State-contingent Framework Research Articles

An indicator for nature-state detection in the state-contingent framework and the case of grain production in Saxony-Anhalt, Germany

Sound policy needs tools for production analysis under uncertainty such as the state-contingent approach. Apart from demonstrating the potential applicability of the approach for the grain producing sector in Saxony-Anhalt, Germany, this contribution addresses one of the key challenges to its empirical applications in the context of agricultural production analysis under uncertainty, namely the definition of the states of nature and the construction of environmental indicators to detect the occurrence of the so-defined states. This study proposes such an environmental indicator based on biophysical crop yields and suggests two methods for statistical data clustering to evaluate its values. To the extent to which simulated data reflect key characteristics of the real data this contribution presents estimates for the state-contingent production technology. The hypothesis of an output-cubical technology is rejected in contradiction to assumptions implicit to conventional production analysis under uncertainty.

Beyond GDP: Is there a law of one shadow price?

This paper builds a welfare measure encompassing household disposable income, unemployment and longevity, which are valued either from life satisfaction data (“subjective shadow prices”) or from calibrated utility functions (“model-based shadow prices”). The two different sets of shadow prices are shown to be broadly consistent once a number of conditions are fulfilled: i) running life satisfaction regressions at the country level rather than at the individual level to reduce the downward bias on the income variable due to measurement errors; (ii) valuing the unemployment risk in a state-contingent framework rather than under the veil of ignorance; (iii) disentangling relative risk aversion parameters for unemployment and vital risks; (iv) calibrating the utility function on adult lifespan rather than life expectancy at birth.

Comparison of production risks in the state-contingent framework: application to balanced panel data

In a balanced panel data setting, this article proposes an empirical application of the state-contingent (SC) framework for production uncertainty. The SC approach (e.g., Chambers and Quiggin 2000) casts production decisions under uncertainty as the decision to select a portfolio of Arrow-Debreu SC outputs, scheduled to be delivered in the contingent states of nature. Under some stationarity assumptions on the SC decisions (i.e., no technical change, time-invariant states of nature, time-invariant SC portfolio decisions) and regularity assumptions on the data generating process (i.e., cross-sectionally homogeneous state realizations), SC technology can be estimated from balanced panel data that are framed as cross-sectional data of partially-revealed SC portfolio decisions. This allows one to simulate an optimal SC portfolio, determined by the interaction between the estimated SC technology and given risk preferences. In the application to Maryland dairy production data, the stochastic technologies of confinement and intensive-grazing dairy systems are compared. Of the two time intervals (years 2000–2004 and ye0ars 2006–2009) separately analyzed, the optimal production decision has generally become riskier for the confinement system and less risky for the grazing system. These contrasting trends appear directly related to the volatile milk prices, feed cost hikes, and increasing organic milk production during 2006–2009. The risk associated with the optimal portfolio is substantially lower under the SC analysis compared to a typical residual-as-uncertainty approach, suggesting that the typical approach may overstate the risk due to uncertainty.

The Time for Austerity: Estimating the Average Treatment Effect of Fiscal Policy

After the Global Financial Crisis, a controversial rush to fiscal austerity followed in many countries. Yet research on the effects of austerity on macroeconomic aggregates was and still is unsettled, mired by the difficulty of identifying multipliers from observational data. This article, reconciles seemingly disparate estimates of multipliers within a unified and state-contingent framework. We achieve identification of causal effects with new propensity-score based methods for time series data. Using this novel approach, we show that austerity is always a drag on growth, and especially so in depressed economies: a 1% of GDP fiscal consolidation translates into a loss of 3.5% of real GDP over five years when implemented in a slump, rather than just 1.8% in a boom.

Measuring the impacts of production risk on technical efficiency: A state-contingent conditional order-m approach

This article studies the influence of risk on farms’ technical efficiency levels. The analysis extends the order-m efficiency scores approach proposed by Daraio and Simar (2005) to the state-contingent framework. The empirical application focuses on cross section data of Catalan specialized crop farms from the year 2011. Results suggest that accounting for production risks increases the technical performance. A 10% increase in output risk will result in a 2.5% increase in average firm technical performance.

Measuring technical and environmental efficiency in a state-contingent technology

Most developed countries support farming activities through policies that are tailored to meet their specific social, economic and environmental objectives. Economic and environmental efficiency have recently become relevant targets of most of these policies, whose sound implementation can be enhanced by monitoring farm performance from a multidimensional perspective. This paper proposes farm-level technical and environmental efficiency measures that recognize the stochastic conditions in which production takes place. A state-contingent framework is used to model production uncertainty. An implementable representation of the technology is developed using data envelopment analysis. The application focuses on a sample of Catalan arable crop farms. Results suggest that technical efficiency is slightly lower in bad than in good growing conditions. Nitrogen pollution can decrease substantially more under good than bad growing conditions.

Production under uncertainty: a simulation study

In this paper we model production technology in a state-contingent framework. Our model analyzes production under uncertainty without being explicit about the nature of producer risk preferences. In our model producers’ risk preferences are captured by the risk-neutral probabilities they assign to the different states of nature. Using a state-general state-contingent specification of technology we show that rational producers who encounter the same stochastic technology can make significantly different production choices. Further, we develop an econometric methodology to estimate the risk-neutral probabilities and the parameters of stochastic technology when there are two states of nature and only one of which is observed. Finally, we simulate data based on our state-general state-contingent specification of technology. Biased estimates of the technology parameters are obtained when we apply conventional ordinary least squares estimator on the simulated data.

Firm behaviour under uncertainty: a simple parametric model

In this paper, we model production technology in a state-contingent framework. We assume that all the firms use the same stochastic technology, but they may have different risk attitudes and information sets, and ex post they may operate in different production environments. Firms maximise ex ante their preference function subject to a stochastic technology constraint; in other words, they are assumed to act rationally, thereby leaving no room for either technical or allocative inefficiency. We provide a simple parametric functional form to represent the state-contingent technology. Using simple numerical examples, we illustrate how optimal input–output choices are dramatically affected when firms have different preferences and information sets. Thus, we show that the observed disparateness of production choices among different firms can actually be attributed to the stochastic nature of the decision environment.

Estimating State‐Contingent Production Frontiers

Chambers and Quiggin (2000) use state‐contingent representations of risky production technologies to establish important theoretical results concerning producer behavior under uncertainty. Unfortunately, perceived problems in the estimation of state‐contingent models have limited the usefulness of the approach in policy formulation. We show that fixed and random effects state‐contingent production frontiers can be conveniently estimated in a finite mixtures framework. An empirical example is provided. Compared to conventional estimation approaches, we find that estimating production frontiers in a state‐contingent framework produces significantly different estimates of elasticities, firm technical efficiencies, and other quantities of economic interest.

The analysis of risky investment: A State-Contingent Approach

The paper extends the stochastic dominance analysis into a state-contingent framework. Unlike the regular stochastic dominance rules in which case the dominance is defined over the states, the efficiency analysis is applicable also for prospects that can be diversified between themselves or with other assets. The paper presents definitions of the state-contingent dominance rules, theorems with respect to the properties of these rules, comparisons between the regular stochastic dominance rules, the state-contingent dominance rules, and demonstrations of some economic implications and uses of these rules.