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Liquidity Risk and Long-Term Finance: Evidence from a
Natural Experiment§
M. Ali Choudhary¶ Nicola Limodio
June 2021
Abstract
Banks in low-income countries face severe liquidity risk due to volatile deposits, which
destabilize their funding, and dysfunctional liquidity markets, which induce expensive
interbank and central bank lending. Such liquidity risk dissuades the transformation of
short-term deposits into long-term loans and deters long-term investment. To validate this
mechanism, we exploit a Sharia-compliant levy in Pakistan, which generates unintended
and quasi-experimental variation in liquidity risk, with data from the credit registry and
firm imports. We find that banks with a stronger exposure to liquidity risk lower their
supply of long-term finance, which reduces the long-term investment of connected firms.
JEL: G21, O16, O12, E58
Keywords: Banking, Investment, Financial Development, Central Banks
§
We are grateful to the editor, Thomas Chaney, and three anonymous referees for their excellent comments.
We thank for their useful suggestions Saeed Ahmed, Asif Ali, Oriana Bandiera, Thorsten Beck, Juliane Be-
genau, Matteo Benetton, Tim Besley, Patrick Bolton, Emily Breza, Markus K. Brunnermeier, Elena Carletti,
Luisa Carpinelli, Angelo D’Andrea, Olivier Darmouni, Nicola Gennaioli, Veronica Guerrieri, Dirk Jenter, Amir
Kermani, Peter Koudijs, Umar Latif, Ross Levine, Rocco Macchiavello, Shafqat Mahmood, Alberto Manconi,
Atif Mian, Adair Morse, Martin Oehmke, Steven Ongena, Daniel Paravisini, Nicola Persico, Jacopo Ponti-
celli, Riaz Riazuddin, Ricardo Reis, Stefano Rossi, Amit Seru, Emanuele Tarantino, Emil Verner, Annette
Vissing-Jorgensen and seminar participants at several conferences, seminars and workshops. Nicola Limodio
acknowledges the award of a STICERD grant and the hospitality of the Research Department at the State
Bank of Pakistan in Karachi. Antonn Park provided valuable editorial assistance. This paper was previously
circulated with the title “Deposit Volatility, Liquidity and Long-Term Investment: Evidence from a Natural
Experiment in Pakistan”. We are responsible for all errors, and this work does not reflect the view of the State
Bank of Pakistan or its Board and is not an official publication of the State Bank of Pakistan.
¶ ali.choudhary@sbp.org.pk, State Bank of Pakistan, I.I. Chundrigar Road, Karachi, Pakistan, and Centre
for Economic Performance, 32 Lincoln’s Inn Fields, WC2A 3PH, London, UK.
nicola.limodio@unibocconi.it, www.nicolalimodio.com, Corresponding Author, Bocconi University, Depart-
ment of Finance, BAFFI CAREFIN and IGIER, Via Roentgen 1, 20136 Milan, Italy.1 Introduction
Despite being a primary driver of long-term finance and development (Gerschenkron (1962),
Caprio and Demirgüç-Kunt (1998), Schiantarelli and Srivastava (1997)), banks are respons-
ible for the scarcity of long-term finance in low-income countries (Levine (2005), World Bank
(2015)). Liquidity risk is a predominant factor behind this empirical regularity for two reasons.
First, banks face unstable funding at the early stages of development, because deposit and
income volatility are particularly intense (Koren and Tenreyro (2007)). Second, low-income
countries frequently present dysfunctional interbank and central bank lending, which makes
expensive, or even impossible, the smoothing of both unpredictable withdrawals and foresee-
able liquidity shortfalls (Levine (1997), Barth et al. (2001), Demirgüç-Kunt et al. (2012)). As a
result, severe levels of liquidity risk prevent banks from transforming short-term deposits into
long-term loans and weaken long-term investment and economic activity.
Two bodies of research have investigated these ideas through different approaches. The
theoretical literature on credit and growth suggests that enhancing financial institutions may
promote intermediation and economic activity, especially by stimulating risk-sharing and an
efficient capital allocation (Townsend (1978), Bencivenga and Smith (1991), Saint-Paul (1992),
Zilibotti (1994), Acemoglu and Zilibotti (1997), Guerrieri and Lorenzoni (2009)). At the same
time, a more recent empirical literature has focused on studying the mechanisms behind the
effects of long-term finance on firms (Breza and Liberman (2017), Breza, Kanz and Klapper
(2018)) and the role of banks in promoting structural transformation (Bustos, Garber and Ponti-
celli (2020)). However, there is scarce direct evidence linking liquidity risk to bank behaviour
and long-term finance.
This research offers an empirical contribution showing how liquidity risk affects banks, long-
term finance and firms in low-income countries. To guide our econometric analysis, we follow
the theoretical work of Prisman et al. (1986), Freixas and Rochet (2008) and Bolton et al. (2011)
and focus on a central mechanism linking liquidity risk and the bank supply of long-term finance.
In this setting, liquidity risk represents both unpredictable withdrawals and liquidity shortfalls
that are foreseeable, but cannot be smoothed because of dysfunctional interbank and central
bank networks.
1As liquidity risk intensifies, banks face a higher funding cost of supplying long-term lending
compared to the short term. This happens because short-term loans offer banks the ability
to smooth deposit shocks in every period by cutting the amounts lent in each period. On
the contrary, long-term loans oblige banks to internalize liquidity risk and borrow expensively
from other financial institutions (i.e. interbank market or central bank loans) to accommodate
deposit outflows. This difference in funding costs makes long-term finance more expensive than
short-term finance, reallocating credit and investment toward the short term.
This mechanism leads us to study two specific questions: 1) Does liquidity risk weaken
the bank supply of long-term finance? 2) Does the reallocation of credit toward the short term
affect firm investment? To answer the first question, we analyze the universe of corporate loans,
containing information on loan amounts, rates, and maturities. To address the second question,
we build a firm-level panel measuring the purchase of imported capital and non-capital goods.
A key challenge to empirically study these questions is to identify changes in the liquidity risk
faced by banks, affecting their supply of long-term finance, which do not alter firms’ demand for
long-term finance. For this reason, our source of quasi-experimental variation in liquidity risk
comes from the application of an Islamic institution in Pakistan, which affects bank deposits
and treats banks heterogeneously through its institutional design: the Zakat. At the beginning
of every Ramadan, the Pakistani government collects a 2.5% Sharia-compliant levy on bank
deposits. To be eligible, an individual needs to hold balances in excess of a deposit threshold,
given by the monetary value of 612.32 grams of silver. As an unintended consequence, this
levy generates a widespread “withdrawal-and-redeposit” phenomenon linked to silver prices:
individuals can avoid the levy by temporarily lowering their deposit by 1 Pakistani Rupee
(PKR) below the threshold, or alternatively pay 2.5% on their entire account if they are 1 PKR
above the threshold.
During this period, individuals frequently readjust their deposits to avoid the levy (by
withdrawing below the threshold), without taking out too much and incurring high costs of
disintermediation (i.e., forgone interest, physical storage, stigma). This happens because both
the level and the volatility of silver prices change the level and volatility of deposits respectively.
In fact, we observe that the international price of silver directly dictates whether an individual
is subject to the levy and affects its level of deposits. As a result, low silver prices imply that
2more individuals are subject to the Zakat deduction, leading to higher withdrawals. At the
same time, because the threshold is announced only a few hours before the payment, higher
volatility in silver prices generates more uncertainty for individuals who may become eligible
due to lower-than-expected silver prices. As a result, a higher silver price volatility makes
deposit fluctuations more intense.
Our identification exploits the level and volatility of silver prices before the Zakat collection
as a source of quasi-experimental variation for the level and volatility of deposits, which is
exogenously determined to the Pakistani economy. We combine this time-series variation with
a measure of bank cross-sectional exposure to the Zakat emerging from two datasets. First, we
exploit a religious map of Pakistan and rely on an institutional feature of the Zakat levy, which
makes only Sunni Pakistanis subject to this deposit deduction and excludes other religious
groups. Second, we employ a novel dataset on branch deposits, which contains information on
the universe of bank branches in Pakistan, their holding of deposits and location. By combining
these two datasets, we can measure the weighted share of deposits for each bank subject to the
Zakat withdrawals.
To investigate the effect of the Zakat levy on Sunni individuals and banks, we analyze a
database containing information on branch-level deposits for the universe of bank branches in
Pakistan between 2000 and 2019. We find that bank branches in Sunni-majority cities exhibit
a 5% average decline in deposits during the Ramadan period compared to branches in other
cities. Moreover, the extent of this decline responds to silver prices: higher silver prices lead to
a reduced decline in deposits, given that fewer individuals are subject to the Zakat levy.
To test the implications of our mechanism, we explore recent data on the universe of corpor-
ate lending, resulting in more than three million loans between 2000 and 2015. In the presence
of a high liquidity risk (low level or high volatility of silver prices), we find that banks with a
higher exposure to Zakat withdrawals offer loans with a shorter maturity (6% for changes in
level, 7% for volatility), a lower lending rate (0.18 points for level, 0.22 for volatility) and lower
amounts (14% for both level and volatility).
The granularity of our credit data leads to establish that our natural experiment affects
firm credit exclusively through a contraction in the supply of long-term finance, in line with
Degryse et al. (2019), Khwaja and Mian (2008) and Choudhary and Jain (2020). Furthermore,
3we observe that liquidity risk induces a stronger decline in maturities, rates and amounts in
the presence of expensive central bank lending. This evidence is corroborated by two different
variables that measure the cost of central bank liquidity: 1) the policy rate set by the State Bank
of Pakistan during its meetings; 2) a novel measure of monetary policy shocks for Pakistan,
which we construct inspired by the work of Romer and Romer (2004).
Despite this credit reallocation toward the short-term, it remains unclear whether a reduc-
tion in long-term finance generates any effect on investment and firms. For this reason, we
test whether such financial shock generates real effects, in line with Breza and Kinnan (2018)
and Verner and Gyongyosi (2020). We employ the universe of international transactions on
imported goods for all Pakistani firms, which identifies the type and sector of the transaction
and the bank offering the trade credit. We use this information to build a firm panel on the
imports of capital and non-capital goods between 2008 and 2019, in line with Chaney (2016)
and Paravisini et al. (2015). Our results indicate that the maturity of firms’ assets follows the
maturity of liabilities. In response to a decline in long-term finance, firms cut the import of
capital goods between 27% and 30% (a proxy for long-term investment), while they reduce
weakly the import of non-capital goods between 6% and 8% (short-term investment).
To address possible confounders, we offer a rich series of alternative specifications to verify
the robustness of our results and, in particular, two placebo tests. First, by using the historical
data around the introduction of the Zakat levy in 1981, we can validate that silver prices did
not generate effects on lending before the introduction of the Zakat levy, but did after 1981.
Our second placebo relies on an alternative Islamic celebration, Eid Adha, and verifies that the
level and volatility of silver prices of this period do not affect loan characteristics. Moreover,
we re-elaborate our analysis in light of the recent papers on shift-share designs (Borusyak
and Jaravel (2017), Borusyak et al. (Forthcoming), Jaeger et al. (2018), Adao et al. (2019),
Goldsmith-Pinkham et al. (2020)) and our findings are robust to the methodology introduced
by Borusyak et al. (Forthcoming).
Our findings indicate that liquidity risk limits long-term finance and investment at the early
stages of development. This research can offer novel insights into the frictions experienced
by banks in low-income countries. As a result, two sets of policy implications extend beyond
this specific context. First, policies that expand financial systems through new sources of
4liquidity (e.g. capital account reforms, financial inclusion) may involve a liquidity risk trade-
off. More bank funding can promote long-term finance, but if these novel liabilities entail a
higher sensitivity to shocks (e.g. exchange rate shocks, business-cycle), then their volatility
may increase risk. Second, promoting interbank markets and central bank facilities in low-
income countries may allow banks to smooth liquidity risk and promote long-term finance.
We document that more than 50% of African countries lack functioning central bank facilities,
while also presenting thin or non-existent interbank markets, in line with D’Andrea and Limodio
(2019).
This paper adds to two ongoing debates. First, it contributes to the literature on long-
term finance and development. This field was pioneered by Levine (1997) and Caprio and
Demirgüç-Kunt (1998), who discuss the role of banks in promoting growth and technological
progress. Schiantarelli and Srivastava (1997) and Schiantarelli and Sembenelli (1997) provide
empirical evidence that long-term finance generates positive effects on profitability, growth,
and productivity. Beck et al. (2005) shows that access to long-term finance is one of the most
important obstacles to firm growth. Moreover, Qian and Strahan (2007) and Bae and Goyal
(2009) provide empirical evidence highlighting that institutions shape loan maturity. Related
to the importance of maturities for firm investment, Breza and Liberman (2017) investigate
an exogenous reduction in credit maturity and find that this reduces the economic activity of
firms, including their trade and network. Kozlowski (Forthcoming) shows theoretically that
trading frictions steepen the yield curve, which reallocates credit and investment toward the
short term.
Second, this paper adds to the literature on the credit constraints of banks and how changes
in liabilities transmit to assets. For example, Paravisini (2008) studies how exogenous changes
in government-to-bank loans affect the supply of credit in Argentina. By relying on a shock
to bank-to-bank loans in Peru, Schnabl (2012) verifies the existence of a transmission to loan
amounts, default and survival. Gilje et al. (2016) study liquidity windfalls in the United States
and observe their transmission to unaffected locations due to the bank branch network. Carletti
et al. (Forthcoming) study a tax reform that affected the maturity of bank liabilities and
cascaded on the maturity of bank assets. Finally, Drechsler et al. (2020) show that maturity
transformation hedges banks from interest rate risk in the United States.
5In Section 2, we present a conceptual framework and the corresponding institutional frame-
work describing the natural experiment. Section 3 presents the databases and identification
strategy. Section 4 illustrates the empirical analysis and results. Finally, we discuss the policy
implications of this paper in Section 5 and offer concluding remarks in Section 6.
2 Conceptual and Institutional Framework
In this Section, we offer a conceptual framework highlighting how liquidity risk can generate a
reallocation of bank credit toward the short term. In addition, we report institutional details
on the source of quasi-experimental variation in liquidity risk induced by the Zakat levy.
2.1 Conceptual Framework
The relation between liquidity risk, lending rates and maturities has been explored by found-
ational contributions in theory, which constitute the basis of this empirical analysis. Our con-
ceptual framework is based on the model of banking with uncertainty and recourse of Prisman
et al. (1986), which has been expanded and innovated by the subsequent work of Freixas and
Rochet (2008) and Bolton et al. (2011). In this specific setting, liquidity risk embodies both
withdrawals that cannot be predicted by the bank and liquidity shortfalls that are foreseeable
but cannot be smoothed because of dysfunctional interbank and central bank networks.
This class of models presents a bank intermediating short-term deposits with short- and
long-term loans. Two forces characterize the key properties of these models. First, depositors
can withdraw a stochastic amount before the long-term loans are back and after the short-
term loans are repaid. Second, the bank faces a non-convex cost of accessing the central bank
facility (or interbank loans) for alternative liquidity. Such non-convexity emerges because small
withdrawals produce zero additional costs, as the bank covers this with its inside liquidity.
However, if such withdrawals are large, the bank needs to borrow from the central bank at a
positive rate, γ. This parameter embeds the “price” of liquidity risk, which we define as liquidity
rate.
These features of the model make explicit that both the first moment of deposit withdrawal,
1
the level m
, and the second moment, the volatility v, increase the “wedge” existing between the
price of short- and long-term loans. These two variables, m and v, describe the “quantity” of
6liquidity risk and, correspondingly, induce a stronger effect in the presence of a higher liquidity
rate, γ.1 This conceptual framework can be summarized by the following proposition.
Proposition
A higher liquidity risk, given by a lower m or a higher v, leads to:
* a change in loan characteristics, with new loans presenting a shorter maturity, lower
lending rate and smaller amount;
* a change in the investment profile of firms, with a reallocation of investment away from
the long-term and toward the short-term.
All the effects on the role of liquidity risk on lending are amplified by the central bank policy
rate, γ. Appendix A reports an example of a simplified setting.
After having introduced this conceptual framework to guide our empirical analysis, we
present the natural experiment which generates exogenous variation in m and v. In the next
Section, we show through our empirical analysis how the Zakat experiment affects the empirical
analogues of m (deposit withdrawals) and v (deposit volatility). After this crucial step, we
investigate how such liquidity risk changes lending and investment.
2.2 Institutional Setting
The Zakat is a poor-giving religious obligation and is formalized in Sharia law. At the begin-
ning of every Ramadan, individuals are expected to donate to the poorest to regenerate their
wealth. In most Islamic countries, the Zakat payment is left to individual contributions, while in
Malaysia, Saudi Arabia and Pakistan, the government directly collects and redistributes some
of these resources.
Pakistan presents the ideal setting for our study because of its unique collection system. In
1981, the Pakistani government introduced a mandatory Zakat payment to the government.2
This was implemented through a Sharia-compliant obligation in the form of a 2.5% levy on
bank deposits that exceed a wealth threshold (the Nisab-i-Zakat). Such threshold, emanating
from local interpretations of the Sharia law, is calculated using the international price of silver
1
These results do not require a risk-averse bank, in fact all of these predictions are based on a simple model
with risk neutrality.
2
Refer to the Zakat and Ushr Ordinance, 1980, available at http://www.zakat.gop.pk/system/files/
zakatushr1980.pdf. For a historical review, refer to Nasr (2004).
7and corresponds to the value of 612.32 grams (approximately 50 tolas, a local measurement
unit). A central characteristic related to the timing of this obligation plays a pivotal role: the
threshold is announced by the State Bank of Pakistan and the Ministry of Religious Affairs
only 2–3 days before the collection, and the obligation applies on only those deposits held in
banks during the first day of Ramadan. The design of the levy creates a notch, because once
a depositor is above the threshold by 1 PKR, the 2.5% applies to the whole deposit amount,
implying a locally infinite marginal levy.
Another key aspect of this levy is given by the targeted audience. Pakistan is an Islamic
republic, with 95% of its population professing the Muslim faith and the remainder composed
mostly of Christians, Hindus, Buddhists, and Animists.3 The majority of Muslim Pakistanis
adhere to the Sunni branch (76%), with the remaining 19% belonging to the Shia. This dis-
tinction plays an important role in our identification because the rules of the Zakat payment
are differentially applied to Sunni followers. Although all Muslims are subject to the Zakat
principle, Sunni Pakistanis are obliged by law to pay through their bank accounts,4 whereas
Shia Pakistanis have been allowed to contribute their Zakat individually since the mid-1980s.5
As a result, we make use of this religious distinction to track which banks are more exposed to
Zakat withdrawals through their exposure to Sunni-majority areas.
To do so, we adopt the religious map of Pakistan elaborated by Dr. Izady and the Columbia
University Gulf/2000 project,6 which is based on a combination of historical data, census in-
formation, and online documentation.7 Figure 2 shows a simplified version of the original
religious map of Pakistan in which we distinguish the religion based on the colour: Sunni-
majority areas are reported in white; Non-Sunni majority areas in black (these can present a
majority of Shias, Hindus, Christians or other religions); while areas that are mixed with Sunni
3
Refer to the 1998 Census collected by the Pakistan Bureau of Statistics, aggregate information available at
https://www.pbs.gov.pk/sites/default/files//tables/POPULATION%20BY%20RELIGION.pdf.
4
The 1980 ordinance allows individuals of any fiqh (sub practice within the Sunni and Shia traditions)
to fill an exemption module. In principle, it would be possible also for Sunni Pakistanis to seek a Zakat
exemption. However, this is rare in some cases because of social stigma and lack of transparency from some
banks. For example, refer to Dawn, “Zakat Exemption Limit Doubled,” http://www.dawn.com/news/647723/
zakat-exemption-limit-doubled.
5
This exemption was discussed between 1982 and 1988 and was implemented in the final correction of the
law in 1989. Refer to Nasr (2004) for a historical and political account of these episodes.
6
The original map is entitled “Pakistan Religions” and is available at this page https://gulf2000.columbia.
edu/maps.shtml
7
To cross-validate the content of the map, we compare the aggregate numbers with the 1998 Census data
collected by the Pakistan Bureau of Statistics and find that these sources are aligned. For more on this, refer
to PBS.
8and other confessions are grey; finally, disputed territories are coloured with dashed lines and
are not part of our analysis.
Despite the good cause of the Sharia-compliant deposit levy, a fraction of Pakistanis avoid
this altogether and give individual donations.8 There is wide anecdotal evidence from newspa-
pers that individuals rush to “withdraw and redeposit”, so that bank deposits are substantially
depleted in the weeks preceding the first day of Ramadan and then more or less quickly rede-
posited. Appendix B.1 reports some newspaper articles and citations related to this behaviour.
As we have just mentioned, Sharia law directly links the threshold to the current price of sil-
ver and Figure 1 shows a scatter plot in which there is a 0.98 correlation between the Zakat
threshold and the international price of silver per ounce in US dollars (USD) on the day of the
announcement.
Before offering statistical evidence on how the Zakat levy affects liquidity risk, it is important
to report four facts regarding the Zakat contribution that facilitate our identification (Appendix
B reports specific evidence on each). First, the Zakat is a mass phenomenon, and the threshold
above which it applies is low. Table B.1 reports the first day of Ramadan and the threshold
in PKR in every year between 2000 to 2015. This table reports two useful elements: 1) the
first day of Ramadan changes in every year due to the Lunar calendar and permits to net out
seasonality; 2) the average of the Zakat thresholds corresponds to 22,761 PKR (corresponding
to 325 USD). Figure B.1 reports that 65% of deposit accounts exceed the threshold on average.
Second, the price of silver can be taken as exogenously determined to Pakistan, which is neither
among the world’s top 20 silver producers nor consumers.9 Furthermore, as shown in Figure B.2,
the correlation between silver price and Pakistani GDP per capita growth is not statistically
different from zero. Third, Figure B.3 shows that silver is more volatile than gold, given that
its market is much less liquid and Table B.2 shows that there is no correlation between the
mean price of silver and its volatility at the quarter–year frequency. Appendix B discusses also
in detail the 2000 Supreme Court Ruling related to the mandatory Zakat payment, highlighting
its inconsequentiality for our empirical strategy.
8
Refer to the work of the Charities Aid Foundation, World Giving Index 2015.
9
See the statistics on silver for 2012 to 2014 provided by the United States Geological Survey, published
by the United States Department of the Interior, available at http://minerals.usgs.gov/minerals/pubs/
commodity/silver/mcs-2014-silve.pdf, and the World Silver Survey 2015, issued by the Silver Institute,
available at https://www.silverinstitute.org/site/publications/. .
93 Data and Identification
This section presents the datasets employed in this analysis and the key elements behind our
identification strategy. After this, we highlight how the Zakat levy induces liquidity risk on
banks and how such a heightened risk affects lending and economic activity.
3.1 Data
Pakistan presents high-quality statistical documentation that is essential for our study. We use
a variety of databases to map the empirical analogues of our theoretical model, listed as follows:
1. Branch and Country Deposit Data. This part includes two datasets. First, the bank
branch deposit dataset which contains the universe of bank branches operating in Pakistan and
provides information on the amounts deposited at all branches of all financial institutions with
a bi-yearly frequency (end of June and December). This dataset identifies each branch through
a unique code and includes information on the corresponding financial institution, the volume
of deposits and location. This information is available for more than 13,000 branches between
2000 and 2019. Second, to measure deposit volatility at high frequency, we employ the daily
version of the “Half-Yearly Scheduled Banks Reference” deposit data. This dataset contains
data on daily deposits from 2007 to 2019. We measure the volatility of deposits by calculating
the standard deviation of the daily growth rate of deposits.
2. The Corporate Credit Information Report. This dataset contains the population of
loans and provides information on all corporate loans at origination issued by all financial
institutions to any corporate entity. It includes specific information on the amount of each
loan, the associated interest rate, the loan initial and end dates, used to infer maturity, and
several additional characteristics. This information is available for more than 32,000 borrowers
and 34 banks between 2000 and 2015. While the universe of loans contains information on more
than five million loans, we restrict our sample to those loans without missing values or spelling
mistakes for the loan maturity, the lending rate, and the amount of the loan. This leads to
a sample of 3,278,051 loans over the 64 quarters between 2000 to 2015 issued by 34 banks to
32,151 firms. Within this group, the subsample of loans to firms borrowing from multiple banks
in a given quarter-year corresponds to 11.5% of all loans.
103. The London Bullion Market Association silver price database. This contains daily silver
prices for the variable Silver Price per Ounce in USD between 2000 and 2019 (1 ounce corres-
ponds to approximately 28 grams), which is the resulting price of the auction that takes place
every day at noon UK time. This is used to measure the level of silver prices, which is aggreg-
ated at quarter-year frequency throughout the paper. We also measure the volatility of silver
prices by calculating the standard deviation of the daily growth rate of silver prices in a quarter.
We report 64 quarter-year observations for these two variables, one per each quarter of the 16
years between 2000 and 2015. Both variables are standardized to simplify their interpretation.
4. Minutes of the Meeting of Monetary Policy Committee at the State Bank of Pakistan.
From the statistical archive of the Pakistan central bank, we extract information on the “policy
rate”, which is the reference interest rate at which the State Bank of Pakistan provides liquidity
to banks. We also digitize the narrative of the state of the economy published on a quarterly
basis, reflecting the monetary policy minutes from the year 2008 to 2019 and produce a measure
of monetary policy shock, in line with the work of Romer and Romer (2004), which is presented
in the next sub-section.
5. Transaction-Level Trade Data. This database is produced by the Federal Board of
Revenue in Pakistan and contains information on the universe of international transactions on
imported goods for all Pakistani firms. This is the most granular dataset used to create the
dataset on “Import of Goods and Services ” at the State Bank of Pakistan.10 Each row in
the database corresponds to a transaction, identifying the importing firm, the bank offering
credit, the type of good, the country of import and amounts of the transaction and additional
identifiers. This information is available for more than 52,000 firms between 2008 and 2019.
In order to study whether firms changed their behaviour in the aftermath of lower long-term
finance, we transform the transaction-level data into a firm-level panel. To do so, we exploit
the first two digits of the HS identifier and identify whether a product is a capital good or
non-capital following the definition of OECD (2015).11 Then we can measure the “Import of
Capital Goods” and “Import of Non-Capital Goods”.
10
Refer to the following link to gain access to the aggregate datasets available from the State Bank of Pakistan:
Import of Goods & Services, visit http://www.sbp.org.pk/publications/import/index.htm.
11
Refer to Table 4, Page 12 of OECD (2015).
11Table 1 reports the summary statistics for all datasets used in this paper. Panel A presents
data on deposits, Panel B shows the characteristics of bank loans from the credit registry
between 2000 and 2015, Panel C summarizes time-series information on the level and volatility
of silver prices and the policy rate and, finally, Panel D presents summary statistics on the
variables used for our firm-level analysis.
3.2 Identification
In this Section, we provide more details on how we identify shocks to liquidity risk. In particular,
we present the time-series variation in liquidity risk, given by the level and volatility of silver
prices in the quarter of Ramadan and the three measures of the bank cost of outside liquidity.
We also describe the cross-sectional variation in bank exposure to the Zakat, through their
deposit distribution in withdrawal-prone areas.
3.2.1 Time-Series Variation
Silver Price Level and Volatility Figure 3 reports the level and volatility in silver price in the
quarter of Ramadan. The average price of silver in the Ramadan quarter is 14.19 USD and
has been steadily rising between 2000 and 2009, it experienced an abrupt increase from 18 to
40 USD in 2011 (like most commodities) and then declined to pre-2011 level in the subsequent
years. At the same time, the average volatility in silver prices is 1.7%, implying that silver prices
fluctuate around 1.7% of their average level in the Ramadan quarter. Such volatility ranges
from 0.6% in 2000 to a maximum of 3.3% and 3.9% in 2008 and 2011 respectively, touching 2%
in the other 3 years (2004, 2006 and 2013).
Policy Rate Replacing deposits with central bank liquidity is generally expensive in Pakistan.
As the left panel in Figure 4 shows the average policy rate between 2000 and 2015 was 10.35%,
in the range of 6.5%–15%. In order to understand the actual cost of outside liquidity for a
bank, it is useful to define a measure of the effective cost of liquidity, the liquidity rate, as
the difference between the policy and average deposit rate. This highlights that in our sample,
there is a 4.75% average premium on liquidity, ranging between 0.85 and 9.5. The State Bank
of Pakistan, the local central bank, is responsible for the conduct of the monetary policy and
defines the policy rate through its policy meetings.
12There is wide evidence showing that the State Bank of Pakistan intervenes strongly to
support banks’ liquidity during the Zakat. This is achieved through liquidity injections, which
can be considered a quantity response. However, we verify that the relatively small extent of
the Zakat withdrawals is not enough to generate changes in monetary policy. In fact, we cannot
reject the null hypothesis of no policy rate adjustment during the Zakat period in Table B.3 in
Appendix B.
Monetary Policy Shocks In order to capture unanticipated shocks in the cost of outside
liquidity for banks, we develop a measure of monetary policy shocks in Pakistan for the period
2008-2019 and employ it for our analysis between 2008 and 2015. The index is an adaptation
of the Romer and Romer (2004) index of monetary shocks in the US, a measure that is free
of endogenous and anticipatory movements. The right panel of Figure 4 shows the monetary
policy shocks, which we use in addition to the policy and liquidity rate in the next subsections.
The derivation of the new index has two key steps. The first is to derive a series for State
Bank of Pakistan’s (SBP) intentions for the SBP policy rate around Monetary Policy Com-
mittee (MPC) meetings. The second is to control for the State Bank of Pakistan’s forecasts
of key macroeconomic variables (inflation, GDP growth and unemployment), to create a meas-
ure of the intended monetary policy actions that is not driven by expectations about future
economic developments. We reach the former through quantitative and narrative records of
newly digitized variables available from 2008 onward, whereas the latter is obtained computing
the residual from a regression of the intended policy rate change on the committee’s forecasts.
Section 1 in Appendix C reports the detailed steps to build this index, which adapts the work
of Romer and Romer (2004) to Pakistan. While there are some key differences with the US,
our results are overall in line with the original computation for the US, as the predictors in our
regression capture a sizeable portion of the variability in the policy rate.
3.2.2 Cross-Sectional Variation
The extent to which the Zakat contribution affects banks depends on how much these are ex-
posed to the withdrawal-and-redeposit phenomenon. We measure this cross-sectional variation
by combining the religious map of Pakistan with the branch deposit data.
13We process these variables through the Theil L index, which is a generalized entropy in-
dex and widely used to measure isolation and distribution (for example in Campante and Do
(2014)). It has several statistical advantages. For example, it is decomposable; hence it can
be analytically separated in within and between-bank variation. Another advantage is that its
information content is preserved through linear transformations. All of these properties are
valuable in light of the empirical setting that we employ in our analysis.
The cross-sectional exposure of bank b to the Zakat, Exposureb , is defined using the average
value of each branch through the weighted sum of three elements for each branch i, Exposureb =
Sunnii × di × log(yi ). These are:
P
i
Sunnii - the religious weight taking unit value if branch i is located in a Sunni-majority
area, 0.5 for a Sunni–mixed, and 0 for a Non-Sunni majority area;
di - a deposit weight, which measures the share of deposits that branch i manages relatively
to the entire stock of deposits that the bank holds;
log(yi ) - the natural logarithm of the distance between branch i and the headquarter in
Kilometres.
The combination of these three variables is key to measure the exposure of a bank to the
Zakat withdrawals. In fact, a bank presents a higher exposure if, other things being equal, it
exhibits: a) a higher share of branches being located in Sunni-majority areas; b) a higher share
of deposits being held in branches operating in Sunni-majority areas; c) branches operating in
more isolated locations in Sunni-majority areas relative to the headquarter of branches, which
makes the operation of liquidity replenishment comparatively more expensive. This last element
is in line with the literature showing that deposit collection and liquidity management is more
costly for banks with a geographically-dispersed branch structure (Berger and DeYoung (2001),
Berger et al. (2017)).
This index displays a mean of 0.41 and a standard deviation of 1.57 for the 34 banks in our
sample. The left panel of Figure 5 reports the distribution of the standardized index, which
shows that most banks are positioned within one standard deviation of the mean exposure.
Some banks present a moderate exposure to the Zakat, more than one standard deviation, and
there is a slight mass of banks recording a particularly low exposure to the Zakat. The right
panel of Figure 5 shows the time-series evolution of this index over time and verifies that we
14cannot reject that this index stays overall constant across the banks. Section 2.1 in Appendix
C offers additional evidence on the time-invariance of this index and on the balance of economic
characteristics across Sunni-majority and non-Sunni-majority cities. In Section 2.2 of Appendix
C, we report an example with two banks displaying the same number of branches, but one bank
presenting a higher exposure to the Zakat than the other, due to a larger number of branches
in locations that are Sunni-majority and further from the headquarter.
4 Empirical Model and Results
This Section investigates how the Zakat levy affects deposits, lending and economic activity. The
first subsection verifies that the level of deposits and their volatility change during the Ramadan
period and depend on silver prices. The second subsection shows that lending responds to the
variation in deposits induced by the Ramadan quarters. Finally, the third subsection shows that
such changes in credit characteristics generate real effects in terms of firm long-term investment.
Each Section presents the empirical models and corresponding results.
4.1 Zakat and Deposits
Newspaper anecdotes discussed in Section 2.2, and presented in Appendix B.1, suggest the
existence of a withdrawal phenomenon prior to Ramadan and redeposit after the Zakat payment.
This implies two sources of liquidity risk: 1) a depletion of the deposit stock in the months
around the Zakat; 2) a higher deposit volatility as individuals adjust their deposit holdings to
remain below the threshold. In this Section, we investigate these two sources and their relation
with silver prices.
To study these issues, we leverage the Branch Deposit Dataset and the availability of branch-
level information on more than 13,000 branches over 20 years. In particular, we exploit one
particular feature of this dataset: its reporting of data takes place at predetermined semester
frequencies (at the end of June and December). While the end of these semesters is always
constant on the Gregorian calendar, it varies relative to the Ramadan period and the Lunar
Calendar. Therefore, we define a variable that maps these two semesters of a each year relative
to their distance from the Ramadan. Given the structure of the Lunar calendar, this distance
changes over time. For example, the deposits reported for December 1998 are classified to
15be reported during the quarter of Ramadan (as the Ramadan begun on December 16, 1998).
However, the deposits reported in December 2008 take place 1 quarter after the beginning of
Ramadan (which begun on September 1, 2008), while those of December 2018 take place 2
quarters after the beginning of Ramadan (which begun on May 16, 2018). It is important to
clarify that observations are not replicated and a branch in one particular year only presents
two observations (end of June and end of December).
For this reason, we introduce a variable to map the position of a semester relative to the
Ramadan, Ik . As a result, I0 takes unit value if the semester s of deposit reporting happens
during the quarter of Ramadan and zero during the others, I−1 takes unit value if the semester s
takes place during the quarter prior to Ramadan, I−2 during the two quarters prior to Ramadan
and, finally, I1 during the quarter following the Ramadan.
This set of variables exhaustively maps the year into four quarter dummies. After this, we
present the following lead-and-lag empirical model:
1
Depositis = a1i + b1s + c1k Sunnii × Ik + e1is
X
(1)
k=−2
which regresses the natural logarithm of deposits of branch i during semester-year s, Depositis ,
over an interaction between the Ramadan indicator Ik and the religious weight Sunnii , which
takes unit value of a branch is located in a Sunni-majority area, 0.5 for a mixed and 0 for a
non-Sunni area. Equation (1) also includes fixed effects for each branch, a1i , and each semester-
year period, b1s , and standard errors are clustered at the branch level. The estimate of c1 for
each period indicates the differential evolution of deposits around Ramadan across branches
depending on the religion of their location.
Figure 6 shows the results of this estimation. Relative to two quarters prior to Ramadan,
there is a slight yet insignificant decline in deposits in the quarter prior to Ramadan, then a
large and significant decline of 5% in the quarter of Ramadan and then a return to zero in
the quarter following Ramadan. This evidence is consistent with a withdrawal-and-redeposit
phenomenon, which takes place differentially at the branch level between Sunni, mixed-Sunni
and non-Sunni areas. This picture also highlights that branches are on parallel trends in their
deposit holding in all quarters, except the Ramadan one, during which Sunni branches observe
16a stronger depletion of their deposit stock. The coefficients of Figure 6 can be found in Table
D.1 in Appendix D.
Following this result, we focus in greater detail on the movements in deposits during the
quarter of Ramadan. In particular, we investigate whether the decline in deposits depends on
the price of silver and its volatility through a difference-in-difference estimation. As a result,
the following empirical model relates
Depositit = a2i + b2t + c2 Sunnii × Ramadant +
+d2 Sunnii × S.P rice + f 2 Sunnii × Ramadant × S.P rice + e2it (2)
the natural logarithm of deposits of branch i at time t, Depositit , to the interaction between
the religious weight Sunnii and a dummy describing the Ramadan quarter, Ramadant ; an
interaction between the religious weight Sunnii and the level of silver prices in the quarter
before Ramadan, S.P rice, and a triple interaction between Sunnii , Ramadant and S.P rice.
In an alternative specification, we also include in equation (2) the volatility of silver price in
the quarter before Ramadan, S.V olatility, to study whether the decline in deposits responds to
the volatility as well as the level of silver prices. In all of these cases, we always include branch
and time fixed effects and cluster at the branch level.
Table 2 summarizes these results through four columns. The first column reports exclusively
the interaction between the religious weight, Sunnii , and the Ramadan dummy, Ramadant .
This replicates the result of Figure 6 and shows that the average decline in branch deposits
during the Ramadan quarter in Sunni branches is 5%, which is statistically different from zero
at less than 1%. The second column also includes the interaction between the religious weight
and the prices of silver and the triple interaction. The first term in this specification clarifies
that the baseline decline in Sunni areas during Ramadan is 9%, while the second term highlights
that outside of the Ramadan period there is no effect of silver prices on deposits in Sunni areas.
Finally, the triple interaction offers evidence that when silver prices are one standard deviation
higher than the average, branches in Sunni areas see a lower decline in deposits at Ramadan
by 5%. This is in line with the functioning of the Zakat levy: when silver prices are higher,
fewer individuals are hit by the levy and withdraw their deposits. Column (3) offers the same
17specification but replaces the level of silver prices, S.P rice, with the volatility in silver prices,
S.V olatility. This shows that the volatility of silver predicts the decline in deposits neither in
general nor during the Ramadan quarter. Finally, we include both the level of silver prices and
its volatility in Column (4) and find similar effects to Column (2). In Appendix D, Table D.2,
we replicate the results of Table 2, including district-time fixed effects to absorb other district
time-varying unobservables and the key results are unchanged.
While high-frequency deposit data at the branch level are not available, we can study instead
the aggregate country-level dataset in Pakistan. This records deposit data at the daily level and
allows us to study the rich within-quarter changes in deposits. Such data is essential because
we can calculate the average volatility of deposits, by computing the standard deviation of
the daily growth rates in deposits at the aggregate level. Once this is done, we combine the
quarter-year description with the Lunar Calendar and create indicators on the position of a
quarter-year period relative to Ramadan Ik , as already discussed. After this, we present the
following lead-and-lag model:
1
D.V olatilityqy = a3q + b3y + c3k Ik + e3qy
X
(3)
k=−2
which regresses the standardized volatility of deposits of quarter q in year y, D.V olatilityqy ,
over the Ramadan indicator dummies Ik and also includes quarter and year fixed effects, a3q
and b3y . The estimate of c3 for each quarter indicates the within-year evolution in deposit
volatility around the Ramadan period. Figure 7 reports these results and shows that relative
to two quarters prior to Ramadan there is a marked increase in volatility in the quarter before
Ramadan and the quarter of Ramadan and then such volatility returns to the yearly average.
To further investigate this and verify the link to silver prices during the quarter of Ra-
madan, we explore the following difference-in-difference model studying the volatility of deposits
D.V olatilityqy
D.V olatilityqy = a4q + b4y + c4 Ramadant−1 + d4 Ramadant +
+f 4 Ramadant−1 × S.V olatility + g 4 Ramadant × S.V olatility + e4qy (4)
18which focuses exclusively on the quarter before Ramadan, Ramadant−1 , and the Ramadan
quarter, Ramadant , the ones that exhibit different behaviours from the other quarters. These
are interacted with the volatility of silver for the corresponding year, as expressed by S.V olatility
(or the price of silver, through S.P rice). Table 3 reports four columns exploring these results.
The first only includes the dummies for the two quarters and shows results in line with Figure
6, indicating that these months present much higher levels of deposit volatility, corresponding
to 1 standard deviation for Ramadant−1 and 0.76 for Ramadant . Column (2) adds to this
specification an interaction of these terms with the price of silver and we cannot reject a zero
effect in both cases: hence the level of silver prices does not affect deposit volatility. In Column
(3), we replace the level of silver prices with the volatility of silver and observe that silver
volatility affects deposit volatility only in the quarter before Ramadan but not in the Ramadan
quarter directly. Finally, Column (4) offers a specification with all interactions and confirms
that deposit volatility is higher in the quarter before Ramadan and increases in silver price
volatility.
4.2 Zakat and Lending
This Section explores the effect of the Zakat levy on lending, focusing in particular on three
characteristics at the centre of this paper: maturities, rates and amounts. We aim at under-
standing how higher costs of liquidity during the Zakat period affect lending and whether the
effects of changes in the level and volatility of deposits interact.
As a result, our first empirical investigation takes the most flexible form and explores a lead-
and-lag design exploiting the Lunar Calendar and the definition of quarterly dummies relative
to the Ramadan, Ik . We investigate the following empirical model
1
a5f b5b c5t d5k Exposureb × Ik +
X
Lendingf bt = + + +
k=−2
1 1
fk5 Exposureb × Ik × S.P rice + gk5 Exposureb × Ik × S.V olatility + e5it
X X
+ (5)
k=−2 k=−2
which regresses the characteristics of a loan (natural logarithm of loan maturity, lending rate,
natural logarithm of loan amount) received by firm f from bank b during the quarter-year t,
Lendingf bt , on an interaction between the cross-sectional exposure of a bank to the Zakat,
19Exposureb , and the Lunar Calendar indicators, Ik . In addition to this, we introduce two
interactions in equation (5) which relate the variables Exposureb and Ik respectively with the
price of silver in the quarter prior to Ramadan, S.P rice, and the volatility of silver, S.V olatility.
Equation (5) includes firm, bank and time fixed effects, a5f , b5b and c5t , and standard errors are
clustered at the bank and city level. Figure 8 reports the results of this specification through
three panels: Panel A for maturity, Panel B for lending rates and Panel C for loan amounts. In
all three cases, the left figure reports the values of d5k , hence the interaction between Exposureb
and Ik , the central figure shows the triple interaction with the level of silver prices, the coefficient
gk5 , while the right figure displays the triple interaction with silver volatility, described by the
coefficient fk5 .
Panel A, B and C in Figure 8 show that banks with a stronger cross-sectional exposure to
the Zakat behave similarly to banks with a weaker exposure two quarters prior to Ramadan and
during the quarter following the Ramadan. However, this does not happen during the quarter
before Ramadan and the quarter of Ramadan. In fact, in this period, there is a divergence
and banks with a stronger Zakat exposure issue loans with lower maturities, rates and amounts
when liquidity risk is stronger (given by lower silver prices or higher silver price volatility).
To offer a compact recap of the previous results, we explore the following difference-in-
difference model
Lendingf bt = a6f + b6b + c6t + d6 Zakatbt +
+f 6 Zakatbt × S.P rice + g 6 Zakatbt × S.V olatility + e6it (6)
in which the characteristics of a loan (maturity, rate, amount) received by firm f from bank b
during the quarter-year t, Lendingf bt , are regressed on the Zakatbt variable, which is defined by
the interaction between the cross-sectional exposure Exposureb and a dummy taking unit value
during the Ramadan and previous quarter. The Zakatbt variable is also included in interaction
with two additional variables to recap the previous findings: the level and volatility of silver
prices during the Ramadan quarter, S.P rice and S.V olatility respectively. As usual, firm, bank
and time fixed effects are included and standard errors are clustered at the bank and city level.
Table 4 summarizes the results of the previous model and constitutes our baseline spe-
cification for investigating how the Zakat affects lending characteristics. It highlights that on
average during the Ramadan and previous quarter, loans from banks with one standard de-
20viation stronger cross-sectional exposure to the Zakat (an index 1.57 points higher than the
average of 0.41) exhibit the following characteristics: a lower maturity by 4.4%, a lower lending
rate by 0.089 points and a smaller, yet insignificant, amount of 2.7%. However, when silver
prices are one standard deviation higher than the average (9.55 USD higher than the mean
of 14.19), then loans issued by the same set of banks during the same period exhibit higher
maturities, lending rates and loan amounts (6%, 0.18 points and 14%). Finally, when silver
price volatility is one standard deviation higher than the mean (0.8% higher than the mean of
1.7%), then loans issued by banks that are one standard deviation more exposed to the Zakat
and during the same period, present lower maturities (by 7.6%), lower lending rates (0.226
points) and smaller amounts (by 14%).
These findings lead us to investigate another point: which maturities are changing in re-
sponse to a higher liquidity risk? To do so, we explore a bank-level version of equation (6) and
aggregate our data by bank, quarter-year and maturity, as reported in the robustness checks
section. This analysis delivers two key messages: 1) higher liquidity risk changes the share
of lending away from loans with a maturity of 4 and 5 years towards loans with a maturity
of 1 and 2 years; 2) even within the loans with a 1-year maturity, there is an increase in the
share of loans that are originated and repaid prior to the Zakat dates and loans with very low
maturities.
We also explore how the interaction between the level and volatility of deposits affects
lending. In the robustness checks section, we discuss that in general this interaction produces
ambiguous effects, as these two moments push liquidity risk in opposite direction. However,
in the presence of high withdrawals (induced by a low level of silver prices), then silver price
volatility is likely to be particularly important. This happens because sudden swings can make
eligible a individuals who may be close to the threshold. As a result, a high volatility of silver
in the presence of low silver prices can introduce an additional layer of liquidity risk, which we
detect in our estimates.
4.2.1 Zakat and Credit Supply
In Table 5, we identify the effect of the Zakat liquidity shock on credit supply by including city-
time and firm-time fixed effects. The first three columns report the results of Table 4, while
21the second set of three columns replicate this specification but replace the quarter-year fixed
effects with their interaction with city fixed effects. As highlighted by Degryse et al. (2019),
this exercise offers important insights into identifying credit supply shocks in firms employing
a single bank. In fact, such specification removes city time-varying factors that could contain
variation in credit demand and, hence, contaminate our results. In fact, by including city ×
quarter-year fixed effects, this specification compares firms operating in the same city and at
the same time, which are connected to banks heterogeneously affected by the Zakat shock.
The last three columns of Table 5 investigate further whether the results of Table 4 are due
to changes in credit supply or credit demand. In this setting, we replace firm and quarter-year
fixed effects with their interaction: hence firm × quarter-year fixed effects. As highlighted
by Khwaja and Mian (2008), this specification offers valuable insights in identifying credit
supply shocks in firms receiving loans from multiple banks at the same time. For this purpose,
we restrict our sample exclusively to firms that are borrowing from more than one bank in
a quarter-year period. This leads to a sample that is smaller than the original (11.5% of its
observations), but still comprises more than 330,000 observations.
In both cases, our findings point to the fact that the bank exposure to the Zakat is a key
driver behind our key results. This is particularly visible once we verify that the first three
columns of Table 5 are extremely close to the remaining last six columns of Table 5 despite
removing a sizeable amount of variation through thousands of fixed effects. It is particularly
important to notice that the point estimates of the effects are nearly equal in statistical terms
across specifications in Table 5. This is a central finding, because it reinforces our evidence
that firms are only affected by the Zakat experiment through changes in credit conditions at
the bank level.
4.3 Zakat and Cost of Liquidity
The next table investigates how loan characteristics react to changes in liquidity risk due to
both fluctuations in silver prices and in the cost of liquidity. Table 6 explores a more extensive
specification of Table 4 by interacting all terms with three proxies for the cost of liquidity.
The first three columns of Table 6 employ the policy rate of the State Bank of Pakistan as a
proxy for the cost of liquidity. The second set of three columns use the liquidity rate, which
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