Fairtrade Wine Price Dispersion in the United Kingdom

AbstractThis paper analyzes wine price dispersion in the United Kingdom. In particular, we are interested in examining whether Fairtrade wines are different from non-Fairtrade wines. Because Fairtrade wines serve an additional social purpose, one may think that consumers search less aggressively for the outlet with the lowest price, thus allowing for a larger price dispersion than for regular wines. We draw on data for about seven thousand wines from South Africa, Fairtrade and non-Fairtrade, sold in the United Kingdom between 2007 and 2012. In a first step, we run a hedonic regression model explaining the wine prices using Ordinary Least Squares (OLS) and Two-Stage Least Squares (2SLS) Instrumental Variable (IV) approaches. In the next step, we regress the squared residuals from the first step on a Fairtrade 0-1 dummy-variable. When using the squared residuals from the OLS model, we find that Fairtrade is a negative determinant of price dispersion. Therefore, Fairtrade wines exhibit a significant lower price dispersion than the comparison group. When using the squared residuals from the IV model, we find mixed results and suspect the presence of a substantial bias due to weak instruments. Finally, in order to avoid IV pitfalls, we ran Fairtrade and Non-Fairtrade wines in separate equations. We find support for the OLS results, i.e., Fairtrade wines appear to exhibit lower price dispersion than their non-Fairtrade counterparts. Whether this is due to consumer search is a priori unclear. (JEL Classifications: L31, L81, Q11)

Download Full-text

What Determines the Financial and Insurance Services Exports: The Case of the United Kingdom

International Journal of Financial Accountability, Economics, Management, and Auditing (IJFAEMA) ◽

10.52502/ijfaema.v3i6.203 ◽

2021 ◽

Vol 3 (6) ◽

pp. 1071-1081

Author(s):

Emmanouil Karakostas

Keyword(s):

United Kingdom ◽

Financial Services ◽

Positive Impact ◽

Essential Element ◽

Political Stability ◽

Financial Sector ◽

State Intervention ◽

Ordinary Least Squares ◽

The United Kingdom ◽

Almost All

The financial sector is a very basic pillar of the international financial system. Almost all countries of the present international economic system participate in international financial services. Today's era, due to intense globalization, constant capital movements, continuous commercial integration and the ever-increasing financial interconnection, have made financial and insurance services an essential element of the present reality. The financial sector is an industry that is very 'sensitive' to the macroeconomic and political stability of countries. This means that countries that are considered unstable cannot have a positive impact on their financial activities. One country that has a strong position in the financial sector is the United Kingdom (UK). The question that can be asked is this: what are the factors that determine the optimal functioning of financial and insurance activities. One answer could be the strong financial institutions of a country. Another answer is the corruption indicator. Or even the existence of intervention by the state apparatus in the financial functions. Of course, these factors must have tangible proof of the functioning of the economy. State intervention, for example, does not entirely mean that it is dysfunctional. This study will seek to create a framework for the analysis of financial services factors. The methodology applied is The Multiple Linear Regression - Ordinary Least Squares (OLS).

Download Full-text

THE IMPACT OF A HAUSMAN PRETEST ON THE ASYMPTOTIC SIZE OF A HYPOTHESIS TEST

Econometric Theory ◽

10.1017/s0266466609100026 ◽

2009 ◽

Vol 26 (2) ◽

pp. 369-382 ◽

Cited By ~ 30

Author(s):

Patrik Guggenberger

Keyword(s):

Least Squares ◽

Hypothesis Test ◽

Ordinary Least Squares ◽

Reduced Form ◽

Structural Parameter ◽

Ratio Test ◽

Two Stage ◽

Weak Instruments ◽

Asymptotic Size ◽

The Impact

This paper investigates the asymptotic size properties of a two-stage test in the linear instrumental variables model when in the first stage a Hausman (1978) specification test is used as a pretest of exogeneity of a regressor. In the second stage, a simple hypothesis about a component of the structural parameter vector is tested, using a t-statistic that is based on either the ordinary least squares (OLS) or the two-stage least squares estimator (2SLS), depending on the outcome of the Hausman pretest. The asymptotic size of the two-stage test is derived in a model where weak instruments are ruled out by imposing a positive lower bound on the strength of the instruments. The asymptotic size equals 1 for empirically relevant choices of the parameter space. The size distortion is caused by a discontinuity of the asymptotic distribution of the test statistic in the correlation parameter between the structural and reduced form error terms. The Hausman pretest does not have sufficient power against correlations that are local to zero while the OLS-based t-statistic takes on large values for such nonzero correlations. Instead of using the two-stage procedure, the recommendation then is to use a t-statistic based on the 2SLS estimator or, if weak instruments are a concern, the conditional likelihood ratio test by Moreira (2003).

Download Full-text

Estimating Average and Local Average Treatment Effects of Education when Compulsory Schooling Laws Really Matter

The American Economic Review ◽

10.1257/000282806776157641 ◽

2006 ◽

Vol 96 (1) ◽

pp. 152-175 ◽

Cited By ~ 335

Author(s):

Philip Oreopoulos

Keyword(s):

United Kingdom ◽

Treatment Effects ◽

The United States ◽

Ordinary Least Squares ◽

Compulsory Schooling ◽

Average Treatment ◽

The United Kingdom ◽

Average Treatment Effects ◽

Local Average Treatment Effects ◽

Local Average

The change to the minimum school-leaving age in the United Kingdom from 14 to 15 had a powerful and immediate effect that redirected almost half the population of 14-year-olds in the mid-twentieth century to stay in school for one more year. The magnitude of this impact provides a rare opportunity to (a) estimate local average treatment effects (LATE) of high school that come close to population average treatment effects (ATE); and (b) estimate returns to education using a regression discontinuity design instead of previous estimates that rely on difference-in-differences methodology or relatively weak instruments. Comparing LATE estimates for the United States and Canada, where very few students were affected by compulsory school laws, to the United Kingdom estimates provides a test as to whether instrumental variables (IV) returns to schooling often exceed ordinary least squares (OLS) because gains are high only for small and peculiar groups among the more general population. I find, instead, that the benefits from compulsory schooling are very large whether these laws have an impact on a majority or minority of those exposed.

Download Full-text

Linear Model IV Estimation When Instruments Are Many or Weak

Journal of Econometric Methods ◽

10.1515/jem-2012-0007 ◽

2017 ◽

Vol 6 (1) ◽

Cited By ~ 1

Author(s):

Michael P. Murray

Keyword(s):

Least Squares ◽

Instrumental Variables ◽

Likelihood Estimation ◽

Ordinary Least Squares ◽

Limited Information ◽

Linear Regression Models ◽

Weak Instruments ◽

Explanatory Variables ◽

Instrumental Variables Estimation ◽

Large Numbers

AbstractEconomists rely frequently on instrumental variables estimation to overcome biases that endogenous explanatory variables cause in ordinary least squares estimation. However, traditional instrumental variables estimators, such as two-stage least squares and limited information maximum likelihood estimation, can suffer persistent estimator biases and size-of-test biases in even very large samples if the instruments used are large in number or are only weakly correlated with an endogenous explanatory variable. This paper reviews strategies for grappling with weak instruments and with large numbers of instruments in linear regression models.

Download Full-text