Empirical tools to assess the sensitivity of directional distance functions to direction selection

Directional Distance Functions (DDFs) are becoming a popular way of measuring efficiency as they encompass the Shephard output and input distance functions as special cases. However, the most critical and still unsolved issue related to DDF remains the selection of the direction along which to measure the distance from the efficient frontier. In this article, we propose some empirical tools which allow to quantify the sensitivity of the efficiency measurement to the selection of the direction. The proposed tools are applied on a dataset on the Italian agricultural sector.     

GMM estimation of panel data models with time-varying slope coefficients

A difference/system generalized method of moments (GMM) model that imposes time-constant coefficients is common in empirical studies using panel data. However, a rejection by the Sargan–Hansen test is sometimes a serious concern for researchers. We highlight the fact that the Sargan–Hansen test for GMM estimators applied to panel data is a joint test of valid orthogonality conditions and coefficient stability over time. A possible reason for a rejection is therefore that the slope coefficients vary over time. One solution is to estimate an empirical model in which the coefficients are time specific. We apply this solution to the system GMM estimator of simple nondynamic Cobb–Douglas production functions for a selection of Swedish industries and find that relaxing the assumption of constant slope coefficients results in more satisfactory outcomes of the Sargan–Hansen test.     

Monetary and fiscal policies' effect on agricultural growth: GMM estimation and simulation analysis

The concept of sustainable economic growth is closely linked with the agricultural growth. This is especially true in the context of under-developed countries. Pakistan is a typical under-developed country that has huge labor force employed in conventional rural economy and more than half of the population relies on agriculture for subsistence. The study examines the agricultural growth through developing a model using the data from agricultural sector of Pakistan for the period 1972–2010. The model is primarily based on input–output reduced form structural equations approach. It is then estimated by GMM, validated and used for deterministic simulation analyses. Finally the validated model is used to critically analyze the impact of fiscal, monetary and energy policies on the agricultural output. We conluded that recent fiscal and monetary policies should be continued, while the energy policy needs to be modified in order to improve the agricultural GDP and reduce the rural poverty situation in the country.     

Solution and estimation of RE macromodels with optimal policy

Macro models of monetary policy typically involve forward looking behavior. Except in rare circumstances, we have to apply some numerical method to find the optimal policy and the rational expectations equilibrium. This paper summarizes a few useful methods, and shows how they can be combined with a Kalman filter to estimate the deep model parameters with maximum likelihood. Simulations of a macro model with staggered price setting, interest rate elastic output, and optimal monetary policy illustrate the properties of this estimation approach.     

Comparing French and US hospital technologies: a directional input distance function approach

French and US hospital technologies are compared using directional input distance functions. The aggregation properties of the directional distance function allow comparison of hospital industry-level performance as well as standard firm-level performance with regard to productive efficiency. In addition, the underlying constituents of efficiency?–?in the short run, congestion and technical inefficiency, and in the long run, scale inefficiency?–?are analysed by decomposing the overall measure. By virtue of using the directional distance function, it is also possible to obtain an estimate of a lower bound on allocative inefficiency. It is found that French and US hospitals use quite different technologies. Long run scale inefficiencies cause most of the French hospitals’?inefficiency, while short run technical inefficiency is the main source of overall productive inefficiency in the US hospitals.     

Engineering robust instruments for GMM estimation of panel data regression models with errors in variables: a note

Econometricians have long recognized the need to account in some way for measurement errors, specification errors and endogeneity to ensure that the ordinary least squares estimator is consistent. This article introduces a new generalized method of moments estimator that relies on robust instruments to estimate panel data regression models containing errors in variables. We show how this GMM approach can be generalized for the panel data framework using higher moments and cumulants as instruments. The new instruments, engineered for greater robustness, are proposed to tackle the pervasive problem of weak instruments.     

Shadow prices of $$\hbox {CO}_{2}$$ emissions at US electric utilities: a random-coefficient,random-directional-vector directional output distance function approach

We estimate the shadow prices of \(\hbox {CO}_{2}\) emissions of electric utilities in the US over the period from 2001 to 2014, using a random-coefficient, random-directional-vector directional output distance function (DODF) model. The main feature of this model is that both its coefficients and directional vector are allowed to vary across firms, thus allowing different firms to have different production technologies and to follow different growth paths. Our Bayes factor analysis indicates that this model is strongly favored over the commonly used fixed-coefficient DODF model. Our results obtained from this model suggest that the average annual shadow price of \(\hbox {CO}_{2}\) emissions ranges from $61.62 to $105.72 (in 2001 dollars) with an average of $83.12. The results also suggest that the firm-specific average shadow price differs significantly across electric utilities. In addition, our estimates of the shadow price of \(\hbox {CO}_{2}\) emissions show an upward trend for both the sample electric utilities as a whole and the majority of the individual sample electric utilities.     

Semiparametric estimation of conditional mean functions with missing data

A new semiparametric estimator for estimating conditional expectation functions from incomplete data is proposed, which integrates parametric regression with nonparametric matching estimators. Besides its applicability to missing data situations due to non-response or attrition, the estimator can also be used for analyzing treatment effect heterogeneity and statistical treatment rules, where data on potential outcomes is missing by definition. By combining moments from a parametric specification with nonparametric estimates of mean outcomes in the non-responding population within a GMM framework, the estimator seeks to balance a good fit in the responding population with low bias in the non-responding population. The estimator is applied to analyzing treatment effect heterogeneity among Swedish rehabilitation programmes.

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Performance of private higher education institutions in Vietnam: evidence using DEA-based bootstrap directional distance approach with quasi-fixed inputs

Vietnam’s higher education has witnessed substantial improvements since the implementation of the Doi Moi (renovation) policy. One of the significant developments is the promotion of establishment and enhancement of the role of private institutions in national education systems. However, the quest to improve the overall performance of the private higher education institutions remains a big challenge for many stakeholders. We assess the performance of Vietnamese private universities using a data envelopment analysis–based bootstrap directional distance approach with quasi-fixed inputs. The results show that there was a large variation in the efficiency levels of private universities within and between academic years and between metropolitan and other private universities. Our empirical findings provide more insights for educational leaders and policy makers on the performance of private higher education institutions and the implications of privatization of the national higher education system.     

Unified efficiency evaluation of regional industries in China: a nonradial directional distance function approach

This article extends a nonradial directional distance function (DDF) to allow decision-making units to adjust inputs in two directions. Based on this nonradial DDF, we evaluate the regional industrial unified efficiency (operational efficiency and environmental efficiency) in China between 2005 and 2014. We find that East China has the highest industrial unified efficiency score, followed by the West and Central China. To avoid methodological biases, three other models are also applied to assess the unified efficiency. The results indicate that a nonradial DDF under natural and managerial disposability (our model) has a greater discriminating power than a nonradial DDF under either of the disposabilities and the radial DDF.     

Specification and estimation of nonstandard profit functions

This paper critically examines appropriateness of the nonstandard profit function (NSPF) as a tool for measuring profit efficiency. We show that there are some fundamental problems with the NSPF approach, the way it is currently specified and estimated. This is true irrespective of whether technical inefficiency is included or not. Instead of estimating profit efficiency directly from the NSPF, we suggest alternative specifications and recommend using the cost function approach and deriving profit efficiency therefrom. Data from US banks are used to estimate the cost function and the NSPF. Results from alternative specifications are then compared and analyzed.

Slack-based directional distance function in the presence of bad outputs: theory and application to Vietnamese banking

In this paper we extend the slack-based directional distance function introduced by Färe and Grosskopf (Eur J Oper Res 200(1):320–322, 2010) to measure efficiency in the presence of bad outputs and illustrate it through an application on data of Vietnamese commercial banks. We also compare results from the slack-based directional distance function relative to the directional distance function, the enhanced hyperbolic efficiency measure (Färe et al. in Rev Econ Stat 71(1):90–98, 1989) and the Farrell-type technical efficiency and confirm that it has greater discriminative power.     

Trade distance functions and the derivation of inverse demand for imports

This article introduces a new representation of trade preferences termed as the trade distance function, which measures the maximum amount by which import quantities must be deflated or inflated to reach the indifference surface. The properties of this function are discussed and employed to derive systems of inverse demand for imported goods. We illustrate its usefulness by proposing two new parametric forms of trade distance functions. While the trade distance function directly yields Hicksian inverse demand functions of imports, they usually lack closed-form representations in terms of observable variables. This problem, however, need not hinder estimation and could be solved by using the numerical inversion estimation method. Results generally indicate that the suggested modelling and estimation methods are operationally, implying that the trade distance function approach is a promising tool of the empirical analysis of import demands subjected to tight theoretical conditions.     

GMM estimation of a structural demand model for yogurt and the effects of the introduction of new brands

The demand structure for yogurt is assumed to be properly described by a one level nested logit model that is applied to aggregate market data. Given the presence of endogenous regressors, suitably lagged endogenous variables (Arellano and Bover in J Econom 68:29–51, 1995; Blundell and Bond in J Econom 87:115–143, 1998) are proposed as instrumental variables. The validity of this set of instruments is discussed and price elasticities and marginal costs are recovered from the demand estimates. Total welfare gains associated to the introduction of two new brands by the same manufacturer are finally computed. Prices and profits decreased and total welfare increased.     

Segmented labour markets and the estimation of wage functions

This paper uses a stratified random sample of private sector employees to estimate wage equations for both the male labour market as a whole and six ‘segments’ of the labour market, segement which are defined with respect to the industry and size characteristics of the employing establishmen. Highly conventional results are obtained when wage equations are estimated for the male labour market as a whole, or within two segements, comprising almost half the survey population. However, one can reject conclusively the hypothesis that the same estimating equation is appropriate for modelling wage determination in all segments and if a ‘human capital’ (HK) model is, within segements, tested against more appropriate alternative hypotheses for wage determination using the Davidson and Mackinnon (1981) methodology for the testing of non-nested hypotheses, the conclusion is that one should reject the HK specification and not reject the alternative.

It is therefore argued that the disaggregation of labour market data reveals differences in the wage determination process that are not consistent with the view that labour markets are ‘dualistic’ but are consistent with the view that are segmented.     

Measuring technical efficiency and shadow price of water pollutants for the leather industry in India: a directional distance function approach

Journal of Regulatory Economics - This paper measures the cost of reducing pollution from the Kanpur leather industry which is a prime source of pollution in India’s largest river basin of...     

Political stability and growth: An application of dynamic GMM and quantile regression

This paper studies the effect of political stability on economic growth by taking 120 developing countries over the period of 1996–2014. We apply relatively advanced dynamic two step system-GMM and quantile regression. Political stability is found to be a key determinant of economic growth. More importantly, political instability (or risk) is found to be higher in the OIC countries and is a deterrent to economic growth. Also, for the lower and middle income OIC countries, political instability appears to affect economic growth more severely perhaps due to the absence of strong economic and political institutions. Moreover, political instability is also found to be significantly higher in the oil-dependent OIC countries. Notably, political instability is likely to affect growth through the channels of investment and human capital accumulation in the developing countries. Finally, the impact of political stability and political instability on growth is found to be equally distributed across the OIC countries with higher or lower growth level. Therefore, the development of political and economic institutions along with human capital development is recommended for all the developing countries in general and the OIC countries in particular.