Innovation and growth in the UK pharmaceuticals: the case of product and marketing introductions

Theoretical literature suggests a positive relationship between innovation and growth (Geroski 2005; Aghion and Howitt 1992) but the empirical literature has reported mixed results with positive links only in some situations, and conditional on firm characteristics (Coad and Rao 2010; Demirel and Mazzucato 2012; Deschryvere 2014; Audretsch et al. 2014). Size, scope, and experience are important factors in determining how much innovative activity a firm undertakes, and whether it results in successful innovation (Acs and Audretsch 1990; Henderson and Cockburn 1996). Recent literature has focused on lack of symmetry in the distribution of growth rates and in the returns to innovation to unravel the types of firms that innovate and contribute to growth most, for instance high growth/superstar firms (Coad and Rao 2008; Capasso et al. 2015; Mazzucato and Parris 2015). However, not all innovations lead to firm growth, as they may in fact cannibalize sales of existing products (Conner 1988; Banbury and Mitchell 1995).

Building on the insights from Hall (1987), Geroski and Machin (1992), Geroski and Toker (1996), Freel (2000), Del Monte and Papagni (2003) and Demirel and Mazzucato (2012) that innovative and/or more R&D active firms outperform non-innovative firms, in this paper, we explore the link between innovation, measured by introduction of additional drugs and pack varieties within a therapeutic class, and revenue growth in the UK pharmaceutical sector. The empirical literature that has investigated this relationship has often focused on innovative inputs, such as R&D intensity and patent counts, rather than innovative outputs, such as introduction of new products (see Roper 1997; Flor and Oltra2004; Coad and Rao 2008; Coad and Rao 2010 and Becheikh et al. (2006) for a review). We follow Cucculelli and Ermini (2012) and use a counts based approach of innovative output as a measure of innovation, but highlight the role of two different types of innovations, product and marketing innovations (two of the four types of innovation accounted for by Tavassoli and Karlsson (2015)), as each strategy can have a separate and distinct impact on growth via product differentiation or by price discrimination.¹

Studies that have used the innovative output approach have often reported lack of a statistically significant positive relationship between growth and innovative output (e.g., Geroski et al. 1997; Bottazzi et al. 2001; Geroski and Mazzucato 2002; Stam and Wennberg 2009). However, as pointed out by Corsino and Gabriele (2011), aggregation across heterogeneous products may hide the true relationship, and hence empirical investigations need to focus on sub-markets where products are relatively homogenous, firms draw on a similar knowledge base, and importantly, target the same type of consumers. Following on from that, we use quarterly sales data from the UK pharmaceutical prescription market during the 2003-2014 period, and measure firms’ sales growth within narrowly defined therapeutic classes, which roughly translates into similar patients (customers), and identify the impact of additional drugs and new pack varieties on growth by these subclasses. We call a firm’s operation within a narrow therapeutic class a business unit, and by focusing on business units as opposed to firms, we are better able to measure the impact of product innovations (drugs) and marketing innovations (additional pack varieties) on revenue growth by relevant sub-markets.

We follow the empirical literature and estimate reduced form equations where we regress growth on lagged size and counts of products and packs at the business unit level in a dynamic panel model (Evans 1987a, 1987b). Indeed, revenue and number of products and packs are equilibrium outcomes, and as such, any omitted demand-side factors can induce positive correlation between revenue growth and introduction of new products and packs. Accordingly, in some specifications, we treat these measures of products and packs as endogenous, and attempt to find an exogenous source of variation for these variables. We also allow for an interaction between these two variables to understand whether they are complementary or substitutable actions within a business unit.

Our main result is that both the introduction of additional drugs (product innovations) and additional packs (marketing innovations) have a significant impact on revenue growth, and the magnitude is larger for new drugs. New drugs also contribute to the growth in the long run since sales keep increasing, perhaps because more patients switch to the newer formulation. On the other hand, additional packs contribute only to short run growth. Nonetheless, given the price regulation for branded drugs in the UK, a marketing innovation of a new pack may still be a viable short-term business strategy as the cost of introducing a new pack is likely to be much smaller than that of introducing a new drug. This may be particularly helpful for small business units as we find that the impact of additional packs (and products) on growth is much stronger for them. When we further restrict the analysis to a selected sample of surviving business units, we observe that the marginal effect of new products and packs gets smaller, and is closer in magnitude to that of larger business units. This suggests that new introductions by small firms give them a boost in revenue growth, and absent such growth, the lack of innovation also lowers their survival probability. Similar to other studies regarding firm size (e.g., Evans 1987a; Dunne and Hughes 1994 or Bottazzi and Secchi 2005, who find some limited evidence), we observe that smaller business units grow faster than larger ones. Our result that the marginal effect of new products is larger for smaller business units is perhaps consistent with Demirel and Mazzucato (2012), who report positive impact of R&D on growth for small firms, albeit as long as they are patenting consistently.

We make three important contributions to the innovation-growth literature. First, we link innovative outputs to growth (rather than inputs) and explicitly recognize that innovative outputs differ in their appropriability conditions and provide evidence that they have different impact on growth. In the pharmaceutical sector, drugs with new molecules or formulations are product innovations that are often protected by Europe-wide patents and market exclusivity, and impact revenue growth by channelling demand to better suited drugs or finding new patients. On the other hand, new drug introductions that exploit size or strength variation may be marketing innovations with lower appropriability and affect revenue growth via a different channel, for instance price discrimination. In turn, the impact of these innovations may differ on revenue growth, they are likely to be substitutes, and hence combining these two distinct forms of innovations into one homogenous measure may be inappropriate. The evidence that we provide is likely to be indicative of the pharmaceutical industry more generally outside of the UK as well. Second, we focus on firm sales within narrowly defined therapeutic areas (which we call business units) rather than overall sales or growth by the firm. This is important for studying the link between innovation and growth, as the new introduction of a specific drug may lead to revenue growth within that therapeutic class, but if the firm has operations in many other larger classes, the overall growth at firm level may be small and the correlation may not be picked up by firm level analysis. While our example is from UK pharmaceuticals, where on average a typical firm operates in 9.48 therapeutic classes, the idea carries over to innovative activities of conglomerate firms outside the pharmaceutical industry. Finally, while several papers have already studied growth or innovation in pharmaceuticals, we add to that literature by documenting the innovation-growth link by size of business units, type of innovative output and which one will have a larger impact on growth.²

The rest of the paper is organized as follows. The next section describes differences in product and marketing innovations, and sets out the hypotheses we test. Section three describes the data and provides descriptive evidence on the relationship between product and pack introductions and growth. Section four outlines our empirical strategy and discusses sources of endogeneity. Section five gives the results and the last section concludes.

In this paper, we use a counts based approach and ask how the introduction of additional products and packs of existing drugs affect the growth of a firm in a therapeutic class (business unit), whether these strategies are substitutes or complements, and whether we should expect results to differ by size of business unit. Firms can introduce new drugs or packs for unilateral reasons if these affect their revenues and/or profits, but may also do so for strategic reasons. Indeed, firms with soon to expire patents can launch additional drugs to deter entry or maintain market shares, or even increase the prices of their branded drugs post generic entry for their brand loyal market segment (Ellison and Ellison 2011; Frank and Salkever 1992; Regan 2008). Similarly, the originator may make a minor tweak on the original drug, say via a dosage change, as part of a ‘product-hopping’ strategy (Hemphill and Sampat 2012; Scott Morton and Kyle 2011). Doing so can prevent a generic drug from gaining market share.³ Our focus is on revenue growth, and not alternative reasons for new introductions per se, and hence we discuss below how or why they may influence growth for a business unit.

To bring either a new drug or a generic copy to the market in Europe, a pharmaceutical firm requires market authorization (MA) from a national authority (Medicines and Healthcare products Regulatory Agency (MHRA) in the UK) or, as of 1995, from the European Medicines Agency (EMA). The process starts with the firm filing for a new drug application (in case of a new molecular entity) or an ‘abridged’ application for generic entry. In the former case, MA is granted after establishing safety and efficacy via three phase clinical trials that take several years to complete while in the latter case, the generic applicant references data for an earlier drug and aims to establish bioequivalence to it.

Drug development is expensive and risky as not all new discoveries make it to the market in the form of a new drug (Torjesen 2015; DiMasi et al. 2016). Further, because patent life is 20 years from initial file date, and significant time is lost in drug development, EU provides two routes that allow innovators to extend the exclusive marketing of their products. The first, available since 1992, is the Supplementary Protection Certificate (SPC) which allows originators to extend the patent for up to five years after the expiration of the original patent, or fifteen years from the first marketing authorization in the EU, whichever is less. Second, there is an explicit data exclusivity period which was introduced in 1984 at the EU level (prior to that, drug approval was at the national level and with varying rules) during which a generic firm may not reference the originators data. Initially, data exclusivity extended either to six years or ten years from the start of MA date depending on the member state (UK had 10 years). In 2005, a new ‘8 + 2(+ 1)’ exclusivity period was introduced at the EU level which provided unified rules of exclusivity across all member states – eight years of data exclusivity during which a generic cannot file for an abridged application, plus two additional years of market exclusivity, i.e., the generic may file the abridged application, but not market the drug, and a final one additional year of market exclusivity for new indication(s) if they constitute a significant clinical benefit.

In the UK, there is an additional step which is particularly relevant for new expensive drugs. National Institute of Clinical Excellence (NICE), which was established in 1999, is charged with undertaking health technology appraisals including those for new medicines as well as providing clinical guidelines for physicians. While NICE is not part of EMA and does not assess safety and efficacy of drugs, it undertakes cost-effectiveness of EMA/MHRA licensed drugs relative to existing practice in the NHS.⁶ Not all new drugs are necessarily reviewed by NICE (Ward et al. (2014) identified 134 new MAs by EMA between 2005 and 2011 and found that only about 54% were apprised by NICE). However if apprised, a positive review means that the drug must be covered by NHS while a negative review means that it will not be covered by NHS (in other cases NICE may give “recommend with restriction” for a smaller population relative to the one listed in original drug approval application). As reported in Jaska et al. (2014a, 2014b), between 2007–2013, the overall negative recommendation rate was 32% but this was mostly driven by oncology related drugs which are often used in hospitals (52% for oncology vs 16% for non-oncology reviews). Nonetheless, drugs that are not eligible to be covered by the NHS are either available over-the-counter (OTC) or face a thin online market. The OTC drugs have reached £2.62bn in Britain in 2016, versus £17.4 billion NHS expenditure in England in 2016/17 (Connelly 2017; N.H.S. Digital 2018).

Use of UK data is attractive, as price is less regulated compared with some of the other EU member states, and because in many member states, reimbursement is often in terms of price per defined daily dose, which lowers the potential for (second-degree) price discrimination via pack variations. Additionally, prices from the UK are often used as comparison when setting prices in other European countries.⁷ Unlike some other European countries that use some form of direct price control, the UK instead employs indirect methods and regulates profit on sales of branded drugs to the National Health Service (NHS) under its Pharmaceutical Price Regulation Scheme (PPRS). The scheme, which was initially introduced in 1957, has evolved over the years, but as of 1986 it applies only to branded drugs. The terms of this scheme are revised approximately every five years. Under the PPRS, firms are free to set prices of branded drugs, which the NHS will reimburse, but prices are indirectly controlled through caps on the overall return on capital for research active firms. Manufacturers can set the price of new drugs without pre-approval by the Department of Health (DH). However, price increases for existing branded drugs need to be justified and approved by the DH, and in practice may even require a reduction of price of some other drugs in the firm’s portfolio to justify the increase in price elsewhere.

We use the 2003–2013 British Pharmaceutical Index (BPI) data series by Intercontinental Marketing Services (IMS), a data set which provides national level monthly sales at the package level for all drugs sold in the UK. The BPI data set contains information in terms of total shipments by nominal sales value and various measures of quantity from wholesalers to retail pharmacies and dispensing doctors, but does not include direct sales from manufacturers to hospitals or to non-pharmacy stores (e.g., grocery stores). Individual drugs in the data are identified by manufacturer, product name, and pack variation. The data also includes information on main/active molecule(s), strength, and form, as well as if the drug is branded or generic, over the counter, or prescription, and if it is reimbursable by NHS or not. We restrict our analysis to prescription drugs covered by the NHS, i.e., over-the-counter (OTC) and non-reimbursable drugs are excluded as these would be sold outside of pharmacies as well, and we do not have complete sales data on those drugs. In 2005, the OTC or self-medication market represents about 12.03% of all pharmaceutical sales in the UK (Habl et al. 2006, p.707). In a later section, we also comment on how our results change if we do not exclude these drugs from the analysis.

For each individual item at the pack level, the data set lists the associated four-digit anatomical therapeutic chemical code (ATC4) and three-digit new form classification code (NFC3). The ATC codes allow drugs to be classified by active ingredients and are further refined by anatomical, therapeutic, pharmacological and chemical subgroups, while the NFC codes provide information on various forms of the drug, e.g., tablet, capsule, extended release, liquid, and cream. In our analysis, we use the combination of four-digit ATC classification and firm identity to define a ‘business unit’, and NFC codes to differentiate among various products within a business unit. We aggregate monthly sales data from individual drugs at the pack level to ATC4 classification by manufacturer and quarter. For example, acid pump inhibitors (A02B2) and psychostimulants (N06B0) produced by Novartis are treated as two separate business units (BUs). The final sample consists of sales data from 218 pharmaceutical firms operating in 385 different ATC4 classes spanning 2,090 business units (i.e., firm-class pairs) observed over 40 quarters (Q2 2003 to Q1 2013) for a total of 56,070 observations.⁸ All sales figures are adjusted for inflation using the consumer price index as deflator with base period set to second quarter of 2003.

As might be expected, sales are highly skewed and we use log of sales (distribution of log sales is relatively symmetric and is given in the Appendix in Fig. 4). Based on tertile distribution of the initial log size of a BU (i.e., first period), we classify them as small, medium, and large and where the cut-off points are 9.84 and 12.50 in log size.⁹ In terms of growth, defined as the change in log sales, we find some variation by the size of the business unit. Figure 1 plots the natural logarithm of sales in period t against the one period lagged value of the same variable (each period is a quarter), and plots the frequency of such pairs on the z-axis. Values along the 45-degree line indicate absence of growth for the BU while those above the line show positive growth. Variation appears to be larger for middle level of sales, and on average, small-sized BUs appear to grow more than very large-sized ones. Nonetheless, there are relatively fewer observations that deviate from the 45-degree line and most are very near the line.

We define a product as a unique combination of proprietary or international nonproprietary name listed in the database, its specific formulation (tablet, extended release, liquid, gel, etc.), the associated ATC4 therapeutic class, and the name of the manufacturer. If any of these change, we count it as different product. Next, for a given product, packs can vary either by size (28 pill pack vs 14 pill pack) or by strength (100MG vs 250MG), and after counting total number of packs per product, we define pack variety (or just variety for short) as the total number of packs for a BU minus the total number of products. Thus, at the BU level, and our pack variety measure begins at zero when each product is available in only one pack size. This is so that when a new product is introduced, it does not lead to an automatic increase in pack varieties offered by the BU as well (since each product must come in at least one pack size).

While we use count of drugs in business unit to measure innovation, a shortcoming of this approach is that it weighs all new drugs equally, even though some drugs may be small tweaks on the original variant as mentioned earlier. In our data, it is not possible to construct measures that capture the innovativeness of new drug. For instance, studies that use patent counts try to overcome this difficulty by using citation count of a patent.

Merger and acquisition activities during the period are handled by IMS by retroactively reassigning the sales and associated products to the end-of-period corporation that owns them as if they owned it for the entire period. Thus, if two firms merged during the observed period, they appear to be a single firm from the start (a similar method is used for instance in Bottazzi and Secchi (2005)) and hence a merger would not lead to an increase in the count of drugs and revenues due to the merger. Generally, a similar rule applies to product line acquisitions; however, here we found some inconsistencies in data which lead to small errors in counts of products. Thus while we use count of drugs as a measure of innovative activity, this is not a perfect measure but based on the sample of cases that we investigated this is not a frequent issue.¹⁰

Figure 2 plots the total sales and average number of product and pack varieties per BU for all ATC4 classes. Consistent with other industry reports of a slow down in manufacturing, our data shows that revenues dropped by 22 percent during the 2004–2012 period. The figure shows that total sales declined over the period, but also that there is some seasonality in sales. More interestingly, while the average number of products per BU is stable around 2.2, there is a decline in the average number of pack varieties starting with 2009 and continues for the next two years until it settles to a new steady state value. While this decline in the number of pack varieties coincides with the global financial crisis, we do not observe a similar decline in pack varieties in the OTC and non-NHS reimbursable drugs (recall we also have data on those drugs that is not included in this analysis). The start of the decline however does coincide with the last PPRS update in the observation period, which was negotiated at the end of 2008 and was effective for the 2009–2013 period. The figure suggests that there is a positive relationship between aggregate sales and average products/varieties per BU, and that perhaps sales are in decline in the UK since firms do not bother introducing new products and packs. This however is not necessarily true. Similar figures drawn at class level indicate different relationships between sales and product and pack introductions at this disaggregated level (see Fig. 5 in the Appendix for some selected classes).¹¹

Finally, and before analyzing growth more systematically in the next section, Table 2 shows growth just before and after a BU introduces a product or a pack, and compares it with the growth of competitors in the same ATC class (the table reports four period moving average of growth before and after introductions). On a class-by-class basis, we find that when a BU introduces a new pack, on average its growth changes from 0.0319 to 0.0507, and for the competitors in the same class but not introducing in that period, growth changes from -0.0103 to -0.0243 (these figures are statistically different from zero). The impact of product introductions has a similar pattern and is given in Table 2.

As our point of entry, we adopt the empirical firm growth model first introduced by Evans (1987a, 1987b) which specifies the change in log size as a linear function of lagged log size, age, other firm specific characteristics of interest, and an additive error term. However, we do so in the context of business units and panel data. Specifically, let \(g_{bt}\equiv (\ln R_{bt}-\ln R_{bt-1})\) denote the revenue growth for business unit b (composed of firm f in anatomical class c) between period t and t − 1. We model the growth equation by an autoregressive distributed lag specification with M lags of the dependent variable and up to L lags of the time varying strategic variables (products and varieties) as In the equation above, the business unit growth in period t is a linear function of lagged growth (with M lags), current and past values of number of products and pack varieties per product (p,v and their interactions up to L lags), and where the latter variables are potentially endogenous. In the final specification, we set M = 1 and L = 4. The specification also includes other relevant variables, prominently the one period lagged size (s_b,t− 1), which we capture with the lagged natural log of revenue. Note than in addition to measuring the impact of size on growth, earlier literature has also highlighted the relationship between size and innovative output (Acs and Audretsch 1988; Graves and Langowitz 1993). Thus, not including size would cause an omitted variable bias as it would be correlated with the variables of interest, i.e., counts of products and packs.

We also include other class-time varying variables such as the number of firms, Herfindahl index, and lagged values of number of products and pack varieties at the class level in the vector x_ct, and incorporate additional exogenous controls that vary at the BU level in x_bt (specifically dummy variables to indicate if the business unit has sales due to generic products and if its products are available via parallel imports in the UK and lagged value of mean drug price in log form).¹² All variables listed here are described in full detail in the Appendix in Table 7 and correlations between them are given in Table 8. Finally, note that while the model includes an interaction term and up to four lags of this term (p_b(t−l)v_b(t−l) where l = 1,2,3,4), we do not include any mixed lags (for instance, two lags of products interacted with three lags of pack varieties). In part, this decision is driven by the desire to keep the model parsimonious, but it is also based on low correlations between product and pack introductions in different periods. For instance, while the correlation in change in number of products and packs from the same lags is fairly high (between .26 and .27, and hence these terms are included in the model), the correlation between mixed lags is typically .05 or less (a full matrix showing correlations in change in products and packs is given in Table 9 in the Appendix).

For short panels, it is common to let the time effect be fixed, here given by u_t and hence we exclude it from the composite error term, which is given by the sum of the business unit unobserved heterogeneity, α_b, and the pure idiosyncratic error term, ε_bt. We assume the idiosyncratic error to be serially uncorrelated and uncorrelated across ATC4 classes. Because of the presence of a lagged dependent variable we follow the dynamic linear panel model literature and take a first difference of Eq. 2 to remove inconsistencies of the parameters associated to the various lags of the dependent variable. Thus, we estimate the growth equation above in first difference form given by

While the first difference form eliminates the unobserved heterogeneity specified earlier as α_b term, it is worth noting that by using a first difference estimator, we also rule out estimating the effect of any time-invariant factors such as the age of the business unit, which may be important (see for instance Cabral and Mata 2003; Coad et al. 2016).

Our paper contributes to the innovation-growth literature in various ways. First, we map innovative output rather than input to growth, and highlight the fact that in the pharmaceutical sector, we have different types of innovations which differ in their appropriability conditions, and hence may have differential impact on growth (Tavassoli and Karlsson 2015). Product innovations are drugs with new molecules or formulations, which often enjoy patent and marketing exclusivity, and can serve a patient base for which earlier drugs were less suitable. These types of innovations differ from marketing innovations, such as a new pack that varies by size or strength and could be introduced due to price discrimination motivation. These pack varieties will not necessarily have any additional marketing exclusivity associated with them. Heterogeneity in these sources of innovations can have an asymmetric impact on revenue growth, and in this work, we emphasize this aspect.

A second contribution is that we refine the empirical analysis at the business unit level rather than at firm level. This distinction is important, since the contribution of each innovation to a business unit growth can get lost at the firm level as many business units not innovating can wash it out. This in fact may be a reason why the literature has not found a robust link between innovative activities and firm performance (for a review, see Coad 2009; Audretsch et al. 2014). Aggregation across heterogenous sub-markets in which a pharmaceutical firm may be operating, i.e., vastly different therapeutic classes, may mask the true relationship between innovative outputs and growth, a point made earlier by Corsino and Gabriele (2011) in a different setting. A fundamental question then is whether the lessons learnt in this current research about the link between innovation and growth at the business unit level imply more generally similar relationship at the firm level? Our data indicate that large business units are associated to firms that are almost double the size of firms associated to small business units. Industries where sub-markets have little or no heterogeneity in products or services, firm and business unit level analysis ought to give similar conclusions. However, when there is significant variation in product sub-markets, inferring links between innovation and growth from business unit level analysis to firm level may not be straight forward. Nonetheless, in our view, in such cases, the appropriate analysis is at a more disaggregated level. For instance, in telecommunications, it may be more useful to study the output innovations within sub-sectors, such as wireless communications, processing systems, long distance carriers, and broadband and data services, and linking those to specific units of AT&T, Verizon, Vodafone etc. that are active in these areas, rather than overall innovative outputs and firm level revenue changes which could be driven by many other factors in these firms.

Finally, our analysis adds to the literature on growth-innovation link by firm size, albeit we do so in the context of pharmaceutical business units and by type of innovations (see Coad 2009). As pointed out in Pagano and Schivardi (2003), small firms are important for job creation and growth, as they intensify competition. In our exposition, we showed that small entities (small business units) are able to get higher returns from innovations, vis-à-vis growth opportunities, particularly product innovations, compared to their larger counter parts. In part this may be because they introduce more innovative products, as for instance indicated by entry order of their products in the therapy class (but it could also be due to other factors not analyzed here, for instance better management or market intelligence). The fact that small business units receive a higher return from product innovations than large business units are evocative of a dynamic market. Also, our finding that new drug introductions generate long run business-unit revenue growth is suggestive that the pharmaceutical market in the UK, through appropriability conditions, is endowed with powerful tools to expand, even when patient bases in therapy class may be slow to grow.

There are several limitations of our work that suggest extensions. While we control for competition using a HHI index at the class level, we have not allowed for an interaction between heterogeneous innovation and competition. Mazzucato and Parris (2015) have studied (for high-growth firms) the effect of R&D investments on growth during periods of intense and soft competition. One could go one-step further and test whether the heterogeneous effect of different innovative outputs on growth is affected by competition, or market structure more generally. This could be executed using different periods of competition, as in Mazzucato and Parris (2015), or by interacting an index of competition with innovative outputs/inputs. Alternatively, it would be useful to study the growth-innovation link in the context of endogenous market power and exit of non-innovating business units. A related possible extension of this study is an investigation of the asymmetric effect of innovation for high-growth and low-growth business units, using a quantile regression as in Coad and Rao (2008) and Capasso et al. (2015). This experiment could be extended to compare growth for top firms/business units (top 10, top 20, and top 50) and study whether skewness of size is informative on the impact of innovative outputs on growth.

In our analysis, we focused on innovation affecting growth, and while we acknowledge the reverse causality and attempt to correct for this source of endogeneity, we do not explicitly model what type of business units are more likely to introduce innovations. Nor do we fully investigate whether the novelty of innovations differ by business unit characteristics, or how these characteristics may interact with market structure to determine future innovation. A conclusion from this study is to facilitate introduction of new drugs relative to pack variation. However, there is need for further research that separates out the types of new drug introduction, distinguishing between radical innovations and small incremental variations of existing drugs such as me-too drugs, or those introduced as part of a product hopping strategy (Hemphill and Sampat 2012; Bokhari and Fournier 2013). The latter strategy may also have some anticompetitive effects and it would be important to test for the separate effects that the two have on growth, with particular focus on small versus large firms (or business units). We hope to investigate some of these issues in future research.