Economic Analysis of Smallholder Vegetable Production in Tigary, Ethiopia. A Case of IPMS’s Alamata Wereda Pilot learning Project

To examine the determining factors on smallholder vegetable producers’ adoption decision to use the new agricultural technology or not, and to interpret the smallholder’s response to this new technology adoption decision in relation to the determining factors, this thesis involves the robust logit model estimation, and elasticity after logit model estimation. To see the impact of the project intervention in the pilot learning Wereda and the trend of vegetable production starting 2004 to 2009 in the area, Heckman treatment effect model and descriptive statistics are estimated (used) respectively. In the robust logit estimation, the study found that education level of the respondent, water sources accessibility, household land holding size, access to credit and households with no experience to employ man labor to their farm activity revealed positive effect while age of the household head, distance of the farm area from the local market (Alamata) and the practice of renting in land for producing vegetable output revealed negative effect on new agricultural technology adoption decisions. The Heckman treatment effect estimation robust our principal hypothesis where our principal hypothesis is project participation has positive effect on the profitability of the project participant and in return this profitability can affect the utility of the smallholder positively which is basically assumed as impact of the project. Besides, membership of any association or farmers’ cooperatives, farmer’s future output market price expectation, being married or coupled and male sex variables indicates positive effect on profitability of the smallholder vegetable producer. Keywords: new agricultural technology, adoption decision, smallholder, vegetabl

Choosing the best model in the presence of zero trade: a fish product analysis

The purpose of the paper is to test the hypothesis that food safety (chemical) standards act as barriers to international seafood imports. We use zero-accounting gravity models to test the hypothesis that food safety (chemical) standards act as barriers to international seafood imports. The chemical standards on which we focus include chloramphenicol required performance limit, oxytetracycline maximum residue limit, fluoro-quinolones maximum residue limit, and dichlorodiphenyltrichloroethane (DDT) pesticide residue limit. The study focuses on the three most important seafood markets: the European Union’s 15 members, Japan, and North America

VAT tax gap prediction: a 2-steps Gradient Boosting approach

Tax evasion is the illegal evasion of taxes by individuals, corporations, and trusts. The revenue loss from tax avoidance can undermine the effectiveness and equity of the government policies. A standard measure of tax evasion is the tax gap, that can be estimated as the difference between the total amounts of tax theoretically collectable and the total amounts of tax actually collected in a given period. This paper presents an original contribution to bottom-up approach, based on results from fiscal audits, through the use of Machine Learning. The major disadvantage of bottom-up approaches is represented by selection bias when audited taxpayers are not randomly selected, as in the case of audits performed by the Italian Revenue Agency. Our proposal, based on a 2-steps Gradient Boosting model, produces a robust tax gap estimate and, embeds a solution to correct for the selection bias which do not require any assumptions on the underlying data distribution. The 2-steps Gradient Boosting approach is used to estimate the Italian Value-added tax (VAT) gap on individual firms on the basis of fiscal and administrative data income tax returns gathered from Tax Administration Data Base, for the fiscal year 2011. The proposed method significantly boost the performance in predicting with respect to the classical parametric approaches.Comment: 27 pages, 4 figures, 8 tables Presented at NTTS 2019 conference Under review at another peer-reviewed journa

Estimating the effect of state dependence in work-related training participation among British employees.

Despite the extensive empirical literature documenting the determinants of training participation and a broad consensus on the influence of previous educational attainment on the training participation decision, there is hardly any reference in the applied literature to the role of past experience of training on future participation. This paper presents evidence on the influence of serial persistence in the work-related training participation decision of British employees. Training participation is modelled as a dynamic random effects probit model and estimated using three different approaches proposed in the literature for tackling the initial conditions problem by Heckman (1981), Wooldrgidge (2005) and Orme (2001). The estimates are then compared with those from a dynamic limited probability model using GMM techniques, namely the estimators proposed by Arellano and Bond (1991) and Blundell and Bond (1998). The results suggest a strong state dependence effect, which is robust across estimation methods, rendering previous experience as an important determining factor in employees’ work-related training decision.state dependence; unobserved heterogeneity; training; dynamic panel data models; generalised method of moments

Robust misspecification tests for the Heckman’s two-step estimator

We construct and evaluate LM and Neyman’s C(α) tests based on bivariate Edgeworth expansions for the consistency of the Heckman’s two-step estimator in selection models, that is, for the 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 instead of testing bivariate normality, testing marginal normality and linearity of the conditional expectations separately have a better size performance. Moreover, the robust variants of the tests have better size and similar power to non-robust tests, which determines that these tests can be successfully applied to detect specific departures from the null model of bivariate normality. We apply the tests procedures to women’s labor supply data

Robust Modeling Using Non-Elliptically Contoured Multivariate t Distributions

Models based on multivariate t distributions are widely applied to analyze data with heavy tails. However, all the marginal distributions of the multivariate t distributions are restricted to have the same degrees of freedom, making these models unable to describe different marginal heavy-tailedness. We generalize the traditional multivariate t distributions to non-elliptically contoured multivariate t distributions, allowing for different marginal degrees of freedom. We apply the non-elliptically contoured multivariate t distributions to three widely-used models: the Heckman selection model with different degrees of freedom for selection and outcome equations, the multivariate Robit model with different degrees of freedom for marginal responses, and the linear mixed-effects model with different degrees of freedom for random effects and within-subject errors. Based on the Normal mixture representation of our t distribution, we propose efficient Bayesian inferential procedures for the model parameters based on data augmentation and parameter expansion. We show via simulation studies and real examples that the conclusions are sensitive to the existence of different marginal heavy-tailedness

The Effects of Intermarriage on the Earnings of Female Immigrants in the United States

This paper investigates the effects of intermarriage on the earnings of female immigrants in the United States. The main empirical question asked is whether immigrant females married to US-born spouses have higher earnings than those of immigrant females married to other immigrants. Using 1970 and 1870 samples of IPUMS data, I estimate an earnings equation through OLS. I also correct for the labor force selection bias using the Heckman procedure. I finally take into account the endogeneity of intermarriage and apply a twostage least squares (2SLS) estimation procedure. I find that there is a positive marriage premium among immigrant females in the United States but a negative intermarriage premium for exogamously married females compared to endogamously married females. My results show that the longer the immigrant stays in the host country, the higher her wages, which is evidence for the assimilation effect over time. I find some evidence for a negative labor force selection bias among immigrant females. In other words, higher human capital women may select themselves out of the labor force, while lower human capital women are working for wages. Among those who are in the labor force, however, married females earn more than singles. I also conclude that being an immigrant from an English-speaking country does not have any impact on wages. Both premiums become statistically insignificant in difference from zero when 2SLS is used as an estimation procedure

On Testing Sample Selection Bias under the Multicollinearity Problem

This paper examines and compares the finite sample performance of the existing tests for sample selection bias, especially under the multi-collinearity problem pointed out by Nawata (1993). The results show that under such multicollinearity problem, (i) the t-test for sample selection bias based on the Heckman and Greene variance estimator can be unreliable; (ii) the standard t-test (Heckman 1979) and the asymptotically efficient Lagrange multiplier test (Melino 1982) have correct size but very little power; (iii) however, the likelihood ratio test following the maximum likelihood estimation remains powerful

Foul or Fair?

This paper gives a short overview of Monte Carlo studies on the usefulness of Heckman?s (1976, 1979) two?step estimator for estimating a selection model. It shows that exploratory work to check for collinearity problems is strongly recommended before deciding on which estimator to apply. In the absence of collinearity problems, the full?information maximum likelihood estimator is preferable to the limited?information two?step method of Heckman, although the latter also gives reasonable results. If, however, collinearity problems prevail, subsample OLS (or the Two?Part Model) is the most robust amongst the simple?to? calculate estimators. --