The Role of Information in Technology Adoption under Poverty

ICT, diffusion, globalization, econometric methods

RURAL LABOR MIGRATION: Migrant Network, Information, and Hysteresis

We investigate a rural household's decision to migrate part or all of its labor to urban areas. Labor migrates only when the expected return passes a hurdle rate that is affected by migration networks. We develop a dynamic model of incomplete information, and characterize the unique pure strategy perfect Bayesian equilibrium. Based on the predictions of the equilibrium, we develop a set of econometric models to estimate the migration decisions. Our results show that more current information helps migration, while the expectation of more information in the future may higher migration.Labor and Human Capital,

AGRICULTURAL PRODUCTION UNDER UNCERTAINTY: A MODEL OF OPTIMAL INTRASEASONAL MANAGEMENT

This paper develops a dynamic model of crop production under uncertainty with intraseasonal input choices. Crop production involves multiple stages, including at least seeding, post emergence fertilizer/pesticide application and harvesting. If the farmer receives new information about the output and/or price during the stages, he may wish to adjust the input use at each stage in response to the future possible information. Whether future information leads to higher or lower input use at earlier stages depends on the production function, in particular whether inputs at different stages are substitutes or complements in the production function.Farm Management,

Multitask Learning Deep Neural Networks to Combine Revealed and Stated Preference Data

It is an enduring question how to combine revealed preference (RP) and stated preference (SP) data to analyze travel behavior. This study presents a framework of multitask learning deep neural networks (MTLDNNs) for this question, and demonstrates that MTLDNNs are more generic than the traditional nested logit (NL) method, due to its capacity of automatic feature learning and soft constraints. About 1,500 MTLDNN models are designed and applied to the survey data that was collected in Singapore and focused on the RP of four current travel modes and the SP with autonomous vehicles (AV) as the one new travel mode in addition to those in RP. We found that MTLDNNs consistently outperform six benchmark models and particularly the classical NL models by about 5% prediction accuracy in both RP and SP datasets. This performance improvement can be mainly attributed to the soft constraints specific to MTLDNNs, including its innovative architectural design and regularization methods, but not much to the generic capacity of automatic feature learning endowed by a standard feedforward DNN architecture. Besides prediction, MTLDNNs are also interpretable. The empirical results show that AV is mainly the substitute of driving and AV alternative-specific variables are more important than the socio-economic variables in determining AV adoption. Overall, this study introduces a new MTLDNN framework to combine RP and SP, and demonstrates its theoretical flexibility and empirical power for prediction and interpretation. Future studies can design new MTLDNN architectures to reflect the speciality of RP and SP and extend this work to other behavioral analysis

PARAMETRIC AND NON-PARAMETRIC CROP YIELD DISTRIBUTIONS AND THEIR EFFECTS ON ALL-RISK CROP INSURANCE PREMIUMS

Normal, gamma and beta distributions are applied to 609 crop yield histories of Ontario farmers to determine which, if any, best describe crop yields. In addition, a distribution free non-parametric kernel estimator was applied to the same data to determine its efficiency in premium estimation relative to the three parametric forms. Results showed that crop yields are most likely to be described by a beta distribution but only for 50% of those tested. In terms of efficiency in premium estimation, minimum error criteria supports use of a kernel estimator for premium setting. However, this gain in efficiency comes at the expense of added complexity.crop insurance, crop yield distributions, kernel, Crop Production/Industries, Risk and Uncertainty,

Pollution Abatement Investment When Firms Lobby Against Environmental Regulation

In this paper, we study a firm’s optimal lobby behavior and its effect on investment in pollution abatement capital. We develop a dynamic framework where a representative firm can invest in both abatement and lobby capital in response to a possible future increase in pollution tax. We show that when the firm lobbies against the scale of the tax increase at a predetermined date, it should act like an occasional lobbyer by investing a lump-sum (optimal) amount in the lobby capital only at that date. But, to delay the new tax, it should act like a habitual lobbyer by investing continuously and at increasing rates over an optimal time period. We show that lobby expenditure crowds out investment in abatement capital and that this effect is stronger the more efficient is the lobbying activity. Further, we show that while uncertainty about the magnitude of the tax reduces the firm’s incentive to lobby, uncertainty about the timing of the new tax increases it.Pollution Abatement, Environmental Regulation

Irreversible Abatement Investment Under Cost Uncertainties: Tradable Emission Permits and Emissions Charges

A major concern with tradable emission permits (TEPs) is that stochastic permit prices may reduce firm incentive to invest in abatement capital or technologies relative to other policies such as a fixed emissions charge. However, under efficient permit trading, the price uncertainty is caused by abatement cost uncertainties which affect investment under both permit and charge policies. The authors develop a rational expectations general equilibrium model of permit trading to show how cost uncertainty affects investment. Differences between the two policies can be decomposed into a general equilibrium effect and a price-versus-quantity effect. Except for the curvature of the payoff functions, uncertainties reduce both effects so that tradable permits in fact help maintain firms\u27 investment incentive under uncertainty

Switching to Perennial Energy Crops under Uncertainty and Costly Reversibility

We study a farmer’s decision to convert traditional crop land into growing dedicated energy crops, taking in account sunk conversion costs, uncertainties in traditional and energy crop returns, and learning. The optimal decision rules differ significantly from the expected net present value rule, which ignores learning, and from real option models that allow only one way conversions into energy crops. These models also predict drastically different patterns of land conversions into and out of energy crops over time. Using corn-soybean rotations and switchgrass as examples, we show that the model predictions are sensitive to assumptions about stochastic processes of the returns. Government policies might have unintended consequences: subsidizing conversion costs into switchgrass reduces proportions of land in switchgrass in the long run.real options, irreversibility, sunk costs, land conversion, biofuel, cellulosic biomass, dynamic modeling, stochastic process, biofuel policy, Land Economics/Use, Resource /Energy Economics and Policy, Risk and Uncertainty, Q42, Q24,