Do Managers Systematically Underestimate The Potential for Waste Reduction?

Transcription

1 Do Managers Systematically Underestimate The Potential for Waste Reduction? LUCA BERCHICCI Rotterdam School of Management Erasmus University, Rotterdam, the Netherlands ANDREW A. KING Tuck School of Bus. at Dartmouth College 114 Buchanan Hall Hanover, NH Tel: Abstract: Scholars have long inferred the managers make systematically biased assessments of the potential for waste reduction, and these biases partially explain reported examples of x-inefficiency in waste reduction. Previous studies have neither developed the underlying logic of this conjecture nor tested it empirically. In this article we do both. We also show that managers indeed underestimate the potential for waste reduction, and we show that well-known heuristics and biases may account for these erroneous estimations. Our research extends the environmental literature, and for the broader literature it uncovers a potential cause for other types of x-inefficiency.

2 Do Managers Systematically Underestimate The Potential for Waste Reduction? Introduction Almost 20 years ago, scholars such as Michael Porter and Claus van der Linde suggested with a growing confidence that firms might be able to be both green and competitive (Porter & Van der Linde, 1995a, 1995b). Their articles and closely allied research had an enormous effect on scholarship, public policy, and business policy. Porter s claim was also part of a broader literature suggesting that managers might systematically miss opportunities to make their firms more profitable. For example, scholars proposed that managers often underestimate the true benefit of inventory reduction, or new market entry, or markets dominated by lower income people (Christensen, 1997; Prahalad & Hammond, 2002). In some cases, the broad predictions of these theories have been tested empirically. For example, many scholars have tested whether waste reduction is associated with higher profitability (Hart & Ahuja, 1996; King & Lenox, 2002; Russo & Fouts, 1997). The underlying mechanisms of these theories, however, have been barely explored. In this paper, we attempt to fill this gap by testing one of the commonly proposed mechanisms for why managers might miss profit opportunities. We explore whether managerial heuristics and biases may be a root cause of missed opportunities for waste reduction among US manufacturers. There is little need to wonder why no previous study has directly tested quantitatively whether managerial expectations provide one cause of missed profit opportunities. Conducting such research seems almost comically infeasible. To study the issue, one must have access to a panel of expectations or predictions from many managers and over many years. One needs to directly measure compare these expectations to what actually happened, and one needs to have access to other measures of activity in order to limit the potential for rival hypotheses. Finally, to avoid problems associated with cross-sectional studies, a longitudinal panel is required. These requirements seem impossible to meet, but remarkably, all (and more) are met by data from a little known part of the US Toxic Release Inventory. The TRI requires technicians in many US facilities to report waste levels for current years and predict waste for the future year. They do this for each waste chemical in the facility and the data have been collected in a consistent manner since 1991.

3 Our research contributes directly to theories of why managers may miss improvement opportunities -- sometimes referred to as "systematic x-inefficiency" (Leibenstein, 1966). It provides the most direct evidence of possible cognitive underpinnings for one specific theory, which says that managers systematically miss opportunities to reduce waste. It also directly tests the role of cognitive heuristics in determining managerial expectations, and it provides the first large scale test of recent theorizing connecting loss aversion and goal setting. It shows that managers respond more strongly when they fail to meet the expectations they have set. It also tests the theory of "under confidence with practice" (Koriat, Sheffer, & Ma'ayan, 2002) as a possible explanation for biased managerial expectations. In summary, this article provides both an important test of practical theory and new empirical information on the role played by managerial heuristics and biases. Theory Several management theories predict that managers systematically miss opportunities to make their firms more profitable. As a result, firms are consistently x-inefficient. That is they are not on the efficiency frontier in turning organizational inputs into valuable outputs. These theories have had important implications four both management scholarship and education because they provide a clear role for advancing productivity. For example, in the late 1980s and early 1990s, several scholars proposed that managers could improve the profitability of their firms by reducing the pollution generated by these firms (Cebon, 1992; Green & Berry, 1985; Kleiner, 1991). The implication of this theory was clear: managers were not investing enough effort in waste reduction. As a result, the average facility or firm could become more profitable by redirecting efforts towards waste reduction. Similarly, theories of lean production suggested that firms systematically held too much inventory and that firms could become more profitable by reducing inventories below current levels (King & Lenox, 2001; Klassen, 2000; Lenox & King, 2004; Pil & Rothenberg, 2003). Finally, theories of disruptive innovation suggest that managers underestimate the potential value of emerging markets and so do not allocate sufficient resources to entering these markets (Christensen, 1997). Once again the implication is that most firms could benefit by redirecting effort towards market exploration. All three of these theories provide multiple explanations for why managers might systematically miss opportunities to improve profits. Each suggests that missing information or misaligned incentives can be important. They also suggest that organizational barriers and inertia can be important contributors to x-inefficiency. Each theory also suggests the managerial perceptions and expectations may play an

4 important role. For example in the case of lean production, scholars propose that managers understanding of the value of inventories may overlook the potential learning benefits created by lower inventory levels (King & Lenox, 2001; Klassen, 2000; Lenox & King, 2004; Pil & Rothenberg, 2003). Similarly, managers facing new markets may frame these new markets using existing performance criteria and so misperceive the potential of these new markets (Christensen, 1997). In the case of pays to be green (PTBG) theory, scholars suggest that managers may not understand how and why waste reduction efforts can be successful (Hart & Ahuja, 1996; King & Lenox, 2002; Russo & Fouts, 1997). Misperceptions of the opportunities for performance improvement can interact with other theories in explaining x-inefficiency. For example if managers believe that they can make little headway in reducing waste in manufacturing operations, they may provide misleading information to higher-up decision makers. Thus, biased perceptions at one level may lead to missing information at another level. Similarly, misunderstanding of the potential costs and benefits improvement may cause the organization to adopt inefficient incentives with respect to improvement efforts. As shown in Figure 1, a misperception of the potential for waste reduction can lead to unexpected waste reductions. If a manager s perception of waste reduction (dashed line) is that it is more difficult than it actually is, he will expect too little waste reduction for any level of effort. Of course, such a simple analysis ignores that the effort level is also chosen by managers. Waste reduction efforts, like many activities, have diminishing returns and economic theory suggests that managers should optimize their firm s performance by spending effort in waste reduction up to the point where the marginal cost of abating waste equals the marginal benefit of waste reduction. Appendix 1 provides an example of a simple calculation of the optimal effort level, and Figure 2 shows the result for one set of parameters. If managers misperceive the true difficulty of waste reduction, they will not choose the optimal effort level and will miss opportunities to profitably reduce waste. Also as shown in the graph, when managers overestimate the difficulty of waste reduction, they may tend to predict less waste reduction than will actually occur. They will also choose an inefficient level of effort and less waste reduction will be done than is optimal. As discussed earlier, this correlation between misperceptions of efficacy (or difficulty) of waste reduction and suboptimal waste reduction is a key part of theories that it pays to be green. Many scholars have argued that managers systematically overestimate the difficulty of waste reduction particularly with respect to the benefits (Palmer, Oates, & Portney, 1995). The same logic has been

5 applied in several other theories of x-inefficiency. For example, scholars of lean production have argued that managers hold too much work-in-process inventory because they overestimate the difficulty of reducing inventory and underestimate the value of doing so. A similar logic exists in several other theories of x-inefficiency. The theory could be best tested by direct access to the managerial mental models of waste reduction difficulty. A close and far more tractable test is that managers will tend to expect less waste reduction than actually occurs (i.e. forecast that the facility will produce more waste than actually occurs). H1: Managers will systematically underestimate the potential for waste reduction. Mechanisms Why would these errors in estimation persist over time? If managers are able to learn from past experience, they should adjust their future predictions until they are unbiased. For underestimation to continue, managers must respond more strongly to overestimation of waste reductions than underestimations? Why might this be? One possible explanation is that predictions of future performance actually become benchmarks against which performance is measured. If the perceived cost of failing to meet a benchmark is greater than the benefit of exceeding it, managers may choose to adjust their predictions so as to increase their chances of success. Why might failing to achieve a goal be more costly than the benefit of exceeding it by an equal amount? One explanation may be that organizations meet out punishments for failure that are disproportionate to the rewards they set for success. Consider a situation where an actor wishes to make an accurate prediction of future improvement. They are uncertain about future outcomes but they have an unbiased expectation of what these might be (see Figure 3). If they are penalized equally whether or not they guess to high or too low, they will make an unbiased prediction. If, however, they are penalized more for error on one side, they will make a biased prediction. Why might they be penalized more for a biased prediction? In many cases a prediction that is too high results only in some excess capacity or excess pending, but a prediction that is too low results in a functional failure. Imagine, for example, making a prediction about how much water to take on a hike. Too much and the hikers expend unnecessary effort on carrying the water, too little and they risk heat exhaustion. Such asymmetric penalties are especially likely to occur in

6 organizations if predictions are used as goals. In this case, failure to meet a goal may be punished more than an equally scaled exceeding of a goal. When accuracy costs are asymmetric, the actor may adjust his expectations (consciously or unconsciously) to be more pessimistic. This notion is popularly understood as sandbagging or expectations management : the process of setting a goal that one has a good chance of meeting. This process of biasing expectations would mean that managers would systematically under predict future improvement. It also suggests that when err on the negative side (failure to meet expectations) they will adjust future scores more strongly than if they err by exceeding expectations. H2a: Managers will update future expectations more strongly when they fail to meet a past expectation than when the overreach a past estimation. In our discussion above, we assume that an actor is communicating to another person who will administer penalties for inaccuracy. As recently pointed out by Heath (1999), however, the same logic can be applied even when an individual is unsupervised. Heath (1999) argues that there are differing cognitive costs of obtaining or failing to meet self-set goals, and he argues that these asymmetric costs can cause people to underestimate what they can achieve and set their expectations too low. He suggests that any predictions of future performance (or goals) divide the space of future outcomes into two regions gains and losses. Individuals who meet their goals will see this as a success while those that fail to meet them will see this as a failure. Prospect Theory predicts that losses are perceived to be more painful than gains even if their magnitudes are equivalent (Kahneman & Tversky, 1979). As a result, individuals who fail to meet a predicted goal suffer more than they would take pleasure in exceeding the goal. Heath notes that the implication of loss aversion is that managers will set expectations low so as order to increase the chance of success. For instance, if a manager predicts he can increase sales by 10% in the next year, then he will perceive a gain of only 8% as a failure or loss, while a growth of 12% will be seen as a success or gain. If people value gains and losses equally then they should make predictions about future performance in an unbiased manner and the probability of success or loss should be 50%. If, however, managers perceive a 2% deficit to be more important than a 2% excess then they will tend to make predictions or set goals that make it more likely they will achieve their goal.

7 In our specific case, this would suggest that managers should be more likely to predict lower levels of waste reduction. To avoid feeling disappointed by not meeting their prediction, they downgrade their prediction to make it more likely they can achieve it. For example, a manager thinks that next year 5% to 10% waste reduction could be achieved. Combining prospect theory and goal setting, we predict he is more likely to set a goal at 5% waste reduction to prevent self-dissatisfaction. H2b: Even when self-supervised, managers will update future expectations more strongly when they fail to meet a past expectation than when the overreach a past estimation. A final explanation for the failure of managers to adjust to past errors in prediction is that become more pessimistic over time about the potential for future improvement. Recent research suggests that when individuals engage in repetitive assignments they become more pessimistic about their own ability. They discover, in other words, how much they don t know and they doubt that they will be able to continue to perform well. Scholars suggest that individuals tend to be overconfident in their ability to learn in their first trial (the predicted degree of learning is greater than the observed one) but become gradually underconfident in the succeeding trials. Koriat, Sheffer, and Ma ayan (Koriat et al., 2002) coined the term underconfidence-with-practice (UWC) for the tendency of people to doubt themselves as they gain more experience. Many mechanisms have been proposed to explain this effect, although with little explanatory power. Our setting provides a good opportunity to conduct a large scale test. If UWC is at work, we should expect managers to overestimate their ability to estimate waste reduction in the first year of their reporting, but then increasingly underestimate the potential for waste reduction as they gain more experience. H3: Managers with greater experience will tend to underestimate future waste reduction. Method Data To test our hypotheses, we make use of the US EPA s Toxics Release Inventory (TRI). Started in 1987, the TRI data collect information from all facilities that process, manufacture or use any of the 612-listed chemical in quantities greater than the EPA s established threshold. And only facilities that have 10 or more employees are required to report to EPA. The TRI data include not only information on the chemicals but also facility characteristics, such as location, industry, production changes and the identity

8 of individuals involved in providing the required information. In 1990 the US congress with the Pollution Prevention Act expanded the TRI to include additional information on efforts in reducing waste. Therefore we use TRI data from 1991 until 2005 because this time-window provides reliable and consistent information about chemical waste generation and source reduction. Our unit of analysis is at the chemical level, which allows us to use more fine-grained and precise data. This means that we are able to use a rigorous differences-in-differences test. That is we measure changes in the rate of reduction of chemical waste each facility produces over time. Our final sample consists of 486,359 observations for a total of 95,980 plant-chemical-year observations. There are 23,733 facilities and they use or produce on average 3.5 toxic chemicals. The TRI data have been used extensively by scholars to measure environmental performance and efficiency (Berchicci, Dowell, & King, 2012; Freudenberg, 2005; King & Lenox, 2002; King & Lenox, 2000; Klassen & Whybark, 1999; Konar & Cohen, 1997). However, two sections of the TRI have been largely overlooked by prior research the identity of the certifiers and technical personnel who report the information and their predictions for the following year s waste generation. We make use of these two relevant sections to test our underestimation hypothesis. The US Environmental protection agency requires managers to report the quantities of waste chemicals they manage, and these data have been used by many previous researchers. The EPA also requires TRI techs (and their form certifiers) to predict waste generation not only for forthcoming two years. These projections are meant to encourage managers to consider their future waste generation and to explore opportunities for source reduction. They are not used by any federal, state, or local regulator. Dependent Variable We will use three dependent variables in our analysis. First, to directly evaluate whether managers systematically predict too much waste, we will compare their predictions to what actually happened. Second, to understand how managers adjust these predictions based on whether they met or did not meet these expectations, we will look at the change in these predictions. Finally, to account for potential motivational changes, we will predict changes in waste reduction trends.

9 1) Prediction relative to actual: Our dependent variable captures the difference between the predicted chemical waste for time t+1 and the chemical waste that was actually generated in time t+1. Managers make the prediction for t+1 in time t. To estimate it, we measure total waste that was generated for a given toxic chemical (waste). In a similar fashion we measure the predicted amount of waste that managers have estimated for the following year. Our dependent variable captures the difference between a managers predictions of future waste output in t+1 and actual waste generated in t+1 for a given chemical (or chemical mixture) and for a given facility. Prediction relative to actual cit Predicted Waste cit 1 1 ln *100 Waste cit 1 (1) Waste cit+1 is the pounds of waste generated for chemical c by facility i in time t+1. Predicted Waste cit+1 is the predicted amount for the same chemical c made by managers in time t for t+1. We utilize the logarithmic transformation of the ratio in equation 1, so that the dependent variable is not bounded by 0 and we multiply the ratio by 100 such that the coefficient will be approximately equivalent to percentage difference in waste per change in the independent variable. Moreover, the logarithmic form of a ratio allows us to calculate the difference between the predicted amount and the actual amount since Equation 1 is identical to the log of Predicted Waste cit+1 minus the log of Waste cit+1. Thus, in the analysis that follows, positive values correspond to an overestimation of waste to be generated in t+1. Or as we suggest in our first hypothesis positive values are consistent with an underestimation of waste reduction. Negative values mean that managers predicted less waste related to year t+1 than the actual amount generated in year t+1. For example, assume one production line in a pharmaceutical manufacturing facility creates 100 lbs of waste ammonia this year. The manager reports this on the TRI form as current waste (year t) for that line of business. The facility expects that next year the production of the drug which entails the creation of waste ammonia will increase of 5% due to market growth. The reporting manager assumes that this will lead to a proportional increase in waste (5% as well). Thus the manager reports an expectation that next year s waste levels will be 105 lbs. 2) Change in predictions: To understand how managers adjust their predictions, we need a second variable. This variable captures the change in the percentage increase (or decrease) in waste they predict for year t+1 relative to year t. For example, a manager in year t-1 might predict that the waste level would increase from the current level (e.g. 100 lbs) by 10% (e.g. 110 lbs) in year t. Then, in year t he or

10 she might predict that this would increase further to 130 lbs (or 18%). We are interested in comparing this year s prediction of an 18% increase to last year s prediction of a 10% increase. é æ Change in Prediction cit+1 = ln Predicted Waste ö æ cit+1 ç - ln Predicted Waste öù cit ê ç ú *100 (2) ë è Waste cit ø è Waste cit-1 øû Predicted Waste cit+1 is the predicted amount for the chemical c made by managers in year t for t+1. Waste cit is the pounds of waste generated for chemical c by facility i in time t. 3) Change in Waste Trends: To understand how waste reduction (or increase) trends are influenced by our independent variables, we create a final DV. This measure captures the changes in waste trends. For example, imagine that between year t-1 and year t waste increased from 1000 to 1200 lbs. (ln(1200/1000) =0.18 ) and further imagine that waste increased from 1200 to 2000 lbs. between year t and t+1 ((ln(1200/1000) =0.51). We wish to note the change growing trend in waste growth ( = 0.33). é æ Change in Waste Trend:CWT cit+1 = ln Waste ö æ cit+1 ç - ln Waste öù cit ê ç ú *100 (3) ë è Waste cit ø è Waste cit-1 øû Mechanisms: Independent variables Prediction Relative to Actual To test hypotheses H2, we want to evaluate how a manager in period t might be influenced by his or her current performance relative to the expectation they set in t-1. This is simply the lagged form of our first DV. This variable measures whether prior managerial expectations are exceeded (or not) by what actually happened. æ Prediction relative to actual:pra cit = ln Predicted Waste ö cit ç *100 (4) è Waste cit ø We also hypothesize that managers may respond to failing to meet a prediction differently than if they exceed it. We capture this potential difference in two ways. First, we create a binary variable measuring if previous expectations for the current period t have not been met or have been exceeded.

11 é æ =1 ln Predicted Waste ö ù cit ê ç *100 < 0 ú ê è Waste Expectation Unmet:EU cit ø ú cit ê æ = 0 ln Predicted Waste ö ú ê cit ç *100 >= 0ú ëê è Waste cit ø ûú (5) We are also interested in the potential for the Prediction relative to actual to have a different effect when the expectation is unmet. We measure this by allowing the slope to be different when the expectation is unmet (Expectation UnmetXPrediction relative to actual). Self-supervision Another overlooked section of TRI forms reports information on the individuals that fill in the figures of a given chemical and check its accuracy. Every form contains the identity of a technical person that fills in the information. And it also includes the identity of a certifier who needs to certify that he or she has reviewed the attached documents and that the submitted information is true and complete and that the amounts and values in this report are accurate based on reasonable estimates using data available to the preparers of this report. (EPA, 2012: 1) We have names and identifiers for both certifiers and technicians. To test H2b, we use a dummy variable that identifies self-supervision. It means that the certifier and the technician is the same person, with the same name and id. In this case, the variable self-supervision is equal to 1 and 0 otherwise. Technician and certifier tenure For each chemical reported, we can use the information on the reporters identity to build two sets of variables to test our UWP hypothesis. First we build a continuous variable named technician tenure that captures the time in which a technician has been reporting at a given facility. We build a similar variable named certifier tenure. To confirm H3, we expect that more experience managers (certifiers and/or technicians) would tend to predict more waste than is actually generated. Control variable Change in production

12 As we discussed in the example above, changes in waste generation are related to changes in production activities. The TRI data include a production ratio, which denotes changes of the production activity of any given chemical from the current year to the next year. We use this variable to control for changes in production volume since an increase or decrease of production may have a direct effect both on the waste generated and on its predicted value. We include the natural logarithm of the production ratio in the analysis multiplied by 100. æ Change in production:cip cit = ln Production ö cit ç *100 (6) è Production cit-1 ø where Production volume cit is the amount of the waste generated for chemical c by facility i in time t. Production ratio captures changes in production activities from year t to year t+1. Positive values correspond to an increase of these activities in next year relative to the previous year. Size We control for the size of the facility as well. Size is the logarithmic form of the number of employees in a given facility. Empirical approach To test hypothesis 1, we conduct several t-tests at the chemical level to examine whether the mean of our dependent variable Predicted relative to actual cit+1 is different from zero in different conditions. If the means of the various tests were different from zero, it would suggest that managers tend to overestimate waste generation. We perform such tests making use of the chemical attributes and facility s characteristics. For example, we compare chemicals that are strictly regulated with those that are not or the core chemicals (those chemicals that have been reported since 1987) with the rest. To investigate the mechanisms and test the remaining hypotheses, we employ a fixed-effect regression at the facility-chemical level. The nice feature of fixed effect regression is the control for all kind of unobserved factors that are constant across time such as industry affiliation or location. These constants can be removed from the equation through differentiating (e.g. first difference). The general model is:

13 ( ) where is Prediction relative to actual, is Expectation unmet, and are certifier tenure and technician tenure respectively are facility plus chemical fixed effects. The total number of fixed effects is thus the number of facilities i multiplied the number of chemical c in each facility. is an error term which is assumed to be independent and identically distributed. To test hypothesis 2a, we introduce the interaction term ( ) We run two fixed-effect regressions where the is Change in prediction and Change in waste trend. To test hypothesis H2b we restrict the sample to include observations with self-supervision only. Therefore the sample drops to XXX observations. Finally, to test Hypothesis 3 we make use of Predicted relative to actual as our dependent variable. In this way we can examine whether an increase of tenure time of certifiers and technicians corresponds an increase in their prediction of more waste relative to the actual waste in year t+1 as UWP theory suggests. Analysis Table 1 shows the descriptive statistics of our sample. Figure 4 presents the trends of production (black line), waste generation (green line), prediction of waste generation (blue line) and the gap between predicted and actual waste generation (red line). The latter is our first dependent variable, Predicted relative to actual cit+1. From the graph it is clear that the forecasted waste generation line is on average always above the actual waste generated line. Therefore the difference between predicted and actual waste is always positive. The drop in production in 2001 is presumably caused by a period of recession which began in 2001 and deepened with the September 11 event. It is not a surprise that the greatest difference between predicted and actual waste is recorded in this year. The overall picture gives us a preliminary evidence of a systematic overestimation of waste generated. Table 2 provides a set of one sample t-tests where the null hypothesis states that the mean of our dependent variable (difference in predicted waste) is equal to zero. In this first table, we run the tests based on different samples defined by specifications of chemical characteristics. We start with the

14 full sample 866,893 observations. The number of observations decreases as we add the next specification. As shown, we run separate t-tests based on whether the lists of chemicals were unchanged since 1987 (the core chemicals), whether chemicals were part of the Pollution Priority list, whether the production did not experience significant changes, whether the chemical were regulated and finally whether the chemicals were recorded with a Form A (for facilities with relatively low quantities of listed toxic chemicals). Overall, we find that the difference between predicted and actual waste is always greater than zero. The only exception regards chemicals reported with Form A. Although the predicted value is significantly lower than the actual value, their contribution is very marginal since they represent only the 0.006% of the whole sample (52 out of 866,893 observations). Table2a presents additional t-tests with other types of specifications. The first three specifications refer to the location of the waste treatment (onsite only, offsite only and both onsite and offsite). The fourth specification includes only those chemicals reported by facilities in environmentally sensitive industries (such as Mining, Oil & Gas, Chemical, Paper, Metal industries). Finally the last specification refers to whether a given chemical was handled with source reduction activities. Again, all our tests suggest that the mean is different from zero and thus the null hypothesis can be rejected. Overall, we find that managers tend to overestimate the waste generation, giving strong support for our underestimation hypothesis (H1). Table 3 presents a series of models that test hypotheses 2a and 2b. All the models are fixed-effect regressions at the chemical level. The first four models have Change in prediction cit+1 as dependent variables, whereas from Model 5 to Model 8 the dependent variable is Change in waste trend cit+1. Model 1 and Model 5 include the control variables only. The coefficient in Change in production cit+1 is negative and significant suggesting that an increase of production volume from time t to time t+1 corresponds a decrease of change in prediction the predicted waste in t+1 relative to the actual waste generated in t+1while size it seems do to affect neither the dependent variables. Model 2 introduces Prediction relative to actual cit that captures the error in prediction between what was predicted for time t and what actually was generated in time t. We estimate a positive coefficient suggesting that positive error in prediction (the amount of waste predicted is bigger than the amount of waste generated) is associated with a similarly signed error in the next period. This suggests that managerial predictions are sticky and they adjust their estimates only slowly as they get information about errors in prediction. However, the coefficients are less than 1, suggesting they reduce the error in the future period.

15 Similarly, a positive error in prediction corresponds to an increase of change in waste trend in time t+1 (Model 6). This is surprising, because it suggests that my expectations of additional waste actually increase when I predicted too much waste in this period. It may be that managers expected a problem or change which did not happen in a given year and now they are even more certain it will happen in the following year. And they are usually right! The small coefficients for Prediction relative to actual cit estimated in the first three models suggest they errors are indeed get smaller. To test H2a and 2b, we allow both a step change and a slope change for positive and negative prediction errors. To do this, we include both Expectation unmet cit and its interaction with Prediction relative to actual cit. Model 3 shows that the interaction term is negative and significant. To better interpret the result, we plot the interaction in Figure 5. The X-axis is the error in prediction (Prediction relative to actual cit ) and it ranges between -100% and +100% that corresponds approximately to one standard deviation below and one above the mean respectively. The blued dotted line corresponds to Expectation unmet cit equal to one. Figure 4 suggests that failing to meet expectations in t causes managers to make overly pessimistic predictions (too much waste) for t+1 (the dotted blue line on the right of the plot). Consistent with H2a, we find that managers respond more strongly to failure to meet expectations than to exceeding them. Indeed, they do not respond at all to exceeding them (the continuous red line on the left of the plot). Indeed, they again tend to make erroneously high predictions! This means that when managers make prediction errors in either direction, they tend to predict too much waste in the next period. This finding has great importance for explaining systematic x-inefficiency. To test Hypothesis 2b, Model 4 includes only a restricted number of observations where technicians and certifiers are the same person. Again, we find a negative and significant effect of the interaction term on our dependent variable. Consistent with H2b, we find that this correction for prediction is true whether or not the individual making the prediction oversees his own work. Thus these findings seem to suggest that both our hypotheses 2a and 2b are supported. To further understand whether errors in predictions could motive managers to reduce waste generation, we run Models 6 to 8. The interaction term in Model 7 and Model 8 are negative and significant. Following the previous approach, we plot the effect graphically. Figure 6 captures the effect of current errors in prediction on predictions of future waste reduction. The continuous red line corresponds to a positive prediction error in time t. As the magnitude of positive prediction errors increases, waste trend increases as well in time t+1. However, when expectations are unmet (the dotted blue line) changes in waste trend tend to be negative. And this effect could not be fully

16 explained by a regression to the mean effect since the blue line lies above. This result suggests that failure to achieve goals increases future waste reduction. In other words, managers are more motivated to find ways to reduce wastes when they fail to reach goals previously set. Table 4 tests the underconfidence with practice hypothesis. We test this hypothesis using Prediction relative to actual cit+1 as dependent variable, because we are interested in how the tenure of the reporters influences the error in prediction in time t+1 relative to time t. Model 1 includes the control variables and Model 2 and Model 3 add technician tenure and certifier tenure respectively. Both of them are significant and positive. It means that managers that have been reporting a given chemical for longer time tend to overestimate future waste generation. These findings suggest that our hypothesis 4 is confirmed. Finally, Model 4 includes all the variables and the direction and significance of their coefficients are unchanged. Robustness test In our previous models the difficulty to predict waste generation may be associated with the difficulty to predict production volume. This prediction difficulty could be due to the level of uncertainty in predicting waste generation or even more critically due to inability to obtain reliable information on the changes in production volume. We run two robustness tests that attempt to reduce this uncertainty by controlling the changes in chemical production. Our first test restricts the sample by keeping constant the changes in production as shown in Table 5. Our second robustness test includes only those cases in which the production volume is unchanged from time t to time t+1 (Table 6). In spite of the substantial decrease in the number of observations, both tables show that the interaction term Prediction relative to actual cit X Expectation unmet cit is still negative and significant both for models with change in prediction cit+1 and for models with change in waste trend cit+1 as dependent variables. Overall, our Hypotheses 2a and 2b are supported. With regard to Hypothesis 3, we find that the effect of certifier and technician tenures is less significant. These results suggest that when the level of uncertainty is lower managers are less subject to under-confidence with practice bias.

17 Discussion Although preliminary, our findings suggest that managers tend to underestimate opportunities for waste reduction by predicting too much waste. And they do so systematically under different conditions. This is an important finding for both the environmental management literature and the broader literature in business administration. If managers are systematically erring in their estimate, and reporting these errors repeatedly over time, optimal choice of investment is made much more difficult. As a result, firms might indeed systematically miss opportunities to be more efficient. Thus our findings provide another solid empirical support for the causes of systematic x-inefficiency. We also show why these errors may persist over time. Managers, we show tend to respond to errors in either direction by increasing forecasts of future waste. And, managers tend to get more pessimistic about the potential for waste reduction as they become more experienced. Perhaps they believe that they have already found all of the low hanging fruit. Our paper also contributes to a larger literature on cognitive heuristics and biases. Consistent with recent theories on goal setting, we find support that managers adjust future predictions more when they fail to meet expectations than when they exceed them. This result is consistent with theories suggesting that managers who fail to meet a goal may be sanctioned more than when they are rewarded when they exceed it. We show that this sense of costly failure is true whether or not the individual setting the goal is selfsupervised. Thus personal perceptions of failure, not organizational penalties, may cause biased predictions. Consistently, we show that failing to meet a goal may motivate managers to work harder at reducing waste. Finally we find evidence in support of the underconfidence with practice conjecture: we show that experience on the job may actually make managers more pessimistic about the potential for further waste reduction. We hope that the bridges we make between different theories and the new data and methods we uncover will provide a new direction for research on x-inefficiency. By getting directly at the predictions of managers, future research may be able to link cognitive heuristics and biases to observed economic conditions. In future research, we hope to further build the theoretical and empirical structures needed to advance along this new research path.

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20 Figure 1: Direct effect of misperception of waste reduction efficacy on reduction predictions. Figure 2: Effect of misperception of waste reduction efficacy on waste reduction effort and reduction predictions.

21 Penalty for Error in Estimate Probability of Result Figure 3: Effect of asymmetric incentive on goal setting. 1 0 Expected performance adjusted down Unbiased Accuracy Penalty Accuracy + Goal Penalty Goal Penalty Actual Performance Probability Perceived probability that minimizes expected penalty Performance Next Year Relative to This Year

22 Log Ratio Figure 4: Trend in production, waste generation and expectation of waste generation. 0.2 Production Forecast Generation Difference in predicted waste Year

23 Change in waste trend ( t+1 rel to t) Predictied relative to actual waste ( for t+1 rel t ) Figure 5: Effect of current prediction error on future prediction 9% 8% 7% 6% 5% 4% 3% 2% 1% Errors in current predictions in either direction cause a predicted increase in 0% -100% -80% -60% -40% -20% 0% 20% 40% 60% 80% 100% Error in Prediction (+ Prediction >actual) Figure 6: Effect of current prediction error on future waste reduction 150% Regression to the mean is mitigated 100% when current predictions were too low. This suggests 50% failure to achieve goal increases future reductions. 0% -100% -80% -60% -40% -20% 0% 20% 40% 60% 80% 100% -50% -100% -150% Error in Prediction (+ Prediction > actual)

24 Table 1. Descriptive statistics (486, 359 observations) Variable Mean Std. Dev. Min Max Prediction relative to 1 actual cit Change in prediction cit Change in waste trend cit Change in production cit Size it Prediction relative to actual cit Expectation unmet cit Technician tenure cit Certifier tenure cit Self-supervision it

25 Table 2: One Sample T-tests. Each specification is added to the prior one. T-test Mean!= 0 Prediction Core chemicals t obs All chemicals ln(predicted waste t+1 / waste t+1 ) Full sample 5.04*** , *** ,893 t obs Chemical not in the pollution priority list And changes in production from last year smaller than 10% 6.49*** , *** , *** , *** ,996 Standard errors in parentheses * p<0.10 Table 2a: One sample t-tests. And not regulated 4.1*** , *** ,209 Form A ONLY 4.43*** 2.5 3, ** 2.3 6,129 ** p<0.05 *** p<0.01 T-test Mean!= 0 Prediction ln(predicted waste t+1 / waste t+1 ) Onsite only Core chemicals t obs All chemicals t obs 6.06*** , *** ,170 Offsite only 4.66*** , *** ,826 Both onsite and offsite 9.24*** , *** ,726 Environmentally sensible 3.45*** , *** ,620 industries Source reduction activities 4.5*** , *** ,266 Standard errors in parentheses * p<0.10 ** p<0.05 *** p<0.01

26 Table 3. Fixed-effect regressions. Teting hypothesis 2 and 2a Change in prediction cit+1 Change in prediction cit+1 Change in prediction cit+1 Change in prediction cit+1 Change in waste trend cit+1 Change in waste trend cit+1 Change in waste trend cit+1 Change in waste trend cit+1 Selfsupervision it only Selfsupervision it only Variable Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8 Change in production cit *** *** *** *** *** *** *** *** (0.003) (0.003) (0.003) (0.008) (0.007) (0.005) (0.005) (0.014) Size it * * (0.213) (0.213) (0.212) (0.897) (0.556) (0.382) (0.379) (1.563) Prediction relative to actual cit 0.007*** 0.081*** 0.134*** 1.212*** 1.381*** 1.415*** (0.001) (0.002) (0.006) (0.002) (0.003) (0.010) Expectation unmet cit *** 1.631*** (0.259) (0.258) (0.758) (0.464) (0.461) (1.321) Prediction *** *** *** *** relative to actual cit X (0.003) (0.008) (0.005) (0.014) Expectation unmet cit Year Dummies Yes Yes Yes Yes Yes Yes Yes Yes Constant * * *** ** *** (1.166) (1.171) (1.169) (4.163) (3.037) (2.097) (2.088) (7.251) Observations R-sq Standard errors in parentheses * p<0.10 ** p<0.05 *** p<0.01

27 Table 4. Fixed-effect regressions. Teting hypothesis 3 Prediction relative to actual cit+1 Model 1 Model 2 Model 3 Model 4 Change in production cit *** 0.061*** 0.061*** 0.061*** (0.004) (0.004) (0.004) (0.004) Size it 0.637* (0.344) (0.344) (0.344) (0.344) Technician tenure cit 1.060*** 0.731*** (0.102) (0.108) Certifier tenure cit 1.373*** 1.118*** (0.111) (0.118) Constant * *** *** *** (1.818) (1.823) (1.822) (1.825) Year Dummies Yes Yes Yes Yes Observations R-sq Standard errors in parentheses * p<0.10 ** p<0.05 *** p<0.01

28 Table 5. Robustness test: constant change in production. Change in prediction cit+1 Change in waste trend cit+1 Prediction relative to actual cit+1 Selfsupervision it only Selfsupervision it only Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8 Model 9 Change in production cit * * * 0.053* 0.052* (0.018) (0.018) (0.044) (0.033) (0.033) (0.090) (0.031) (0.031) (0.031) Size it * (1.004) (0.995) (2.970) (1.830) (1.814) (6.075) (1.737) (1.737) (1.738) Prediction relative to actual cit 0.010** 0.130*** 0.141*** 1.277*** 1.496*** 1.393*** (0.004) (0.007) (0.016) (0.008) (0.013) (0.032) Expectation unmet cit *** *** (1.059) (1.050) (2.428) (1.930) (1.913) (4.967) Prediction relative to actual cit X Expectation unmet cit *** *** *** (0.010) (0.022) (0.018) (0.045) Technician tenure cit (0.503) (0.526) Certifier tenure cit (0.500) (0.523) Year Dummies yes yes yes yes yes yes yes yes yes Constant * (5.412) (5.375) (14.325) (9.866) (9.797) (29.303) (9.565) (9.444) (9.634) Observations R-sq Standard errors in parentheses * p<0.10 ** p<0.05 *** p<0.01

29 Table 6. Robustness test: Change in production is equal to zero. Change in prediction cit+1 Change in waste trend cit+1 Prediction relative to actual cit+1 Selfsupervision it only Selfsupervision it only Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8 Model 9 Size it (1.244) (1.238) (4.099) (2.219) (2.201) (7.469) (1.997) (1.997) (1.997) Prediction relative to actual cit Expectation unmet cit Prediction relative to actual cit X Expectation unmet cit 0.026*** 0.115*** 0.247*** 1.286*** 1.485*** 1.570*** (0.005) (0.008) (0.023) (0.009) (0.014) (0.043) * 4.368* (1.313) (1.306) (3.531) (2.341) (2.322) (6.436) *** *** *** *** (0.011) (0.032) (0.020) (0.059) Technician tenure cit 0.878* (0.525) (0.583) Certifier tenure cit 1.120** (0.563) (0.624) Year Dummies yes yes yes yes yes yes yes yes yes Constant (6.743) (6.720) (19.584) (12.028) (11.950) (35.689) (10.437) (10.412) (10.461) Observations R-sq Standard errors in parentheses * p<0.10 ** p<0.05 *** p<0.01

30 Appendix 1: Simple analysis of endogenous effort choice and expectations of waste reduction. This simple model assumes that the marginal benefit of waste reduction ( ) is a and the marginal cost of waste redcution ( ) are both linear where and A > 0. People also have a perceived marginal cost of waste reduction PX and that P > 0. We assume people pick a total effort level to exert on waste reduction based on their perceive costs and real benefits. They then exert this effort level but achieve waste reduction according the actual marginal cost of waste reduction. Figure A.1. show the basic setup. Figure A.1 Assumptions: The planned effort is thus: ( ) The actual waste reduction level is thus: ( )