Examining at macro level the impact of generic prices on market shares and the persis- tence of the latter, I estimate a dynamic panel data model and employ the FE, OLS and System GMM estimator. Appendix [2.5] presents the FE and OLS estimates obtained for the first specification. Table 2.4 below contains the System GMM estimates. For the most
Table 2.4: System GMM – Coefficients Mgitm.l1,P
Ratio gitm andP
Ratio gitm xT
instrumented using 3rd Lags:
Dep.Variable: Mgitm Spec.1 Spec.2
Lag Generic Market Share (Mgitm.l1) 0.7593*** 0.7392***
(0.069) (0.062) Generic-to-Brand Price Ratio (PRatio
gitm ) -0.0741** -0.1020**
(0.029) (0.032)
PRatio
gitm * Time Trend: (P
Ratio gitm xT) – 0.0032** (0.001) Generic Entry -0.5321* -0.6140* (0.251) (0.243) Generic Entry2 0.0084 0.0120* (0.004) (0.005) Branded Substitutes -0.0579* -0.0210 (0.029) (0.017) Generic Substitutes 0.0169 0.0025 (0.015) (0.013) Drug Portfolio Size 0.0080 0.0136 (0.014) (0.011) Umbrella Brand Size 0.0011 -0.0044 (0.013) (0.009) Package Size 0.0027 0.0057 (0.003) (0.003) Time Trend -0.0040 -0.1391**
(0.005) (0.049) Year (2002,· · ·,2007) yes yes
Arellano-Bond test AR(1) 0.000 0.000 Arellano-Bond test AR(2) 0.017 0.018 Arellano-Bond test AR(3) 0.616 0.595 Hansen test: chi2(2)/chi2(3) 0.094 0.299 Instruments (Overid. Restr.) 18 (2) 20 (3) Wald chi2(15)/(16) 1332.59 1743.45 Prob>chi2 <0.001 <0.001 G x (T-1) 20283
* p<0.05, ** p<0.01, *** p<0.001
Notes: An observation in the regression is the monthly turnover share of a generic prod- uct within the bestseller segment. Two-step standard errors robust to heteroscedasticity and autocorrelation, clustered at firm level (68 clusters), in parentheses. The Windmeijer correction for two-step standard errors has been employed. The reference year is 2002.
part, the same explanatory variables turn out to have a significant effect. Whereas the size of the effects varies somewhat across the three estimations, the direction of the effects is the same. Notably, the GMM coefficient of the lagged dependent variable lies well in the range of the FE and OLS estimate. Recall that the FE and OLS estimates provide a useful bound for the true parameter estimate for the lagged dependent variable (Mgitm.l1). Given a second
order autocorrelation of the error terms – as indicated by the Arellano-Bond autocorrela- tion test – I use market share lags of third order as instruments in the GMM regression.52
The Hansen-test of over-identifying restrictions does not reject the joint null hypothesis that instruments are valid and that excluded instruments are correctly excluded from the esti- mated market share equation. The estimated coefficient of the lagged dependent variable’s amounts to roughly 0.76, indicating that generic market shares are quite persistent over time.
Consistent with empirical evidence, this result implies that first-mover advantages can be substantial in generic drug markets. Independent generic first-movers (early entrants) achieved on average a market share of 13.6% (59.1%) in the first month of entry. After one year generic first-movers (early entrants) had retained on average a market share of 10.5% (39.2%). After two years they had retained on average a market share of 10.2%, and 31.3% respectively. Even after three years of generic competition, independent generic first-movers (early entrants) had still retained a market share of 8.1% (29.5%) within the generic bestseller segment. The small impact of generic prices on market shares proves to be one important explanatory factor of market shares’ persistence. Just like lagged market shares, generic-to-brand price ratios are instrumented using third order lags. The coefficient of the generic-to-brand price ratio variable amounts to roughly -0.07, which implies that generic manufacturers achieve only a 0.7 percentage point higher market share by offering a 10 percentage point lower price. Given a standard deviation of 14.9 percentage points across generic products (see Appendix[2.4])53, generic prices tend to affect market shares very little. The entry of one generic firm reduces the market share of other generic firms by roughly 0.5 percentage points. Between three and 39 generic companies have entered the 35 drug markets by December 2007. With a standard deviation of roundabout eight generic en- trants, the effect of entry on firms’ market shares is large, considering that the vast majority of generic firms obtain a small market share only. The number of on-patent, branded sub-
52Effectively, I use market share lags of second order as instruments for the lagged dependent variable. 53Appendix[2.1] also illustrates that generic price deviations on a scale of 10 percentage points are common.
stitutes also has a significantly negative impact on market shares in the first specification. An additional branded drug in the same ATC1 therapeutic field lowers market shares by about 0.06 percentage points. Given a standard deviation of 5 branded substitutes, the ef- fect explains hardly any variation in the data. All other effects are insignificant at a 5% level. The number of off-patent, generic substitute drugs in the same ATC1 therapeutic field has no impact on generic market shares. Neither does the size of firms’ drug portfolio or um- brella brand, or products’ package size. There is no evidence of a time trend in market shares.
In the second specification generic-to-brand price ratios are further interacted with the time trend to examine the impact of generic prices on market shares over time. The inter- action term is instrumented for using third order lags. Its inclusion in the empirical model does not alter the results notably. The coefficient of the lagged dependent variable decreases slightly to 0.74, confirming that market shares are persistent over time. The effect of generic- to-brand price ratios increases somewhat. Firms achieve a 1 percentage point higher market share by offering a 10 percentage point lower price. Given a standard deviation of 14.9 per- centage points across products, the impact of prices on market shares is again very small. Moreover, their effect diminishes by 0.0032 percentage points in each month of generic com- petition. After roughly 32 months the effect of a price change is essentially zero. Overall, I find support for the first hypothesis, stating that differences in generic-to-brand price ratios have a small and marginally decreasing impact on generic market shares over time.
The entry of one generic firm has in turn again a negative, yet slightly larger and non- linear effect on firms’ market share. The entry of one generic firm lowers market shares by about 0.6 percentage points, and the effect is decreasing by 0.01 percentage points with each additional entrant. Given a standard deviation of roughly eight entrants, the effect is notable. Recall that the most firms obtain a minuscule market share only. Except for the time trend, all other effects are again insignificant. The coefficient of the time trend amounts to -0.14, indicating that market shares drop by about 0.1 percentage points in each month of generic competition. Given a standard deviation of roughly 15 months, the effect does not explain much of the variation in the data. Overall, estimates show that the previous market share and the number of generic entrants determine firms’ market share most strongly. Generic prices affect market shares only minimally, and their effect is decreasing over time.