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How do firms' partnering strategies impact the size of their partner-based retail networks? We draw on agency theory to address this question in the context of franchising. Our econometric analyses (based on nine years of longitudinal balanced panel data) include assessment of data nonstationarity and estimation of a dynamic panel data model that accounts for unobserved heterogeneity and endogeneity. Our findings indicate that franchisee network size is driven more by franchisor strategies that mitigate agency costs than by strategies that simply lower entry and ongoing costs and barriers for franchisees.

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This article investigates the effects of the choice of lag length on the estimation of long run cointegration relationships using the Johansen estimation procedure. This issue is of particular interest to applied researchers using time series data, see for example Awokuse (2005), Bacchiocchi et al. (2005), Gallegati (2005), Gomes and Paz (2005), Hasan (2005) and Pieroni and Ricciarelli (2005), among many others. An empirical example is used to demonstrate some of the issues that applied researchers face when they wish to use cointegration analysis. First, the number of lags to include in the model must be determined. However, different lag length selection criteria often lead to a different conclusion regarding the optimal lag order that should be used. Second, as demonstrated in this article, the choice of lag length can drastically affect the results of the cointegration analysis.