Shin and Stulz (1998)

1. Shin and Stulz (1998) • Internal capital markets allow diversified firms to fund projects that because of informational asymmetries and agency costs, the external capital market would not finance • If the internal capital market functions perfectly, then investment by a segment in a diversified firm should depend only on its investment opportunities, not on its cash flow; only firm-level cash flow should matter • However, much recent empirical work shows that diversification is not successful: on average, diversified firms are valued less than matching portfolios of specialized firms by 13-15% • There is also evidence that diversifying acquisitions are associated with decreases in shareholder value • This paper investigates whether diversification is unsuccessful because internal capital markets function imperfectly

2. Results • This paper uses segment data from Compustat • The authors find evidence that the internal capital market is active but imperfect: • Investment by a segment of a diversified firm does depend on the cash flow of the firm’s other segments, but significantly less so than on its own cash flow • The investment by segments of highly diversified firms is larger and less sensitive to their cash flow than the investment of comparable single-segment firms • The sensitivity of a segment’s investment to the cash flow of other segments does not depend on whether its investment opportunities are better than those of the firm’s other segments. This suggests that resource allocation is inefficient

3. Causes of Internal Capital Market Failure The authors discuss possible sources of ICM failure: • If the firm’s management pursues its own objectives at the expense of shareholder value it might use the ICM to finance pet projects with negative NPV • Division managers might expend resources to influence resource allocation and by doing so create deadweight losses An efficient internal capital market should allocate limited funds to maximize firm value, but the authors’ results suggest that firms tend to ignore differences in investment opportunities across segments This is a form of “socialism” within the firm

4. Methodological Issues • To assess the dependence of a segment’s investment on other segments’ cash flows, the authors regress investment on segment cash flows, proxies for segment investment opportunities, and other segments’ cash flows • Recall Lamont’s problem; it is not possible to compute Tobin’s q at the segment level because we do not observe the market value of a segment • Given this, they use the median q for the specialized firms in the segment’s industry, where industry is defined at the 2-digit SIC level • This is probably a poor measure because the 2-digit SIC level is very aggregated and individual firms in the same segment differ substantially • The authors acknowledge that if they poorly estimate investment opportunities, they might overstate the impact of a segment’s cash flows on investment if cash flow proxies for investment opportunities

5. Scharfstein and Stein (2000) • These authors also note that a large body of evidence suggests that diversification is value reducing: diversified firms trade at a discount compared to comparable specialized firms • This could occur if internal capital markets are inefficient, as work by Lamont (1997) and Shin and Stulz (1998) suggests • But in terms of theory, it is not obvious why internal capital markets would be inefficient • In fact, theoretical work by Stein (1997) suggests that internal capital markets (headquarters) add value, even though top management values private benefits in his model • The authors provide a model where division managers can engage in influence activities as well as productive activities • In order to ensure that managers spend their time in productive activities, HQ must effectively bribe them, and optimally does so through capital allocation rather than direct cash payments

6. Matsusaka (2001) • This is another theory paper that attempts to explain why diversified firms trade at a discount relative to single-business firms • However, the approach is quite different from one that emphasizes inefficient resource allocation and internal problems • The paper shows how taking a dynamic approach can explain several facts that appear puzzling if we look only at cross-sectional data viewed through static models • The author introduces a dynamic model in which a firm is composed of a set of organizational capabilities that can be profitable in multiple businesses • Diversification is a search process by which firms seek businesses that are good matches for their capabilities

7. Basic Idea • Organizational capabilities are valuable • Given this, it may not be optimal for a firm to go out of business as sales of its products decline (and its value drops) • Rather, it may be optimal to search for a new industry to operate in • The process of searching is fraught with uncertainty, and in some cases the uncertainty can only be resolved with experimentation – this requires entering new industries (diversifying) • Thus, diversification can be optimal even if specialization is efficient; the search process is necessary in order to find a match • Once a match is found, the firm can specialize again • Along the way, some experiments might fail; thus, diversification may look like a mistake in the short run and diversified firms would in general trade at a discount relative to stand alone firms (who have already found a good match for their capabilities)

8. Maksimovic and Phillips (2002) • The authors begin by noting that several studies claim that conglomerates destroy value and do a poor job of investing across business segments • Key explanations for these findings require imperfections in firm governance (agency theory) or financial markets (incorrect valuation of industry segments) • Typically, studies assume that conglomerates and single-industry firms possess similar abilities to compete, but that conglomerates have simply chosen to operate in more industries • The authors argue that firms differ in their ability to exploit market opportunities; it is necessary to develop a neoclassical model with this feature in order to properly explore what neoclassical theory suggests before appealing to alternative explanations

9. Approach • The authors develop a profit maximizing neoclassical model of optimal firm size and growth across different industries based on differences in industry fundamentals and firm productivity • In the model, a conglomerate discount is consistent with profit maximization • The model predicts how conglomerate firms will allocate resources across divisions over the business cycle and how their responses to industry shocks will differ from those of single-segment firms • The authors proceed to use plant-level data and find that the growth and investment of firms are related to fundamental industry factors and individual segment level productivity • The majority of conglomerate firms exhibit growth across industry segments that is consistent with optimal behavior

10. Basic Ideas in the Model • Firms with a comparative advantage arising from skill in a particular industry have higher growth and attain a larger size in that industry • As a firm’s returns to growing within an industry diminish, the firm moves into other industries • The optimal number and size of industry segments a firm operates depends on its comparative advantage across industries • Firms that are very productive in a specific industry have higher opportunity costs of diversifying • Thus, in equilibrium, singles-segment firms are more productive than conglomerates of the same total size • Also, the larger segments of conglomerates are relatively more productive than smaller segments

11. Demand Shocks in the Model • The effect of demand shocks on the growth of a conglomerate’s segment depends on the segment’s productivity • The same positive shock that causes high-productivity firms to increase their market share can cause low-productivity firms to decrease their market share • This implies that empirical tests of how conglomerates invest in response to shocks are misspecified if they do not control for the productivity of the firm’s segments • Further, demand shocks faced by a segment of a conglomerate firm affect the growth rates of other segments, despite the absence of agency costs and financial market imperfections • If a firm’s segment is more productive than its other segments, a positive demand shock for that segment decreases the growth rates of other segments; models of inefficient capital markets suggest the opposite, and they test this hypothesis

12. The Model • As in Lucas (1978), managerial talent varies across firms • Some firms are more productive in some industries • Any given manager is better at managing a small firm than a large firm; there are diseconomies of scale within firms • Firms optimally produce in industries in which they have a comparative advantage • Consider a population of firms that can operate in Industry A, B, or both • All firms are price takers and produce a homogeneous output • Firms use two inputs: capital capacity and labor • For tractability, assume that each unit of capacity produces one unit of output

13. Formal Structure

14. Optimization

15. More Industries • The authors use numerical results to confirm that the patterns persist across multiple industries • These more general cases yield testable predictions about the relation between diversification and relative productivity • In each example, there are 10 industries with exogenous output and input prices and cost parameters • There are 25,000 potential firms; each one is assigned firm-specific ability for each industry (high ability is the same as high productivity). They compare two cases: • A firm’s ability is one industry is independent of its ability in all others. Each ability assignment d is drawn from a normal distribution with mean 1 and standard deviation .5 • A firm has a common ability parameter that is drawn from a normal distribution with mean 0 and s.d. .25. This measure is added to the segment specific measures from 1.

16. Numerical Results • For each firm, they determine the number of industries the firm operates in and its size (output) in each one • They end up with simulated one-segment firms, two-segment firms, etc. • In case 1., the productivity of highly diversified firms in each segment is lower than the productivity of more focused firms because it is very unlikely that any single firm is productive in all industries; firms that choose to produce in a lot of industries tend to have mediocre ability in all of them • A simple OLS regression using the simulated data shows that firms’ mean productivity is positively and significantly related to their focus, measured by the Herfindahl index, and size • This is essentially a conglomerate discount

17. Numerical Results • In case 2, the main segments are still more productive than the others • The main difference is that now firms that choose to operate in many segments are more productive in general • However, again a simple OLS regression shows that firms’ mean productivity is positively and significantly related to focus and size • Thus, both cases are consistent with a conglomerate discount • However, agency-based empire-building models are also consistent with a conglomerate discount • In order to differentiate between the two models the authors consider the effects of demand shocks

18. Price Shocks and Growth

19. Agency Models The authors’ tests can detect only some types of agency behavior: • Agents may invest optimally but divert a portion of the proceeds for their own benefits as higher overhead at the firm level or overpayments for acquisitions. This diversion might not be detectable. • Agents may over-invest but still allocate resources according to marginal returns. Comparative statics results would not differ from the model’s predictions. • Agents might misallocate resources across segments. This is the type of agency behavior the internal capital markets literature has been most concerned with. Such misallocations would lead to the rejection of the model.

20. Empirical Analysis • The null hypothesis is that the growth and investment decisions of conglomerate firms are consistent with the model • The authors use three approaches to test the model: • They calculate the productivities of conglomerates and of single-segment firms and examine whether they are in accord with Proposition 1 • They compare the growth and investment patterns of conglomerates to those of single-segment firms. They test Proposition 3 vs. the inefficient cross-subsidization emphasized in the ICM literature • They identify a subsample of failed conglomerates that were split up or experienced substantial decline in the number of segments they operated to examine whether such firms were less likely to follow policies similar to those the model predicts (optimal policies)

21. Data • They examine an unbalanced panel of firms for the period 1972-92 • To be in the sample, firms had to have manufacturing operations producing products in SIC codes 2000-3999 • They use data from the Longitudinal Research Database (LRD) of the Center for Economic Studies at the Bureau of the Census • The LRD contains detailed plant-level data on the value of shipments produced by each plant, investments broken down by equipment and buildings, and the number of employees • The LRD tracks about 50,000 manufacturing plants every year in the Annual Survey of Manufactures (ASM) • The ASM covers all plants with more than 250 employees; smaller plants are selected every fifth year to complete a five-year panel • The survey is not voluntary; all data has to be reported

22. Sample Construction • They aggregate each firm’s plant-level data into firm industry units at the three-digit SIC code, which yields 374,339 segment-year observations • These segments may not correspond to divisions of firms or the segments in Compustat (which also differ from actual divisions) • They do not capture any headquarters or division-level costs that are not reported at the plant level (like overhead or R&D) • They classify a firm as a multi-segment firm if it produces more than 2.5% of its sales outside its principal three-digit SIC code • They also report results where they use a 10% cutoff • For multiple-segment firms, they classify each segment into main and peripheral; main segments account for at least 25% of the firm’s total shipments

23. Sample Construction • They need to compute growth rates for some of their analysis so they lose observations that are noncontiguous; the number of observations falls to 279,000 • Of these observations, 76% are single-segment firm-years • Then they exclude segments that have continuously compounded annual growth rates greater than 100% in absolute value; the number of segment-year observations falls to 266,103 • On the whole, the data is superior to Compustat, where reporting biases have greater effects. Firms have considerable flexibility in how they report segments for Compustat • However, the data here is still limited, mainly because there is no coverage outside manufacturing

24. Variables The two main dependent variables: • A firm’s segment growth • Investment The primary independent variables: • Segment productivity • The change in aggregate industry shipments • They include a segment’s lagged size and the lagged number of plants in the segment as control variables • The subtract out the industry average in each year from all segment-level variables except for the number of plants • They also consider how growth is related to segment operating margin and value added per worker

25. Productivity of Industry Segments • Their primary measure of performance is total factor productivity (TFP); they calculate TFP for all firm segments at the plant level • TFP takes the actual amount of output produced for a given amount of inputs and compares it to a predicted output • Here, TFP is (a residual in a regression plus a plant fixed effect) divided by the standard deviation of TFP for the industry • They compute segment-level TFP by aggregating plant TFPs • Their output measure is value of shipments; all inputs and outputs are deflated using information from Bartelsman and Gray (1994) • To compute the predicted output, they use a translog production function for each industry; this is a second-order approximation to an arbitrary production function (use a Taylor series expansion) • For each industry, they estimate this production function using an unbalanced panel with plant-level fixed effects; they include all plants in the industry in the 1974-92 period

26. Industry Variables • To measure industry demand, they use industry shipments at the four-digit SIC code level deflated using a price deflator from the Bartelsman and Gray (1994) National Bureau of Economic Research (NBER) database • They aggregate this data to the three-digit level • They detrend this data by regressing the value on a yearly time trend; they measure industry shocks as the difference between the actual and predicted value • They also code years as expansion years when both real and detrended aggregate industrial production increase relative to the previous year (and as recession years when both decline) • They also investigate the impacts of industry cash flow on segment growth, but they do not present these results (they are similar to those presented)

27. Results • The average segments of single-segment firms are more productive than those of conglomerate firms for all size categories except for the smallest size segments (small focused firms are not more productive than the small segments of conglomerates) • There is a strong negative relation between a segment’s size rank in a conglomerate and its productivity; this is consistent with the hypothesis that conglomerates are larger in segments in which they have a comparative advantage • Holding within-conglomerate rank of a segment constant, its productivity increases as the total number of segments the firm operates in increases; this is consistent with the hypothesis that large conglomerates have capabilities that are portable across several industries

28. Growth and Productivity over the Business Cycle • There is a strong positive association between growth and productivity in expansions; conglomerates grow their productive segments at much faster rates than their unproductive segments • In recessions, firm growth also increases with productivity, and conglomerates cut growth much more in their peripheral segments than in their main segments • Further, as the model predicts, the differences between the growth rates of high and low productivity firms are lower during recessions than expansions • They compare industry adjusted annual growth rates of the most productive quartile of firm segments with the quartile of least productive segments and find that the difference was 2-2.5 percentage points higher in expansion years

29. Growth and Relative Productivity with Industry Shocks • They examine the effect of productivity and industry fundamentals on the real growth rates of conglomerate and single-segment firms using multivariate regressions • They measure the dependent variable, industry-adjusted segment growth, in real 1982 dollars, subtracting out the industry average each year • Productivity and segment size are also industry adjusted by subtracting the industry averages in each year • The econometric specification takes the model’s predictions into account: the growth of a segment should depend on the interaction between the segment’s productivity and the sign of the demand shock in the industry, and the relative productivity and demand conditions of the firm’s other segments should also matter

30. Variable Construction They use two variables to measure how the growth of a conglomerate firm’s segment is affected by the firm’s other segments • They measure the productivity of the other segments by weighting the TFP of each segment by its predicted sales • They test for the interaction between the segment’s shock and the shocks in other segments by interacting the segment’s relative industry demand with the other segments’ weighted productivity. Relative industry demand is 1, 0, or -1 when the segment’s change in shipments at the industry level is greater, equal, or less than the firm’s median segment. Their theoretical model predicts that this variable should have a negative effect The regressions also control for the segment’s TFP, the total number of plants owned by the firm, the log of firm size, and a segment effect (this is a random effect but they get the same results with fixed effects)

31. Results • Growth is positively related to segment productivity • The number of plants is negatively related to growth • The real change in industry shipments times segment productivity has a positive effect, as the model suggests • The segment’s growth rate is negatively related to relative demand times the other segments’ relative productivity, as the model suggests

32. Main vs. Peripheral Segments • The authors find that both main and peripheral segments’ growth rates have positive sensitivity to relative productivity • Peripheral segments are more sensitive to productivity than the main segments • When they examine investment, they find that both conglomerate and single-segment firms’ investment is sensitive to productivity and fundamental industry factors • The interaction of productivity with industry shipments is positive and significant for peripheral and main segments; the interaction of relative demand and the firm’s other segments’ productivity is negative • These findings reinforce the authors’ conclusion that conglomerate firms do not insulate their peripheral segments; there is no cross-subsidization

33. Conglomerates that Failed • The authors compare conglomerates that were broken up to those that survived by defining significant restructuring as a 25% or greater decrease in the number of segments a conglomerate operates by 1992 (the last year of their data) • The model does not hold in the period prior to the restructuring of conglomerates that experienced significant restructuring; there is an insignificant relationship between growth and the interaction of change in shipments and own segment TFP and between growth and the interaction of relative demand and other segments’ weighted TFP • This suggests that such firms might have experienced agency problems, but even for these firms there is not a positive association between other segments’ relative productivity and growth (so there is no evidence of cross-subsidization)

34. Relation to the Literature • Prior studies have used industry Tobin’s q measured using single-segment firms to proxy for investment opportunities for conglomerate firms • This is appropriate only if firms have homogeneous investment opportunities, which is not the case • The authors demonstrate that conglomerates have systematically different productivities than single-segment firms • Their finding of lower productivity is consistent with the conglomerate discount observed in the data, but the model shows that the discount is consistent with value-maximizing diversification

35. Relation to the ICM Literature • The authors find that firms grow their more productive segments when those segments experience a positive demand shock and shrink these segments when other segments are more productive and experience positive relative demand shocks • The previous ICM literature suggests that segments in conglomerates invest more than single-segment firms in bad industries and invest less in good industries • The differences may arise because the authors control for productivity at the industry level by aggregating plant-level data, whereas previous work relies on measuring industry q

36. Conclusions There are four main results: • Plants of conglomerate firms are less productive than are plants of single-segment firms of a similar size, except for firms of the smallest size; this is consistent with a conglomerate discount and can be rationalized with a model where firms maximize value • The productivity, size, and investment patterns in conglomerate firms’ segments is consistent with a simple value-maximizing model; firms are larger and invest more in segments where they have a comparative advantage • The growth of both productive and unproductive segments is consistent with the value maximizing model; segment growth is strongly related to industry fundamentals and segment productivity, and there is no evidence of cross-subsidization • Only failed conglomerates appear to exhibit agency problems