Heckman's Two-step Estimator Research Articles

Drivers of willingness to pay for reforestation of urban ecosystems in Ghana

PurposePopulation growth and urbanization pose several threats to terrestrial ecosystems, especially in forest ecological zones worldwide. This study examines the drivers of average willingness to pay (WTP) to restore urban forests in a developing country.Design/methodology/approachIt utilizes survey data of households and employs a robust Heckman two-step estimator with bootstrapping to address the research objective.FindingsThe study underscores the role of income, gender, education and perception of the health benefits of forests as the underlying determinants of restoration bids by respondents. These drivers have a positive and statistically significant effect on forest restoration. Education and gender appear to be the most effective by magnitude, followed by the perception of health benefits, then income. Attention is therefore drawn to relevant economic, sociocultural and psychological factors towards the goal of forestry to improve well-being in urban centres.Originality/valueThis paper seeks to add methodological insights to the literature on reforestation and land use changes in the Accra metropolitan area and the local population’s WTP for reforestation in this area. In principle, this is a case study informing about the values people hold for forests in Ghana and Africa, where a knowledge gap exists with respect to their socio-economic valuation.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-09-2022-0618

Monte Carlo Simulation for Modified Parametric Of Sample Selection Models Through Fuzzy Approach

The sample selection model is a combination of the regression and probit models. The models are usually estimated by Heckman’s two-step estimator. However, Heckman’s two-step estimator often performs poorly. In the context of the parametric method, Monte Carlo simulations are studied. The goal is to simulate and test as early as possible so that we can anticipate the problem of the accuracy of a model. The best approach is to take advantage of the tools provided by the theory of fuzzy sets. It appears very suitable for modeling vague concepts. It is difficult to determine some of the criteria and arrive at a quantitative value. Fuzzy sets theory and its properties through the concept of fuzzy number. The fuzzy function used for solving uncertain of a parametric sample selection model. Estimates from the fuzzy are used to calculate some of equation of the sample selection model. Finally, estimates of the Mean, Root Mean Square Error (RMSE) and the other estimators can be obtained by Heckman two-step estimator through iteration from some parameters and some of values.

Gender Inequality in Literacy Status, and its Effects on Households Economic Well-Being in Burkina Faso: A Semiparametric Bi-Variate Sample Selection Modeling Approach

This paper models the gender inequality in literacy status, and its effects on households economic well-being in Burkina Faso using A semi-parametric bi-variate sample selection modeling approach. This approach compared to the classic Heckman two-step estimator is methodologically innovative because it deals simultaneously with non-random sample selection using conventional systems of two equations, non-linear covariates' effects using spline approach, and the non-normal bivariate distribution using copula functions. Using data from the National Survey on Households Living Conditions in Burkina Faso, the graphical results by the Lorenz curves combined with the numerical Atkinson and Gini coefficients suggest that inequality in overall per-capita consumption spending (Economic well-being) among households headed by literates is higher than that of their illiterate counterparts for 2009 and 2014. However, independently of the head of household's literacy status, the level of inequality in total economic well-being decreased from 2009 to 2014. The analysis of poverty by literacy status using the poverty indices of Watts, Sen, Foster (alpha=1) suggests that poverty among households headed by literates is lower than that of their illiterates counterparts for both years. Without distinction of literacy status however, overall poverty decreased nationally between 2009 and 2014. The semi-parametric bivariate results do support the existence of gender inequality in literacy status, with women heads of households having relatively less chances of having a literate status than their men counterparts. The results also show that although this gender inequality in literacy status does not translate into inequalities in non-food wellness, it does however for food-wellness with female headed households having 38.9% less per-capita food consumption spending than their men counterparts. Combining both food and non-food consumption spending, total economic well-being seems to also exhibit gender inequality as female headed households now have relatively 26.7% less combined per-capita consumption spending.

Educational Attainment, and the Joint Outcomes of Employment and Consumption in Burkina Faso: A Semi-Parametric Bivariate Modeling Approach

With UN member states formally adopting the Education 2030 Agenda through the Incheon Declaration, comes the need to evaluate the effect of education on the joint outcomes of employment and consumption for each member state, so as to get a clear picture of its value as an effective policy leverage for sustainable development in the country. To this end, this paper looks at the effects of educational attainment on the two key aspects of consumer behavior (labor force participation, and consumption spending) in Burkina Faso, using a Semi-parametric Bivariate modeling approach, and data from the National Survey on Household Living Conditions. The results show that not only is the semi-parametric modeling approach methodologically innovative because it deals with the simultaneity of labor force participation and consumption spending using conventional systems of two equations, with non-linear covariates' effects using spline approach, and non-normal bivariate distribution using copula functions; it also performs better than the classic Heckman two-step estimator based on the AIC and BIC criterion. Most importantly, it is found that a typical head of household's education level plays a positive and significant role in his/her likely participation in the labor market, and also Simultaneously contributes to higher household's per-capita food consumption and non-food consumption spending in burkina faso. Moreover, the positive and significant correlation coefficients between labor force participation and consumption spending on food and non-food items, suggest that the assumption of joint occurrence of the two processes is important. This assumption leads to a more accurate account of the effects of education and that of the other covariates in the system. Overall, the results do support the importance of education as an effective policy leverage for fighting against food insecurity and poverty in burkina faso, although care must be taken to reduce the disparities between rural and urban areas.

The BMTE Command: Methods for the Estimation of Treatment Effects When Exclusion Restrictions are Unavailable

We present a new Stata command, bmte (bias-minimizing treatment eects), which implements two new estimators proposed in Millimet and Tchernis (2012) designed to estimate the eect of treat- ment when there exists selection on unobserved variables and appropriate exclusion restrictions are unavailable. In addition, the bmte command estimates treatment eects from several alternative estimators that also do not rely on exclusion restrictions for identication of the causal eects of the treatment, including: 1) Heckman's two-step estimator (Heckman 1976, 1979); 2) a control function approach outlined in Heckman et al. (1999) and Navarro (2008); and 3) a more recent estimator proposed by Klein and Vella (2009) that exploits heteroskedasticity for identication. By implementing two new estimators alongside pre-existing estimators, the bmte command provides a picture of the average causal eects of the treatment across a variety of assumptions. We present an example application of the command following Millimet and Tchernis (2012).

FUZZY PARAMETRIC SAMPLE SELECTION MODELS OF MARRIED WOMEN FOR NON-PARTICIPATION BY MLE : CASE STUDY THE MPFS-1994

Models with sample-selection biases are widely used in various fields of economics such as labour economics (see Maddala, Amemiya, and Mroz). The models are usually estimated by Heckman's two-step estimator. However, Heckman's two-step estimator often performs poorly (see Wales and Woodland, Nelson, Paarsch, and Nawata). The data used in this study originated from the survey was conducted by the National Population and Family Development Board of Malaysia under the Ministry of Women, Family and Community Development of Malaysia, called the Malaysian Population and Family Survey 1994 (MPFS, 1994). The survey was conducted through a questionnaire, were randomly and specifically for married women. The data set focus on married women which provides information on wages, educational attainment, household composition and other socioeconomic characteristic. The Original sample data based on Mroz (1987), there are 4444 records married women. It is necessary to use the maximum likelihood method to estimate the models in such cases. For solving uncertainty data of a parametric sample selection model, in this paper needs to consider the models estimation using fuzzy modeling approach, called Fuzzy Parametric Sample Selection Model (FPSSM). Fuzzy Parametric sample selection model (FPSSM) is builds as a hybrid to the conventional parametric sample selection model. Finally, the result showed, FPSSM by Maximum Likelihood Estimator (MLE) estimates of the mean, Standard Deviation (SD).

Household demand for improved water supply services in Mekelle City, Northern Ethiopia

With population growth and urbanization, demand for improved water services has been growing. It is imperative therefore to examine different factors that influence demand for improved water services and the resultant welfare changes. Using cross-sectional household survey data collected through structured questionnaire from ten administrative units in Mekelle City, we estimate household willingness to pay models and identify major determinant factors of demand for improved water service. In order to help us do this, we considered selection issues and estimated models using the Heckman Two-Step Estimator. Our results show that the amount of bid (amount of money households would be willing to pay) that households (already connected to private taps) would be willing to pay is positively associated with household income, ownership of the house, price of vended water and the practice of water purification. For households who are not connected to private taps, the amount they would be willing to pay for (improved) private tap connection is positively associated with formal education, housing status and gender. We also investigate the welfare gains and losses as a result of improved water service. Analytical results show that, as the number of households who subscribe to improved water service increases, there is a gain in surplus for households and revenue (producer surplus) for the municipality.

Robust Misspecification Tests for the Heckman's Two-Step Estimator

This article constructs and evaluates Lagrange multiplier (LM) and Neyman's C(α) tests based on bivariate Edgeworth series expansions for the consistency of the Heckman's two-step estimator in sample selection models, that is, for marginal normality and linearity of the conditional expectation of the error terms. The proposed tests are robust to local misspecification in nuisance distributional parameters. Monte Carlo results show that testing marginal normality and linearity of the conditional expectations separately have a better size performance than testing bivariate normality. Moreover, the robust variants of the tests have better empirical size than nonrobust tests, which determines that these tests can be successfully applied to detect specific departures from the null model of bivariate normality. Finally, the tests are applied to women's labor supply data.

Foreign direct investment and export spillovers: Evidence from Vietnam

The existing IB literature suggests that the presence of foreign firms in a country can benefit domestic firms through the formation of inter-firm linkages. These linkages can take various forms. By making use of firm level data from Vietnam's manufacturing sector, this paper examines the impact of horizontal and vertical (backward and forward) linkages between domestic and foreign firms on (i) the decision of domestic firms to export and (ii) the export share of domestic firms. This paper considers only transactional linkages. The empirical analysis is based on Heckman's two-step estimator in selection models. It is shown that the presence of foreign firms in Vietnam, through horizontal and forward linkages, significantly affects the decision of domestic firms to export as well as their export share. This result continues to hold when we take into account factors such as the (a) level of technology of domestic firms, (b) ownership structure of domestic firms, (c) orientation of foreign firms and (d) geographical proximity to foreign firms.

Selection, Investment, and Women's Relative Wages Over Time*

In theory, growing wage inequality within gender should cause women to invest more in their market productivity and should differentially pull able women into the workforce. Our paper uses Heckman's two-step estimator and identification at infinity on repeated Current Population Survey cross sections to calculate relative wage series for women since 1970 that hold constant the composition of skills. We find that selection into the female full-time full-year workforce shifted from negative in the 1970s to positive in the 1990s, and that the majority of the apparent narrowing of the gender wage gap reflects changes in female workforce composition. We find the same types of composition changes by measuring husbands' wages and National Longitudinal Survey IQ data as proxies for unobserved skills. Our findings help to explain why growing wage equality between genders coincided with growing inequality within gender.

Testing and Correcting for Non-Random Selection Bias due to Censoring: An Application to Medical Costs

Censoring is a common problem with medical cost data. Methods from traditional survival analysis are not directly applicable to estimate medical costs since patients accumulate costs with different rate functions over time, leading to negatively biased estimates. Heckman's two-step estimator results in large variances when identical explanatory variables that influence selection are included in the structural equation, i.e. when there are no exclusion restrictions. This paper provides a systematic treatment of the correction for nonrandom sample selection bias of medical cost data where the selection rule is described by a censored regression model. The proposed method first uses the duration of time a patient is tracked for the selection, rather than a binary variable, namely whether or not the duration is censored. Second, using Tobit residuals instead of the inverse Mills ratio in the structural equation allows us to decrease large variances introduced by the Heckman model when there are no exclusion restrictions. We show that the resulting estimators are consistent and asymptotically normal. Simulation studies confirmed our results. Moreover, we derive a simple test to determine possible sample selection bias due to censoring. Data from a study on the medical cost of breast, prostate, colon, and lung cancer is used as an application of the method.

What Factors Determine the Allocation of Aid by Arab Countries and Multilateral Agencies?

This article provides a statistical analysis of the determinants of Arab aid allocation using Heckman's two-step estimator. It is found that poorer, Arab, Islamic and Sub-Saharan African countries are more likely to receive some positive amount of Arab aid (gate-keeping stage). The same is true for countries not maintaining diplomatic relations with Israel as well as those with voting patterns in the United Nations General Assembly similar to Saudi Arabia. Arab and more populous countries also receive a higher share of the total aid allocated (level stage). The same is true for Islamic countries in the case of bilateral aid and countries with voting similarity in the case of multilateral aid. Donor interest, in particular Arab solidarity, plays a clear role at both stages, whereas recipient need as measured by a country's level of income only affects the gate-keeping stage, not the level stage.

Estimation of sample selection bias models

Econometric models with sample selection biases are widely used in various fields of economics, such as labor economics. The Maximum Likelihood Estimator (MLE) is seldom used to estimate models because of computational difficulty, while Heckman's two-step estimator is widely used to estimate these models. However, Heckman's two-step estimator sometimes performs poorly. In this paper, methods of calculating the MLE are analysed, and finite sample properties of the MLE and Heckman's two-step estimator are compared using Monte Carlo experiments and empirical examples.

ON THE EFFECTS OF MISSPECIFICATION ERRORS IN MODELS WITH GENERATED REGRESSORS†

This paper considers the consistency and efficiency of two-step estimators of a structural equation when the auxiliary equation is misspecified. Underspecification generally leads to the two-step estimator and the estimator of the error variance being inconsistent. When the errors of the structural and auxiliary equations are uncorrelated, a two-step estimator based on an overspecified auxiliary model will generally lead to a loss of efficiency compared with the two-step estimator based on a correctly specified auxiliary model. The impact of underspecifying and overspecifying the auxiliary equation used to generate initial parameter estimates for Heckman's two-step estimator of models with sample selection is analyzed. Copyright 1994 by Blackwell Publishing Ltd

Estimation of sample selection bias models by the maximum likelihood estimator and Heckman's two-step estimator

In this paper, methods of estimating models with sample selection biases are analyzed. Finite sample properties of the maximum likelihood estimator (MLE) and Heckman's two-step estimator are compared using Monte Carlo experiments.

A note on the estimation of models with sample-selection biases

Econometric models with sample-selection biases are widely used in various fields of economics, such as labor economics. Heckman's two-step estimator is widely used to estimates these models. This paper points out some limitations and problems of Heckman's two step-estimator