Effect of price and in-store promotion on sales: a study of distinct regions in an emerging market

Increasing competition caused by globalization, high growth of some emerging markets and stagnation of developed economies motivate Consumer Packaged Goods (CPGs) manufacturers to drive their attention to emerging markets. These companies are expected to adapt their marketing activities to the particularities of these markets in order to succeed. In a country classified as emerging market, regions are not alike and some contrasts can be identified. In addition, divergences of marketing variables effect can also be observed in the different retail formats. The retail formats in emerging markets can be segregated in chain self-service and traditional full-service. Thus, understanding the effectiveness of marketing mix not only in country aggregated level data can be an important contribution. Inasmuch as companies aim to generate profits from emerging markets, price is an important marketing variable in the process of creating competitive advantage. Along with price, promotional variables such as in-store displays and price cut are often viewed as temporary incentives to increase short-term sales. Managers defend the usage of promotions as being the most reliable and fastest manner to increase sales and then short-term profits. However, some authors alert about sales promotions disadvantages; mainly in the long-term. This study investigates the effect of price and in-store promotions on sales volume in different regions within an emerging market. The database used is at SKU level for juice, being segregated in the Brazilian northeast and southeast regions and corresponding to the period from January 2011 to January 2013. The methodological approach is descriptive quantitative involving validation tests, application of multivariate and temporal series analysis method. The Vector-Autoregressive (VAR) model was used to perform the analysis. Results suggest similar price sensitivity in the northeast and southeast region and greater in-store promotion sensitivity in the northeast. Price reductions show negative results in the long-term (persistent sales in six months) and in-store promotion, positive results. In-store promotion shows no significant influence on sales in chain self-service stores while price demonstrates no relevant impact on sales in traditional full-service stores. Hence, this study contributes to the business environment for companies wishing to manage price and sales promotions for consumer brands in regions with different features within an emerging market. As a theoretical contribution, this study fills an academic gap providing a dedicated price and sales promotion study to contrast regions in an emerging market.

Revisiting the synthetic control estimator

The synthetic control (SC) method has been recently proposed as an alternative to estimate treatment effects in comparative case studies. The SC relies on the assumption that there is a weighted average of the control units that reconstruct the potential outcome of the treated unit in the absence of treatment. If these weights were known, then one could estimate the counterfactual for the treated unit using this weighted average. With these weights, the SC would provide an unbiased estimator for the treatment effect even if selection into treatment is correlated with the unobserved heterogeneity. In this paper, we revisit the SC method in a linear factor model where the SC weights are considered nuisance parameters that are estimated to construct the SC estimator. We show that, when the number of control units is fixed, the estimated SC weights will generally not converge to the weights that reconstruct the factor loadings of the treated unit, even when the number of pre-intervention periods goes to infinity. As a consequence, the SC estimator will be asymptotically biased if treatment assignment is correlated with the unobserved heterogeneity. The asymptotic bias only vanishes when the variance of the idiosyncratic error goes to zero. We suggest a slight modification in the SC method that guarantees that the SC estimator is asymptotically unbiased and has a lower asymptotic variance than the difference-in-differences (DID) estimator when the DID identification assumption is satisfied. If the DID assumption is not satisfied, then both estimators would be asymptotically biased, and it would not be possible to rank them in terms of their asymptotic bias.

Dynamic D-Vine copula model with applications to Value-at-Risk (VaR)

Regular vine copulas are multivariate dependence models constructed from pair-copulas (bivariate copulas). In this paper, we allow the dependence parameters of the pair-copulas in a D-vine decomposition to be potentially time-varying, following a nonlinear restricted ARMA(1,m) process, in order to obtain a very flexible dependence model for applications to multivariate financial return data. We investigate the dependence among the broad stock market indexes from Germany (DAX), France (CAC 40), Britain (FTSE 100), the United States (S&P 500) and Brazil (IBOVESPA) both in a crisis and in a non-crisis period. We find evidence of stronger dependence among the indexes in bear markets. Surprisingly, though, the dynamic D-vine copula indicates the occurrence of a sharp decrease in dependence between the indexes FTSE and CAC in the beginning of 2011, and also between CAC and DAX during mid-2011 and in the beginning of 2008, suggesting the absence of contagion in these cases. We also evaluate the dynamic D-vine copula with respect to Value-at-Risk (VaR) forecasting accuracy in crisis periods. The dynamic D-vine outperforms the static D-vine in terms of predictive accuracy for our real data sets.