owned, a substantial number of the villages have common lands governed by the local village ..... where fl is the marginal product of forest labor and so forth.
Population Growth, Income Growth and Deforestation: Management of Village Common Land in India
April 2002
Andrew D. Foster Brown University Mark R. Rosenzweig University of Pennsylvania Jere R. Behrman University of Pennsylvania
The research reported in this paper was supported in part by grants NIH HD33563 and NIH HD30907. We are grateful to Nauman Ilias and Joost Delaat for able research assistance.
I. Introduction Increasing concern about the phenomena of global warming and declining bio diversity has led to an increase in attention to the disappearance of the world’s forests, particularly in the developing world. Forests cover about 40% of land in tropical nations, and deforestation is especially acute in Asia. Between 10 and 20 million hectares of tropical forest are cleared each year, with annual rates of deforestation between 1980 and 1990 averaging 1.2% in Asia, compared with 0.9% in the developing world (Ehui and Hertel 1989; World Bank 1992). At this rate, an additional 50 million hectares, or about 16 per cent of Asia's remaining tropical forests, will be lost by the year 2000. The Asian Development Bank (ADB) claims that "there are four major environmental problems ... arising from population pressures, lack of development, and the development process itself" and three of these four problems in Asia - "land degradation and depletion of natural resources; ...pollution of soil, water and air; and consequences of global warming due to excessive discharge of green house gases to the atmosphere" - are intimately associated with deforestation (Jalal 1993, p. 2). The ADB further claims that India has the most severe and diverse environmental problems in the region - in part because of the enormous numbers of poor people residing in rural areas1 . While a variety of global economic forces affect the trends in deforestation in developing countries, recent research has emphasized the importance of local-level processes such as agricultural encroachment and product extraction through firewood collection and animal grazing that are importantly influenced by the fact that forest resources in most developing countries are not privately owned (e.g., DasGupta 1995, Loughran and Pritchett 1997, Filmer and Pritchett, 1996, Agarwal and Yadama 1997,
1
According to World Bank 1990 estimates, 30% of the poor in the world, defined on the basis of a common poverty line, live in rural India. 1
Jodha 1985, Ligon and Narain forthcoming, López 1997).2 The nonprivate ownership of forests lands, as well as of grazing and waste lands, has raised questions of whether there historically has been and/or currently is a "tragedy of the commons" that characterizes forest operations. Several researchers, most notably Jodha (whose empirical work has been largely on India), have investigated whether, in the absence of regulatory institutions, rapid population growth actually leads to degenerative patterns of use and the gradual depletion of common property resources (CPRs) (Jodha 1985, 1990; Repetto and Homes 1984). Although as popularly conceived depletion of such resources is a straightforward consequence of rapid population growth, these studies suggest that traditionally many CPRs have been well-managed by local institutions so that historically the effects of rapid population growth have been, in Jodha's (1985, p. 247) words, "mediated by institutional factors and often overshadowed by pressures arising from changing market conditions." Indeed, the Joint Forest Management initiative in India introduced in the 1980s, which gave villages increased claim to revenues from timber harvests, was predicated on the notion that, given proper incentives, villages could effectively manage common forest resources. (Kumar et al. 2000). Considerably less attention has been given to the related question of how income growth in general, and technical change in agriculture in particular, has impacted forest resources and how these are mediated by management regimes. One important barrier to improved understanding of the technological, behavioral and organizational forces affecting global deforestation has been the absence of adequate data. Many empirical studies of land and forest use are based on case studies over short time periods that do not permit identification of the longer-term consequences of population change or technical progress and cannot easily be generalized. Other studies have used cross-country data (e.g., Cropper and Griffiths (1994)) to examine the relationship between economic growth, population change and deforestation. But
2
In India, for example, Bowonder (1982) reports that 96% of the total forest areas is owned by the government. 2
these studies do not characterize common-property governance regimes and thus cannot be informative about whether there are local populations externalities present due to an inefficient local landmanagement regime. In this paper, we utilize a newly-assembled data set that combines at the village level longitudinal household survey data and satellite images of land use that cover a wide area of rural India over a 29-year period to address limitations to knowledge about how agricultural technical change, rural industrialization and population growth affect land management in general and the management of forest exploitation in particular.3 A key feature of the data is that while in most villages all land is privately owned, a substantial number of the villages have common lands governed by the local village council (panchayat). We use the data to address the question of whether forest areas are first-best locally managed in areas in which community management of lands is present. To clarify and to assess the role that local land management plays in determining the rate of deforestation in the face of changing incomes due to agricultural productivity change and rural industrialization as well as the effects of population pressures, we develop a three-sector generalequilibrium representative agent model. The model distinguishes between the effects of population growth on forest area and density that operate through the influence of wages, land rents, and income and those that operate net of these economic or "pecuniary" effects. An important implication of the model is that the proposition that forest area is first-best efficiently managed, as would be the case given complete markets for land and labor, imposes testable restrictions on the effects of population change on forest area net of its effects on equilibrium wages, land rents and incomes and of household size. We establish that these tests have power against the alternative hypothesis that forest labor is unmonitorable but forest
3
Foster and Rosenzweig (2002) also uses these data to examine alternative explanations for increases in forest cover in India over the last three decades but does not develop a formal model of forest land allocation nor consider, either theoretically or empirically, how the effects of economic change on forest resources is mediated by common ownership of forest land. 3
land allocations are second-best efficient given this limitation. India is a particularly interesting and useful setting in which to study the determinants of deforestation. Over 20 percent of all land in India is designated as forest land, 96 percent of which is publicly managed common land. Moreover, India is a country that has experienced rapid population growth and since the late 1960's has been a major beneficiary of the “green revolution,” which has substantially augmented crop productivity growth. Large-scale studies of the effects of agricultural productivity growth or population change on forests in India have been limited, however, by the fact that governmental data sources provide information only on designated forest lands, including newlydesignated land for forests on which tree planting may have only been recently initiated rather than on actual tree coverage or forest biomass.4 Information on actual land use, based on satellite images, is thus necessary to evaluate the effects on actual forest biomass of population trends and the agricultural productivity increases. Figure 1 plots the (i) proportion of land in India designated by the government as forest land for each Census year from 1951 through 1991 and for 1999 and (ii) our satellite-based estimates of the proportion of land of land with trees surrounding the sample villages in our nationally-representative survey data for the same years starting in 1971 when satellite data first became available.5 Contrary to popular belief, the government statistics (Anon 1997) indicate that the amount of land set aside for forests has increased in India since 1951, increasing from 12.3% in 1951 to over 23% in the 1990's. Moreover, our data, based on satellite imagery for our national sample of Indian villages, indicate that the increase in set-asides for forest land has been accompanied, with a lag, by increases in forests - the estimated proportion of land covered by forests increased from just over 10% in 1971 to over 24% in
4
Cropper and Griffiths (1994) were unable to estimate any significant relationships for Asia or South Asia using official government statistics on forests. 5
The earliest images are actually for 1972. The construction of the measures of forest growth are discussed below. 4
1999. During the same 29-year period that forests surrounding our sample villages have experienced growth on average, the average population size of the villages has increased by approximately 91% and yields of the hybrid-seeds associated with the green revolution have almost tripled. Moreover, as documented below, over the same period the villages have electrified, access roads have substantially improved and factories have been set up in almost all villages. This does not necessarily imply that rural economic development and population growth have not adversely affected forests. These aggregate trends mask important differences in the experiences of Indian villages. For example, 41% of our sample villages experienced a decline in forests between 1971 and 1999. To identify the roles of population change and rural development and to assess the efficiency of local land management thus requires the comparison of the changes in forests across villages over time with their changes in population size, agricultural productivity and rural infrastructure. Section II of the paper describes the theoretical framework that guides the empirical analysis, highlighting the mechanisms by which technical change in agriculture, rural industrialization and population growth affect forest area and use and contrasting perfect-markets and unmonitorable labor regimes to consider the effects of forest-management institutions on these relationships. Section III describes the data sources and the construction of the variables. Section IV presents the reduced-form estimates and Section V the structural estimates. These results indicate that the effect of income growth on forests is importantly conditioned by the specific mechanisms causing incomes to increase. Our structural estimates indicate that the principal mechanism through which both agricultural technical change and population growth cause deforestation is through their effects on pushing up the value of land for growing crops. Rural industrialization has had little impact on forest survival because industrialization did not appear to raise land rents appreciably, in contrast to the effects of the improvements in agricultural productivity, and because changes in income per se appear to have only
5
weak direct effects on forest exploitation. These results suggest that the “green” revolution in India, while forestalling the negative income consequences of a Malthusian equilibrium, was less green than first thought. Our estimates also are also consistent with the hypothesis that the difficulty of monitoring the extraction of forest is a contributing factor, along with agricultural technical change, to inhibiting the growth of forests. II. Theory To elucidate the paths by which agricultural technical change and population growth affect deforestation and to establish a test for whether the amount of forest area and deforestation reflect suboptimal forest management, we formulate a simple general equilibrium model consisting of a set of regional economies (villages) in which households attempt to optimally allocate labor across forest, agricultural and manufacturing activities. The three-sector model is specified under two regimes: a benchmark model in which all markets for production inputs, including that of forest labor, are perfect and a model in which labor time spent in the extraction of forest resources cannot be monitored. For clarity of exposition we suppress regional subscripts and assume that in each village (1) the number and size of households and thus population growth is exogenous and (2) households are identical. We also assume that periods are of sufficient length such that the extraction of forest resources in one period does not influence the output of forest products in subsequent periods. Total available land per household a.t is divided at time t into forest land, aft, and agricultural land agt. Per-household total labor supply, i.e., family size, is denoted l.t and labor supply by the household in the forest, agriculture and manufacturing sectors are denoted lft, lgt, and lmt, respectively. The technologies associated with the extraction of forest products - fuelwood and fodder6 - and the agricultural and manufacturing sectors are, initially, f(0.2 (NDP) and the mean NDVI of those areas with an NDVI exceeding 0.2. The product of these two measures was also constructed as a measure of overall biomass attributable to forests (NDT). The MSS images have a spatial resolution of approximately 80 meters in four bands of the spectrum. Each “path” and “row” pair uniquely determine a geographical area of approximately 185 kms square for the MSS images. A number of criteria were used to select specific scenes. First, it was desirable to have scenes for each location that corresponded as closely as possible to the crop-years covered by the ARIS (1970-71) and REDS (1981-1982) surveys. The most important constraints in matching by crop-year are that the first Landsat satellite, Landsat 1, was not launched until late in 1972 and the availability of the relevant scenes for India is limited after 1980. Second, in order to control for seasonal variation in vegetative cover, scenes were selected for a given area that were at similar points within the crop-cycle and corresponded to periods within the year during which there is minimal presence of standing crops, which can be difficult to distinguish from forest area using satellite imagery. Preliminary analysis suggested that the months of January and February were best. Third, because areas covered by clouds cannot be used to assess vegetative cover, images were selected with little or no cloud cover. The choice of the winter months was also useful in this regard because the cloud-laden monsoon period was excluded. We were reasonably successful in meeting all of these criteria. Ninety-six percent of the scenes corresponding to the ARIS survey came from late 1972 and early 1973, with the scenes corresponding to the REDS survey distributed between years 1977 and 1980. The average number of years between these scenes across path-row combinations is 5.1, with 75 percent of the observations spanning the interval between 4 and 7 years. In addition, 81 percent of the selected scenes came from January and February,
18
with all scenes coming from the November-April period. Finally, the level of cloud cover for the selected scenes never exceeds 2 on a 0-7 scale, with 0 denoting complete absence of clouds and 7 complete cloud cover. For each of the selected 146 scenes corresponding to a specific path-row-day combination we obtained positive transparency images for both the near infra-red and red bands of the spectra. These images were then scanned at a resolution of 300 pixels per inch. Images were then registered to latitude and longitude using data on the locations of the four corners of each image. The intensity of the images was then adjusted using the grey-scale bands printed on the side of each image. Scans of these two images were combined to construct a measure of NDVI for each of 6.5 x 106 pixels in each scene. Based on these, we obtained a distribution of the values of the NDVI pixels within a 10km radius of each village for each of the surveys. On average there were 53,904 pixels within the desired radius for each village. Selection and analysis of the Landsat VII images from early 1999 followed a roughly similar procedure, with the exception that images were available digitally making it unnecessary to manually geo-register and intensity-correct the images. Coverage of the relevant villages required 85 distinct pathrow combinations for this satellite. Images were selected to have low cloud cover and correspond to the time-period of the year for the early images. The images are at a resolution of 30 meters but were resampled to a resolution comparable to that of the 1971-1982 images before the NDVI measures were calculated. The selected Landsat VII images were collected between November 1998 and April 1999 and had cloud cover of less than 20 percent. Due to the limited availability and high cost of Landsat images for the South Asian region during the 1980s and early 1990s, an alternative source was used to construct NDVI measures to correspond to the 1991 Indian census. In particular, we obtained NDVI images compiled by the USGS based on data collected from the AVHRR satellite in 1992. Because these images have a lower resolution (1.1
19
kilometer) than the Landsat images and because measures of vegetative cover may be importantly affected by the resolution used19 we resampled these images to a higher resolution based on the content of the 1999 images. In particular, selected 1999 images were first sampled to a resolution of 80m and then averaged to a resolution of 1km. We then constructed a linear regression equation relating NDVI in the 80m images to that in the 1km images for 1999 and determined the variance of the resulting residual. This regression equation combined with random draws from the corresponding error distribution were used to construct NDVI images with a nominal resolution of 80m based on the original 1km AVHRR images. B. Village-level Economic and Demographic Variables The national NCAER surveys and the 1991 Indian Census village-level data provide information on variables describing the economic environment of the villages matched to the satellite data on forests over the 1971-99 period. The 1970-71 round of the ARIS data provides information on household structure (age-sex composition); income by source, agricultural inputs, outputs and costs, by item, and wage rates and labor supply for 4,659 households in 259 villages that were selected based on a stratified random survey design. The data set contains sample weights reflecting the stratified sample design so that population statistics, necessary for aggregation and merging, can be obtained from the survey data. Also provided is information on village population size, village-level land prices, for irrigated and unirrigated land, and on village infrastructure, including whether the village was electrified and the presence of rural industry and whether or not the village was located in a district participating in the Intensive Agricultural District Program (IADP), a national program instituted in the late 1960's that
19
This arises from the non-linearity of the forest-cover measure. Consider, for example, a 10km radius image consisting of 50,000 pixels, 40 percent of which have an NDVI of .3 and 60 percent of which have an NDVI of .4. At this resolution our measured forest cover (NDVI>.2) is 40 percent. Now suppose the image is degraded by a factor of 100 so that the village now contains 500 pixels. In the extreme case that the forest and non-forest pixels are independently distributed across the village then, using the same .2 cutoff one would obtain a forest cover measure of only 3.6 percent. 20
provided agricultural resources and subsidies of agricultural input (seeds, fertilizer)in areas believed to be those that would be subject to the most significant productivity improvements as a consequence of the green revolution.. The 1982 REDS data provide similar information for the 1981-82 crop year on a subset of the original 1970-71 households as well as data on a new, random sample of households based on the same survey design as in the ARIS and on a complete census of households in the original 259 ARIS villages. However, because of political constraints, all households in the state of Assam were dropped from the sampling frame. The panel and the new households together number 4,947 and, based on the sample weights, are representative of the entire national rural Indian population (except for Assam) in 1981-82. The REDS data also provide sampling weights for all households, as described in more detail in Vashishtha [1989], thus permitting construction of a representative data set at the village level for 198182 that can be matched with that from 1970-71. In 1999, NCAER under our direction carried out a survey of the same villages as in the 1970-71 ARIS, excepting those in Jammu and Kashmir states, collecting information consistent with that collected at the village-level in 1982. To construct a measure of agricultural technology, information from the three surveys on crop outputs and acreage planted by crop, type of land and seed variety (high-yielding (HYV) or not) was used to construct a Laspeyres index of HYV crop yields on irrigated lands combining four HYV crops (corn, rice, sorghum and wheat) using constant 1971 prices for each of the villages for the three survey years. The 1970-71 ARIS and the 1981-1982 and 1999 REDS data sets thus provide a consistent set of rural agricultural wage rates, land prices, HYV crop productivity, and rural industry measures and information on the size and numbers of households for up to 253 villages spread all over India for the years 1971, 1982 and 1999. We also obtained information from the 1991Indian Census on a subset of the 1999 survey villages. The Indian Census provides data for every village in India on population size, number of households and road types for 1991.Using as matching information village, tehsil and block names we
21
were able to match 234 of the 253 villages in the 1999 survey.20 The 1999 REDS provides histories of the electrification of villages, which were used to determine which of the villages were electrified in 1991. Based on the village geo-codes, we also matched information on annual rainfall to each of the villages in each of the four relevant years using information on the nearest weather station from the set of 30 weather stations reporting data to the National Climate Data Center over the 29-year period. Figure 2 maps the location of the survey villages as well as the weather stations. Table 1 provides the means and standard deviations for all variables for each of the four years, along with the data source for the variables, and the number of villages in each round for which there is survey or Census data. As can be seen, the data indicate that India experienced economic development over the 29-year period spanned by the data: HYV crop productivity more than tripled, real agricultural wages grew by 150%, the proportion of villages that were electrified rose from less than a third in 1971 to almost 93% in 1999, and the proportion of villages with a factory increased from 14% to 95%. At the same time the average population of the villages increased by almost 91.7% and the proportion of land with forest more than doubled. An important feature of the 1999 survey data is the identification of those villages with common or local-authority (panchayat) governed lands. Approximately 56% percent of the villages had village commons, so that it is possible to examine the relationship between forest growth and property rights and, in particular, carry out the tests, described above, of efficiency in the management of forest resources. In particular, we can assess whether the estimates of the equilibrium equations more closely
20
The sources for village population sizes for the ARIS and the 1981-82 REDS surveys were the 1971 and 1981 Censuses of India. Surprisingly, a non-trivial number of the villages in the Census data do not report population or household size. The fraction of non-reporting villages for the years 1971, 82, 91 are .055, .279, and .051, respectively. Population estimates for the 1999 village survey are missing for 13.1% of the villages. Similarly, 12.4% of the villages in 1991 and 15.7% in 1999 had no information on number of households so that it was not possible to compute average household size. In the econometric analyses reported below, we include observations with missing values for population and household size by setting the missing values to zero and adding to the specification dummy variables indicating that these variables were not available. 22
conform to the restrictions of the first-best model in villages in which there is no common land compared with villages with joint management of forest resources, presumably established as a response to the high cost of labor monitoring.21 Table 2 provides information on forest area and density, population, crop productivity, wage rates and prices from 1971 through 1999 for the sample villages stratified by whether or not they had locally-managed common lands. The figures indicate that the proportion of land area devoted to forests in the initial period was 39% higher in common-land villages compared with those villages without government-controlled land. Villages with common lands also had agricultural land that was almost 8% more productive than villages without common lands. The fact that common-land villages in 1971 had both a greater proportion of land under forest and greater crop productivity than villages without common lands might suggest that common-land governance succeeds in protecting forests despite high returns to alternative land use. However, it is also possible that conditions favoring crop productivity also favor tree growth, or that forest land is of lower quality than crop land such that where more land is devoted proportionally to agriculture, average crop productivity is lower. Given land and climate heterogeneity, it is not possible to distinguish between these hypotheses from cross-sectional associations. Table 2 also indicates that forest cover increased slightly more in the common-land villages over the last 29 years - the proportion of land covered by forest grew by 135% in common-land villages and by 114% in the other villages. However, crop productivity growth in the villages with common land was 41.9 percentage points slower than that in the common-land villages. This suggests, in contrast to the cross-sectional relationships in 1971, that the inability to monitor and enforce forest exploitation in the
21
Bardhan [1993] discusses the notion that incentives to undertake group management of common resources may be importantly related to local ecological conditions. Narain [1999] presents evidence that the costs of monitoring forest resources is closely related to the degree of forest degradation and considers the implications of this relationship for group management of resources. 23
face of rural economic growth may have attenuated forest growth. However, common-land villages experienced faster population growth compared with the other villages over the same period. A more systematic examination of the roles of population and economic growth in affecting the change in forests is thus needed that takes into account land and climate heterogeneity and the interrelationships among population, technical change, and the demand for forest products in assessing the efficiency of commonland forest management and identifying the consequences for forests that emanate from improvements in agricultural productivity. IV. Estimates of the Effects of Productivity Growth by Sector and Population Growth on Factor Prices, Income and Forests We first estimate log-linear approximations to reduced-form equations relating the variation in agricultural productivity, population size (number of households and household size) and rural infrastructure (electricity availability and access road quality) to the equilibrium values of the village wage, agricultural land price, average household income, factory presence and the measures of forest coverage. The reduced-form estimating equations are given by (14) where z = r, the log of the average price of land in the village; w, the log of the village male agricultural wage rate; y, average log of household income in the village; presence of a factory, and Af, the share of village land area under forest and forest density, measured both by NDP and NDT, respectively. 2 t is an index of agricultural productivity, measured by the four-crop productivity index; 0, represents industrial infrastructure and is measured by dummy variables indicating whether the village was electrified and had a paved access road; et represents actual weather conditions at time t and is measured by the annual amount of rainfall in the nearest weather station;22 lt is the average log of household size in the village,
22
The relevant years for the reduced-form wage and land price equations are the NCAER ARIS and the two REDS survey years (1971, 1982 and 1999). The income equations are estimated using data from 1971 and 1982, because the 1999 village survey does not provide household income measures. For 24
and Nt is the log of the population in the village, tt is a set of dummy variables capturing year effects.23 Finally, < captures village-specific attributes of weather and soil as well as proximity to urban areas and markets, and ,zt is a time-varying, village-specific shock. As the relationships in Table 2 suggest, estimation of (14) by ordinary least squares (OLS) using variation across villages at one point in time may be misleading because the unmeasured environmental variable
