in the interior of Forest Reserves are unlikely to see this change in boundary, and as
a consequence their permitted land titles are unlikely to change.
The panel dataset for this study is thus divided into two types of villages: The
first group of villages is a group of 257 villages that has ‘ambiguous property rights’
over the duration of the study period, caused in large part by changing forest
legislation and by changing forest reserve boundaries. These villages witnessed
frequent changes and had ambiguous property rights or APR villages and constitute
38 % of the study sample. The second group consists of villages that claim to have
no secure rights consistently and are likely to be located deep inside Forest
Reserves, where changing Forest boundaries create no ambiguity. The latter
group of villages are called ‘no secure property rights’ villages NPR villages.
Two other features of the survey are that (i) village headmen provide responses
to questions and, (ii) the biennially conducted survey records modal values of
variables. Data are collected via questions such as: ‘What is the mode of transport
most (popularly) used by households in the village?’ or, ‘What is the method of sale
for most households?’ “What is the most popularly grown short run (long run) crop
this year?” For crops other than paddy rice, crop area, the number of households
growing the crop and other attributes are recorded only for the short-run or long-run
crop that is ‘most popular’. This means no crop is tracked for all years, other than
paddy rice, unless that crop is ‘popular’ every year.
19 Furthermore, crops are
tracked in groups i.e. ‘short run crops’ or ‘long run crops’. Other challenges with
the data including absence of price data and agricultural practices are discussed and
addressed in Puri (2006). Main characteristics about villages included in this dataset
are presented in Tables 8.4 and 8.5.
8.4 Characteristics of Data and Hypothesized Effects
In this section I discuss the hypothesized effects that different village level attributes are expected to have on two main agricultural variables: on agricultural area
within a village and on average intensity of cultivation within a village.
The intensity of cultivation variable requires a brief discussion. Boserup (1965)
in her classic exposition of factors governing agricultural expansion in developing
countries, especially in Asia, defined agricultural intensification as “. . .the gradual
change towards patterns of land use which make it possible to crop a given area of
land more frequently than before.” (pp. 43). In this definition she thus departed
from the definition of intensification that measured increased use of inputs per
hectare of cropped area. In this study, I use this Boserup measure to understand
intensity of cultivation: Intensity of cultivation is measured by a variable that is the
response to the question “What percentage of agricultural land is being used ( for
19 Village headmen are also asked questions about “the second most important short (long) run
crop” and the “third most important short (long) run crop”. Data on these is scarcer.
8 Using Mixed Methods to Assess Trade-Offs Between Agricultural Decisions. . .
139
a consequence their permitted land titles are unlikely to change.
The panel dataset for this study is thus divided into two types of villages: The
first group of villages is a group of 257 villages that has ‘ambiguous property rights’
over the duration of the study period, caused in large part by changing forest
legislation and by changing forest reserve boundaries. These villages witnessed
frequent changes and had ambiguous property rights or APR villages and constitute
38 % of the study sample. The second group consists of villages that claim to have
no secure rights consistently and are likely to be located deep inside Forest
Reserves, where changing Forest boundaries create no ambiguity. The latter
group of villages are called ‘no secure property rights’ villages NPR villages.
Two other features of the survey are that (i) village headmen provide responses
to questions and, (ii) the biennially conducted survey records modal values of
variables. Data are collected via questions such as: ‘What is the mode of transport
most (popularly) used by households in the village?’ or, ‘What is the method of sale
for most households?’ “What is the most popularly grown short run (long run) crop
this year?” For crops other than paddy rice, crop area, the number of households
growing the crop and other attributes are recorded only for the short-run or long-run
crop that is ‘most popular’. This means no crop is tracked for all years, other than
paddy rice, unless that crop is ‘popular’ every year.
19 Furthermore, crops are
tracked in groups i.e. ‘short run crops’ or ‘long run crops’. Other challenges with
the data including absence of price data and agricultural practices are discussed and
addressed in Puri (2006). Main characteristics about villages included in this dataset
are presented in Tables 8.4 and 8.5.
8.4 Characteristics of Data and Hypothesized Effects
In this section I discuss the hypothesized effects that different village level attributes are expected to have on two main agricultural variables: on agricultural area
within a village and on average intensity of cultivation within a village.
The intensity of cultivation variable requires a brief discussion. Boserup (1965)
in her classic exposition of factors governing agricultural expansion in developing
countries, especially in Asia, defined agricultural intensification as “. . .the gradual
change towards patterns of land use which make it possible to crop a given area of
land more frequently than before.” (pp. 43). In this definition she thus departed
from the definition of intensification that measured increased use of inputs per
hectare of cropped area. In this study, I use this Boserup measure to understand
intensity of cultivation: Intensity of cultivation is measured by a variable that is the
response to the question “What percentage of agricultural land is being used ( for
19 Village headmen are also asked questions about “the second most important short (long) run
crop” and the “third most important short (long) run crop”. Data on these is scarcer.
8 Using Mixed Methods to Assess Trade-Offs Between Agricultural Decisions. . .
139