Project Update

DNRME 18018-Variation 1 The impact of MyFueINT on

Retail ULP prices in the Northern Territory

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Executive Summary

1. MyFueINT was in troduced in ttie Northern Territory (NT) on 1 November 2017 to provide real-time information abou t fuel prices a t service stations across the region.

2. Our analysis suggests tha t this co inc ided with on increase in NT ULP prices of1.08 cents per litre (+0.67% change in total price). However, these results need to be trea ted with caution due to da ta limitations & contem poraneous influences.

3. The increase in ULP prices was not limited to Darwin. Significant upward increases were found in Alice Springs (1.10 cents) and Katherine (1.05 cents).

4. This increase is likely due to the low levels of com petition in the NT (com pared to NSW). In the presents of low levels of retail com petition, the MyFueINT potentia lly enabled greater tac it collusion am ong fuel retailers. Similar cases have occurred in Perth, Germ any and Chile.

5. In terms of annual costs, the estimated 1.08 cents increase in average monthly ULP price is equ ivalent to a loss o f $11.80 a year per passenger vehicle in Darwin and $13.40 in the NT. This amounts to a total annual loss in consumer surplus of approxim ate ly $0.73 million in Darwin and $1.12 million across the NT.

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1. IntroductionThe aim of this report is to em pirically assess w hat im pact the MyFueINT scheme had on ULP Prices in the Northern Territory. The scheme was in troduced on the 1 November 2017 to provide consumers with real time da ta on retail ULP prices. Similar schemes have been im plem ented in Western Australia (2001) and in New South Woles (2016). These schemes ore designed to

(a) Reduce the am ount o t price information asymmetry fa ce d by fuel consumers

(b) Increase price com petition betw een fuel retailers

Our previous analysis ot the FuelCheck schem e in NSW provided some ev idence that ULP prices declined in metro Sydney (but not in rural NSW) otter its in troduction. At the some time, os m entioned in tha t report, on im portant m ediating fac to r was the degree o t com petition am ong ULP retailers. Due to differences in the level o t retail com petition in NT, such a schem e may not result in a significant decline in ULP prices. In tact, several case studies suggest tha t such scheme could yield higher ULP prices by fac ilita ting collusion am ong retailers.

This report is structured os follows. Section 1 provides some background. Section 2 discusses the da ta , while Section 3 report the results ot our em pirical Analysis. Section 4 concludes.

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2. BackgroundIn the absence of strong com petition, it is possible thot ULP price monitoring schemes such os MyFueINT con ieod to increoses in retoil fuel prices os they potentio iiy fociiito te toc it collusion om ong ULP retoiiers. Box 1 below illustrates.

The possibility of tacit collusion in the NT retaii fuel market

Various studies (Bundeskartellamt, 2011; Byrne and De Rocs, 2017; Ezrachi and Stucke, 2017; Luco, 2016) have posited the opportunity for fuel watch type schemes to allow retailers to engage in what can be termed ‘tacit collusion’ whereby they utlise the retail fuel price information to not only avoid competing on price, but to keep prices high. Three examples (Chile, Germany, Perth) are provided below. Note that in order for tact collusion to operate successfully, there needs to be an oligopolistic market with the aim of engaging in non-price competition rather than price competition.

Chile

This scheme was found to reduce rather than increase price competition, with petrol stations’ margins rising by 10% on average following the prices being posted on the government website. We note however, that although the margins increased across Chile, the effect was not uniform with the margins highest in areas with low or non­existent consumer search (low-income areas), while rising increased the least (or even fell), in areas with high search intensity (high-income areas).

Germanv

The German government suspected that an oligopoly of 5 firms largely controlled the off-motorway petrol station business, and in order to promote competition, required the petrol stations to report to its government’s transparency unit any price changes for gasoline or diesel fuel in real time. However, rather than lower prices, the enhanced market transparency actually saw rising prices instead.

Perth

Four major oil firms (BP, Caltex, Mobil and Shell) and two supermarket chains (Coles and Woolworths) dominate Perth’s concentrated retail petrol market. In 2001, the government introduced a petrol price transparency program called Fuelwatch. It proved useful in promoting tacit collusion (Ezrachi and Stucke, 2017). According to Byrne and De Roos (2017) rivals could see on-line the prices for every petrol station in the market, and after 2:30 pm each day, tomorrow’s prices. The market leader, BP, through trial- and-error and experimentation, eventually facilitated tacit collusion, which significantly increased retail margins and led to price stability.

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2. The DataData from Informed Sources was provided for the period January 2012 to January 2018. The da ta was aggrega ted in the following groups:

• Darwin

• A lice Springs

• Kafherine

• Tennant Creek'

General Trends. Figure 1 below depicts the doily average ULP prices across the above regional centres, as well as average daily ULP prices for the entire NT (top figure). These reveal tha t daily ULP prices app ea r to have shifted upwards perm anently across various centres after 1 November 2017.

One possible explanation for these increases could be that they were driven by an increase in international crude oil prices. However an exam ination o f trends in infernotionai crude oil prices (see Figure A1 in Appendix A) ind ica te tha t while crude oil prices were slowly trending upw ard in this period, they did not reveal a sharp increase around 1 November 2017. This is confirm ed by the ACCC (2018).

Figure 1 Doily ULP Data across NT and regional Centres.

NT ULP prices (cents)180

160

140

120

100

80

60

40

20

0as CO CO CO as

The data from Tennant Creek contalnted m any missing oberservations. For this reasons it was not analysed separately. However, the data was included in th e aggregate analysis of average m onthuly ULP price for NT.

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Darwin ULP prices (cents)

150

145

140

135

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120

115CO

OCO 00 CO o o o

COCOHONS O OO O O o o o o o o o o o o o o o o o

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V

Alice Springs ULP prices (cents)

r o O O O O O O O O O O O O O ' I r t r t O O O O O O O O O p p O O O O O

Katherine ULP prices (cents)

165

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125 so W 09 CO 00 COa o o o a o

fso

09 CO 00o

CO OS 00 CO 00 Wo o o o o ap«.

o o ofs

oo o o o o o o o o o a

Monthly prices. Figure 2 below reports m onttily overage prices for NT and the international oil price (reported in $AUD). These again reveal that ULP prices were rising both on an international level, as well as in the NT. However, the price rise appear to be relatively sharper in the NT, relative to the increase in international prices.

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Figure 2 Daily ULP Dote across NT and Regional Centres.

Monthly price (AU$ per barrel - Brent) and ULP average monthly price in NT

80

60

40

0

• NTPrice(ce

•AU8rent

Comparison to other capital cities. Figure 3 com pares monthly average Darwin ULP prices to the overage of the four other m ajor cities (Brisbane; Adela ide; Melbourne and Perth). Com paring the ULP price in Darwin to four other m ajor cap ita l cities (Brisbane, Adela ide, Perth and Melbourne), Darwin prices ore largely higher than the overage (ACCC 2018).

Figure 3 Comparison of Darwin ULP with four major cities.

Monthly ULP Prices

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160

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3. Modelling ResultsA m odel of log monthly ULP retail prices was deve loped using a num ber ot time series and control variables (details in Appendix B). The main explanatory factors used to model retail ULP include:

• International oil prices. We expect a positive and significant influence of international crude oil price on ULP retail prices.

• ULP retail prices o f the previous time period. We expect that if oil prices were high in the preceding period, this would have a positive influence on the next period.

• Demand factors (proxied by a time trend). We expect tha t as dem and for ULP price rises over time, this will hove a positive im pact on ULP prices.

• A dum m y variable to capture the e ffect o f MyFueINT (step dum m y). If MyFueINT is effective, w e would expect this to hove a negative and significant im pact on ULP prices in NT

• A constant term.

These variables were chosen by ba lancing two key requirements: that the m odel is effective os it con accou n t for most o f the observed variation; that it is parsimonious in the sense that it predicts the ULP prices with as few variables as possible. An intuitive m ethod to check the explanatory m odel is to com pare its fitted values of ULP with actua l ULP prices, as well as using it to deve lop out-of-sam ple forecasts and com paring these w ith actua l trends. These exercises show tha t fitted values closely follow actua l ULP prices and the m odel is quite powerful in being ab le to forecast monthly ULP prices (see Appendix B). No seasonality in the monthly ULP prices was observed.

This m odel was used to study the log of monthly ULP retail prices in Darwin, Alice Spring, Katherine and NT as a whole (where the NT results include Tennant Creek). Table 1 below reports the results. The overall perform ance is strong in the sense that the forecasting ability ot the m odel is sound (see Appendix B). At the some time it should be noted that results need to be trea ted with care for three reasons.

1. Working with the monthly da ta , there exist only 3 observations after the introduction of the MyFueINT in November 2017 (November, Decem ber 2017 and January 2018). This low number o f observations implies that we cannot conclude with certa inty w hat the sustained im pact of the MyFueINT scheme is on ULP prices.

2. Important changes took p lace in the sample size and the number of retailers reporting their prices over time. In particular, after the im plem entation of MyFueINT, the da ta is likely to be more dispersed. This has an observable im pact on the da ta as there appears to be greater variability in the log of monthly prices. We con not rule out the possibility tha t changes in the sampling strategy m ay be responsible for variation in the ULP price.

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3. The error term exhibits serial correlation which suggest that the results suffer from om itted variab le bias.

Table 1: M odel Results

Variables NT Darwin Alice Springs K atherine

Log of ULP Price Monthly Average (cents) (Previous Month)0.616594***

(0.04642)

0.647295***

(0.05174)

0.689527***

(0.04960)

0.645717***

(0.04322)

Constant1.12259***

(0.1691)

1.02790***

(0.1640)0.977580***

(0.1683)

0.901943***

(0.1440)

Log of Brent Crude Oil Spot Price Monthly Average (AU$ per Barrel) (Previous Month)

0.177177 ***

(0.02175)

0.163572***

(0.02388)

0.131757***

(0.02137)

0.107610***

(0.03089)

S tep dummy (policy)0.0794011*(0.01359)

0.0483799***(0.01274)

0.0964480**(0.01363)

0.0501788**(0.01179)

Dummy_March 20130.00649015**

(0.02187)

0.00842109***

'(0.01728)

Dummy_1_2D15-0.0617439***

(0.02208)-0.0816310***

(0.02287)

-0.0393447***

(0.01835)

Trend0.000295109***

(0.0001954)

0.000817442***

(0.0001796)

no. of obsrevatoins 72 72 72 72

Adi 0.974669 0.974144 0.970133 0.979106

***Significont at 1%, ** significant at 5%, * significant at 10%. All SEs are calculated using Heteroscedasticity Consistent (HCSE) robust SEs.

Note that the dependen t variab le is ULP fuel price for NT (column 2), Darwin (column 3), A lice Springs (column 4) and Katherine (column 5). Each row reports the param eter estimate for the explanatory variable listed in the first column. The top numbers report the sign and m agnitude of the param eter estimate. The stars ind ica te the a lpha level (see tab le footnote). The bottom num ber in brackets report the standard error for the param eter estimate. A large standard error implies that that there is low certa inty abou t the size o f the param eter estimate.

In terms of the explanatory variables, both international oil prices and ULP prices of the previous period ore found to hove a strong positive e ffec t on retail ULP prices across all observed regions. This conforms to our prior expectations. In add ition the param eter estimate for the trend variab le is positive and significant. This captures the influence of dynam ic factors on ULP prices, such os growing dem and and population growth. It is worth noting that two other events are found to significantly im pac t NT ULP prices: 1) a negative international oil price shock in January 2015 (see Dummy_l_2015), this had a negative im pac t on NT ULP prices and is consistent with our results on NSW ULP prices (see previous report). 2) Merger activ ity in March 2013, whereby Puma Energy purchased Ausfuel also led to greater concentra tion in the retail fuel market (ACCC 2015).

Impact of MyFueINT. The results for Darwin show that the step dum m y entered from Novem ber 2017 onwards to measure this e ffec t was significant and possessed a positive sign (0.0483799). The e ffec t on ULP prices in Darwin was significant a t the a lpha = 1 per cent level (see Darwin Column in Table abo ve ). This means that w e can reject with 99% confidence the null Hypothesis that MyFuelCheck schem e had no significant im pac t on retail ULP prices in Darwin. Note the m agnitude o f this e ffect

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appears to be small, but ttiis is because retail ULP prices were studied in log form .2 in normal terms, tine m agnitude of ttiis im pact is abou t 1.05 cents. A simiior e ffect was found w tien examining overage monthily ULP prices for NT.

Other NT regions. The same m odel was used to study ULP price trends in Alice Springs, Katherine (see third and fourth column). Here it is worth noting that MyFueINT was also found to hove a significant and positive im pact on ULP prices. This suggests that price increases where not just confined to Darwin, but appear to be spread across the Northern Territory. It also suggests that contem poraneous influences ore not responsible for the Darwin results. NT m edia (2017c) reported tha t one independent retailer closed down in Darwin around the time of the introduction of MyFueINT. This cou ld be another possible explanation for why w e find prices spike upwards around November 2017. Yet the closure of a petrol station in Darwin cannot accoun t for why price spike were observed in Alice Springs and Katherine as well. This suggests tha t the price rises are more likely the result o f collusion fac ilito fed by the MyFueINT, bearing in mind the 3 caveats listed above.

Robustness ctieck. To further verify our results, a ratio o f Darwin’s ULP price fo the average of ULP price for Brisbane, Perth, Melbourne and Adela ide was ca lcu la ted (see Table B2 in Appendix B). This ratio was then used as a dependen t variable in the m odel and tested for policy e ffec f through the step dummy. The results revealed that policy e ffec t was statistically significant with the expected positive sign (NTdumm71), such that the ULP price in Darwin, relative to other cities, was found to have risen after the introduction o f the MyFueINT policy. The m agnitude o f the rise was similar to our previous results (1.01 cents). However, it is worth noting that the overail model fit was lower relative to the m odel reported in Table 1. This is because accounting for price differences between major cities is a relatively more com p lica ted exercise and ideally requires more variables to accou n t for cross-city differences in ULP retail prices.

5. ConclusionsThe main conclusion of this study is that the MyFueINT scheme had a small but significant positive im pact on retaii ULP prices in NT. Significant e ffect were found across Darwin, Alice Springs and Katherine. In terms of annual costs, the estimated1.08 cents increase in average monthly ULP price is equ ivalent to a loss of $11.80 a year per passenger vehicle in Darwin and $13.40 in the NT. This amounts to a total annual loss in consumer surplus o f approxim ately $0.73 million in Darwin and $1.12 million across the NT.

At the some time, it should be noted that the results o f the study should be trea ted with caution due to 1) limitations of the data , 2) changes in the sampling m ethodoiogy 3) om itted variable bias.

Logs models are typically used to study tim e series when there is heteroscedasticity present In the data._______________________________________________________________________________________________________in

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1. ReferencesABS (2017) Survey of Motor Vehicle Use, Australia, 12 months ended 30 June 2016, Cat. No.9208.0. Available at: http://www.abs.gov.aU/ausstats/abs@.nsf/mf/920B.0

ACAPMAg (2017) Fuel prices to increase under MyPuel NT scheme. Last accessed 8 March 2018. Available at: http://acapmag.com.au/home/2017/07/fuel-prices-increase-myfuel-nt- scheme/

ACCC (2015) ACCC announces first regional petrol market study in Darwin, 10 march. Available at: https://www.accc.gov.au/media-release/accc-announces-first-regional-petrol- market-study-in-darwin

ACCC (2015a) Report on the Darwin petrol market, November. ISBN 9781922145659

ACCC (2017) Report on the Cairns petrol market. May. ISBN 9781920702106

ACCC (2018) Report on the Australian petroleum market. December Quarter 2017, June. ISBN 9781920702250ACCC

Bundeskartellamt (2011) Fuel sector inquiry. Final report. Available at: http://www.bundeskartellamt.de/SharedDocs/Publikation/EN/Sector%20lnquiries/Fuel%20Se ctor%20lnquiry%20-%20Final%20Report.pdf? blob=publicationFile&v=14

Byrne, D. P. and De Roos, N. (2017) Consumer search in retail gasoline markets. Journal of Industrial Economics, Vol. LXV, No. 1, pp. 183-193

Department of Treasury and Finance (2017) Northern Territory implementation of MyFuel NT: mandatory retail fuel price reporting scheme, 12 January. Available at: www.treasury.nt.gov.au/PMS/Publications/Economics/CP-ECO-MFNT.docx

Ezrachi, A. and Stucke, M. E. (2017) Algorithmic collusion: problems and counter-measures, OECD document. Available at:https://one.oecd.org/document/DAF/COMP/WD(2017)25/en/pdf

Luco, F. (2016) Who benefits from information disclosure? The case of retail gasoline. Working Paper, Department of Economics, Texas A&M University September 28. Available at https://cf00f56d-a-62cb3a1 a-s-sites.googlegroups.com/site/flucoe/home/lnfo_disclosure.pdf?attachauth=ANoY7cosrnBIBTk0h-NiO3m8AWL4iyvCbrOZ3saStlFICbZ9eMvUMVckNxS2LGM6l3JsbqOEAXT4rypxMvwYp-xsweYjtKxgXU30v5CFwOW5CaOI3z5vlzqctbPptxvmMhUsiPDEF6RmJaFOYpY5kOyVbwdf9xRbB4Nxe7Eiujo2tbxFI6rEBD29dXZ7Zn5EMsm4SBKwV4L7yZg10mZ6X3yYoOff4czdivO%3D%3D&attredirect=0

Northern Territory news (2017) Spike in fuel questioned, 31 October

Northern Territory news (2017a) Fuel tracker blamed for petrol price hike, 2 November

Northern Territory news (2017b) Fuel site fails to end hike, 14 November

Northern Territory news (2017c) Fuel could be cheaper, 15 November.

Northern Territory news (2017d) Petrol ‘rip-offs’ in sight of watchdog, 22 November.

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Northern Territory news (2017e) Territory drivers iook for help at the bowser, 22 December.

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Appendix A: Further FiguresFigure A1: International Crude Oil Prices (AU$ per Barrel). Source: EIA

AU$BrentDaily price (per barrel) from 1®* October 2017 to 31®' January 2018

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0

*0=

B1 Forecasting ResultsThe set o f figures below com pared the fitted values of the m odel with the ac tua l ULP retails prices. If these two m ove closely together, it is on ind ication that the m adel is functioning well. In add ition, the predictive pow er of the m odel was tested by conducting out-of-sample forcasts which essentially gets the m odel to pred ict how prices will deve lop in a selected tim e-period (see blue bar). The predictions are then com pared with ac tua l trends within the same period. These figures show tha t the madels perfarm well across all regions.

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Fig;Bl Darwin

[ laA IkftSprin^ F ittftll

5J5

5.05

4.83

Fig. B2 Alice Springs

1—— LtiK adttrm P Fittedl5.10

5.00

Fig.B3 Katherine

[ ' tnpckeC yD ------

3.10

5.00

4.90

Fig. B4 NT

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B2 Robustness ChecksTable B2: Modelling the difference between Darwin and the average of four ofher cities.

EQ; Modelling LnRatioDarwin by OLSThe dataset is: E:\Research\2018\oil prices\Data\final

data\Analysis\UL\NTOXMET. xlsxThe estimation sample is: 3 - 7 3

Coefficient Std. Error HCSE t-HCSE t-prob Part. R 2LnRatioDarwin_l 0.626404 0.08094 0.09112 6. 87 0.0000 0.4172Constant -0. 234524 0. 06776 0. 08809 -2.66 0.0097 0. 0970LnAUBrent_l 0.0591378 0. 01600 0.02059 2.87 0.0055 0. nilNTdumm71 0.0223598 0.01916 0.006292 3. 55 0. 0007 0. 1606dumm37 0.0767092 0. 03373 0.01270 6. 04 0. 0000 0. 3559

sigma 0. 0321511 RSS 0.0682238238R"2 0. 723041 F(4, 66) = 43.08 [0.000]**Adj.R'R 0.706256 log-likelihood 145. 897no. of observations 71 no. of parameters 5mean(Y) 0. 0797406 se (Y) 0. 0593214

AR 1-2 test: ARCH 1-1 test: Normality test: Hetero test: Hetero-X test: RESET23 test:

F(2,64) = 6.1211 [0.0037]**F(l, 69) =2. 4098e-008 [0.9999]Chi'2(2) = 2.4422 [0.2949]F(5,64) = 1.7196 [0.1428]F(6,63) = 1.4604 [0.2064]F(2,64) = 0.72390 [0.4888]

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Appendix C: Consumer surplus

DARWIN

Consumer surplus was ca lcu la ted using information provided by ttie Australian Bureau of Statistics (ABS) 2017 Cat. No. 9208.0 (Survey o f Motor Vetiicle Use, Australia, 12 montlns ended 30 June 2016).

From Table 6 of Cat. No. 9208.0 w e obtain tFie average rate of petrol consumption in NT for passenger cars, wInicFi is given as 10.7 litres per 100 kilometres (or 0.107 litres per kilometre). While w e a c c e p t that fuel consumption is likely to be higher In an urban setting like Darwin's w e are unable to procure the figure for Darwin, though w e acknow ledge it is likely to be greater than 10.7 litres per 100 kilometres driven

Table 8 of Cot. No. 9208.0 provides information on the average kilometres travelled in Darwin for passenger vehicles, which is 10,500 kilometres. Thus, over 10,500kms, the am ount o f petrol consumed would be 10,500*0.107 = 1,123.501. Given a penalty of $0.0105 per litre the perceived introduction of MyFueINT this would end up costing 1123.50*0.0105=$ 11.80 a year per passenger vehicle in Darwin.

To ge t total negative consumer surplus w e need to know how many kilometres were covered in passenger vehicles in Darwin in this time (to reiterate this period covers 1 July 2015 to 30 June 2016). Table 27 of Cot. No. 9208.0 provides information on total kilometres travelled within Darwin using passenger vehicles, and that figure stands a t649,000,000 kilometres. Given our previous ca lcu la tion on litre per kilometre consumption, this yields a tota l consumption o f 69,443,000 litres of pefrol. Multiplying this by the perceived MyFueINT penalty of $0.0105 per litre gives us an estimated negative consumer surplus of $729,151.50.

Robustness Check

Changes in the figure o f tofal kilometres travelled will obviously alter the negative consumer surplus. Going back to Table 27, if w e include trips not just within Darwin but also those betw een Darwin and other ports o f NT as well as between Darwin and another cap ita l city then 777,000,000 kilometres are covered, yielding tota l petrol consumption of 83,139,000 litres. Applying the estimated MyFueINT perceived penalty to this yields a negative consumer surplus value of $872,959.50.

A slight difference in definition is available from Table 7 (total kilometres travelled, by state/territory of registrafion by type of vehicle by area o f operation), and this gives (for Darwin and passenger vehicles) a total o f 716,000,000 kilometres travelled. This will provide us with an estim ated petrol consumption of 76,612,000 litres. As w e estimate each litre is now $0.0105 more expensive, negative consumer surplus would be $804,426.00.

1 A

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Thus, given different possible figures, w e ca lcu la te the negative consumer surplus for the consumption o t petrol in Darwin (using kilometre and passenger vehicles figures covering 1 July 2015 to 30 June 2016) to hove been between $729,151.50 and $872,959.50 had MyFueINT been in p lace.

NT (ABS definition used is detaiis on intrastate travei)

Table 8 ot Cot. No. 9208.0 provides information on the average kilometres travelled intrastate tor passenger vehicles, w hich is 11,600 kilometres. Thus, over 11,600kms, the am ount ot petrol consumed would be 11,600*0.107 = 1,241.201. Given a penalty ot $0.0108 per litre the perceived introduction o t MyFueINT this would end up costing 1241.20*0.0108=$ 13.40 a year per passenger vehicle in NT.

To ge t to ta l negative consumer surplus w e need to know how many kilometres were covered in passenger vehicles in NT in this time (to reiterate this period covers 1 July 2015 to 30 June 2016). Table 27 o t Cat. No. 9208.0 provides information on total kilometres travelled (excluding ‘all outside state/territory ot registration’) using passenger vehicles, and that figure stands a t 1,034,000,000 kilometres. Given our previous calcula tion on litre per kiiometre consumption, this yields a tota l consumption o t 110,638,000 litres ot petrol. Multiplying this by the perceived MyFueiNT penalty ot $0.0108 per litre gives us on estim ated negative consumer surplus ot $1,194,890.40.

Robustness Check

A slight d ifference in definition is availab le from Table 7 (total kilometres travelled, by state/territory o t registration by type ot vehicle by area ot operation), and this gives (tor NT and passenger vehicles) a tota l o t 1,027,000,000 kilometres travelled intrastate. This will provide us with on estimated petrol consumption ot 109,889,000 litres. As we estimate each litre is now $0.0108 more expensive, negative consumer surplus would be $1,186,801.20.

Thus, given different possible figures, w e ca lcu la te the negative consumer surpius tor the consumption ot petrol in NT (using kilometre and passenger vehicles figures covering 1 July 2015 to 30 June 2016) to hove been between $1,186,801.20 and $1,194,890.40 had MyFueiNT been in p iace.

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