Download Benchmarking Financial Sector Development

Benchmarking financial sector development
Technical Paper to accompany the IOM Guidance Paper
OPM1, August, 2015
1. Introduction
Tracking financial system development over time or comparing one country’s performance
with its peers is a simple straightforward way to benchmark performance and inform policy
design and implementation. However, using additional data points, it is possible to construct
a more meaningful benchmark which takes into account a variety of structural variables
unique to the country. By estimating a predicted level of financial sector development, we
may isolate an estimation of the impact of specific policy changes and market intervention.
This paper introduces a benchmarking model which FSDs can use to compare the level of
financial sector development in their country against where they could expect to be given
certain variables of the market structure such as income and population. The difference
between where the specific country is and where it is expected, may therefore be linked to
policy and market interventions, or lack thereof.
2. Benchmarking financial systems
a. Overview
Tracking indicators of financial development over time is an important starting point.
However, it does not allow us to necessarily attribute this deepening or broadening process
to specific interventions or policy changes. Financial development, as captured by these
indicators, can change over time for many reasons unrelated to interventions and policies. A
better way to track financial development is therefore to compare a country’s development
relative to peer countries. However, given the differences in their characteristics there are
many other factors that may explain cross-country, cross-time variation in financial system
development.
Further, it is possible to utilize data from financial systems across the world for a richer basis
for benchmarking. Following Beck et al. (2008), Barajas et al. (2013) and Beck and Feijen
(2013) this note suggests use of a synthetic benchmark, or a benchmark that does not
compare country to country but rather compares a country to its own benchmark which is
synthesized from a multi-dimensional analysis of what has happened in other countries in the
same year. This benchmarking exercise is based on the concept of the financial depth
frontier. This frontier represents the equilibrium of supply and demand, variously affected by
market frictions. These market frictions are factors such as fixed cost components of
financial service provision and the risk of providing financial services, especially lending and
insurance services, due to information frictions and limitations in perfectly diversifying
idiosyncratic risks.2 This financial depth frontier is therefore determined by two classes of
1
This paper was a sub section of a background paper on measuring financial sector development,
prepared by Thorsten Beck (Cass Business School, City University London and CEPR), for this IOM
work.
2
Information frictions refer to the limited information lenders have about characteristics and actions of borrowers,
which will increase the cost of bank lending and reduce its availability. Limitations to diversification refer to
concentrated lending to specific borrowers, sectors or geographic units that are exposed to similar shocks and/or
the inability to diversify such risk using financial markets or specific financial instruments, such as derivatives.
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variables: (i) structural characteristics of the socio-economic environment in which financial
institutions and markets operate and which impose a limit on their development and (ii) longterm policy variables that either foster or limit financial deepening. We consider the former to
be characteristics that are in many ways inherent to the country and that would be difficult to
influence through interventions, while the latter are characteristics that can be improved
through targeted policy and market level interventions and in which FSDs can potentially play
a role.
b. The model
In the following, we focus on structural characteristics and define the structural depth line
as the level of financial development predicted by structural country characteristics that are
not directly related to policies and/or the financial sector. The gap between the actual level
of financial system development and the structural depth line may then be attributed
to different policies.
To derive this structural depth line, we use the following regression equation:
FDi,t = βXi,t+∑i,t (1)
where FD is the log of an indicator of financial development, X is an array of structural
country-specific factors, and the subscripts i and t relate to countries and years, respectively.
The predictive variables that make up X are the country characteristics that theory predicts to
be associated with the level of financial development in a country. These are:
(i)
the log of GDP per capita and its square (to account for possible non-linearities)
proxy for general demand and supply-side constraints related to low income;
(ii)
the log of population proxies for market size, in line with the above discussion on
scale economies;
(iii)
the log of population density proxies for geographic barriers and thus the ease of
financial service provision;
(iv)
the log of the age dependency ratio is included to capture demographic trends
and corresponding savings behaviour;
(v)
dummy variables for off-shore centres, transition countries and oil-exporting
countries are included to control for specific country circumstances, as these
countries face specific challenges and development experiences that impact their
financial systems.
Barajas et al. (2013) and De la Torre, Feyen and Ize (2013) use this regression model to
predict a large number of financial sector indicators capturing:
(i)
the depth,
(ii)
efficiency,
(iii)
stability and
(iv)
outreach
and of different segment of the financial system, including
(i)
banking,
(ii)
capital markets and
(iii)
contractual savings institutions.
While the significance levels of the models and the predictive variables vary, especially when
fewer data points are available, existing data confirm the validity of this regression model and
the significance of these structural socio-economic indicators as predictors of financial sector
development.
2
In line with the concept of a financial depth frontier, we assume that the gap between the
financial development predicted by the model above and the actual values can be explained
by an array of macroeconomic, regulatory, institutional and market structure variables.
Indeed, Barajas et al. (2013) were able to relate the gap between predicted and actual
financial development, as measured by Private Credit to GDP, to an array of policies and
institutions.
3. Benchmarking Uganda’s financial systems – an example
In the following, the frontier concept and benchmarking are illustrated using the example of
Uganda. Specifically, we predict values for different financial development indicators for
Uganda over several years based on the regression model presented in the previous
section. We compare these predicted values with actual values. To show how this approach
to benchmarking compares to the simple approach of benchmarking against peers, we also
present regional and income group medians, i.e. medians for the Sub-Saharan African group
and the low-income group. While it is interesting to consider both types of benchmarks, the
synthetic benchmark is arguably more appropriate, as it takes into account the structural
factors that significantly influence financial system development.
Figure 1 shows the actual and predicted value of Private Credit to GDP over the ten-year
period 2003 to 2012. We can see that for most of the period the actual value was below the
predicted value, while starting in 2010, there does not seem a significant difference between
the actual and the predicted value. This suggests that in the years until 2010, Private Credit
to GDP was below the value predicted by structural factors, while it has caught up with this
value since then. This might suggest that recent policy changes and interventions might have
been successful. Figure 2, on the other hand, shows no such convergence of the actual to
the predicted level for the other financial depth indicator, Bank Deposits to GDP.
Figure 1 Benchmarking Private Credit to GDP in Uganda
It is interesting to note that from 2003 to 2012 both regional and income group medians in
Figures 1 and 2 increased more than the synthetic benchmark. This suggests that Uganda
faces certain structural constraints that have for some reason been less of a constraint in
other countries (either because the structural conditions are more favourable or because
policy interventions have mitigated their effect) in the region and its income group. As a
result, while Uganda has managed to catch up to the predicted value for private sector
lending, it has still made less progress than its peers.
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Figure 2 Benchmarking Bank Deposits to GDP in Uganda
Figure 3 shows that branch penetration in Uganda has been in line with the predicted value
and the income group median, but below the regional median across Sub-Saharan Africa.
On the other hand, account penetration has been above the predicted value and the income
group median, while it has been again below the regional median (figure 4).
Figure 3 Benchmarking Branch Penetration in Uganda
Figure 4 Benchmarking Account Penetration in Uganda
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Figures 5 and 6 show that net interest margins and overhead costs have been well above
the predicted value as well as income group and regional medians, clearly indicating the
inefficiency of the Ugandan banking system. This gap seems somewhat more pronounced
for the case of net interest margins than in the case of overhead, suggesting that the high
interest margins (as well as spreads) are not only explained by cost inefficiencies but also by
other policy and market factors.3
Figure 5 Benchmarking Net interest margins in Uganda
Figure 6 Benchmarking Overhead Costs in Uganda
4. Conclusions
This short note introduces a benchmarking model that takes into account various factors
influencing financial sector development and allows for a better comparison across countries
and over time than simple peer country comparisons. If FSDs find this type of analysis
useful, further work and analysis can compare how institutional and policy variables (which
FSDs have greater influence over) relate to predicted levels of financial sector development
(See, Barajas et al. 2013).
3
See Beck and Hesse (2009) for an in-depth exploration of margins and spreads in Uganda.
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References
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Cold, Too Hot, or Just Right? Assessing Financial Sector Development Across the
Globe.” IMF Working Paper 13/81
Beck, Thorsten. 2007. “Efficiency in Financial Intermediation: Theory and Empirical
Measurement” in: Microfinance and Public Policy: Outreach, Performance and Efficiency,
edited by Bernd Balkenhol, Palgrave, MacMillan
Beck, Thorsten and Robert Cull. 2014. “Banking in Africa: A Progress Report.” Revue
d’Economie Financière, forthcoming.
Beck, Thorsten, Asli Demirgüç-Kunt, and Ross Levine. 2007. “Finance, Inequality and the
Poor.” Journal of Economic Growth 12, 27–49.
-Kunt and Sole Martinez Peria . 2007.“Reaching Out: Access
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Financial Possibility Frontier,” Background Paper for World Development Report,
Washington, D.C.
Beck, Thorsten and Heiko Hesse. 2009. “Why are Interest Spreads so High in Uganda?”,
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