“Paper or Plastic… or Reusable?” An Analysis of the D.C ...“Paper or Plastic… or...

“Paper or Plastic… or Reusable?” An Analysis of the D.C. Disposable Bag Tax on Worker Productivity & Learning

Becca Taylor1

Second Year Paper April 15, 2013

The District of Columbia’s 2010 disposable plastic bag tax was designed to decrease the use of the plastic bags polluting the city’s waterways. By adding an additional step to the production process of retail stores, this tax also significantly affected store and worker productivity. Using a difference-in-differences empirical strategy and high frequency supermarket scanner data, I find that worker productivity fell 5% during the first weeks of the DC bag tax. However, this decline was temporary with worker productivity recovering to pre-tax levels within twelve months of the tax— evidence that workers learn and adapt to policy changes. Furthermore, I find that the bag tax changed social interactions between coworkers, with productivity spillovers increasing well beyond their pre-tax levels and potentially mitigating the negative productivity shock of the bag tax. I. Introduction

Economists often wonder how new technologies and procedures affect worker productivity.

Does adding an additional step to a production process lead to a permanent decline in worker

productivity, or, are productivity declines temporary as workers learn and adjust to new procedures? If

workers learn and productivity declines are temporary, what are the determinants of learning and how

much time does the learning process take? Furthermore, do workers learn simply from their own

experiences or do they also learn from the experiences of their coworkers and peers performing the same

task? This paper will address these questions by analyzing how worker and store productivity changed

after a disposable bag tax altered the production process of grocery store cashiers in the District of

Columbia (DC).

On January 1st, 2010, in an effort to curb river pollution caused by discarded disposable plastic

bags, the District of Columbia implemented the Anacostia River Cleanup and Protection Act, a law

requiring all stores that sell food items to charge a 5 cent tax per disposable bag issued. A disposable

1 I would like to thank my advisor Sofia Villas-Boas, Enrico Moretti, Michael Anderson, Maximilian Auffhammer, Elisabeth Sadoulet, Seth Garz, Xi Lu, Geoffrey Barrows, Aluma Dembo, Daniel Gross, Daniel Tregeagle, Jesse Unger and Erin Wolcott for useful comments and discussion during the writing of this paper. All errors are my own.

Becca Taylor ARE Second Year Paper

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bag tax is a fee levied on the customers of retail stores at checkout for the disposable bags they use to

carry their purchased goods home. The goal of a bag tax is both to curtail the use of plastic bags and to

raise tax revenue earmarked for litter cleanup.2 To make the tax salient to customers, the DC law

requires that the tax be charged at the point of purchase of the bag and it cannot be included in the price

of other items.

This analysis is motivated by the fact that disposable bag policies significantly alter the

production processes of grocery store cashiers. With a disposable bag tax policy, a cashier can no longer

simply ask the customer “Paper or Plastic?” Instead, a cashier must ascertain: 1) whether a customer has

brought reusable bags, 2) whether the customer would like to pay 5 cents for each plastic disposable

bags issued or to purchase reusable bags, 3) how many disposable or reusable bags the customer would

like to buy and 4) how to pack the various types of chosen bags efficiently and securely. While the

literature is rich in examining how disposable bag policies alter consumer behavior3, no study has

investigated how worker productivity changes and evolves in response to disposable bag policies.

Therefore, the first goal of this paper is to quantify whether and how a bag tax reduces store and worker

productivity. The testable hypotheses are that A) the bag tax decreased worker productivity in the form

of longer transactions times and subsequently longer checkout lines, and B) the decline in productivity

was temporary as workers learned how to adjust to the new policy. To empirically test the productivity 2 As the first city in the U.S. to charge a fee for the use of disposable bags, the bag tax legislation put D.C. on the forefront of a nationwide effort to promote reusable shopping bags. States as diverse as Arizona and Pennsylvania have since considered disposable bag taxes and several cities in California (including San Francisco, Los Angeles, Santa Monica, San Jose, and Sunnyvale as well as the counties of Marin, Santa Clara, Santa Cruz and Alameda) are regulating the use of disposable bags with bans on plastic bags and 10 cent fees for paper bags. 3 In response to the growing number of governments considering bag tax legislation, several studies have analyzed how bag taxes affect consumer behavior. Homonoff (2012) studied the impact of a bag tax that went into effect in Montgomery County, Maryland by collecting data on the disposable and reusable bag habits of customers in the months before and after the tax’s implementation. Homonoff (2012) finds that while 82 percent of customers in Montgomery County use at least one disposable bag per shopping trip before the tax, this estimate declines by 42 percentage points after the tax implementation. Convery, McDonnell and Ferreira (2007) examine a bag tax in Ireland and find that within the first year of a €0.15 tax per bag, bag consumption fell a drastic 94 percent. Other papers look at slightly different policies, such as the ban of plastic bags under a certain thickness in South Africa (Dikgang, Leiman and Visser, 2012 & Hasson, Leiman, and Visser, 2007) and in China (He, 2010).


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slowdown and worker learning hypotheses, I will use difference-in-differences identification strategies

with measures of cashier and store productivity constructed from high-frequency scanner data. The

main measure of productivity is the number of transactions time-stamped by a worker per ten-minute

interval.

The scanner data also afford the unique opportunity to explore a second, related research

question—Does a bag tax alter worker productivity and knowledge spillovers? In other words, does a

bag tax change peer effects and how coworkers respond to one another? Workers may learn simply

from their own experiences or they may also learn from the experiences their coworkers and peers

performing the same task. Economists have been wondering how peer effects influence worker

productivity since Alfred Marshall (1980) hypothesized that interactions on-the-job may generate

positive externalities across workers (Lucas, 1988; Kandel and Lazear, 1992; Paarsch and Shearer, 1999;

and Gaynor, Rebitzer, and Taylor, 2004). However, the first economists to study and quantify

productivity spillovers between grocery store cashiers were Alexandre Mas and Enrico Moretti (“MM”)

in their 2009 paper, “Peers at Work”. I add to this literature by first extending the MM analysis to a

different empirical setting—replicating their methodology on a similar dataset but with a different group

of stores and a different point in time. Second, I analyze how the potential productivity spillovers

change after a policy shock adds an additional step to the production process. Does a fast-learning

cashier—someone who has quickly adapted to asking customers whether they want disposable bags and

to handling the varying reusable bags customers bring—influence the productivity of those working in

her vicinity? In other words, do knowledge spillovers offset the productivity declines from a policy

change? Or alternatively, does a shock to how a cashier rings-up her customers obstruct her ability to

monitor coworkers, consequently negating peer effects and increasing the opportunity to free-ride?


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By measuring a policy shock’s effect on worker productivity, worker learning, and worker

productivity spillovers, this research has implications for both Industrial Organization and Labor

Economics. The results will become increasingly important as more and more cities, counties, and

states across the country consider similar bag laws. If productivity changes from these policies are

significant and persistent, policymakers will need to reevaluate the way in which they calculate the

success of a bag tax.

The remainder of this paper is organized as follows. Section II presents an empirical model of

productivity determinants in an environment where exogenous productivity shocks and productivity

spillovers may operate. Section III describes the data and how the various productivity measures are

constructed. Section IV presents empirical results and Section V provides robustness checks. Section

VI considers potential threats to the validity of this study. Finally, Section VII discusses broader

impacts and Section VIII concludes.

II. Empirical Framework

This section develops an empirical model for how productivity can be affected by 1) an

exogenous policy shock, specifically the disposable bag tax, and 2) social interactions among coworkers.

First, to assess the impact of the bag tax on productivity, the empirical model follows a difference-in-

differences strategy and takes the following form:

𝑌𝑠𝑐 = 𝛽0 + 𝛽1(𝐷𝐶𝑠 ∗ 𝑃𝑜𝑠𝑡𝑐) + 𝛽2 ∗ 𝐷𝐶𝑠 + 𝛽3 ∗ 𝑃𝑜𝑠𝑡𝑐 + 𝜌𝑋𝑠𝑐 + 𝜑𝑠 + 𝜏𝑐 + 𝜀𝑠𝑐; (1)

where 𝑌𝑠𝑐 is a measure of the average worker productivity in store 𝑠 on calendar date 𝑐, 𝑃𝑜𝑠𝑡𝑐 is an

indicator for observations after the implementation of the DC tax, 𝐷𝐶𝑠 is an indicator for observations at

a DC store, 𝑋𝑠𝑐 is a set of controls, 𝜑𝑠 is store fixed effects and 𝜏𝑐 is calendar date fixed effects. The

coefficient of interest is 𝛽1, the coefficient on the interaction of Post and DC, which measures the effect


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of the tax on worker productivity in DC relative to changes in productivity in Beltway stores. If 𝛽1 is

negative, then productivity decreased due to the bag tax.

Hypothesis 1(a): The bag tax led to a decline in stores’ average worker productivity (ie. β1 < 0).

I next use a variant of equation (1) to investigate whether worker the declines in productivity are

permanent or temporary. After aggregating the data into weekly store averages, I run equation (1)

sixteen times— each regression comparing the observations of a week in 2009 to its counterpart in 2010.

I also compare the weeks in 2009 to those in 2011. More formally, I estimate:

𝑌𝑠𝑤𝑦 = 𝛽0

𝑤𝑦 + 𝛽1𝑤𝑦(𝐷𝐶𝑠 ∗ 𝑃𝑜𝑠𝑡𝑤𝑦) + 𝛽2

𝑤𝑦 ∗ 𝐷𝐶𝑠 + 𝛽3𝑤𝑦 ∗ 𝑃𝑜𝑠𝑡𝑤𝑦 + 𝜌𝑋𝑠

𝑤𝑦 + 𝜑𝑠 + 𝜀𝑠𝑤𝑦 (2)

where 𝑤 indicates the week of the observation, 𝑤 = {1, 2, 3, 4, 5, 6, 7, 8}, and 𝑦 indicates the

comparison year, 𝑦 ={2010, 2011}. If 𝛽1𝑤𝑦 decreases in magnitude over time, then the productivity

shock was temporary and diminished as workers learned and adjusted to the policy change.

Hypothesis 1(b): The productivity downturn from the bag tax decreases over time (ie.

�𝛽11,2010� > �𝛽1

2,2010� > �𝛽13,2010� > �𝛽1

4,2010� > … > �𝛽18,2011� )

Lastly, I consider a framework for how the marginal utility of effort can depend on coworker

effort. MM (2009) note that in many jobs, such as clerical occupations, construction, agriculture and

retail, employers cannot perfectly observe the exact contribution provided by each individual worker to

the production of the total output. This is especially true when the number of employees working on the

task is large. Grocery store cashiers are no exception. Since customers typically choose the shortest line

available, the length of each line is generally equal for all cashiers working at a given time. While

managers can see the length of each cashier’s line, they cannot perfectly observe how hard a cashier is

working, especially since they have attention-demanding responsibilities beyond monitoring cashiers.

Since cashiers know that their productivity cannot be perfectly observed by management, they

have an incentive to put forth less effort and free-ride off the productivity of their peers. At the same


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time, coworkers on a team have an easier time monitoring one another than their managers.

Consequently, if a cashier begins to slack off, she will be observed by her team. The desire not to look

bad in the eyes of peers, or to be ostracized for being a slacker, might mitigate the incentive to free-ride.

In other words, absent peer effects a worker will exert less effort following the introduction of a high-

productivity coworker to her shift since the worker’s marginal benefit of effort declines as the effort of

coworkers increases. Peer pressure can potentially mollify this externality.

At the same time, the ability to monitor one another may lead to knowledge spillovers between

cashiers. Suppose a worker is highly productive because she has developed an innovative technique for

ringing customers. Peers working in her proximity may adopt this technique after witnessing its

success. Thus a worker may exert more effort following the introduction of a high-productivity

coworker from whom she can learn. MM (2009) formalize the intuition as follows, assume productivity

of worker 𝑖, working in store 𝑠, in calendar date 𝑐, at time 𝑡 can be modeled by:

𝑦𝑖𝑡𝑐𝑠 = 𝜃𝑖 + 𝛿1�̅�−𝑖𝑡𝑐𝑠 + 𝜋𝑁𝑡𝑐𝑠 + 𝜏𝑅𝑖𝑐𝑠 + 𝛾𝑡𝑑𝑠 + 𝜀𝑖𝑡𝑐𝑠, (3)

where 𝜃𝑖 is a worker’s fixed effects and �̅�−𝑖𝑡𝑐𝑠 is the peer effects measure—the average permanent

productivity of all the coworkers who are active in period 𝑡, excluding the coworker in question. 𝑁𝑡𝑐𝑠 is

the number of workers on duty at the given time of day, 𝑅𝑖𝑐𝑠 is an indicator for checker i’s register

location, and 𝛾𝑡𝑑𝑠 is a vector of interactions for all possible combinations of hour of the day, day of the

week and store. Taking first differences over time produces the baseline estimating equation:

∆𝑦𝑖𝑡𝑐𝑠 = 𝛿0 + 𝛿1∆�̅�−𝑖𝑡𝑐𝑠 + 𝜋∆𝑁𝑡𝑐𝑠 + 𝜀𝑖𝑡𝑐𝑠 (4)

where 𝛿1 is the coefficient of interest and represents the effect of permanent coworkers’ productivity on

worker i’s current productivity. If 𝛿1 is negative, cashiers exposed to more productive peers free-ride.

If 𝛿1 is positive, spillovers are present and cashiers will increase their effort when exposed to faster

peers. Lastly, if 𝛿1 is zero then both spillovers and free-riding are absent. MM find strong evidence of


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positive productivity spillovers from the introduction of highly productive personnel into a shift. In their

baseline estimation they find a significant 𝛿1 = 0.1500. This means that a 10 percent increase in

coworker permanent productivity is associated with a 1.5 percent increase in reference worker

productivity. In other words, the return to introducing a high-productivity worker into a group is

greater than her individual contribution. However, will positive spillovers dominate the free-riding

effect after a productivity shock? I hypothesize that 𝛿1 pre-tax is not the same as 𝛿1 post-tax, since it

will be harder for cashiers to monitor one another.

To test whether 𝛿1 changes after the bag tax goes into effect, I add interactions of �̅�−𝑖𝑡𝑐𝑠 with

indicators for DC stores and the post-tax period (DC*Post, DC, &Post) to the baseline estimate,

∆𝑦𝑖𝑡𝑐𝑠 = 𝛿0 + 𝛿1∆�̅�−𝑖𝑡𝑐𝑠 + 𝛿2∆�̅�−𝑖𝑡𝑐𝑠 ∗ 𝐷𝐶𝑠 ∗ 𝑃𝑂𝑆𝑇𝑐 + 𝛿3∆�̅�−𝑖𝑡𝑐𝑠 ∗ 𝐷𝐶𝑠 +

𝛿4∆�̅�−𝑖𝑡𝑐𝑠 ∗ 𝑃𝑂𝑆𝑇𝑐 + 𝜋∆𝑁𝑡𝑐𝑠 + 𝜀𝑖𝑡𝑐𝑠 (5)

The coefficient of interest in this specification is now 𝛿2. If 𝛿2 = 0, then productivity spillovers remain

unchanged by the bag tax.

Hypothesis 2: Productivity spillovers between workers are altered by the bag tax (ie. 𝛿2 ≠ 0).

Equations (4) and (5) are estimated in 3 steps. The first step is to estimate 𝜃𝑖 using the following

equation:

𝑦𝑖𝑡𝑐𝑠 = 𝜃𝑖 + 𝑀′𝜔𝐶𝑖 + 𝜋𝑁𝑡𝑐𝑠 + 𝜏𝑅𝑖𝑐𝑠 + 𝜇𝑃𝑂𝑆𝑇𝑐 + 𝛾𝑡𝑑𝑠 + 𝜀𝑖𝑡𝑐𝑠. (6)

The term 𝜔𝐶𝑖 is a vector of all possible interactions from the set {Cil,…, Cik} and

𝐶𝑖𝑙 = �1 𝑖𝑓 𝑤𝑜𝑟𝑘𝑒𝑟 𝑙 𝑖𝑠 𝑜𝑛 𝑑𝑢𝑡𝑦 𝑎𝑡 𝑡𝑖𝑚𝑒 𝑡, 𝑜𝑛 𝑐𝑎𝑙𝑒𝑛𝑑𝑎𝑟 𝑑𝑎𝑡𝑒 𝑐, 𝑎𝑛𝑑 𝑠𝑡𝑜𝑟𝑒 𝑠, 0 𝑖𝑓 𝑖 = 𝑙, 0 𝑖𝑓 𝑤𝑜𝑟𝑘𝑒𝑟 𝑙 𝑖𝑠 𝑛𝑜𝑡 𝑜𝑛 𝑑𝑢𝑡𝑦 𝑎𝑡 𝑡𝑖𝑚𝑒 𝑡, 𝑜𝑛 𝑐𝑎𝑙𝑒𝑛𝑑𝑎𝑟 𝑑𝑎𝑡𝑒 𝑐,𝑎𝑛𝑑 𝑠𝑡𝑜𝑟𝑒 𝑠.

Less formally, the term 𝜔𝐶𝑖 is the set of dummy variables for every possible combination of coworker

composition. Second, the fixed-effect estimates from equation (6) are used to construct a measure of

average coworker productivity in every shift, denoted �̅�−𝑖𝑡𝑐𝑠. Third, �̅�−𝑖𝑡𝑐𝑠 and 𝜃𝑖 are used to estimate


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equation (4) and (5). Note that since �̅�−𝑖𝑡𝑐𝑠 is derived from estimated quantities, the standard errors have

to be adjusted to account for the sampling variability in this term. Specifically, MM use a Bayesian

parametric bootstrap that takes the estimated variability from simulated draws of the estimated fixed

effects to adjust the standard errors. I refer you MM (2009) for full description of this procedure.4

Please note the implications of using a model in first differences over time. Because the model is

in first differences, only variation within a given day for a given worker is used to estimate 𝛿1 and 𝛿2.

Since all cashiers do not switch shifts at the same time, the staggered nature of shifts produces the

necessary variation in personnel composition. The central assumption of the model is that permanent

productivity of workers entering and exiting shifts within a day is orthogonal to changes in the

productivity of other workers in the shift, aside from behavioral responses of workers to their peers.

MM explain that the plausibility of this assumption as follows:

“This assumption is plausible because scheduling of shifts in the stores in our study is unsystematic, and management’s only role in scheduling shifts is to determine how many workers are on duty at every point in time. Moreover, scheduling is determined two weeks prior to a shift, so that the entry and exit of workers due to shift changes is predetermined relative to transitory shocks to productivity.”5

III. Summary Statistics and Data Description:

Quantifying the shock to worker productivity at retail stores after a bag tax requires a very

detailed dataset. With this purpose in mind, I obtained unique transaction-level scanner data from a

national supermarket chain. The dataset covers 4 months in the pre-tax period (December 2008, January

2009, February 2009, and December 2009) and 5 months in the post-tax period (January 2010, February

2010, December 2010, January 2011 and February 2011). Due to constraints in requesting data from the

4 As observed by MM (2009), “An important feature of this procedure is that it permits for arbitrary covariances in the error term between every pair of time periods for a given checker in a given day. In this way we are allowing for possible serial correlation.” 5 MM (2009) also empirically test the validity of this assumption, which can be seen in section IVD of their paper.


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supermarket chain, the sample includes only the peak transaction hours between 3pm and 7pm each day

of the week.6 In total, I have 270 days, 1080 hours and 6480 ten-minute intervals in my sample.

Each observation in the dataset corresponds to a purchased product and includes information on

the name and Universal Product Code (UPC) of the item, the store location, the cashier ID, the register

number, the time the transaction began and a transaction identifier used to link all purchases within a

given transaction. An attractive feature of this dataset is that it includes a line item for whether or not

the customer was charged for the use of a store-provided plastic bag in the post-tax period in DC. This

allows me to create measures of 1) the percent of customers using at least one disposable bag in the days

following the implementation of the tax and 2) the change in disposable bag demand in each DC store

over time. On average, 37% of the transactions in DC during the tax period paid a bag tax, with

individual stores ranging between 25% and 47% of their transactions paying the tax. Figure 1 shows the

average percent of transactions per day in DC that demanded at least one disposable bag. On January

1st, 2010—the first day of the bag tax—55% of the transactions were charged a 5 cent tax.7 By January

19th, only three weeks later, this percent had fallen to 38% as customers reacted to the tax.8

FIGURE 1: Daily Percent of Transactions in DC Paying the Bag Tax

0.3

0.35

0.4

0.45

0.5

0.55

0.6

1-Jan 6-Jan 11-Jan 16-Jan 21-Jan 26-Jan 31-Jan 5-Feb 10-Feb 15-Feb 20-Feb 25-Feb

Perc

ent P

ayin

g Ta

x

2010

2011

Snowmageddon

6 However, as I will discuss later, this constraint works to my advantage when constructing measures of worker productivity. 7 Note that Figure 1 shows a substantial drop in disposable bag demand in January 2010 when the tax was first implemented, but does not show a similar change in behavior in January 2011. This eases the concern, raised by Homonoff (2012), that seasonal fluctuations in disposable bag use could be confounding the effect of the tax. 8 Snowmageddon: Between January 30th and February 10th there was a large drop in the number of transactions and an increase in the usage of plastic bags, mostly likely due to Snowmageddon, a series of three nor’easters that dumped 60 inches of snow on the DC area over 12 days, shutting down roads, railways, airports, schools and businesses. After the storm, disposable bag issuance declined again, and by early 2011, settled around a 37.5% average. Snomageddon days are dropped from the sample for the remainder of this paper so that lower productivity from extreme snow-days does not interfere with the productivity analysis.


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Next, in order to understand how productivity changed due to the bag tax, I construct measures

of worker productivity for pre- and post-tax comparisons following the methods of MM (2009). MM

calculate worker productivity by first summing the number of items a worker scanned over each ten-

minute interval. They then divide this number by the total number of seconds that the worker was in a

transaction, where a transaction is defined as the time between when the first item was scanned to when

the payment was completed and the receipt for the transaction was given to the customer. Unfortunately,

the supermarket chain no longer keeps record of the various times needed to construct this measure.

Instead, just one time stamp is available per transaction. Yet since the sample only includes peak hours,

I can make the assumption that transactions occur back-to-back, with little or no downtime in between

transactions for cashiers. Holding this assumption in mind, I construct the following measure of worker

productivity: Measure 1—the number of transactions time-stamped per worker per ten-minute interval.9

9 In regards to the productivity spillover analysis, MM make a strong case for why these data are well-suited for the purpose. First, they note that since the measure of productivity is near to being continuous, it is possible to identify instantaneous changes in individual productivity. Second, not only do we know the composition of workers at any moment in time, we can also pinpoint the exact contribution of each member’s output in the group. On the other hand, the productivity measure is not without flaws. While the measure captures the quantitative aspects of productivity, it does not measure the qualitative aspects, such as friendliness and care in handling items, or other aspects of performance, such as absenteeism, which could be affected by the bag tax.


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As summarized in Table 1, the dataset spans 10 stores in the District of Columbia (DC) that were

affected by the 2010 Bag Tax and an additional 41 stores in the Beltway around DC that did not

experience a bag tax during the sample period and can be used as controls. Stores that were not in

operation for the entirety of the 9 month sample have been discarded from the analysis.10 In the pre-tax

period, DC stores have on average 5.731 standard cashiers11 working at a time while Beltway stores

typically have 4.763. On average, DC customers buy slightly fewer items per transaction and DC stores

process 77 more transactions per hour than their Beltway counterparts. In regards to productivity before

the tax, Beltway and DC workers have average productivity levels for Measure 1 of 5.482 and 5.302

respectively. This suggests that Beltway workers are slightly more productive in the pre-period.

FIGURE 2: Distribution of Productivity (Measure 1)

Kernel density estimates of Measure 1, averaged over workers and 10-minute intervals to obtain daily store averages. I used an Epanechnikov kernel and bandwidth = 0.30. Thick lines denote DC obs. & black lines denote obs. in the post-tax period.

Figure 2 shows the stores’ daily averages for Measure 1 as kernel densities, comparing DC to

Beltways stores pre and post-tax. Note that the data have been averaged over workers and 10-minute

10 Stores that were renovated during the sample period were also discarded from the analysis. 11 This sample does not include cashiers working self-checkout lines, customer-help lines, or gas pump lines.

0.2

.4.6

Den

sity

2 4 6 8 1010-Min Worker-Store Averages

DC Pre-Tax DC Post-TaxBeltway Pre-Tax Beltway Post-Tax

y ( )


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intervals to produce one observation per store per day. Further note that even though all cashiers in the

sample perform the same tasks and use the same technology, this figure indicates that there is wide

variation in average worker productivity levels across stores and days for Measures 1. Lastly note that

while the Beltway kernel densities look similar pre and post-tax, the DC post-tax density is shifted

markedly to the left of the pre-tax density.

Figure 3 illustrates how Measure 1 changes over time for both DC and Beltway workers. On the

vertical axis is the percent deviation of productivity Measure 1 from its average level, de-trended for

month fixed effects. While productivity deviations follow a similar pattern in both DC and the Beltway

prior to the bag tax, their monthly averages diverge in January 2010 with DC stores’ productivity

approximately 4% below average and Beltway stores only 0.46% below average. Together, Figure 2

and 3 provide strong initial data evidence that the DC bag tax led to a decline in productivity.

FIGURE 3: Average Percent Difference from Monthly Average Worker Productivity (Measure 1)

Jan Feb Dec Jan Feb DecDC 0.6180 1.6493 -0.3093 -4.0516 -6.0766 -0.8384Beltway -1.2321 1.4018 -0.6619 -0.4570 -3.8254 0.6622

-8

-6

-4

-2

0

2

perc

ent d

iffer

ence

2009 2010

The vertical axis denotes the percent deviation of Measure 1 from its average level in DC and Beltway stores, de-trended for month fixed effects. IV. Results

A. The Effect of the DC Bag Tax on Productivity

With data on average daily worker productivity for each of the 10 DC stores and 41 Beltway

stores before and after the implementation of the DC tax, I estimate equation (1) on the log of


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productivity Measure 1—the average number of transactions scanned per worker per 10 minute interval

in a given store. I chose a log-linear form because it is more convenient to think of percent changes in

worker productivity instead of fractions of a transaction. Table 2 presents the results for three

specifications of the model. The specification in column (1) controls for the DC indicator, the post-tax

indicator and DC/Post-tax interaction. Furthermore, it includes controls for the average number of items

scanned per 10 minute interval, the average number of cashiers on shift, and store and day fixed effects.

The estimated coefficient on 𝛽1 indicates that the tax caused worker productivity to decrease by 1.99%,

however, this estimate is not statistically different from zero. Column (2) replicates the specifications

from the first column, but with the sample period trimmed to only include the years 2009 and 2010.

Now the 𝛽1 estimate is approximately -0.0318 and significant. This indicates that the bag tax led to a

3.18% decline in worker productivity, though this downturn did not persist longer than a year.


14

The data for the first two specifications were aggregated over workers and 10-minutes intervals

to obtain daily averages for each store. Column (3) replicates the specification from the second column,

but with the data aggregated into pre and post-tax store averages. This reduces the number of

observation to 102 (51 stores by 2 periods). Bertrand, Duflo, and Mullainathan (2004) ask “How much

we should trust differences -in-differences estimates?” and find that when more than two periods of data

are used, there is a potential for a large number of dependent observations within each cross-sectional

unit. One of the solutions they test and recommend is to collapse the data until the dependence issue

disappears. The purpose of the specification in column (3) is to address this concern. Note that even

after aggregating the data, the point estimate for 𝛽1 is statistically different from zero and further

indicates that productivity fell 3.68% due to the bag tax.

Second, I investigate whether these productivity declines are permanent or temporary. Using

equation (2), I run regressions comparing the weeks in January and February 2009 to their counterparts

in 2010. I also compare the weeks of 2009 to 2011. Figure 4 shows the evolution of the 𝛽1𝑤𝑦 estimates

over time. The estimates from the 2009 to 2010 comparisons indicate that in the first eight weeks of the

bag tax, productivity fell between 3-6.5% from the previous year. Conversely, the 2009 to 2011

comparisons produce 𝛽1𝑤𝑦 estimates that are not statistically different from zero. These results further

indicate that the productivity shock was temporary, and not permanent, consistent with the hypothesis

that cashiers adapted to the policy change within a year of its implementation. However, counter to

Hypothesis 1(b), the 𝛽1𝑤𝑦 estimates do not monotonically decrease in magnitude over time. In other

words, even though cashiers adapt to the policy change and the 𝛽1𝑤𝑦 estimates trend toward zero over

time, this learning is not linear as hypothesized. A limitation of this analysis is that, without data from

the remaining nine months of the year, I cannot pinpoint when worker productivity first returned to pre-


15

tax levels. Currently, I can only conclude that the learning process took longer than eight weeks but less

than a year.12

FIGURE 4: Evolution of 𝛽1𝑤𝑦 Estimates Over Time

Janwk1

Janwk2

Janwk3

Janwk4

Febwk1

Febwk2

Febwk3

Febwk4

Janwk1

Janwk2

Janwk3

Janwk4

Febwk1

Febwk2

Febwk3

Febwk4

β1 -0.058 -0.060 -0.048 -0.047 -0.059 -0.048 -0.051 -0.031 -0.019 -0.031 -0.015 -0.008 -0.010 -0.023 0.002 0.011Min 90% CI -0.102 -0.107 -0.094 -0.100 -0.114 -0.090 -0.083 -0.058 -0.068 -0.075 -0.070 -0.067 -0.056 -0.068 -0.041 -0.028Max 90% CI -0.015 -0.013 -0.003 0.006 -0.003 -0.006 -0.020 -0.004 0.030 0.013 0.040 0.051 0.036 0.022 0.044 0.049

-0.125-0.100-0.075-0.050-0.0250.0000.0250.0500.075

Estim

ates

of β

1

2009 to 2010 Comparison 2009 to 2011 Comparison

10% confidence intervals are calculated using clustered standard errors. Measure 1 = Trans/10 mins. Data is averaged over workers, 10-minute intervals, and days to obtain weekly averages for each store. The 𝛽1

𝑤𝑦 coefficients are estimate by running equation (2) sixteen times— eight regressions comparing the observations of weeks in 2009 to their 2010 counterparts and eight regressions comparing 2009 weeks to 2011 weeks. Third, having found that the bag tax temporarily lowered productivity, I examine whether the tax

affected the productivity of all DC stores in the same manner. In other words, was the treatment effect

heterogeneous? Are certain store characteristics associated with larger declines in productivity? Table 3

presents the results from the specification in Table 2 column (3), but with additional indicators. I split

the DC stores equally into two groups based on their strength in each of the following four

characteristics: i) disposable bag demand on the first day of the tax, ii) the change in disposable bag

demand experienced in the first two weeks of the tax, iii) the average number of items purchased per

transaction in each store, and iv) the median income of the census tract in which the store is located.

Columns 1-4 present the results of the diff-in-diffs regressions that include each of these paired dummy

variables interacted with the treatment effect. DC stores with higher demand for disposable bags on the

first day of the tax, low change in disposable bag demand over the first two weeks, smaller average

transaction sizes, and higher median incomes saw largest declines in productivity. To parse out which 12 I am presently in the process of obtaining additional data from the supermarket chain (for the months of March, April and May, 2009 & 2010) in order to more fully understand worker learning after the bag tax.


16

characteristics influence productivity more heavily, I include all four characteristics in the specification

of the model in column (5). The results indicate that starting with a higher demand for disposable bags

and not changing to reusable bags within two weeks of the tax implementation are correlated with larger

productivity declines. In other words, customers that use disposable bags and don’t switch to reusable

bags quickly get a double hit from the bag tax. Not only do they pay for the disposable bags they use,

they also pay in the form of longer transaction times and longer lines.


17

B. The Effect of the DC Bag Tax on Productivity Spillovers

Switching to the model of productivity spillovers, the unit of the analysis is now at the individual

worker level. Due to the labor intensive nature of coding the MM model for each store, I focus on 27

stores—nine in DC, nine in Maryland, and nine in Virginia. The Maryland stores are all in

Montgomery County (MC), a county neighboring DC to the northwest. MC offers a unique control area

as it passed a similar 5-cent bag tax initiative two years after DC. While the MC bag tax is not in my

sample period, by using MC stores I attempt to control for any unobservable factors that may be related

to both a county passing a bag tax and to store productivity. Since MC and Virginia are more affluent

and less diverse than DC, I chose the 9 stores from each area by matching on the median incomes levels

and racial composition of the stores’ census tracts.13

First I examine the pre-tax period (Table 4, odd columns). Using Measure 1 on the data for all

27 stores, I estimate equation (4) and find a highly significant 𝛿1 = -0.1359. This coefficient indicates

that a 10 percent increase in coworker permanent productivity is associated with a 1.36 percent decrease

in reference worker productivity. Looking at DC, Virginia, and Maryland individually the estimates for 13 U.S. Census Bureau, 2007-2011 American Community Survey.


18

𝛿1 are -0.1632, -0.2288, and -0.0836 respectively, though the Maryland estimate is not statistically

significant. Next I run equation (4) on a sample that contains both the pre-period and the bag tax

productivity shock months: Jan & Feb 2010 (Table 4, even columns). While the estimates for VA and

MD do not change noticeably, the coefficient for DC is cut nearly in half (from -0.1632 to -0.0872).

Moreover, the estimate is no longer statistically significant, indicating that productivity spillovers did

change in DC during the bag tax.

To further explore how/whether 𝛿1 changed due to the bag tax, I estimate the model in equation

(5), which includes interaction terms between the change in coworker permanent productivity, a DC

indicator, and a tax period indicator (Table 5, Column 1). While the coefficient on the change in

coworker permanent productivity is still negative, I find a large and positive estimate on the interaction

between coworker permanent productivity, the tax indicator and the DC indicator. Specifically, I

estimate 𝛿2 = 0.3722 at a 90% confidence interval and reject that null hypothesis that 𝛿2 = 0. This

coefficient indicates that the bag tax does change the productivity spillovers between workers.

Furthermore, the magnitude of 𝛿2 is large enough to offset the negative 𝛿1 coefficient (-0.0911 + 0.3722

= 0.2811!). Thus we can conclude that productivity spillovers are large and positive in the post-tax

period.

Lastly, I look at the heterogeneity of the change in spillovers over worker types. Equations (4)

and (5) assume that the spillover effect is the same for all workers. However, whether or not a worker is

high or low ability may influence the spillover effect. To allow for the spillover effect to vary

depending on the skill level of the focal worker, I add to equation (5) interactions with a dummy equal to

one if worker 𝑖′𝑠 permanent productivity is above average in her the store (Table 5, Column 2). While

the spillover coefficient is large and negative for workers who are below average (-0.1977), it is near

zero for those who are above average (-0.1977 + 0.1819 = -0.0158). During the post-tax period, both


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low and high workers witness increased spillovers, with a large and positive coefficient for workers who

are above average (-0.1977 + 0.1819 + 0.5691 + -0.3977 = 0.1556) and an even larger coefficient for

workers who are below average ( -0.1977 + 0.5691 = 0.3714). This indicates that during the bag tax

months, productivity spillover increased more so for low skill workers than for high skill workers.

V. Robustness and Validity Checks

A. Robustness Check: The Effect of the DC Bag Tax on Productivity

To test the robustness of the results in Table 2, I run placebo tests on the specification

corresponding to column (2) of Table 2. Table 6 evaluates time placebos where, after dropping the


20

actual treatment period from the sample, I construct five month-long placebo policy shocks with the

remaining months in the sample. None of the placebos produce 𝛽1′s statistically different from zero.

Table 7 presents estimates from store placebos, created by randomly drawing 12 stores from the

Beltway to be marked as “treated”.14 This drawing procedure was completed 5 times. The first four

14 I used Excel’s random number generator. No treatment store is included in the control group.


21

random placebos produce 𝛽1’s that are again not statistically different from zero while the fifth

regression finds a significant but positive 𝛽1. Taken together, these placebo tests indicate that the

estimation results are not picking up spurious correlations of productivity over time and stores.

B. Robustness Check: The Effect of the DC Bag Tax on Productivity Spillovers

To test the robustness of the productivity spillover model, I explore three further specifications.

First, I look at the spillovers for just those workers who worked in both the pre and post-tax periods

(Table 5, Column 3). The purpose of the specification is to test whether the presence of new workers is

driving the change in productivity spillovers. While the estimate on 𝛿1 is the same, the estimate on 𝛿2 is

slightly higher, indicating that new workers are not driving the increase in productivity spillovers during

the bag tax period.

Second, I look at how the number of coworkers at a store affects productivity spillovers. While

all stores have on average 4 to 6 cashiers working at a time, the stores vary greatly on the number of

employees that worked at least once during the sample period. Some stores only have 21 cashiers that

worked between 3-7pm in the sample and other stores have as many as 78 cashiers. This variation could

stem from stores having different numbers and types of cashiers on their payroll (part-time vs full-time)

or because certain stores may have more shift changes during the 3-7pm window. However, the

differences between stores are not due to worker turnover, as they persists even after cashiers who did

not work in every month of the sample period are dropped. Either way, the intuition is that cashiers in

stores with more personnel will be less likely to work together on a regular basis and thus peer effects

will be less strong for these cashiers. To test whether the number of cashiers employed affects my

results, I estimate equation (5) on a sample that excludes stores with less than 30 workers and then again

on a sample that excludes stores with more than 75 workers (Table 5, Columns 4 & 5). The over 30

sample is comprised of 21 stores—6 DC, 9 Maryland and 6 Virginia store, while the under 75 sample


22

has 23 stores—6 DC, 9 Maryland, and 9 Virginia. While both samples have similar estimates of 𝛿1, the

over 30 sample has a larger and more significant estimate of δ2 than the under 75 sample. These results

indicate that that number of workers at a store does not alter baseline productivity spillovers, whereas

the change in productivity spillovers due to the implementation of the bag tax is greater at stores with

more workers.

VI. Potential Threats to Validity

A potential concern in replicating the methodology of MM (2009) is the fact that the measure of

worker productivity used here is not exactly the same as their measure. While Measure 1 includes the

entire transaction period from scanning to eliciting payment to bagging, their measure only looks at

scanning time, information I don’t have in my dataset. To test whether or not their measure (Measure

MM) is fundamentally different from Measure 1, I estimate the model using their data and compare the

results for each measure. In Column 1 of Table 8, I estimate equation (4) using Measure MM and find a

positive relationship between changes in average coworker permanent productivity and changes in

individual productivity, with 𝛿1 = 0.1500. This result matches the estimate from their paper and

alleviates any concern over not coding their model correctly. As mentioned above, they conclude that

this estimate of 𝛿1 indicates that positive spillovers dominate any free-riding effects. Next, I estimate

equation (4) using Measure 1 with their data (Column 2). I now find a significant negative relationship

between changes in average coworker permanent productivity and changes in individual productivity.

However, my measure is designed to measure productivity in peak shopping hours. Consequently, I

drop all hours outside the 3pm-7pm window from the MM dataset and estimate the model again

(Columns 3 & 4). Moreover, I only look at months Dec, Jan, and Feb to match the months in my

sample. While the estimate of 𝛿1 with Measure MM does not change, the estimate for Measure 1 is


23

markedly lower, though still negative. Note that the 𝛿1 found in column (4), using Measure 1 and the

trimmed MM data, is in similar to the 𝛿1’s estimated for DC &Virginia in columns (3) & (5) of Table 4.

Why is their estimate of 𝛿1 positive while mine is negative? I hypothesize that my measure is

biased downward since I am unable to control for the presence of baggers—employees who assist

cashiers in putting groceries into bags. There are often fewer baggers on shift than cashiers and

therefore baggers can be thought of as a constrained common resource shared by cashiers. The MM

dataset doesn’t have data on baggers either, however, because their measure only looks at scanning time,

the presence of baggers does not strongly influence the swiping productivity of cashiers. On the other

hand, Measure 1 takes into account the entire length of each transaction, and thus the unobserved

presence of baggers could have a large negative effect on less productive cashiers. The intuition is that

baggers are instructed to go to check-out lines where they are needed most, ie. the lines where cashiers

are working quickly. Since baggers are a scarce resource, more productive workers impose an


24

externality on the less productive workers by attracting baggers to their lines. Consequently, baggers

make productive cashiers even more productive and less productive cashiers less so. With my measure

of worker productivity I can no longer conclude that a negative 𝛿1 estimate indicates negative

productivity spillovers and free-riding. However, while the causes of spillover effects may not be as well

identified with my data, examining how 𝛿1 changes due to the bag tax is still a valid and informative

exercise.

For instance, consider the results in Section V which indicate that productivity spillovers for DC

workers changed from negative to positive due to the implementation of bag tax. There are two potential

explanations for this change. First, it could be that the bag tax makes baggers less of an asset as baggers

and cashiers alike struggle to use fewer disposable bags, to pack more in each bag, and to deal with the

idiosyncrasies of reusable bags. On the other hand, it could be that the bag tax induced less productive

workers to learn from their more productive peers in dealing with the policy shock. A future paper

could test which explanation prevails by examining the spatial orientation of cashiers. Registers are

oriented in a store such that when a checker is in position facing the customer, she is facing one set of

registers but not another set. Cashiers can more easily monitor coworkers in front of themselves than

those behind. If we found evidence of productivity spillovers increasing during the bag tax when a

cashier was watching her more productive coworkers (but not when she was being watched and not

before the tax), that would be evidence in support of production shocks leading to information

spillovers. MM perform this spatial test for their analysis and find that spillovers are strongest when

workers are being watched by more productive peers and not when they are watching them—evidence

in favor of the peer pressure hypothesis over social learning.

Before concluding this analysis, there are a two other potential confounders to lay bare and

consider. First, the bag tax may induce some customers to switch to self-checkout lines, either because


25

they prefer to pack their own re-usable bags themselves or because they want to try to avoid paying the

bag tax. In self-checkout lines, customers self-report whether or not they used plastic bags. If not

closely monitored, customers may choose to under report plastic bag usage. Since self-checkout

transactions have been removed from the sample, the change in the number of transactions in DC stores

during the treatment period may be due to an increase in the use of self-checkout lines. Another issue is

that the bag tax may have induced DC stores to open more self-checkout lines. If customers with easy

to scan or quick transactions switch to the new self-checkout lines while customers with relatively more

difficult transactions stay in traditional checkout lines, store productivity in DC may appear lower than it

actually is.

To test for this confounder, I go back to the original data and compare the number of transactions

and items scanned in self-checkout lines before and after the tax in both the DC and Beltway stores.

During my sample period, only 6 DC stores and 15 Beltway stores had self-checkout lines. Of the stores

with self-checkout lines, the average number of self-checkout lines open at any given time is slightly

less than 4. This is true for DC and Beltway stores, both before and after the bag tax. In regards to

changes in customer preferences for self-checkout lines, Beltway stores self-checkout lines process 4.6

more transactions per day on average in the post-tax period than in the pre-tax period (an 8% increase)

while the DC stores process about 4.8 more transactions per day (an 8.7% increase). Therefore it does

not appear that customers in DC acted differently toward self-checkout lines due to the bag tax.

The last confounder is, again, the omission of baggers. Besides negatively biasing the

coefficient 𝛿1, the inability to control for the presence of baggers may cause a second problem in

estimating a change in worker productivity spillovers. The possibility that DC stores changed their

policies towards baggers in reaction to the bag tax —ie. hiring more/less baggers or retraining baggers to

handle re-usable bags—while Beltway stores did not may bias my results. Now let’s consider the sign


26

of the bias. Fully retrained baggers would bias δ2 towards zero, as they would remain an externality on

the less productive workers. Conversely, hiring more baggers would dampen their negative externality

and bias δ2 upwards. While there is no way to see baggers in the data, from newpapers reports and

anecdotal evidence it appears that DC stores did not change their policies towards baggers within the

first few months of the bag tax implementation.15 This was not true two years later when Montgomery

County stores faced a similar bag tax. Learning from their sister stores in DC, Montgomery County

stores reported retraining “employees to load bags more tightly, knowing customers paying for each one

would be eyeing their packing prowess” (Shaver and Zapana, 2012).

VI. Discussion of Broader Impacts

The results above indicate that it takes workers at least eight weeks to learn how to adjust to the

bag tax procedure change and return to previous levels of productivity. Suppose instead that workers

did not learn. What would a 3-5% permanent decline in worker productivity look like and how would it

affect customers and retail grocery stores? A 5% decline in productivity means that each transaction is

approximately 6 seconds slower, with every checkout line processing 6 transactions less per day during

the peak hours. While 6 seconds might not seem like a lot of time, a customer not only has to wait the

extra 6 seconds for their own transaction, they also have to wait the 6 seconds for those in line in front of

them. For example, if a customer enters a line behind 4 others, she will have to wait 30 seconds longer

on average after the tax. How much is 30 seconds worth? For someone whose wage is $12/hr, 30

seconds is worth 10 cents—double the cost of a disposable bag. And this time cost is paid by all

customers to some degree, whether or not they pay for disposable bags.

I also find that productivity slowdowns are greater in stores where customers do not switch to

reusable bags. This result has policy relevance since Homonoff (2012) finds that males and minorities

15 Which is not surprising since DC was the first city in the U.S. to experience a disposable bag tax.


27

are significantly more likely to use disposable bags and pay the tax. Perhaps men and minority

customers value disposable plastic bags for purposes beyond holding groceries, such as carrying lunch

to work or lining small trashcans. For these individuals, 5 cents might match their willingness to pay.

However, some may continue to use disposable bags after the tax because they are not aware of the

eventual savings they’ll get from buying reusable bags. An interesting study would be to look at the

demand for other types of plastic bags sold at grocery stores (eg. lunch bags and garage bags) before and

after a plastic bag tax and a bag ban. Either way, in a county or city with heterogeneous demand for

disposable bags, policymakers and official should consider policies that 1) educate those who are least

likely to adopt reusable bags about the potential savings, both to their pocketbook and to the

environment, and 2) that make the transition to reusable bags easier for consumers, especially in the first

couple weeks of a bag tax. For instance, local governments could subsidize stores for giving away

reusable bags, especially in areas with large minority and male populations.

In regards to store welfare, since the bag tax lowers customers’ demand for plastic bags, grocery

stores no longer need to purchase as many disposable bags to give away. One manager in this retail

chain reported to the Washington Post that within the first couple months of the bag tax his customers

were using half the disposable bags they used prior the tax— an average 6,000 bag decline per week.

Given that each plastic bag costs 4 cents, this is approximately a $240 savings per week per store.

Moreover, retail stores usually pass the cost of the disposable bags onto their customers by incorporating

it into the price of groceries. If stores do not adjust their prices down to account for the bags they no

longer buy, the savings for the store may be even larger. On the flip side, if price changes are stickier

going down than going up, customers could end up paying for disposable bags twice! Furthermore, the

DC bag tax law also stipulates that stores get to keep at least 1 cent per 5 cent bag fee collected. For the


28

store above that sold 6,000 disposable plastic bags per week, bag tax revenue would be at least $60,

bringing total bag tax revenue/cost savings to $300 per week.

On the negative side, grocery stores could be hurt by a bag tax if the ensuing productivity

slowdowns lead to temporarily lower revenues. While the results indicate that productivity fell 5%

during the initial weeks of the bag tax—an average decline of 30 transactions per store per 4 hour

window each day—I do not know if these transactions were made-up in other hours of the day.

Consequently I cannot measure whether daily store revenue decreased due to the bag tax. However,

knowing that their customers don’t like waiting in line, grocery stores may choose to open up more

lines.16 To get back to the same level of transactions per line from before the tax, stores would need to

open approximately 0.5 more lines. Given that cashiers make $12/hr, this would cost stores $168 per

week. The cost of adding another half worker to the peak hour shift is greatly outweighed by a store’s

bag tax revenue plus their savings revenue from purchasing less disposable bags. However, these back-

of-the-envelope calculations do not include the potential costs of having to retrain cashiers and baggers

to pack varying types of bags.

In regards to productivity spillovers, I find that more productive workers have a large and

positive effect on the productivity of their peers during the bag tax period. If the missing variable bias

of baggers was the reason that 𝛿1 was negative to begin with and the bag tax made baggers less of an

asset (and therefore less of an externality on lower productivity workers), this could be evidence that

positive spillovers were present all along. On the other hand, it could be that the bag tax led to larger

productivity spillovers as less productive workers learn from their more productive peers. This finding

is important because it implies that by rearranging the mix of workers in a given shift so that the less

productive workers can watch the more productive workers, a supermarket could reduce the productivity

decline caused by the bag tax, and other future policy shocks. 16 This did not happen in this DC in 2010.


29

VII. Conclusion

Overall, retail grocery stores in DC experienced a significant productivity shock due to the

disposable bag tax. All customers—whether or not they brought a bag, bought a bag or juggled their

groceries without a bag—faced longer checkout lines after the implementation of the tax. Moreover, the

productivity declines were worse in stores where customers did not quickly switch to reusable bags.

However, these declines in productivity were temporary, lasting no longer than a year, as cashiers

learned to adapt to the procedural changes of the tax. In regards to differences in peer effects due to the

bag tax, the declines in worker productivity were tempered by increased positive productivity spillovers

between cashiers. Looking towards future research, it will be interesting to examine at how productivity

and peer effects evolve in other regions of the country after the implementation of disposable bag

policies, especially since DC was the first city in the U.S. to impose such a tax. Furthermore, a

comparison of productivity shocks after a bag tax versus a complete plastic bag ban will provide yet

another intriguing topic to consider.

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