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juniperpublishers-etoaj · 6 years ago

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Total Costs in The Brazilian Efficiency Model of Distribution System Operators: An Analysis - Juniper Publishers

Juniper Publishers - Open Access Journal of Engineering Technology

Abstract

This study analyses the efficiency of electricity distributors in Brazil by considering total costs. The impact of the inclusion of total costs is evaluated with four different efficiency models using Data Envelopment Analysis and Stochastic Frontier Analysis. The analyses are conducted using a sample of 60 companies over two periods of time. The years 2008 to 2010 are used to calculate the efficiency frontier, and the years 2011 to 2012 are used to validate the methodology. The results show that, on average, the total costs estimated by benchmarking methods are approximately 7% lower than those observed in 2011 and 2012, that is, utilities need to reduce their total annual costs by approximately R$40 million on average.

Keywords: Efficiency; Electricity distributors; Methodologies; Electricity sector; Competitive; Environment; Incentive regulations; Operating costs; Distribution system operators; Territorial extension; Efficiency scores; Environmental variables; Tariff reviews; Remuneration; Minor components costs

Abbrevations: DSOs: Distribution System Operators; CR: Capital Remuneration; RD: Regulatory Depreciation; MC: Minor Components Costs; AC: Additional Costs; DEA: Data Envelopment Analysis; CRS: Constant Returns to Scale; VRS: Variable Returns to Scale

Introduction

Since 1990, number of infrastructure sectors around the world, including the electricity sector, have initiated long reform processes, replacing rate of return regulation with incentive regulation. Although the structures and methodologies adopted by the electricity sector have changed since the reforms, the main objective of efficiency improvement has been maintained [1].

Rate of return regulation, which was widely used before the reform process, had an adverse effect. Specifically, it encouraged companies to overinvest to obtain greater capital remuneration. This effect is known in the literature as the Averch-Johnson effect [2]. In this scenario, consumers are penalized by having to pay high tariffs.

Following the reform process, incentive regulation has become popular in the electricity transmission and distribution segments because it incentivizes companies to become more efficient [3]. Under this type of regulation, benchmarking techniques are applied to detect inefficiencies during the electricity transport process. In short, these techniques aim to compare similar companies in a competitive environment [4].

In Brazil, rate of return regulation is partially employed in the definition of capital costs, whereas incentive regulation is fully applied in the calculation of operating costs. However, economic regulation best practices follow a different trend: the adoption of incentive regulation for capital and operating costs. This practice is based on the existence of a potential trade-off between the two costs [1]. If they partially adopt rate of return regulations for capital costs and incentive regulations for operating costs, companies will simultaneously seek to raise the former and reduce the latter [5].

In this context, the present study proposes the use of total costs for the efficiency analysis of Brazilian distribution system operators (DSOs) from an incentive regulation perspective.

Several studies analyzing the efficiency of Brazilian DSOs have been published, but, to the best of our knowledge, no study has evaluated the economic effect of the adoption of total costs in the efficiency model. Xavier, Lima, Lima, and Lopes [6] propose an alternative form of efficiency analysis for Brazilian DSOs motivated by the great territorial extension. Despite the use of total costs with physical variables as a proxy, their study does not analyses the economic impact. Costa, Lopes, and Matos [7] evaluate operating cost models proposed by Brazilian regulators and discuss their main inconsistencies. Corton, Zimmermann, and Phillips [8] investigate the effect of incentive regulation on the operating costs of Brazilian DSOs, focusing on service quality. Altoé, Júnior, Lopes, Veloso, and Saurin [9] analyse the relationship between technical efficiency and some financial variables related to capital management using operating costs, costs related to service quality, and non-technical losses. Gil, Costa, Lopes, and Mayrink [10] examine the statistical correlation between efficiency scores and environmental variables using operating costs as inputs.

Despite the previous research, studies that investigate the incentive regulation effects on the total costs of Brazilian electricity distributors are still necessary. At the moment, this proposal is subject to an internal study by Brazilian regulator. However, given the global trend, a shift towards total costs will become essential. Thus, this study provides empirical evidence of the impact of adopting total costs on efficiency analysis by comparing four different models.

Brazilian Electricity Distribution Regulation

Since 2003, DSOs have been regulated by a price cap model, which specifies an average rate under which tariffs should be adjusted considering inflation and productivity targets (X factor). The electricity distribution segment has completed three tariff reviews (2003-2006, 2007-2010, and 2011-2014) and is completing the fourth (2015-2018). During a tariff review, capital and operating costs are redefined.

Capital Costs

Capital costs consist of capital remuneration (CR) and regulatory depreciation (RD). CR is the product of the remuneration rate and the net remuneration base, which corresponds to recognised investments and is not depreciated. RD is the product of the average depreciation rate and the gross remuneration base, which corresponds to total recognised investments.

In the fourth tariff review, the previous asset base was maintained and updated by the inflation index. New assets were valued according to the concept of the optimised and depreciated replacement cost, and a utilization index was applied to all accepted assets to reduce overinvestment.

A reference price base is used to calculate the average minor components costs (MC) and additional costs (AC), which make up the final fixed asset value (replacement new value-RNV), according to Equation 1:

RNV=ME+MC+AC (1)

Where:

ME-main equipment, such as circuit breakers and current transformers;

MC-fixed components associated with a particular constructional standard, such as control cables and insulators;

AC-setting up the good, consisting of design, management, assembly, and freight costs.

ME is valued according to the company’s price base, whereas MC and AD are valued according to the reference price base, which has created an incentive mechanism within capital costs.

The reference price base is structured in a modular way such that a module is associated with each type of ME according to the company’s group. The regulator applies a clustering technique to segregate 63 DSOs into five groups to take into account different levels of investment in electricity distribution systems. Each company has an average group cost considering differences between the concession areas. Once the prices of the ME, MC, and AC are known, the RNV is calculated.

Operating Costs

The Brazilian regulator applies Data Envelopment Analysis (DEA) as an efficiency analysis, with operating costs as an input. The outputs are the underground network, the over ground network, the high-voltage network, distributed energy, the number of consumers, non-technical losses, and service quality. The sample has 61 DSOs, with mean values for the variables during 2011, 2012, and 2013. The analysis preserves non-decreasing returns to scale and the input orientation. The regulator creates a confidence interval around efficiency scores because DEA has a deterministic aspect.

From these restrictions, an operating cost target is set to be reached over the regulatory period. At the time of review, the target is compared to real operating costs. The difference between real and target costs determines a regulatory trajectory. Part of the difference is incorporated at review time, and the remaining portion is considered in X Factor [11].

International Electricity Distribution Regulation

Unlike in the early years of reform, when regulators were worried about operating costs, a current emerging question is how to ensure that utilities set efficient investment levels. Over the years, DSOs have improved their performances in response to incentive regulations. However, significant investment is needed over the next few years, and this need, combined with incentives to reduce costs, accentuates a new challenge between efficiency and investment [12].

This broad view of total costs has several motivations, including the trade-off between operating and capital costs, the freedom of companies to choose different strategies, and the trade-off between cost efficiency and quality.

An analysis that segregates operating, and capital costs encourages substitution between these cost categories [13]. Consider a benchmarking model in which operating costs are the only input and the distribution network is the only output. Utilities will increase investments by focusing on maximizing output and the return to capital, resulting in greater operational efficiency; however, tariffs will increase.

Companies can adopt different combinations of operating and capital costs to operate and improve their networks [1]. When total costs are considered, a DSO is free to choose an optimal cost composition.

In addition, total costs play an important role in service quality analysis. As more DSOs invest in network reliability, total costs and quality improvement marginal costs will be higher. Therefore, a total cost model is more appropriate to evaluate this possible trade-off [14].

Finally, a total cost model is considered one of the best regulatory practices, according to Haney and Pollitt [15]. A similar result is presented by Mesquita [16], who investigates aspects of the efficiency analyses currently employed by European and Latin American countries. The analysis considers ten European countries and eight Latin American countries and finds that most of the countries surveyed use total costs.

However, adopting total costs in efficiency models can also mean a strong incentive to reduce capital costs and may jeopardize long-term investments [17]. The possible adverse effect of discouraging investment and jeopardizing the future performance of energy distribution networks has been pointed out as one of the possible causes for the non-adoption of total costs by the Brazilian regulator. However, the regulator recognizes its use as an international trend:

‘Discussions like this point toward benchmark model based on total cost, which has been a trend in international regulatory experience. However, a breakthrough in this direction requires a much deeper study and certainly a space for methodological transition and adaptation of agents’ [18].

This adverse effect is not observed by Cullmann & Nieswand [19] when analyzing incentive regulation effects on the investment behavior of 109 German DSOs. The results show an increase in investments from 2009 for both public and private companies. The authors conclude that an analysis of investment decisions should include all institutional aspects of incentive regulation.

From a similar perspective, Poudineh & Jamasb [20] explore the determinants of the investment decisions of 129 Norwegian DSOs in the period from 2004 to 2010. The results show that the main factors influencing these decisions are the rate of return under the previous period’s investment, socio-economic costs, and the lifespan of useful assets.

Cambini, Fumagalli, & Rondi [21] investigate the relationship between incentives, service quality, and the investment levels of Italian DSOs. The results indicate a causal relationship between incentives and investment levels, and, in the process of performance improvement, penalties are more effective than rewards are.

Benchmarking Methods

The most recent advances in the field of efficiency, microeconomics, and econometrics studies are focused on efficiency frontier analysis. Given the impossibility of observing theoretical efficiency frontiers, efficiency is determined by empirical boundaries, estimated by observing the minimum use of inputs given an output level or the maximum output given an input level. This study uses DEA and Stochastic Frontier Analysis (SFA) in estimating the efficiency of Brazilian DSOs.

Data Envelopment Analysis

DEA is a nonparametric methodology that uses real data to measure the relative efficiency of a DMU. It was proposed by Charnes, Cooper & Rhodes [22] to address the efficiencies of companies operating in constant returns to scale (CRS) and further extended by Banker, Charnes & Cooper [23] to variable returns to scale (VRS).

This efficiency analysis can be focused on input reduction or output expansion. The result from an input-oriented model is the maximum reduction possible in the inputs level for a given level of output. With an output-oriented focus, the model seeks the maximum output quantities that can be generated by the actual level of inputs used by the company. The efficiency scores can vary from 0 to 1, where 1 denotes the efficient company

The majority of the DEA models consider either CRS or VRS. For CRS model, outputs and inputs increase (or decrease) by the same proportion along the frontier. Where the technology exhibits increasing, constant or decreasing returns to scale along different segments of the frontier, the VRS model is indicated. The CRS model assesses the overall technical and scale efficiency, while a VRS model measures only the technical efficiency.

The efficiency score of the ith company of N companies in CRS models takes the form specified in Equation 2, where θ is a scalar (equal to the efficiency score) and λ is a Nx1 vector that represents the weight of each Decision-Making Unit in the construction of the reference company. Assuming that the companies use E inputs and M outputs, X and Y represent ExN input and MxN output matrices, respectively. The input and output column vectors for the ith company are represented by xi and yi respectively. In Equation 2, company i is compared to a linear combination of sample companies which produce at least as much of each output with the minimum possible amount of inputs. The Equation 2 is solved once for each company.

For VRS models, a convexity constraint Σλ = 1 is added that ensures that the company is compared against other companies of a similar size.

Stochastic Frontier Analysis

SFA, a parametric method, was originally developed by Aigner, Lovell, and Schmidt [24] and Meeusen and Broeck [25] and allows the estimation of the inefficiency associated with a production function or cost.

The stochastic frontier consists of

(i) a deterministic component,

(ii) a stochastic component representing random error in the estimation of the frontier, and

(iii) an inefficiency component for each company. It is calculated, in most studies, using an input-oriented Cobb- Douglas functional form with stacked data, as in Equation 3.

The SFA model allows the error to be disaggregated into two independent components, vit and uit, and to be uncorrelated with the explanatory variables [26].

The component vit is random noise that represents deviations of the deterministic component from the frontier due to the non-inclusion of an explanatory variable or measurement error. We adopt the assumption that the error vit is independent and identically distributed and normally distributed with a zero mean and constant variance. This error term has all the characteristics of the error term used in the classical linear regression model.

The uit component is a positive error term that reflects the cost inefficiency of firms. This term indicates the cost excess relative to the stochastic frontier. When this component is null, the firm is at the efficiency frontier. Aigner, Lovell, and Schmidt [24] propose using the half-normal distribution as the probability distribution for this term, as in Equation 4:

This model is referred to as SFA-ALS. Even today, this is the most common specification used in SFA models found in the literature. Subsequently, other distributions have been proposed for the u term, the most common of which are the exponential, normal truncated, and gamma distributions [26].

Methodology

Choice of variables

The choice of inputs and outputs is a crucial aspect of benchmarking methods, especially for DEA, as the discriminatory power of these methods decreases as the number of variables increases [27]. Therefore, a researcher needs to be parsimonious in choosing variables, opting for those that best describe the evaluated process.

There is no consensus on the best variables to describe the electricity distribution process. Jamasb and Pollitt [13] investigate the most frequently used variables in benchmarking studies. Among inputs, the following stand out: operating costs, number of employees, transformer capacity, and network extension. With regard to outputs, distributed energy and the number of consumers are the most common choices.

This study uses monetary and physical variables that are widely adopted in benchmarking studies as well as non-technical losses and service quality indicators. The monetary variables are operating and total costs. The physical variables are the same as those adopted by the Brazilian regulator in the current tariff cycle, namely, the underground network, the over ground network, the high-voltage network, distributed energy, and the number of consumers. Non-technical losses and the service quality indicators are also the same as those adopted by the Brazilian regulator that consider the difference between actual and expected values [18].

Data

An efficiency analysis is conducted using data from 60 Brazilian DSOs from 2008 to 2012. The dataset can be found at the website of the Brazilian regulator (www.aneel.gov.br) and was divided into two periods: 2008 to 2010 for the efficiency frontier calculation and 2011 to 2012 for the model validation.

The methodology used to calculate capital costs was the same as that used by the regulator in Technical Note 185/2014 from the Economic Regulation Superintendence [18]. Operating costs and outputs were the same as those from Technical Note 66/2015 from the Economic Regulation Superintendence database [11]. Table 1 shows sample descriptive statistics.

This data shows great variability between companies, especially for underground networks, which are only found in the capitals of large countries.

Models

Four distinct models are evaluated in Table 2: three DEA models and one SFA model. The first two models were selected to evaluate the impact of total costs on efficiency analysis. This choice was based on the literature review presented in Section 3. The last two models were included in the analysis to validate the DEA results using SFA, a guideline recommended by Bogetoft and Otto [28].

Results

The proposed methodology was applied to the four models defined in Section 5.3 using data from sixty Brazilian DSOs from 2008 to 2010. Models 1, 2, and 3 were based on DEA using an input orientation and non-decreasing returns to scale. Model 4 applied SFA and was estimated using an input-oriented cost function. Table 3,4 shows the estimated results.

The results indicate that DSOs have average efficiency scores of 0.70, 0.84, 0.80, and 0.81 in Models 1, 2, 3, and 4, respectively, which indicates room for improvement.

Model 1 considers ten utilities as efficient, including three small and seven large companies. Two of them, Eletropaulo and Light, are located in high consumer density areas. Others that have reached the frontier do not have such high densities, which implies relatively efficient input management. Other utilities have an average efficiency of 0.67. This inefficiency can be explained by low load densities and dispersed consumers, which make such areas expensive and challenging for energy distribution. Three CPFL Energia DSOs are considered efficient: Piratininga, CPFL Paulista, and RGE. These results suggest a possible advantage associated with holding characteristics, as Semolini [29] also concludes. Twenty-nine utilities have efficiency scores under 0.67, including AME, Ene. Paraíba, Ene. Sergipe, CEMIG, and CEEE. The first three are located in the Brazilian north or northeast, which are characterized as less urbanized regions with the lowest monthly income [30]. Analysis indicates that these companies should reduce operating costs by 55% on average.

Model 2, which considers total costs as inputs, indicates lower efficiency levels for three DSOs (Piratininga, CPFL Paulista, and Light). New companies are considered efficient, such as, for example, CEB, Coelce, and Cosern. Comparatively, these companies have partial productivities that are higher than their segment averages, especially for total costs and the highvoltage network ratio. Therefore, some companies’ efficiencies decrease under Model 2, whereas those of others increase, and the segment average efficiency rises from 0.70 to 0.84. The efficiency scores have a correlation of 0.76 with those of Model 1. Light is located at the efficiency frontier in Model 1. However, with total costs, the DSO receives a score of 0.90; a reduction of 10% in its efficiency. On the other hand, Cepisa achieves better results. Under Model 1, it has an efficiency of 0.59 compared to Celtins, Coelba, and João Cesa. Under Model 2, the company obtains a score of 0.88, and its peers are Celtins and Coelba. This evidence indicates that Model 1 can penalise companies that are efficient in total costs and can favour those that are efficient in operating costs.

Model 1 can distort the incentives given to companies. For example, Coelce obtains an efficiency of 0.80 in Model 1 and of 1.00 in Model 2. These results corroborate the existence of a possible trade-off between operating and capital costs. Therefore, models with total costs are more appropriate for efficiency analysis [1]. In fact, Model 1 does not capture the aspect of DSOs’ total costs.

In contrast with the previous models, Model 3 considers only seven companies to be efficient. CEB, Coelce, and Cosern have lower scores following the changes to the model, such as the exclusion of service quality and non-technical losses and the aggregation of the distribution network. Some companies, such as Coelba and RGE, remain on the frontier in all three models. The results of Model 3 results have a 0.89 correlation with those of Model 2. In addition, the efficiency of Light is considerably lower in Model 3, with a value of only 0.61. The company obtained scores of 1.00 and 0.90 in Models 1 and 2, respectively. This change can be explained by inclusion of the non-technical loss variable, given that difference between the expected and real values is minimal.

Model 4 estimates efficiency using SFA and estimates the cost function using the Cobb-Douglas functional form. An exponential probability distribution is used to estimate the inefficiency term of the u error. The coefficient on the logarithm of the products is shown in Table 5.

Table 5 shows that all estimates of the product coefficients are significant at the 5% level. The significance of the variance parameters of the error components, σ and λ, validate the use of the SFA stochastic model. We observe that the most important product is the distributed energy, which has an importance of almost 50% between the three products. The sum of the coefficients of the three products is 1.01, indicating the possibility of constant returns to scale. The results of the application of this model have a 0.76 correlation with those of Model 3, since Model 3 is constructed using the same inputs and products as this model is.

Of the sixty DSOs, thirteen companies have efficiencies greater than 0.95, and only two companies have efficiencies less than 0.5. Of these two DSOs, one is João Cesa, with a score of 0.45, but in Models 1, 2, and 3, this company is considered a benchmark. This company has the smallest outputs in the sample, and this fact may be distorting its efficiency.

Discussion

To analyses the economic impacts of the different models, we calculate:

(1) the average segment efficiency for each model,

(2) each distributor’s score divided by the average segment efficiency,

(3) the product of the previous result and the average real total cost from 2008 to 2010, and

(4) the comparison of the previous result with the average real total cost from 2011 to 2012. The results can be seen in Table 6,7.

Comparing the total costs estimated by Model 2 and the real values, we find a necessary average reduction of R$37 million, which is approximately 7% of real total costs. A similar result was found by Yu, Jamasb, and Pollitt [29], who analyse the efficiency of twelve English DSOs from 1995 to 2003. Of the sixty companies evaluated, thirty-three exhibit total costs that are higher than those defined by DEA. According to Model 2, AME needs to reduce cost by R$166 million or, in percentage terms, 35% of its total costs. Another inefficient large company is Ampla, which spends R$331 million more relative to others. Other DSOs have lower real total costs; RGE is a member of this group, with a real total cost of R$575 million versus an expected cost of R$643 million.

Coelce also uses comparatively fewer inputs, about 12% fewer than expected. Some companies have real and expected values that are very close, requiring no decrease or increase. These companies include Coelba, CPFL Paulista, and Light.

Model 3 suggests an average reduction of R$49 million, or approximately 9% of real total costs. Giannakis et al. [1] make a similar diagnosis when evaluating UK utilities between 1991 and 1999. About half of companies need to reduce their costs. This model does not include the quality and non-technical losses variables, as in other studies [1,14,29-33,]. AME remains inefficient, needing to reduce costs by R$162 million, which is R$4 million less than in Model 2. Ampla needs to reduce costs by R$364 million. As in the previous model, some utilities prove to be efficient, such as, for example, RGE, which spent R$100 million less than expected. Coelce maintains its good performance in this model, and AES Sul has an appropriate level of total costs.

Model 4 presents the lowest required cost reduction, with a value of approximately R$34 million, or 6% of costs. This result is to be expected since SFA considers data error. This model does not include environmental variables since they were not significant. These results corroborate previous work, such as that by Yu et al. [29], who conclude that environmental factors do not have significant economic or statistical impacts on the overall performances of English DSOs. The model finds the sharpest reductions with respect to Boa Vista (58%) and João Cesa (51%). In the previous models, the latter is considered efficient, with opportunities to increase total costs by 3% and 8%, respectively, in Models 2 and 3. Another utility with a similar result is Eletropaulo, which can increase total costs by R$236 million in Model 2, can increase them by R$86 million in Model 3, and should reduce costs by R$172 million in Model 4. Elektro moved in the opposite direction, as it is evaluated positively by Model 4 but needs improvement in Models 2 and 3.

Finally, when analyzing the results of all models, we find that, in average percentage terms, the total costs estimated by the benchmarking methods are not considerably smaller than those defined by the Brazilian regulator.

Conclusion

Efficiency analysis is receiving considerable attention from regulators in the electricity sector, especially in the distribution segment. Due to the natural monopoly characteristics of the electricity distribution process, utilities are not subject to market forces.

This study simulated a virtual competitive scenario among Brazilian utilities. DEA and SFA were used for efficiency analysis. Both methods calculate an efficiency frontier based on the evaluated company’s inputs and outputs to evaluate the impact of total costs.

The novelty of this study is in the use of total costs as inputs in efficiency models, specifically in the Brazilian case. Although total costs have already been evaluated by other studies, mainly in European countries, they have not been applied in a country with a considerable distribution segment growth rate, such as Brazil.

Four different models were studied. Comparing Model 1 and Model 2 allowed us to evaluate the impact of total costs on efficiency, whereas the comparison between Model 3 and Model 4 was useful to understand the robustness of the results. In the first comparison, 88% of utilities had a higher efficiency score in Model 2, with a mean difference of 0.14. In the second comparison, the efficiencies of 39 companies increased with SFA, with a correlation between the results of 0.76.

When evaluating the impact of the use of incentive regulations in total costs, we find that DSOs need to reduce their costs by an average of R$ 40 million per year, which is around 7% of total costs. This efficiency gain will affect consumers, who will pay lower tariffs.

This study evaluated the efficiency of Brazilian DSOs using total costs as an input; future studies could focus on superefficient Brazilian companies.

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theleaderdotinfo-blog · 7 years ago

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Alfies Golf Society Saurines de la Torre -Thursday 13th December 2018 has been published at http://www.theleader.info/2018/12/16/alfies-golf-society-saurines-de-la-torre-thursday-13th-december-2018/

New Post has been published on http://www.theleader.info/2018/12/16/alfies-golf-society-saurines-de-la-torre-thursday-13th-december-2018/

Alfies Golf Society Saurines de la Torre -Thursday 13th December 2018

21 members from Alfie's GS travelled to Saurines de la Torre GC for the final event in the calendar for 2018 which would decide who would take the honours for the Championship Golfer of The Year. The weather was cold and wet making the golf a real challenge. The course was in reasonable condition bearing in mind the rain we have had in the last few weeks. The presentation was held at The Street restaurant in the evening as this was also the Alfies Christmas Dinner and a good time was had by all. As the golfers came off the course the positions of the winners was changing all the time with the final stages deciding who came out on top. Our winner of the day in the silver division was Jim Dempsey with a great score of 40 stableford points Second place went to Wayne Stevenson with a score of 33 Stableford points. The Winner of the gold division was Ian Ingledew our society Captain with a fantastic score of 40 stableford points. Second place went to Nick Lee who also had 40 points but lost out on count back. Nearest the pin on the 10th– Sponsored by ‘The Street Restaurant’ went to Tony Hall Nearest the pin on the 18th– sponsored by ‘Alfies Bar‘ went to Ian Ingledew Nearest the pin in 2 shots on the 7th - sponsored by ’The Celtic Drop’ went to Jim Dempsey The Blind pairs was won by Neil Oliver and Steve Bicks So the outcome of the main events was:- Championship Golfer of the Year – Ian Connell 180 points Runner up Golfer of the Year – Jim Dempsey 157 points 3rd Place Golfer of the Year – Ian Ingledew 154 points Most Improved Golfer of the Year 2018 – Steve Barlow Winner of the Eclectic Cup 2018 – Rita Potters Many thanks to our sponsors, our committee for their hard work in the background, to Cat and Liz at Alfies Bar for their continued support throughout the year, and to Sandie Hall for organising the charity raffle and prizes. And finally thanks to all the members for supporting Alfies Golf Society throughout 2018.

#Alfies Golf Society

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endenogatai · 7 years ago

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This early GDPR adtech strike puts the spotlight on consent

What does consent as a valid legal basis for processing personal data look like under Europe’s updated privacy rules? It may sound like an abstract concern but for online services that rely on things being done with user data in order to monetize free-to-access content this is a key question now the region’s General Data Protection Regulation is firmly fixed in place.

The GDPR is actually clear about consent. But if you haven’t bothered to read the text of the regulation, and instead just go and look at some of the self-styled consent management platforms (CMPs) floating around the web since May 25, you’d probably have trouble guessing it.

Confusing and/or incomplete consent flows aren’t yet extinct, sadly. But it’s fair to say those that don’t offer full opt-in choice are on borrowed time.

Because if your service or app relies on obtaining consent to process EU users’ personal data — as many free at the point-of-use, ad-supported apps do — then the GDPR states consent must be freely given, specific, informed and unambiguous.

That means you can’t bundle multiple uses for personal data under a single opt-in.

Nor can you obfuscate consent behind opaque wording that doesn’t actually specify the thing you’re going to do with the data.

You also have to offer users the choice not to consent. So you cannot pre-tick all the consent boxes that you really wish your users would freely choose — because you have to actually let them do that.

It’s not rocket science but the pushback from certain quarters of the adtech industry has been as awfully predictable as it’s horribly frustrating.

This has not gone unnoticed by consumers either. Europe’s Internet users have been filing consent-based complaints thick and fast this year. And a lot of what is being claimed as ‘GDPR compliant’ right now likely is not.

So, some six months in, we’re essentially in a holding pattern waiting for the regulatory hammers to come down.

But if you look closely there are some early enforcement actions that show some consent fog is starting to shift.

Yes, we’re still waiting on the outcomes of major consent-related complaints against tech giants. (And stockpile popcorn to watch that space for sure.)

But late last month French data protection watchdog, the CNIL, announced the closure of a formal warning it issued this summer against drive-to-store adtech firm, Fidzup — saying it was satisfied it was now GDPR compliant.

Such a regulatory stamp of approval is obviously rare this early in the new legal regime.

So while Fidzup is no adtech giant its experience still makes an interesting case study — showing how the consent line was being crossed; how, working with CNIL, it was able to fix that; and what being on the right side of the law means for a (relatively) small-scale adtech business that relies on consent to enable a location-based mobile marketing business.

From zero to GDPR hero?

Fidzup’s service works like this: It installs kit inside (or on) partner retailers’ physical stores to detect the presence of user-specific smartphones. At the same time it provides an SDK to mobile developers to track app users’ locations, collecting and sharing the advertising ID and wi-fi ID of users’ smartphone (which, along with location, are judged personal data under GDPR.)

Those two elements — detectors in physical stores; and a personal data-gathering SDK in mobile apps — come together to power Fidzup’s retail-focused, location-based ad service which pushes ads to mobile users when they’re near a partner store. The system also enables it to track ad-to-store conversions for its retail partners.

The problem Fidzup had, back in July, was that after an audit of its business the CNIL deemed it did not have proper consent to process users’ geolocation data to target them with ads.

Fidzup says it had thought its business was GDPR compliant because it took the view that app publishers were the data processors gathering consent on its behalf; the CNIL warning was a wake up call that this interpretation was incorrect — and that it was responsible for the data processing and so also for collecting consents.

The regulator found that when a smartphone user installed an app containing Fidzup’s SDK they were not informed that their location and mobile device ID data would be used for ad targeting, nor the partners Fidzup was sharing their data with.

CNIL also said users should have been clearly informed before data was collected — so they could choose to consent — instead of information being given via general app conditions (or in store posters), as was the case, after the fact of the processing.

It also found users had no choice to download the apps without also getting Fidzup’s SDK, with use of such an app automatically resulting in data transmission to partners.

Fidzup’s approach to consent had also only been asking users to consent to the processing of their geolocation data for the specific app they had downloaded — not for the targeted ad purposes with retail partners which is the substance of the firm’s business.

So there was a string of issues. And when Fidzup was hit with the warning the stakes were high, even with no monetary penalty attached. Because unless it could fix the core consent problem, the 2014-founded startup might have faced going out of business. Or having to change its line of business entirely.

Instead it decided to try and fix the consent problem by building a GDPR-compliant CMP — spending around five months liaising with the regulator, and finally getting a green light late last month.

A core piece of the challenge, as co-founder and CEO Olivier Magnan-Saurin tells it, was how to handle multiple partners in this CMP because its business entails passing data along the chain of partners — each new use and partner requiring opt-in consent.

“The first challenge was to design a window and a banner for multiple data buyers,” he tells TechCrunch. “So that’s what we did. The challenge was to have something okay for the CNIL and GDPR in terms of wording, UX etc. And, at the same time, some things that the publisher will allow to and will accept to implement in his source code to display to his users because he doesn’t want to scare them or to lose too much.

“Because they get money from the data that we buy from them. So they wanted to get the maximum money that they can, because it’s very difficult for them to live without the data revenue. So the challenge was to reconcile the need from the CNIL and the GDPR and from the publishers to get something acceptable for everyone.”

As a quick related aside, it’s worth noting that Fidzup does not work with the thousands of partners an ad exchange or demand-side platform most likely would be.

Magnan-Saurin tells us its CMP lists 460 partners. So while that’s still a lengthy list to have to put in front of consumers — it’s not, for example, the 32,000 partners of another French adtech firm, Vectaury, which has also recently been on the receiving end of an invalid consent ruling from the CNIL.

In turn, that suggests the ‘Fidzup fix’, if we can call it that, only scales so far; adtech firms that are routinely passing millions of people’s data around thousands of partners look to have much more existential problems under GDPR — as we’ve reported previously re: the Vectaury decision.

No consent without choice

Returning to Fidzup, its fix essentially boils down to actually offering people a choice over each and every data processing purpose, unless it’s strictly necessary for delivering the core app service the consumer was intending to use.

Which also means giving app users the ability to opt out of ads entirely — and not be penalized by not being able to use the app features itself.

In short, you can’t bundle consent. So Fidzup’s CMP unbundles all the data purposes and partners to offer users the option to consent or not.

“You can unselect or select each purpose,” says Magnan-Saurin of the now compliant CMP. “And if you want only to send data for, I don’t know, personalized ads but you don’t want to send the data to analyze if you go to a store or not, you can. You can unselect or select each consent. You can also see all the buyers who buy the data. So you can say okay I’m okay to send the data to every buyer but I can also select only a few or none of them.”

“What the CNIL ask is very complicated to read, I think, for the final user,” he continues. “Yes it’s very precise and you can choose everything etc. But it’s very complete and you have to spend some time to read everything. So we were [hoping] for something much shorter… but now okay we have something between the initial asking for the CNIL — which was like a big book — and our consent collection before the warning which was too short with not the right information. But still it’s quite long to read.”

Fidzup’s CNIL approved GDPR-compliant consent management platform

“Of course, as a user, I can refuse everything. Say no, I don’t want my data to be collected, I don’t want to send my data. And I have to be able, as a user, to use the app in the same way as if I accept or refuse the data collection,” he adds.

He says the CNIL was very clear on the latter point — telling it they could not require collection of geolocation data for ad targeting for usage of the app.

“You have to provide the same service to the user if he accepts or not to share his data,” he emphasizes. “So now the app and the geolocation features [of the app] works also if you refuse to send the data to advertisers.”

This is especially interesting in light of the ‘forced consent’ complaints filed against tech giants Facebook and Google earlier this year.

These complaints argue the companies should (but currently do not) offer an opt-out of targeted advertising, because behavioural ads are not strictly necessary for their core services (i.e. social networking, messaging, a smartphone platform etc).

Indeed, data gathering for such non-core service purposes should require an affirmative opt-in under GDPR. (An additional GDPR complaint against Android has also since attacked how consent is gathered, arguing it’s manipulative and deceptive.)

Asked whether, based on his experience working with the CNIL to achieve GDPR compliance, it seems fair that a small adtech firm like Fidzup has had to offer an opt-out when a tech giant like Facebook seemingly doesn’t, Magnan-Saurin tells TechCrunch: “I’m not a lawyer but based on what the CNIL asked us to be in compliance with the GDPR law I’m not sure that what I see on Facebook as a user is 100% GDPR compliant.”

“It’s better than one year ago but [I’m still not sure],” he adds. “Again it’s only my feeling as a user, based on the experience I have with the French CNIL and the GDPR law.”

Facebook of course maintains its approach is 100% GDPR compliant.

Even as data privacy experts aren’t so sure.

One thing is clear: If the tech giant was forced to offer an opt out for data processing for ads it would clearly take a big chunk out of its business — as a sub-set of users would undoubtedly say no to Zuckerberg’s “ads”. (And if European Facebook users got an ads opt out you can bet Americans would very soon and very loudly demand the same, so…)

Bridging the privacy gap

In Fidzup’s case, complying with GDPR has had a major impact on its business because offering a genuine choice means it’s not always able to obtain consent. Magnan-Saurin says there is essentially now a limit on the number of device users advertisers can reach because not everyone opts in for ads.

Although, since it’s been using the new CMP, he says a majority are still opting in (or, at least, this is the case so far) — showing one consent chart report with a ~70:30 opt-in rate, for example.

He expresses the change like this: “No one in the world can say okay I have 100% of the smartphones in my data base because the consent collection is more complete. No one in the world, even Facebook or Google, could say okay, 100% of the smartphones are okay to collect from them geolocation data. That’s a huge change.”

“Before that there was a race to the higher reach. The biggest number of smartphones in your database,” he continues. “Today that’s not the point.”

Now he says the point for adtech businesses with EU users is figuring out how to extrapolate from the percentage of user data they can (legally) collect to the 100% they can’t.

And that’s what Fidzup has been working on this year, developing machine learning algorithms to try to bridge the data gap so it can still offer its retail partners accurate predictions for tracking ad to store conversions.

“We have algorithms based on the few thousand stores that we equip, based on the few hundred mobile advertising campaigns that we have run, and we can understand for a store in London in… sports, fashion, for example, how many visits we can expect from the campaign based on what we can measure with the right consent,” he says. “That’s the first and main change in our market; the quantity of data that we can get in our database.”

“Now the challenge is to be as accurate as we can be without having 100% of real data — with the consent, and the real picture,” he adds. “The accuracy is less… but not that much. We have a very, very high standard of quality on that… So now we can assure the retailers that with our machine learning system they have nearly the same quality as they had before.

“Of course it’s not exactly the same… but it’s very close.”

Having a CMP that’s had regulatory ‘sign-off’, as it were, is something Fidzup is also now hoping to turn into a new bit of additional business.

“The second change is more like an opportunity,” he suggests. “All the work that we have done with CNIL and our publishers we have transferred it to a new product, a CMP, and we offer today to all the publishers who ask to use our consent management platform. So for us it’s a new product — we didn’t have it before. And today we are the only — to my knowledge — the only company and the only CMP validated by the CNIL and GDPR compliant so that’s useful for all the publishers in the world.”

It’s not currently charging publishers to use the CMP but will be seeing whether it can turn it into a paid product early next year.

How then, after months of compliance work, does Fidzup feel about GDPR? Does it believe the regulation is making life harder for startups vs tech giants — as is sometimes suggested, with claims put forward by certain lobby groups that the law risks entrenching the dominance of better resourced tech giants. Or does he see any opportunities?

In Magnan-Saurin’s view, six months in to GDPR European startups are at an R&D disadvantage vs tech giants because U.S. companies like Facebook and Google are not (yet) subject to a similarly comprehensive privacy regulation at home — so it’s easier for them to bag up user data for whatever purpose they like.

Though it’s also true that U.S. lawmakers are now paying earnest attention to the privacy policy area at a federal level. (And Google’s CEO faced a number of tough questions from Congress on that front just this week.)

“The fact is Facebook-Google they own like 90% of the revenue in mobile advertising in the world. And they are American. So basically they can do all their research and development on, for example, American users without any GDPR regulation,” he says. “And then apply a pattern of GDPR compliance and apply the new product, the new algorithm, everywhere in the world.

“As a European startup I can’t do that. Because I’m a European. So once I begin the research and development I have to be GDPR compliant so it’s going to be longer for Fidzup to develop the same thing as an American… But now we can see that GDPR might be beginning a ‘world thing’ — and maybe Facebook and Google will apply the GDPR compliance everywhere in the world. Could be. But it’s their own choice. Which means, for the example of the R&D, they could do their own research without applying the law because for now U.S. doesn’t care about the GDPR law, so you’re not outlawed if you do R&D without applying GDPR in the U.S. That’s the main difference.”

He suggests some European startups might relocate R&D efforts outside the region to try to workaround the legal complexity around privacy.

“If the law is meant to bring the big players to better compliance with privacy I think — yes, maybe it goes in this way. But the first to suffer is the European companies, and it becomes an asset for the U.S. and maybe the Chinese… companies because they can be quicker in their innovation cycles,” he suggests. “That’s a fact. So what could happen is maybe investors will not invest that much money in Europe than in U.S. or in China on the marketing, advertising data subject topics. Maybe even the French companies will put all the R&D in the U.S. and destroy some jobs in Europe because it’s too complicated to do research on that topics. Could be impacts. We don’t know yet.”

But the fact of GDPR enforcement having — perhaps inevitably — started small, with so far a small bundle of warnings against relative data minnows, rather than any swift action against the industry dominating adtech giants, that’s being felt as yet another inequality at the startup coalface.

“What’s sure is that the CNIL started to send warnings not to Google or Facebook but to startups. That’s what I can see,” he says. “Because maybe it’s easier to see I’m working on GDPR and everything but the fact is the law is not as complicated for Facebook and Google as it is for the small and European companies.”

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williamsjoan · 7 years ago