This study investigates the linkages between changes in agricultural land use and population growth in India. We have employed long-term time series and a panel dataset of 1869 samples (267 districts × 7 time points from 1961 to 2021) to determine this. We theorize that there is an inverted “U-shape” relationship between changes in population growth and agricultural land. Our findings suggest a positive impact of population growth on the change in cultivated land. However, this relationship was not static during 1961–2021. We found a two-stage split relationship with a breakpoint in 1981. Prior to the 1980s, there was a 12% expansion in cultivated land in response to a unit increase in population growth. During the post-1980s, with a unit decline in population growth, there was a 5% reduction in cultivated land. The findings were reaffirmed through several robustness checks: analyses using alternative outcome variables, alternative break points in a segmented regression model, and spatial modeling. From a policy perspective, this study advances the need for the reduction of population growth rate in high-fertility states and the adoption of superior and green technology for agricultural intensification and diversification to stop cropland expansion at the cost of environmental sustainability.

Spatial econometric models allow for interactions among cross-sectional units through spatial weight matrices. This paper parameterizes each spatial weight matrix in the widely used spatial Durbin model with a different instead of one common distance decay parameter, using negative exponential and inverse distance matrices. We propose a joint estimation approach of the decay and response parameters, and we investigate its performance in a Monte Carlo simulation experiment. We also present the results of an empirical application on military expenditures. Indirect effects in particular appear to be sensitive to different parameterizations.

This study aims to deepen our understanding of social investment expansion proposing a political learning mechanism to link existing institutional and political explanations. When resources are limited, increased spending in social investment often comes at the expense of politically costly retrenchment of established social insurance policies. Previous studies suggest that this trade-off results in existing entitlements crowding out new policies, and that party ideology plays less of a role in determining social policy expansion. I argue that this is because parties face an electoral dilemma, as individual preferences for social investment and social insurance have been shown to differ between groups that partly overlap in their voting behaviour. Applying a policy diffusion framework to the analysis of childcare expenditure, this study proposes that policymakers learn from the political consequences of past decisions made by their foreign counterparts and update their policy choice accordingly. The econometric analysis of OECD data on childcare expenditure shows that governments tend to make spending decisions that follow those of ideologically similar cabinets abroad and that left-wing governments with a divided electorate tend to reduce childcare expenditure if a previous expansionary decision of a foreign incumbent is followed by an electoral defeat. The findings have implications for the study of the politics of social policy development.

This study scrutinizes spatial econometric models and specifications of crop yield response functions to provide a robust evaluation of empirical alternatives available to researchers. We specify 14 competing panel regression models of crop yield response to weather and site characteristics. Using county corn yields in the US, this study implements in-sample, out-of-sample, and bootstrapped out-of-sample prediction performance comparisons. Descriptive propositions and empirical results demonstrate the importance of spatial correlation and empirically support the fixed effects model with spatially dependent error structures. This study also emphasizes the importance of extensive model specification testing and evaluation of selection criteria for prediction.

Although the impacts of income, population growth, and other important determinants of land-use change have been widely studied, there is less understanding of how spatial spillovers matter. Utilizing a spatial econometric approach, we investigate the main determinants of natural landscape conversion, focusing on quantifying local and global spatial spillovers. The empirical investigation applies to the Edmonton Metropolitan Region and the Calgary Regional Partnership in Canada. Key results include: (1) determinants of land conversion have significant spillover effects; (2) income, population density, road density, natural land endowment and land suitability for agriculture are all found to have influences on natural land conversion both in the own and neighboring areas; and (3) local (i.e., within the immediate neighboring areas) and global (in the entire study region) spillovers are different in strength and direction. Our work provides useful information for understanding the spillover issues in land conservation, resource governance, and optimal conservation design.

Distinguishing substantively meaningful spillover effects from correlated residuals is of great importance in cross-sectional studies. Both forms of spatial dependence not only hold different implications for the choice of an unbiased estimator but also for the validity of inferences. To guide model specification, different empirical strategies involve the estimation of an unrestricted spatial Durbin model and subsequently use the Wald test to scrutinize the nonlinear restriction of common factors implied by pure error dependence. However, the Wald test’s sensitivity to algebraically equivalent formulations of the null hypothesis receives scant attention in the context of cross-sectional analyses. This article shows analytically that the noninvariance of the Wald test to such reparameterizations stems from the application of a Taylor series expansion to approximate the restriction’s sampling distribution. While asymptotically valid, Monte Carlo simulations reveal that alternative formulations of the common factor restriction frequently produce conflicting conclusions in finite samples. An empirical example illustrates the substantive implications of this problem. Consequently, researchers should either base inferences on bootstrap critical values for the Wald statistic or use the likelihood ratio test which is invariant to such reparameterizations when deciding on the model specification that adequately reflects the spatial process generating the data.

This study supplements spatial panel econometrics techniques with qualitative GIS to analyse spatio-temporal changes in the distribution of integrated conservation–development projects relative to poaching activity and unauthorized resource use in Volcanoes National Park, Rwanda. Cluster and spatial regression analyses were performed on data from ranger monitoring containing > 35,000 combined observations of illegal activities in Volcanoes National Park, against tourism revenue sharing and conservation NGO funding data for 2006–2015. Results were enriched with qualitative GIS analysis from key informant interviews. We found a statistically significant negative linear effect of overall integrated conservation–development investments on unauthorized resource use in Volcanoes National Park. However, individually, funding from Rwanda's tourism revenue sharing policy did not have an effect in contrast to the significant negative effect of conservation NGO funding. In another contrast between NGO funding and tourism revenue sharing funding, spatial analysis revealed significant gaps in revenue sharing funding relative to the hotspots of illegal activities, but these gaps were not present for NGO funding. Insight from qualitative GIS analysis suggests that incongruity in prioritization by decision makers at least partly explains the differences between the effects of revenue sharing and conservation NGO investment. Although the overall results are encouraging for integrated conservation–development projects, we recommend increased spatial alignment of project funding with clusters of illegal activities, which can make investment decision-making more data-driven and projects more effective for conservation.

Spatial econometric models become increasingly popular in various subfields of political science. However, the necessity to specify the underlying network of dependencies, denoted by $\boldsymbol{W}$, prior to estimation is a prevalent source of criticism since the true dependence structure is rarely known and theories mostly provide insufficient guidance. The present study investigates the effects of this network uncertainty which is a special case of model uncertainty that arises from uncertainty about the correct specification of $\boldsymbol{W}$. It advocates Bayesian model averaging (BMA) as a superior approach to this problem, located at the intersection of theory and empirics. Conducting Monte Carlo experiments, I demonstrate that, while the effect estimates are robust toward a misspecification in the functional form of $\boldsymbol{W}$, uncertainty in the neighborhood definition can bias the effect estimates derived from spatial autoregressive models. In contrast to alternative techniques, BMA directly addresses network uncertainty, correctly identifies the true network structure in the set of feasible alternatives, and provides unbiased effect estimates. Two replication studies from different subfields of the discipline illustrate the benefits of this approach for applied research.

According to spatial models of political competition, parties strategically adjust their ideological positions to movements made by rival parties. Spatial econometric techniques have been proposed to empirically model such interdependencies and to closely convert theoretical expectations into statistical models. Yet, these models often ignore that the parties’ ideological positions are latent variables and, as such, accompanied by a quantifiable amount of uncertainty. As a result, the implausible assumption of perfectly measured covariates impedes a proper evaluation of theoretical propositions. In order to bridge this gap between theory and empirics, the present work combines a spatial econometric model and a Bayesian dynamic item response model. The proposed model accurately accounts for measurement uncertainty and simultaneously estimates the parties’ ideological positions and their spatial interdependencies. To verify the model’s utility, I apply it to recorded votes from the sixteen German state legislatures in the period from 1988 to 2016. While exhibiting a notable degree of ideological mobility, the results indicate only moderate spatial dependencies among parties of the same party family. More importantly, the analysis illustrates how measurement uncertainty can lead to substantively different results which stresses the importance of appropriately incorporating theoretical expectations into statistical models.

This study identifies clusters of certified organic operations in the United States and determines the form of spatial autocorrelation present in the operations’ distribution. We identify large hot spots of organic operations along the West Coast and in the Midwest and Northeast with some variation based on how we define an organic operation. Further analyses suggest that organic operations do not necessarily follow the same geographic patterns as nonorganic agricultural and general business establishments. Spatial autoregressive models confirm the presence of significant spatial dependence in the distribution of certified organic operations for a number of different definitions of an organic operation.

Fracking is a controversial practice but is thriving in many areas. We combine a comprehensive data set on local bans and moratoria in the state of New York with local-level census data and spatial characteristics in a spatial econometric analysis of local fracking policies. Some factors, including location in the Utica shale, proportion of registered Democrats, and education level, increase the probability of restrictions on fracking. Extent of local land development, location in highly productive petroleum areas, and number of extant oil and gas wells are among factors that have a negative impact on the likelihood of a ban or moratorium.

U.S. county-level net migration data and a general spatial model are used to examine the effects of various amenities on migration decisions. Results suggest that higher county cancer risks and the presence of superfund sites in a county, or a higher ranking on the Environmental Protection Agency's hazard ranking system, reduce the relative attractiveness of a county to prospective migrants, while natural amenities on balance attract migrants, ceteris paribus. The results also reveal spatial dependence among contiguous counties in terms of net migration behavior.

This study examines the drivers of land use in a shifting cultivation system with forest fallow. Forest fallow provides on-farm soil quality benefits, local hydrological regulation, and global public goods. An optimal control model demonstrates that farmers have an incentive to fallow less than is socially optimal, though market failures limiting crop production can have a countervailing effect by encouraging fallow. An econometric model estimated using data from the Brazilian Amazon suggests that fallowing does not result from internalization of local fallow services but instead is associated with poor market access and labor and liquidity constraints.

Using a unique spatial database, a hedonic model is developed to estimate the value to nearby residents of open space purchased through agricultural preservation programs in three Maryland counties. After correcting for endogeneity and spatial autocorrelation, the estimated coefficients are used to calculate the potential changes in housing values for a given change in neighborhood open space following an agricultural easement purchase. Then, using the current residential property tax for each parcel, the expected increase in county tax revenue is computed and this revenue is compared to the cost of preserving the lands.

The purpose of this paper is to investigate the formation of hotspots of organic operations (geographically close areas that have positively correlated high numbers of organic operations), paying particular attention to the role of the organic certifying agent. We analyze the association of county-level factors related to policy, economics, demographics and organic certifiers with the probability that a county is in a hotspot or coldspot (geographically close areas that have positively correlated low numbers of organic operations) of organic operations. The results suggest that a high presence of government run organic certifying agents, as well as a high presence of private organic certifying agents who provide outreach services, are both positively associated with the probability that a county belongs to a hotspot. Other factors, such as the level of property taxes and the distance of the county from the nearest interstate, are also significantly correlated with the probability that a county is in a hotspot. Understanding factors associated with organic hotspots is important given the surge in momentum in the organic industry and the concerns that demand for organic products may be outpacing domestic supply. In particular, understanding the role that certifiers play in the formation of organic hotspots is important, as certain services provided by certifiers may be indicative of the level of communication between organic operations and their communities. The results of this paper may encourage public institutions that approve and regulate organic certifiers to provide incentives for offering outreach services, and private institutions interested in promoting organic operations to work more closely with certifying agents as a means to boost organic hotspots.

Zoning decisions related to residential lot size and density affect residential land value. Effects of size on residential parcel value in Roanoke County, VA, are estimated with fixed effects hedonic models. Parcel size; elevation; soil permeability; proximity to urban areas, malls, and roads; and location influence parcel value, but the effects vary by value of construction and development status. Parcel value per square meter declines with increasing parcel size. The estimated relationships could be used to evaluate zoning decisions in terms of land values and tax revenues if model estimation uncertainties and responses by developers to zoning strategies are considered.

Panel data are used in almost all subfields of the agricultural economics profession. Furthermore, many research areas have an important spatial dimension. This article discusses some of the recent contributions made in the evolving theoretical and empirical literature on spatial econometric methods for panel data. We then illustrate some of these tools within a climate change application using a hedonic model of farmland values and panel data. Estimates for the model are provided across a range of nonspatial and spatial estimators, including spatial error and spatial lag models with fixed and random effects extensions. Given the importance of location and extensive use of panel data in many subfields of agricultural economics, these recently developed spatial panel methods hold great potential for applied researchers.

This article contributes to the small literature on the relationship between the range of local public services and population size. Using new data on French local jurisdictions, we test the hypothesis that larger jurisdictions provide a broader range of public goods (the so-called “zoo effect”, Oates (1988)). We take advantage of the fact that, in France, many municipalities recently joined together, forming groups of municipalities (or communities) in order to achieve economies of scale. Using spatial econometrics, we find some evidence for the existence of a zoo effect in French communities. In other terms, larger communities provide a broader range of services than smaller ones. The intensity of the zoo effect is higher in urban than in rural areas.