key factor
which contributes to increases in output. As in the earlier case, average temperature
differences (the difference between the mean maximum and mean minimum temperature) have a positive effect on increasing nonagricultural output, presumably
Economic Impacts of Climate Change in India’s Cities
289
uses; this may explain the positive impact of extreme temperatures on nonagricultural output.
Human capital (measured by literacy rate for 2001) has the expected positive
impact on the district’s output. A one percentage point increase in the literacy rate
of the district increases its non-primary output per capita by `60,630. This is reasonable to expect. Human resources with specific skills are required to utilize
natural resources to produce output, both agricultural and nonagricultural.
Finally, the ratio of manufacturing to services employment has a negative impact
on nonagricultural output, demonstrating the silent services revolution that has
taken over India’s cities.
A specification reported in Table 2 excludes rainfall, since there was limited
number of observations for this variable. Hence, removal of rainfall explains the
larger sample size for the specification. This specification continues to exhibit the
same robust signs for most variables as in the model with rainfall, except the human
capital variable which becomes insignificant. The impact of average temperature
differences on nonagricultural income becomes somewhat decreased, once rainfall
is removed. The one variable which now has the expected impact on nonagricultural output is the road length per 1000 population which has a positive impact on
nonagricultural output per capita. This is highly likely since roads represent access
to markets, jobs, and public services; hence, they facilitate greatly the production of
goods and services. Specifically, for every 1 km of extra (both pucca and katcha)
road per 1000 population, there is increase in the non-primary output to the extent
of nearly Rs. 2336 per capita.
A considerable number of studies discuss the impact of climate change in coastal
or riverine locations). As discussed earlier, the impacts of climate change are
usually more pronounced in coastal than they are in inland areas. In coastal
districts/cities, the land available for productive economic activity (such as buildings used to house manufacturing or service firms) would be limited compared to
that for inland districts. Hence, while coastal locations would lead to lower agricultural output, we nonetheless estimate the impact of coastal locations on nonagricultural output. For this reason, Table 3 summarizes the impact of climate change
on district output per capita, by including a dummy for coastal districts of the
country.
We estimate two specifications when we control for the effects of a district’s
coastal location, as before, one with rainfall included, and a second specification
with rainfall excluded, to benefit from the larger number of observations that would
result. In the first specification, with rainfall included, we find that the human
capital measured by the literacy rate has a positive impact on nonagricultural
output, which is highly plausible to expect, since human capital is the key factor
which contributes to increases in output. As in the earlier case, average temperature
differences (the difference between the mean maximum and mean minimum temperature) have a positive effect on increasing nonagricultural output, presumably
Economic Impacts of Climate Change in India’s Cities
289