This chapter quantifies the benefits that would arise from the implementation of the OECD’s recommendations. It is only possible to quantify benefits for a subset of recommendations, but the OECD estimates that implementing these would generate around EUR 325 million annually by lowering prices and interest rates for consumers and businesses, corresponding to 0.8% of Tunisia’s 2021 GDP. This figure is likely an underestimate of the actual resulting benefits because it was not possible to quantify the effects of all individual recommendations due to the limited availability of detailed data. It also excludes the dynamic benefits of competition, which can be substantial, but which are difficult to estimate.
Competition Market Study of Tunisia's Retail Banking Sector
10. Quantification of consumer benefits
Abstract
This chapter describes the general framework for assessing the benefits of competition. It then illustrates the methodology for calculating changes in consumer surplus and quantifies the expected benefits of the OECD’s recommendations focussing on selected proposals that include the introduction of a credit information bureau, the introduction of a registry for movable assets, the introduction of a price comparison website for current accounts and granting a banking license to La Poste.
10.1. The benefits of competition
The market study aimed to identify regulations, market features and business practices in the retail banking sector that may prevent competition from working as well as it could. If competition is not effective, productivity and economic growth suffer.
Each recommendation set out in Chapter 9 is likely to have an impact well beyond individual consumers in the segments assessed. When consumers can shop around and freely choose products and services, firms are forced to compete, innovate more and be more productive (Nickell (1996[1]); Blundell et al. (1999[2]); Griffith, Redding and Van Reenen (2004[3])). Industries in which there is greater competition experience faster productivity growth. These conclusions have been confirmed by a wide range of empirical studies and summarised by (OECD, 2014[4]). Competition may stimulate productivity via different channels. It can create incentives to invest in research and development, and it can provide opportunities for more efficient firms to enter and gain market share at the expense of less efficient firms. Increased competition in one sector can also have spillover effects and improve productivity in related sectors. The following paragraphs provide a brief description of some work in these three areas.
Nickell (1996[1]) argues competition affects productivity growth mainly via two mechanisms: managerial capacity and innovation. With respect to managerial capacity, competition makes profits more responsive to managerial effort, which encourages shareholders to ensure managerial effort is high and inefficiency is low. When it comes to innovation, in a more competitive environment, cost-reducing improvements in productivity generate larger increases in profits, raising incentives to invest. Nickell finds that a high degree market power reduces productivity levels in the long run and that competition intensity (measured as reduced rents) is associated with higher productivity growth. Disney, et al. (2003[5]) use data on around 140 000 establishments in the United Kingdom over the period 1980‑92, and employing a similar methodology to Nickell (1996[1]), find that competition has a positive effect on firms’ productivity.
Similar positive effects of competition on productivity growth are also found in other countries. These include: (Januszewski et al., 2002[6]) in Germany; (Koke et al., 2005[7]) in the UK and Germany; (Okada, 2005[8]) and (Funakoshi et al., 2009[9]) in Japan; (Aghion, Braun and Fedderke, 2008[10]) in South Africa; (Ospina and Schiffbauer, 2010[11]) for 27 countries in Eastern Europe and Central Asia; and (Tang and Wang, 2005[12]) in Canada.
A different set of studies investigates the relationship between competition and productivity development at an economic sector level. This body of work studies the net effects of competition on productivity growth across firms and focuses on market efficiency, i.e. whether more productive firms can attract more resources, resulting in higher productivity at the sector level.
Arnold et al. (2011[13]) investigated the effects of anti-competitive regulation, as measured by the OECD’s product market regulation (PMR) indicators. They found that productivity growth is generally faster and the reallocation of resources towards the highest-productivity firms was stronger in countries and industries with lighter regulatory burdens.
Other studies have looked at the spillover effects of competition in related markets. For example, a lack of competition in upstream markets may generate barriers to entry in downstream markets (Bourlès et al., 2013[14]). (Barone et al., 2008[15]) showed that manufacturing productivity growth was harmed by regulations reducing competition in services, especially financial services and energy provision.
In addition to the evidence that competition fosters productivity and economic growth, many studies have shown the positive effects of more flexible PMR.1 These studies have analysed the impact of regulation on productivity, employment, research and development, and investment, among other variables. Differences in regulation also matter and can reduce significantly both trade and foreign direct investment (FDI) (Fournier, 2015[16]).2 By fostering growth, more flexible PMR can improve the sustainability of public debt, which is particularly important in countries such as Tunisia (OECD, 2018[17]). A particularly large body of evidence attests to the productivity gains from more flexible PMR. At the company and industry level, restrictive PMR is associated with lower multifactor productivity (MFP) levels (Nicoletti, Scarpetta and Lane, 2003[18]) and (Arnold, Nicoletti and Scarpetta, 2011[13]).3 The result also holds at the aggregate level (Égert, 2017[19]).4 Anti-competitive regulations have an impact on productivity that goes beyond the sector in which they are applied, an effect that is more important for sectors closer to the productivity frontier (Bourlès et al., 2013[14]).5 Specifically, a large part of the impact on productivity goes through the channel of investment in research and development. Moreover, lowering regulatory barriers in network industries can have a significant impact on exports (Daude and de la Maisonneuve, 2018[20]).
10.2. Quantifying the benefits of increased competition
The OECD’s quantification exercise draws on the standard analytical framework used in previous OECD competition assessment reports. The framework is built on the classical diagram of consumer surplus, i.e. the difference between consumers’ willingness to pay and the price they pay. Savings in the price paid by consumers can be interpreted as an increase in their surplus. The framework allows us to take into account consumer demand in retail banking in Tunisia and estimate the benefits, in terms of price reductions, to be expected following the implementation of the OECD’s recommendations. However, the benefits derived from this framework are partial because they look at only one product in question, and static, because they do not take into account changes in productivity and income. Box 10.1 presents the theoretical framework used to calculate consumer surplus.
This section quantifies the annual benefits of the OECD recommendations in the current accounts and the bank loans sectors. This section does not quantify the impact of the recommendations in the mobile payments sector due to a lack of revenue data. To quantify benefits in the current accounts and the bank loans sectors the section focusses on the proposals to introduce a credit information bureau, a registry for movable assets, a price comparison website for current accounts and granting a banking license to La Poste. The choice of these recommendations was led by the availability of studies showing the price impact of these interventions and by the availability of data on revenues in the relevant segments.
The OECD recommendations are expected to increase competition in Tunisia’s retail banking sector and generate benefits for consumers and small businesses:
Current account sector. Price comparison websites for current accounts increase the ability of consumers to access and compare information about these products. This increases banks’ incentives to compete.
Bank loans sector: Credit information bureaus reduce credit risk by reducing adverse selection and moral hazard in lending markets. Credit information bureaus also have a positive impact on competition, as they reduce the information advantages of larger banks, which gather data from their larger customer bases. Moreover, a registry for movable assets reduces credit risk and facilitate access to finance by notifying parties of the existence of a security interest in movable assets and establishing the priority of creditors. This may reduce the length of court proceedings.
Across retail banking sector. Granting a banking license to La Poste should increase competition across the retail banking sector, including current accounts and lending markets. La Poste is already an important player in the current account market. However, the lack of a banking licence hinders La Poste’s ability to offer a realistic alternative to the country’s banks, because consumers and small businesses may use their current account to establish a banking relationship and obtain credit.
As described in Box 10.1, the calculation of the annual consumer benefits generated by the recommendations requires three key inputs: 1) an estimate of the revenues of the product ; 2) an estimate of the elasticity of demand of the product ; and 3) an estimate of the percentage change in price due to the recommendation. Assuming the estimates of the inputs used do not vary significantly over time, these benefits are expected to be realised annually. The following sections describe the sources used to estimate each input and provide an estimate of the consumer benefits generated by the selected recommendations.
The quantification of benefits focuses on price reductions, but several other benefits are not quantified. For example, cheaper access to finance will reduce costs and increase efficiency in other parts of the economy, and may lead to the entry of new firms, increasing innovation and choice for consumers.
Box 10.1. Measuring changes in consumer surplus
A demand curve describes the relationship between the price of a good or service and the quantity of that good or service that consumers are willing to buy at any given price. Demand curves typically have a downward slope as consumers are willing to buy larger quantities at lower prices. The sensitivity of consumers to price is illustrated by the elasticity of demand, which measures the change in the quantity demanded as a result of a change in price.
The impact of a recommendation that increases competition can often be examined as a movement from one point on the demand curve to another. For example, the introduction of a price comparison website makes it easier for consumers to identify the lowest prices subject to other product characteristics, putting downward pressure on prices. Graphically, the change is illustrated for a constant elasticity demand curve (i.e. a level of demand at which consumers’ sensitivity to price does not change). shows the equilibrium in the baseline before the recommendation; shows the equilibrium point once the recommendation is implemented. The new equilibrium is different from previous one in two important ways: lower price and higher quantity. These properties are a well-known result of many models of competition.
Figure 10.1. Changes in consumer surplus
Under the assumption of constant elasticity of demand, the equation for consumer benefit is:
For small price changes, the basic formula for such a standard measure of consumer benefit from improving competition is:
Where is the standard measure of consumer benefits; is the percentage change in price related to a restriction; is sector revenue; and is demand elasticity. Thus, the key inputs to calculate the benefits generated by a recommendation are estimates of: 1) the percentage price change; 2) the elasticity of demand; and 3) the revenues of the sector.
When elasticity is not known, past OECD competition assessments have used a value . Under this assumption, the expression above simplifies as:
According to the diagram, revenues can either increase or decrease, depending on the price elasticity of demand. By moving to the competitive equilibrium,, rents (the area “”) are eliminated as the price falls from to . The increase in consumer surplus (), is explained by the areas “” and “”. When the equilibrium is shifted to , activity in volume terms increases from to . At the same time, total revenue will also include the area “”. Therefore, total revenue is explained by the areas “”“” at the equilibrium , and the areas “”“” at the equilibrium . If the absolute value of the price elasticity of demand is lower than 1, then the area “” is larger than the area “”, in which case sales value decreases. Conversely, if the price elasticity of demand is higher than 1, the area “” is larger than the area “”, in which case sales value increases.
Source: OECD (2019[21]), OECD Competition Assessment Toolkit, https://www.oecd.org/competition/assessment-toolkit.htm.
10.2.1. Revenues in Tunisia’s retail banking sector
This section describes the assumptions used to estimate the annual revenues () in the bank loans and current accounts segments that will benefit from improved competition.
Bank loans
Given that data on revenues from bank loans was not available, this section describes the assumptions used to obtain a reasonable estimate of revenues from bank loans. The BCT provided data on the lending portfolios of banks broken down by loan category. For each loan category, the BCT publishes the average market interest rate, which is then used to calculate the cap on lending interest rates (see Section 5.2.1). Revenues from bank loans are estimated by multiplying total outstanding loans by prevailing average market interest rates. Table 10.1 shows the estimates of revenues for four categories of business loans of Tunisia’s ten largest banks.
Table 10.1. Interest rate revenues from selected bank loans
Sources: Information request to BCT; OECD calculations.
These estimates rely on several simplifying assumptions due to data constraints. It is assumed that the prevailing average market interest rate applies to all outstanding loans, while instead it applies only to loans granted in the specific six‑month period. This is a reasonable assumption as Figure 5.6 shows that the cap on lending interest rates (and thus the market average) did not change substantially between 2015 and 2020. It is also assumed that interest rate is not compounded. Finally, the estimate of revenues is based on the data available, which covers only the ten largest banks in Tunisia (Figure 2.3 shows that the ten largest banks accounted for slightly less than 80% of banking assets).
Current accounts
The BCT provided the OECD with annual data on the number of current accounts and non-interest revenues generated by current accounts for a six‑year period between 2015 and 2020 for 23 banks. These data did not include current accounts held at La Poste, which accounted for a significant proportion of the total (see Chapter 4). Table 10.2 presents the aggregate of banks’ non-interest revenues generated by current accounts in 2020, in TND and converted into EUR.
Table 10.2. Non-interest revenues from current accounts
Source: Information request to BCT; OECD calculations.
Figure 10.2 shows that the total non-interest revenues on current accounts increased significantly between 2015 and 2019 dipping in 2020 at the beginning of the COVID‑19 pandemic (see also Chapter 4). Calculations of the annual benefits used revenues from 2020, as these were the latest available figures. This represents a conservative assumption, as using the current account revenues for 2020 may lead to a lower estimate of the benefits of the OECD’s recommendations if revenues in this sector recover and follow their pre‑pandemic trend.
Figure 10.2. Non-interest revenues from current accounts, 2015‑20
Sources: Information request to BCT; OECD calculations.
10.2.2. Demand elasticity
This section describes the assumptions used to select the estimate of the elasticity of demand () for bank loans and current accounts. As described in Box 10.1, the elasticity of demand represents consumers’ sensitivity to price, in other words, the change in the percentage quantity demanded due to a 1% price rise. Elasticity is negative to reflect the downward slope of the demand curve. A high elasticity value means consumers are very price‑sensitive and thus the quantity demanded will decrease significantly if price increases. An elasticity value close to zero means that consumers are not very sensitive to price and a price change has little effect on the quantity demanded.
Bank loans
Several papers have estimated the elasticity of demand for different types of credit. Karlan and Zinman (2019[22]) estimated the elasticity of demand for micro-loans in Mexico in the range of ‑1.1 to ‑2.9. This was slightly more elastic than other estimates. For example, Gross and Souleles (2002[23])’s estimate of elasticity of demand for credit cards in the US was between ‑0.8 and ‑1.3, and (Dehejia, Montgomery and Morduch (2012[24])’s estimate of elasticity of demand for micro-loans in Bangladesh was between ‑0.39 and ‑1.04. To estimate the benefits arising from the OECD’s recommendations in the lending markets, Section 10.2.4 uses values equal to ‑1 as a reasonable mid-point estimate.
Current accounts
No estimate of elasticity of demand for current accounts was found, so Section 10.2.4 considers a value of elasticity equal to ‑2, consistent with previous OECD competition assessments. An elasticity of ‑2 means that a 1% price increase results in a 2% decline in the quantity demanded.
10.2.3. Assumptions underlying average price impacts
This section describes the available evidence on the expected price effects () resulting from the recommendations in the current account and bank loan segments, chosen based on the availability of information on the impact of similar recommendations in other countries.
Bank loans
(Love, Martínez Pería and Singh, 2016[25]) estimated the impact of the introduction of a registry for movable assets on the access to and costs of finance, and loan maturity (see Box 9.7). Using a difference‑in-difference approach and controlling for firms’ characteristics, fixed country and time effects, their baseline estimate suggests that interest rates in countries with such registries are 2.9 percentage points lower than in countries without them, which corresponds to a 22.3% reduction in average prices.6 The reduction is larger among smaller firms.
Martínez Pería and Singh (2014[26]) estimated the impact of the introduction of a credit information bureau on the access to and costs of finance, on loan maturity and other outcomes. They estimated that interest rates are 1.3 percentage points lower in countries with a credit information bureau, corresponding to a 9.3% reduction in average prices.7
Current accounts
Remedies to increase consumers’ ability to make informed decisions when choosing banking products are common across jurisdictions. These can take many forms, from requirements for financial institutions to provide meaningful information to consumers, to services to facilitate account switching. A common tool to allow simple price comparisons are price comparison websites (PCWs), which aim to help consumers easily identify the lowest available prices, subject to products’ characteristics. Several studies have provided estimates of the impact of these remedies. (Civic Consulting, 2011[27])assessed the impact of PCWs on prices paid by consumers. In a cross-country, cross-product study, (Civic Consulting, 2011[27]) found that PCWs were associated with estimated savings of around 7.8%.
Finally, granting a banking license to La Poste is expected to have an impact across the retail banking sector, including current accounts and bank loans. The price effect of granting a banking license to La Poste is based on the meta-studies contained in the OECD Competition Assessment toolkit that estimate that the benchmark price change when regulation limits the ability of some types of suppliers to provide a good or service is around 15% (OECD, 2019[21]).
Conclusions on price impacts
Table 10.3 provides a summary of the estimated price reduction used to calculate the benefits resulting from the implementation of the OECD’s recommendations. As described above, each sector is impacted by several recommendations. To use a conservative approach, when multiple recommendations affect the same sector, the price effects are not added together. Instead, only the largest price effect is considered.
Table 10.3. Estimates of selected recommendations’ price effects
Sources: Love, Martínez Pería and Singh (2016[25]), Collateral Registries for Movable Assets Does Their Introduction Spur Firms’ Access to Bank Finance?, J Financ Serv Res 49, 1‑37 (2016) https://doi.org/10.1007/s10693-015-0213-2; Martínez Pería and Singh (2014[26]), The Impact of Credit Information Sharing Reforms on Firm Financing, https://doi.org/10.1596/1813-9450-7013; Civic Consulting (2011[27]), Consumer market study on the functioning of e‑commerce and Internet marketing and selling techniques in the retail of goods Final Report Part 1: Synthesis Report, https://op.europa.eu/en/publication-detail/-/publication/24877d5b-a4a0-11e5-b528-01aa75ed71a1.
10.2.4. Estimates of the benefits arising from selected OECD recommendations
Using the expression in Box 10.1, Table 10.4 shows that the benefits arising from the OECD recommendations will generate at least EUR 325 million annually in lower prices and interest rates for consumers and businesses.
Table 10.4. Estimates of selected recommendations’ consumer benefits
Sources: Information request to BCT; OECD calculations.
10.3. Conclusions
As discussed in Chapter 9, the OECD’s recommendations are mutually reinforcing, so many of their benefits will be realised only when they are implemented together. It is therefore strongly recommended that all the packages of recommendations are consider holistically. Given the limited availability of data, this chapter provides a quantification of the impact of the OECD’s recommendations in the current account sector and in the bank loans sector and it does not quantify the impact of the recommendations in the mobile payments sector due to a lack of revenue data.
OECD’s recommendations together are expected to generate around EUR 325 million annually in lower prices and interest rates for consumers and businesses. However, this is a significant underestimate, as many recommendations are not quantified, and this estimate excludes the non-price benefits of competition, which can be substantial, but which are difficult to estimate.
For example, Love, Martínez Pería and Singh (2016[25]) found that firms in countries that had introduced registries for movable assets were 8% more likely to access bank finance and 7% more likely to have access to bank loans. This suggest that such reforms have effects beyond reductions in price. Moreover, credit information bureaus have effects beyond price reduction. For example, Sutherland (2018[28]) found that credit information bureaus reduce borrowers’ switching costs, especially those of smaller and newer firms.
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Notes
← 1. The methodology followed in this project is consistent with the PMR developed by the OECD. To measure a country’s regulatory stance and track progress of reforms over time, in 1998, the OECD developed an economy-wide indicator set of PMR (Nicoletti, Scarpetta and Boylaud, 2000[29]); this indicator was updated in 2003, 2008 and 2013.
← 2. Fournier (2015[16]) found that national regulations, as measured by an economy wide PMR index, have a negative impact on exports and reduce trade intensity (defined as trade divided by GDP). Differences in regulations between countries also reduce trade intensity. For example, convergence of PMR among EU member states would increase trade intensity within the European Union by more than 10%. Fournier (2015[16]) studied the impact of heterogeneous PMR in OECD countries and concluded that lowering regulatory divergence by 20% would increase FDI by about 15% on average across OECD countries. He investigated specific components of the PMR index and found that command-and-control regulations and measures protecting incumbents (such as antitrust exemptions, entry barriers for networks and services) were especially harmful in reducing cross-border investments.
← 3. Arnold, Nicoletti and Scarpetta (2011[13]) analysed firm-level data in 10 countries from 1998 to 2004 using the OECD’s PMR index at the industry level and found that more stringent PMR reduced firms’ MFP.
← 4. Égert (2017[19]) investigated the drivers of aggregate MFP in a sample of 30 OECD countries over a 30‑year period.
← 5. The study of 15 countries and 20 sectors from 1985 to 2007 estimated the effect of regulation of upstream service sectors on downstream productivity growth.
← 6. Given that the average interest rate paid in their sample was 13%, the average percentage price reduction was 2.9%/13%=22.3%.
← 7. Given that the average interest rate paid in their sample was 14%, the average percentage price reduction was 1.3%/14%=9.3%.