Comments welcome

[Corruption and the Delivery of Heath and Education
Services]{.smallcaps}

[Omar Azfar]{.smallcaps}[^1]

Table of Contents

Summary [2](#summary)

1\. Introduction [3](#introduction)

2\. The nature of corruption in the health and education sectors
[5](#the-nature-of-corruption-in-the-health-and-education-sectors)

3\. The effect of corruption on education outcomes
[10](#the-effect-of-corruption-on-education-outcomes)

4\. The effect of corruption on health outcomes.
[17](#the-effect-of-corruption-on-health-outcomes.)

5\. The causes of corruption in the health and education sectors
[26](#the-causes-of-corruption-in-the-health-and-education-sectors)

6\. Emerging data sets and ongoing studies.
[34](#emerging-data-sets-and-ongoing-studies.)

7\. Recommendations [39](#recommendations)

Conclusions [42](#conclusions)

References [43](#references)

Appendix A. Empirical Methods for the Analysis of Corruption and
Governance
[48](#appendix-a.-empirical-methods-for-the-analysis-of-corruption-and-governance)

# [ ]{.smallcaps} 

#  **Summary**

This paper summarizes the expanding literature on corruption in the
health and education sectors.

It begins with a discussion of the nature of corruption in health and
education and presents a typology of different kinds of corruption in
the provider-client (doctor-patient, teacher-student) relationship, in
the provider-supplier relationship (procurement etc.), the
payer-supplier relationship (insurance fraud etc.) and in the service
delivery organization itself.

The paper then reviews the evidence on the effect of corruption on
health and education outcomes. It summarizes the findings of Gupta et
al., who find that corruption levels across countries undermine health
(measured by child mortality and infant mortality) and education
outcomes (repeater and dropout rates). It then discusses the results of
Rajkumar & Swaroop, who show that reducing corruption increases the
effectiveness of public expenditure on health and education. It then
reports the results of some replications of these results by Azfar &
Lee. Next, it reviews the results of Azfar & Gurgur and Azfar, Kähkönen
& Meagher who show, using cross-municipal data from the Philippines,
that corruption appears to undermine the delivery of health and
education.

Next comes an examination of the causes of corruption with a focus on
the health sector. Various forms of accountability tend to matter. Azfar
et al. show that voting patterns appear to discourage corruption across
Philippine municipalities. Several chapters in Di Tella and Savedoff's
book on Latin American hospitals show that forms of
accountability---like the probability of detection for absenteeism or
the ease of dismissal---may reduce corruption. Some of the chapters also
offer hard and soft data comparisons for corruption. One paper that
shows that accountability may have reduced corruption in Salvadorian
schools is also discussed.

The next section reviews promising work currently underway at the World
Bank on quantitative service delivery surveys (QSDS) and public
expenditure tracking surveys (PETS).

The paper concludes with some tentative policy suggestions.

# **1. Introduction**

Corruption is widespread in the health and education sectors of
developing countries, and not uncommon in some developed countries.
Exams are sold in Indonesia and Pakistan. Some reports state that there
are 20,000 ghost schools in Pakistan, the expenses for which find their
way into the pockets of bureaucrats. Teachers routinely pay between
\$200 and \$1400 for jobs and then collect their monthly salaries of
\$70-\$100 without working, sharing these salaries with their
supervisors for turning a blind eye to their absenteeism. Consequently
as many as 32% of teachers in Pakistan never show up for classes (Burke
2000), while many more who buy their jobs or get them as favors are
incompetent or even illiterate. In Thailand, teachers pay bribes to
board members for promotions and transfers, and board members in turn
bribe people to vote for them. One report states that, as a rule of
thumb, 70% of teachers fail to show up for work on a typical day in the
poorest countries (Bennet). In another case, a Thai education fund of
103 Billion Baht (2.2 million dollars) was reportedly rendered
ineffective as loans meant for the poor were given to the rich and
well-connected---many of whom were not even students (Bangkok Post
2001). There is widespread corruption in textbook procurement, with the
consequence that the prices of textbooks in some countries are many
times their prices elsewhere.

The health sector is scarcely better. Studies of corruption in formerly
communist countries have shown that the health sector is considered one
of the most corrupt sectors in Slovakia, Tajikistan and Ukraine, with
bribes routinely demanded for treatments that are supposed to be free
(Lewis 2000). Large leakages are reported for health expenditures in
Uganda and Tanzania (Reinikka and Svensson 2002). The dilution of
vaccines in Uganda has allegedly led to the emergence of resistant
strains of bacteria (Ruzindana 1998). In Colombia and elsewhere,
corruption has allegedly led to the bankrupting and closure of
hospitals. Studies of hospitals in various Latin American counties have
uncovered indicative evidence of theft, absenteeism and kickbacks for
procurements (Di Tella and Savedoff 2001). Nor is corruption in the
health sector exclusive to developing countries. Some studies have
estimated numbers as high at \$100 billion for hospital fraud in the
US---this is more than the GDP of most developing countries. Doctors in
the US and Germany routinely get valuable gifts from drug companies
whose drugs they prescribe. Nor are not-for-profit organizations
immune---even in relatively well-governed countries like the US,
prestigious organizations have been accused of accepting large donations
and recommending drugs without proper scrutiny.

Given the prevalence of these various practices it is hardly surprising
that there are some statistically significant relationships between
corruption and health and education outcomes. To some extent it is not
surprising that corruption---the abuse of office for personal
gain---would affect outcomes. I can think of three reasons why it might
not affect outcomes, but none seem to be true in practice:

1: Governments cannot do anything to improve health or education
outcomes

2: Corruption is a minor concern in health and education

3: The form of corruption is efficiency enhancing or efficiency neutral
in the health or education sectors.

Since 1) is patently false, in this paper I will review empirical
evidence on points 2 and 3 above. The evidence suggests that corruption
is a major concern in the delivery of health and education and the form
of corruption is often efficiency reducing.

Still there may be other valid reasons why corruption may not affect
service delivery and it is important to examine evidence on whether
health and education outcomes are in fact worse in places with more
corruption. I shall also review this evidence which suggests that
corruption does appear to undermine service delivery, although the
evidence is inconclusive on this score.

The paper begins with a description of the nature of corruption in the
health and education sectors in section 2 and then summarizes the
results on the relationship between corruption and health and education
outcomes in sections 3 and 4. Section 5 examines the causes of and
possible remedies for corruption in the health sector looking at
evidence from Latin American hospitals. (No such empirical evidence is
currently available for the education sector.) Section 6 describes the
various data sets that are currently available or being created, which
can help identify the causes, consequences and remedies for corruption
in the health and education sectors. Section 7 concludes.

# **2. The nature of corruption in the health and education sectors**

Both health and education offer plentiful scope for corruption. In each
sector corruption can take place in procurement, recruitment, the theft
of money and supplies, absenteeism, induced demand for unnecessary goods
and services, and the solicitation of bribes for services. Understanding
the nature of corruption in these two sectors will both help evaluate
the impacts of corruption on outcomes and help design more effective
responses to it.

Some of these manifestations of corruption may be worse than others in
terms of their effect on service delivery. To some extent one can answer
the question of whether corruption affects service delivery by looking
at the kinds of corruption that are more prevalent. If corruption takes
the form that patients bribe nurses and doctors for services then, [if
there were no wide discrepancies in incomes]{.underline}, we might
expect that the resulting price mechanism would improve the
effectiveness of health services[^2]. If incomes varied widely, the
impact of bribery on health outcomes would be ambiguous, because while
the bribes would deter the relatively well-off from frivolous
treatments, the bribes would also deter the indigent from seeking
necessary treatments. Other forms of corruption in the health
sector---like the dilution of vaccines, the pilferage of refrigerators
(which breaks the vaccine cold chain), or the prescription of
unnecessary treatments---have clearer effects on worsening health
outcomes.

Two sources that provide a good description of the nature of corruption
in parts of the health sector are Di Tella and Savedoff (2001) and Cohen
et al (2002). The paper on Argentina by Schargrodsky et al. (2001) in Di
Tella and Savedoff mentions the incidence of corruption in the purchase
of pharmaceuticals, meals, cleaning services and physical inputs. In
addition, corruption takes place in staff appointments, the theft of
supplies, bribes for scheduling surgeries, absenteeism and the diversion
of patients to private practices. Additionally, in Peru there is
evidence of induced demand for Caesarian births which are in excess of
the medically required number of around 10% and highest in the
private-sector hospital (Alcazar and Andrande 2001---see section 5 for
more details). This highlights the possibility of more corruption in the
private sector, and suggests the need for caution about thinking of
privatization as a panacea for corruption.[^3]

The chapter by Gideon et al. in DiTella and Savedoff on corruption in
Bogota hospitals categorises corruption into doctor-patient,
hospital-payer, and hospital-supplier relationships (Table 1, below,
follows this format). In the first relationship, a doctor may improperly
use public facilities for a private practice and also be absent or lazy
during duty hours to increase demands for the private practice. In the
hospital-payer relationship it can take the form of the falsification of
bills, a problem which is likely to become more important as developing
countries move to more insurance and reimbursement systems. In the
hospital-supplier relationship corruption may take the form of either
explicit kickbacks or more subtle gifts not explicitly tied to the
awarding of contracts.

Many chapters in Di Tella and Savedoff concentrate on this last form of
corruption, corruption in procurement, perhaps because data on this form
is more readily available than on other forms. The hard data on
corruption in procurement is not on the corruption itself, which the
perpetrators are careful to conceal, but on the prices charged, which
are observable. Price differences themselves do not provide conclusive
evidence of corruption---a hospital may pay higher prices because of
emergency purchases, poor bargaining or general negligence. In response
to this concern, the authors do find evidence in the responses of health
workers which link higher prices to corruption. It is also not clear how
important these distinctions are: repeated emergency purchases or
neglect are forms of mismanagement and, therefore, worthy of
investigation or response, whether or not they are instances of
corruption. Furthermore, for practical purposes, the most effective
anti-corruption strategy may well be to provide incentives to reduce
neglect and corruption at once, rather than trying to excise corruption
surgically while consciously avoiding related forms of misgovernance.

Cohen et al. (2000) divide corruption in the pharmaceutical sector into
various stages: approval and registration, selection, distribution and
service delivery. They provide a corruption vulnerability assessment for
each stage. They demonstrate the complex scope of the problem and the
possible detail of the diagnosis if one takes a hard, sector-specific
look at the problem. They also produce a set of recommendations on how
to deal with the problem. With refreshing realism they categorize the
possible responses both in terms of their expected effectiveness and
their political feasibility.

Corruption in the education sector can also take various forms, many of
which parallel those in the health sector (c.f., Table 2 below). There
can be corruption in the purchase of textbooks, desks, blackboards, and
other supplies, as well as in the purchase of cleaning services and
meals. Corruption in the procurement of school construction can make
construction cost between two and eight times what similar buildings
cost for private use (Bennet 2000). There is also frequent theft of
supplies and funds. On one trip with a senior official in Pakistan where
textbooks and school bags were distributed to the students, IRIS
researchers found (only because we defied protocol and went for a hike
on which we met some parents) that the principal had requisitioned that
all the materials be "returned" to him.

There is widespread corruption in many countries in the hiring of
teachers---for instance, the reported hiring of ghost teachers in
Pakistan. Some reports state that 32% of Pakistani teachers never show
up to teach (Burke 2000). Other reports (e.g., Bennet) suggest that this
number may be even higher in some developing countries. Absenteeism is
frequent in the schools of many countries; this is often related to the
sale of jobs, as mentioned above.

Teachers and principals may solicit bribes for admitting students or
giving better grades. One barely disguised way of doing this is to ask
students to pay for private tuitions. Teachers may teach badly to
increase the demand for private tuitions. Examiners may allow students
to cheat on exams or take exams for each other. Educational institutions
may issue false certifications.

There is also corruption at the interface of payers and schools and
universities. Student loans may be given to people who are neither needy
nor students. A 103 Billion Baht Thai education fund was rendered
ineffective by such practices. The implementation of voucher schemes may
lead to the proliferation of ghost schools that process vouchers without
providing education---similar to fraudulent health reimbursement
systems.

**Table 1. The nature of corruption in the health sector**

  -------------------- -------------------- ------------------------ ------------
  **Patient-Doctor**   **Payer-hospital**   **Hospital-supplier**    **Within
                                                                     hospital/
                                                                     ministry**

  Bribes for treatment Fraudulent billing   Kickbacks for purchase   Sale of jobs
                       for fictional        orders for drugs,        and
                       treatment            equipment, supplies,     promotions
                                            meals and cleaning       and
                                            services                 transfers

  Induced demand for   Patients with        Bribes for approval of   Theft of
  unnecessary          coverage get         drugs                    funds
  procedures e.g.      prescriptions for                             
  Caesarian deliveries those without                                 

  Diluted vaccines                          Doctors bribed by drug   Theft of
                                            companies for            supplies
                                            prescribing their drugs  

  Absenteeism                               Non-profits and other    Fraudulent
                                            organizations accept     billing for
                                            donations for            expenses
                                            recommending drugs       

  Negligence                                Kickbacks for            
                                            construction             

  Bribes for illegal                                                 
  procedures like                                                    
  abortions                                                          
  -------------------- -------------------- ------------------------ ------------

**Table 2. The nature of corruption in the education sector**

  --------------------- ------------------ ------------------------ -------------------
  **Student-Teacher**   **Payer-school**   **School-supplier**      **Within
                                                                    school/ministry**

  Bribes for admission  Ghost schools for  Kickbacks for purchase   Sale of jobs and
                        processing         orders for textbooks,    promotions and
                        vouchers           equipment, supplies,     transfers
                                           meals and cleaning       
                                           services                 

  Bribes for grades and Inflating number   Bribes for approval of   Theft of funds
  promotions            of students to get textbooks                
                        reimbursements                              

  Induced demand for    Loan officers      Board members bribed by  Theft of supplies
  private tuitions      bribed to give     publishing houses for    
                        loans to rich      selecting their          
                        students or        textbooks                
                        non-students                                

  Absenteeism           Students who stay  Bribes for turning blind Fraudulent billing
                        in university for  eye to photocopying      for expenses
                        decades to collect textbooks and violating  
                        stipends           intellectual property    

  Teaching badly                           Kickbacks for            Under-allocation to
                                           construction             education

  Sale of exams and                                                 
  bribes for letting                                                
  professional exam                                                 
  takers take exams for                                             
  students                                                          
  --------------------- ------------------ ------------------------ -------------------

# **3. The effect of corruption on education outcomes**

Several studies---notably Gupta et al., Rajkumar and Swaroop, Azfar and
Gurgur and Azfar et al.---have shown negative effects of corruption on
education outcomes in cross-sectional analyses. Additionally, Mauro
(1998) and Knack and Sanyal (2000) have shown that corruption reduces
the share of public expenditures on education. Knack and Sanyal's
results are stronger because they actually control for the general
quality of government in their regression, which indicates that it is
corruption itself rather than the generally poor quality of government
that is leading to the underallocation of resources to education. Azfar
and Lee (2002) have also investigated the relationship between
corruption and spending on health and education, and have found, like
Mauro, that there is a clear relationship between corruption and
education expenditures but only a fragile relationship between
corruption and health expenditures---which disappears if income is
controlled for.

Before discussing the results of Gupta et al. and Rajkumar and Swaroop,
it is worth discussing the corruption variable they use. As a measure
for corruption, they use the International Country Risk Guide's (ICRG)
index for corruption in government. It is best to think of this index as
a measure of integrity, since higher numbers denote more honest
governments. The definition from Knack and Keefer (1995) who introduced
the variable to the literature is

Corruption in government: Lower scores indicate "high government
officials are likely to demand special payments" and "illegal payments
are generally expected throughout lower levels of government" in the
form of "bribes connected with import and export licenses, exchange
controls, tax assessments, policy protection, or loans." Scored 0-6.
High numbers denote better government.

The index is available annually for around 130 countries, beginning in
the mid-1980s. Many researchers use this index because other corruption
indices either have insufficient country coverage or time-series
coverage, or both.[^4]

Gupta et al. and Rajkumar and Swaroop's result, can only be interpreted
as the impact of a governance variable on the dependent variable. The
different governance variables are too highly correlated to be able to
disentangle the effects of one from the other. Perhaps the best way to
view the existing evidence is that it establishes the importance of good
governance as a whole, one aspect of which is reducing the level of
corruption. However, Anderson et al.'s micro-level findings for Georgia,
(admittedly only one, possibly unrepresentative country), on the sale of
jobs and on the prevalence of corruption in the law enforcement sector,
do suggest that corruption would contribute to wider misgovernance.

Gupta et al. showed that in univariate regressions, corruption is
correlated with each of: school enrollment, repeater (failure) rates,
dropout rates, continued schooling through grade 5, and illiteracy. This
equation (despite its potentially serious omitted variable problems) can
be interpreted as demonstrating the total effect of corruption on
education outcomes---if we accept that the level of income is endogenous
to the level of corruption.[^5] The coefficient of 0.36 on dropout rates
implies that improving the integrity of government by two would halve
the dropout rate. The coefficients on illiteracy and repeater rates are
a little lower at 0.24, implying that an improvement of two points in
the integrity index would reduce the dropout rate by around 40%. After
controlling for income, corruption is correlated with repeater rates and
dropout rates but not with enrollment, persistence to grade 5 or
illiteracy. The coefficients on repeater rates and dropout rates each
fall by about a third, implying correspondingly smaller effects of
corruption on education outcomes. The coefficient of 0.20 on dropout
rates implies that a two-point improvement in the quality of government
would reduce dropout rates by around 33%.

Gupta et al. also conduct a multivariate analysis of dropout rates,
controlling for average female education among adults, public education
spending, the dependency ratio and urbanization. They find a
persistently significant effect of corruption on dropout rates, but the
coefficient drops to 0.13. This implies that a two-point improvement in
the integrity of government would reduce dropout rates by around 22%.

Because the equilibrium levels of income are probably affected to some
extent by the level of corruption, but also by other variables, the
actual effect of corruption on dropout rates is probably somewhere
between the 22% estimated in the multivariate regression and the 50%
estimated in the univariate regression. It is also worth noting that the
coefficients might also be biased downwards because of measurement error
in the corruption variable.

At first glance, there are serious endogeneity problems in looking at
the effect of corruption on education outcomes. Improving education
could lead to increased civic virtue or the competency of the
bureaucracy, either of which could reduce corruption. These concerns,
however, are largely allayed by the inclusion of adult education levels
on the right-hand side of the regression (female education levels are
quite highly correlated with total adult education levels). Having
controlled for the education levels of the working and voting
population, it is unclear that current dropout rates would affect the
level of corruption.

However, Gupta et al., like many economists, take these concerns
seriously and provide the reader with instrumental variable estimates of
the effect of corruption on education outcomes. In fact it is not clear
whether the instrumental variable analysis is more credible than the OLS
regressions. The instruments they use are from Treisman's analysis of
the causes of corruption (2000): democracy, income, the share of
Protestants in the population and a unitary form of government. All of
these could easily have a direct impact on education outcomes, any one
of which could create spurious results in the instrumental variable
analysis. For instance valuing education may be one part of the
Protestant ethic (with the exception of France, compulsory primary
education was first tried in mostly Protestant countries). Democracy and
income might be even more suspect as instruments. Gupta et al. do
conduct the test of over-identifying restrictions to test for this
concern, but this test is very weak in small samples and does not
reliably demonstrate that the instruments are well chosen (Newby 1985,
Nakamura and Nakamura 1985). One important lesson that these econometric
caveats have for the policy maker is that it is generally far better to
understand the mechanisms by which one variable might affect another and
to collect micro-level data on these interactions than it is to rely on
statistical relationships among instrumental variables, because the
latter demonstrate causality only when a host of technical conditions
are satisfied (the appendix contains an elaboration of this
methodological point).

Rajkumar and Swaroop (2001) show that reducing corruption (or, improving
the quality of government) improves education outcomes largely by
improving the effectiveness of public expenditures. It is quite
intuitively plausible that if corruption leads to large leakages in
public funds allocated to education, public expenditure on education is
likely to be less effective[^6]. Rajkumar and Swaroop estimate an
equation of the determinants of dropout rates using data from two years
1990 and 1997, with the aim of examining whether public expenditure on
education is more effective in well-governed countries. They control for
per-capita GDP, the level of corruption, public education spending,
female education, income inequality, a dummy for predominantly Muslim
countries, a dummy for East Asia, ethno-linguistic fractionalization,
access to safe water, degree of urbanization, percentage of population
under 5, the adult illiteracy rate and distance from the equator. They
also include a variable (corruption rating × public education spending)
that allows the impact of corruption and public education spending to
vary with the level of the other variable. Their results show that while
public education expenditure would have no perceptible impact on dropout
rates if the integrity rating was 2, it would have a significant and
meaningful effect in the integrity rating was 4. The implied coefficient
of 0.6 at the higher integrity level indicates that a doubling of public
expenditure would reduce dropout rates by 70%.

As a part of this project Azfar and Lee have attempted to replicate
these findings for other measures of education outcomes, but have been
unable to do so. In fact the coefficient on the interaction term often
takes the opposite sign. This might be driven by differences in the data
sets or outcome variables used by the two sets of authors (Azfar and Lee
use data from 3 years, 1990, 1995 and 1997 and use enrollment and
illiteracy rates as outcome variables, as compared to Rajkumar and
Swaroop who use data from two years 1990 and 1997 and repeater rates as
the outcome variable). It does, however, seem wise to caution the reader
that the results of Rajkumar and Swaroop may not be robust to changes in
the sample and outcome variable.

Azfar and Gurgur (2001) and Azfar, Kähkönen and Meagher (2001) examine
the effect of corruption on education outcomes across municipalities in
the Philippines. Since their papers focus mostly on health, their
methodology is described in greater detail in the next section on
corruption and health outcomes. They use two measures of outcomes:
students' scores in the National Elementary Aptitude Test (NEAT) and
households' satisfaction ratings with schools. Their corruption measure
is derived from surveys of education officials. With this measure, they
conduct regressions at the municipality level. Of the 80 municipalities
surveyed, they are able to use data on 70 municipalities for
satisfaction ratings, but only 44 for NEAT scores, as data was not
available for many schools. They find a clearly significant effect of
corruption on satisfaction ratings (t-stats from 2.16 to 2.64) but only
insignificant effects on NEAT scores (t-stats from 1.30-1.64).

**Table 3 The effect of corruption on education outcomes**

+-----------------+------------------------------------------+---------+
| **Authors and   | **Results**                              | **      |
| data**          |                                          | Signifi |
|                 |                                          | cance** |
+-----------------+------------------------------------------+---------+
| Gupta, Davoodi  | Clear univariate relationships between   | Sig.    |
| and Tiongson    | corruption and various education         | OLS     |
|                 | outcomes: school enrollment, repeater    | results |
| Cross country   | rates, dropout rates, persistence to     |         |
| data 72-111     | grade 5, illiteracy rates                | Several |
| observations    | (t-stats\>2.71).                         | insig.  |
|                 |                                          | OLS     |
| ICRG data but   | Results become weaker and generally      | results |
| also repeat     | insignificant after controlling for      | (signs  |
| with Kaufmann   | income (repeater and dropout rates       | unre    |
| et al. Graft    | remain significant).                     | ported) |
| index           |                                          |         |
|                 | Results for dropout rates remain         |         |
|                 | significant after controlling for        |         |
|                 | various other variables, including       |         |
|                 | female education, public spending on     |         |
|                 | health and urbanization. Result holds in |         |
|                 | instrument varialbe (IV) estimation      |         |
|                 | using Treisman's equation to predict     |         |
|                 | corruption. Results don't hold in        |         |
|                 | multivariate analysis for other          |         |
|                 | education variables.                     |         |
+-----------------+------------------------------------------+---------+
| Rajkumar and    | Controlling for several variables        | Sig.    |
| Swaroop         | including female education, income,      | OLS     |
|                 | urbanization, and distance from the      | r       |
| ICRG data.      | equator they show that public education  | esults. |
|                 | spending has a greater effect on         | (But    |
| Two year (1990  | repeater (failure) rates the higher is   | Azfar   |
| and 1997) panel | the quality of government --measured     | and Lee |
| data            | both as the absence of corruption and    | could   |
|                 | the quality of the bureaucracy. Results  | not     |
| 148-169         | hold in an IV estimation using           | rep     |
| observations    | neighbouring countries' expenditures and | licate) |
|                 | governance quality as instruments.       |         |
+-----------------+------------------------------------------+---------+
| Azfar, Kähkönen | Controlling for income, voting, media    | One     |
| and Meagher     | exposure, inequality, urbanization and   | sig.    |
| 2001, Azfar and | social differences, Azfar et al. find    | OLS     |
| Gurgur 2001     | that corruption is significantly related | result  |
|                 | to satisfaction with education services  |         |
| Cross municipal | but only insignificantly with NEAT       | One     |
| data from the   | scores.                                  | insig.  |
| Philippines.    |                                          | OLS     |
| Corruption data |                                          | result  |
| based on        |                                          |         |
| education       |                                          |         |
| officials       |                                          |         |
| responses.      |                                          |         |
|                 |                                          |         |
| 44              |                                          |         |
| municipalities  |                                          |         |
| for NEAT        |                                          |         |
| scores, 70 for  |                                          |         |
| satisfaction    |                                          |         |
| ratings (80     |                                          |         |
| sampled)        |                                          |         |
+-----------------+------------------------------------------+---------+

# 

Summary: four results are of the right sign; of these, three are
significant at 5%. One result is of the wrong sign.

The effect of corruption on education outcomes (t-stats below
coefficients)

+---------+----------+----------+----------+-------+-------+-------+
|         | Authors  | G, D & T | G, D & T | G, D  | R & S | A & L |
|         |          |          |          | & T   |       |       |
+---------+----------+----------+----------+-------+-------+-------+
|         | Es       | Un       | Co       | Contr | Contr | Contr |
|         | timation | ivariate | ntrolled | olled | olled | olled |
|         | method   | re       | for      | for   | for   | for   |
|         |          | gression | income   | in    | in    | in    |
|         |          |          |          | come, | come, | come, |
|         |          |          |          | f     | f     | f     |
|         |          |          |          | emale | emale | emale |
|         |          |          |          | ed.   | ed.   | ed.   |
|         |          |          |          | etc.  | etc.  | etc.  |
+---------+----------+----------+----------+-------+-------+-------+
|         |          | 3.1      | 3.2      | 3.3   | 3.4   | 3.5   |
+---------+----------+----------+----------+-------+-------+-------+
| De      | Ind      |          |          |       |       |       |
| pendant | ependent |          |          |       |       |       |
| v       | variable |          |          |       |       |       |
| ariable |          |          |          |       |       |       |
+---------+----------+----------+----------+-------+-------+-------+
| School  | Co       | -0.03    | 0.01     |       |       | 2.33  |
| enr     | rruption |          |          |       |       |       |
| ollment |          | (3.30)   | (0.82)   |       |       | (     |
|         |          |          |          |       |       | 0.81) |
+---------+----------+----------+----------+-------+-------+-------+
|         | Corr\*Ed |          |          |       |       | 0.25  |
|         | Exp.     |          |          |       |       |       |
|         |          |          |          |       |       | (     |
|         |          |          |          |       |       | 0.45) |
+---------+----------+----------+----------+-------+-------+-------+
| R       | Co       | 0.24     | 0.15     |       | 0.08  |       |
| epeater | rruption |          |          |       |       |       |
| rates   |          | (3.47)   | (2.16)   |       | (     |       |
|         |          |          |          |       | 0.58) |       |
+---------+----------+----------+----------+-------+-------+-------+
|         | Corr\*Ed |          |          |       | 0.43  |       |
|         | Exp.     |          |          |       |       |       |
|         |          |          |          |       | (     |       |
|         |          |          |          |       | 2.44) |       |
+---------+----------+----------+----------+-------+-------+-------+
| Dropout | Co       | 0.36     | 0.20     | 0.13  |       |       |
| rates   | rruption |          |          |       |       |       |
|         |          | (7.02)   | (3.39)   | (     |       |       |
|         |          |          |          | 2.00) |       |       |
+---------+----------+----------+----------+-------+-------+-------+
| Pers    | Co       | -0.04    | -0.00    |       |       |       |
| istence | rruption |          |          |       |       |       |
| to      |          | (4.30)   | (0.30)   |       |       |       |
| grade 5 |          |          |          |       |       |       |
+---------+----------+----------+----------+-------+-------+-------+
| Ill     | Co       | 0.23     | 0.11     |       |       | 1.209 |
| iteracy | rruption |          |          |       |       |       |
| rates   |          | (2.71)   | (1.34)   |       |       | (     |
|         |          |          |          |       |       | 0.39) |
+---------+----------+----------+----------+-------+-------+-------+
|         | Corr\*Ed |          |          |       |       | -     |
|         | Exp.     |          |          |       |       | 0.231 |
|         |          |          |          |       |       |       |
|         |          |          |          |       |       | (     |
|         |          |          |          |       |       | 0.35) |
+---------+----------+----------+----------+-------+-------+-------+

Regressions reported in columns 3.4 and 3.5 were run with both
corruption and the interaction of corruption and public expenditure as
right hand side variables.

G,D&T Gupta, Davoodi and Tiongson

R&S Rajkumar and Swaroop

A&L Azfar and Lee

#  **4. The effect of corruption on health outcomes.**

The same four papers (Gupta et al., Rajkumar and Swaroop, Azfar and
Gurgur and Azfar, Kähkönen and Meagher) also examine the effect of
corruption on health outcomes (see Table 4). These results are perhaps a
little clearer for health outcomes. One of the papers (Gupta et al.)
even offers panel data results on health outcomes.

Gupta, Davoodi and Tiongson (2000) show that levels of corruption are
clearly related to child mortality and other health outcomes. They find
highly significant relationships in univariate regressions between
corruption and all child mortality (under 5), infant mortality (under
1), births attended by health staff, immunization and low-birthweight
babies (the lowest t-statistic is 5.61, dependent variables are in
logs). Of course, this regression has a serious omitted variable problem
since poorer countries have higher levels of corruption. If, however, we
are interested in the total effect of corruption (broadly defined) on
health outcomes, and accept that differences in income are largely
created by differences in the quality of governance, then the
coefficient does allow an interpretation of improvements in the quality
of government on health outcomes. The coefficient on the log of child
mortality and infant mortality is 0.37 and 0.35, implying that a two
unit change in the corruption rating would halve child and infant
mortality. The coefficient on low-birthweight babies is significantly
lower at 0.14, implying that an increase of 2 points in the corruption
score would only lower the number of low-birthweight babies by 30%.

Gupta et al. also estimate the effect of corruption on health outcomes
after controlling for differences in income. The impact of corruption on
health outcomes now becomes significantly smaller, with the coefficients
falling by around 60%, though with the exception of immunization (t=1.4)
they remain significant. The coefficients of 0.13 and 0.14 on child and
infant mortality imply that a two-point improvement in the integrity of
government would reduce child or infant mortality by 40%. Azfar and Lee
have repeated these regressions and found these effects to be
significant as reported by Gupta et al.

In fact, several other variables, like female education, may also reduce
child mortality (Filmer and Pritchett 1999) and be related to the level
of corruption (Swamy et al.), so a more accurate assessment of the
impact of corruption on health outcomes would involve a multivariate
regression controlling for income, female education, urbanization and
other factors. Gupta et al. show that even controlling for per capita
income, female education, public health spending, the dependency ratio
and urbanization, corruption has a significant impact on child
mortality, though the coefficient falls by half. The coefficient of 0.07
indicates that a two-point improvement in the integrity of government
would reduce child mortality by 20%.

Gupta et al. also attempt an instrumental variable analysis to overcome
reverse causality problems---for instance, poor health status increasing
the bargaining power of health providers and hence the level of
corruption. It seems unlikely that the demand effects created by poor
health outcomes would really have an impact on corruption ratings by
international investment agencies, but Gupta et al. do find their
results hold even in instrumental variable analyses.

As with education, it is not clear whether the instrumental variable
analysis is more credible than the OLS regressions. As mentioned before,
the instruments they use are from Treisman's analysis of the causes of
corruption (2000). All of these could easily have a direct impact on
health outcomes, any one of which could create spurious results in the
instrumental variable analysis. Unitary governments may, for instance,
be better than decentralized governments at managing the cold chain for
vaccines. Gupta et al. do conduct the test of over-identifying
restrictions to test for this concern, but as mentioned above this test
is weak and unreliable in small samples and does not reliably
demonstrate that the instruments are well chosen.

A more serious endogeneity/omitted variable problem is suggested by the
recent work of Acemoglu, Johnson and Robinson (2001) and Easterly and
Levine (2002) who find that settler mortality is an important
determinant of institutional quality. The story, eloquently told, is
that settler mortality affected the character of colonization, with
European colonists settling in low mortality areas and only
expropriating from high mortality areas. Thus low mortality areas have
more Protestants, more democracy and less corruption. If one is to
believe this story then mortality causes corruption not vice versa. The
econometric problem is serious because some of the main causes of
mortality, like malaria, remain persistent to the present day. The
instrumental variable methods of Gupta et al. do not resolve this
concern, because the share of Protestants and democracy are also
endogenous to mortality. Controlling for settler mortality in the child
mortality regression seems one way of dealing with this problem. Azfar
and Lee (2002) ran this regression as a part of this project and found
that the relationship between corruption and child mortality does not
change substantially when settler mortality is included in the
regression.

Gupta et al. do conduct a panel data analysis which may allay these
concerns. They find that over time countries that improved their quality
of governance more also reduced child mortality by more. This appears
less likely to be affected by the omission of some core mortality risk
variable. However, some cautions are in order because child mortality
data is often imputed from neighbouring years, which can create a large
number of artificial observations that could create spurious
statistically significant relationships.

Rajkumar and Swaroop (2001) conduct an analysis of the impact of
corruption on the effectiveness of health expenditures much like their
analysis of education. They show that reducing corruption or improving
the quality of government improves health outcomes, largely by improving
the effectiveness of public expenditures. The intuition behind this is
that if public funds allocated to health are stolen, or diverted to less
productive uses, they are less likely to improve health outcomes.
Formally, Rajkumar and Swaroop do this by estimating an equation of the
determinants of child mortality using the following data from two years
1990 and 1997: per-capita GDP, the level of corruption, public health
spending, female education, income inequality, a dummy for predominantly
Muslim countries, ethno-linguistic fractionalization, access to safe
water, degree of urbanization, percentage of population under 5 and
distance from the equator. They also include a variable (corruption
rating × public health spending) which allows the impact of corruption
and public health spending to vary with the level of the other variable.
Their results show that while public health expenditure would have no
perceptible impact on child mortality if the integrity rating was 2, it
would have a significant and meaningful effect in the integrity rating
was 4. The implied coefficient at the higher integrity level of 0.22
indicates that a doubling of public expenditure would reduce child
mortality by 30%. The results for infant mortality are similar.

Mauro (1998) has raised the concern that corruption may distort public
expenditure away from health and education. One interpretation of the
results of Rajkumar and Swaroop is that this may be rational, as public
health and education expenditure is less effective in more corrupt
countries. However, since all public and private expenditure could well
be less productive in misgoverned economies, no such inference is
possible.

What the results of Rajkumar and Swaroop do imply is that simply
increasing public health spending, as is increasingly common in the
conditionalities of the IMF and the World Bank, may not improve health
outcomes. Rather, it may be more effective---if more difficult---to
insist on reforming the quality of governance in the health and
education sectors simultaneously. In fact the Bank and the Fund do now
try to insist on improving governance in many countries. Both public
spending on health and education, and improvements in the quality off
governance will have a bigger effect on health and education outcomes,
the higher is the level of the other variable.

Azfar and Lee (2002) attempted to replicate these findings but were
unable to do so. The coefficient on the interaction term is clearly
insignificant (t\<1.00) and becomes negative and significant (!) if life
expectancy is used as a dependant variable. This could be driven by
differences in the data sets used by the two sets of authors (Azfar and
Lee use data from 1995 in addition to data from 1990 and 1997 used by
Rajkumar and Swaroop). We tentatively caution the reader that the
results of Rajkumar and Swaroop might not be robust.

These sorts of studies on a cross-national level have also been
conducted on a sub-national level. In a study of the Philippines, for
example, IRIS staff (Azfar, Kähkönen and Meagher 2001; Azfar, Gurgur,
Kähkönen, Lanyi and Meagher 2000; Azfar and Gurgur 2001) examined the
effect of corruption on health and education outcomes. They randomly
selected 4 municipalities each from 20 randomly selected provinces, for
a total of 80 municipalities, which allowed them to do a cross-municipal
analysis.

To measure corruption, they used responses to questions about specific
instances of corruption asked of public officials (stealing funds,
stealing equipment, taking bribes, buying jobs, getting paid and not
working), and general questions about corruption asked of municipal
officials and households. Corruption indices were constructed using
answers from these different questions.

The data were cleaned of respondent bias by using answers to a question
about national corruption: since national corruption is the same for
everyone in the sample, different ratings for national corruption must
almost by definition reflect respondent bias, and these differences can
be used to filter responses about local corruption. This was an
important step, as respondent bias seems to account for 15% of the
differences in perceptions of municipal corruption.

The next step was conducting consistency checks on the data, which
generally check whether municipalities deemed highly corrupt by one set
of respondents are also rated highly corrupt by other respondents.
Reassuringly, the data passed all consistency checks and Azfar et al.
moved to the next step of the analysis where they examined the effect of
these corruption measures on several health outcomes. As measures of
health outcomes, they used knowledge of required immunizations by health
officials, an index of satisfaction ratings and waiting times from
households, and reports on increases in immunizations and decreases in
diseases from health officials. In each regression they used different
sources of data for the dependent and independent variables to minimize
the effect of any respondent bias that was not already filtered out.

Their strongest finding was a significant and clear effect of corruption
on the knowledge of required immunizations by physicians, controlling
for income levels, voting rates, media exposure, delays in salary
payments and the supply of medicines. The estimated the impact of
corruption on satisfaction ratings and waiting times at clinics. Here
they found that the expected effects for either dependent variable was
in the right direction but not statistically significant. However, a
variable combining waiting times and satisfaction ratings was
significantly predicted by public officials perceptions of corruption
(at 10% significance). Finally, with regard to increases in
immunizations and reductions in diseases, the corruption variable had
the right sign but was only significant (at 10%) for certain
regressions. Nevertheless, since the knowledge of required immunizations
had a clear and significant impact on the outcomes, it appears that
corruption does undermine the delivery of health and education in the
Philippines. Figure 2 below summarizes these results.[^7]

\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_

**Figure 2. The Consequences of Corruption in the Philippines (Azfar et
al.)**

######## **Corruption**

Knowledge of required immunizations

Satisfaction and waiting time

Health performance

Solid lines significant results

Dashed lines marginally significant or insignificant results

\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_

This cross-district study supplements similar findings at the
cross-national level (Gupta et al.) in several important ways. First, it
uses independent data focusing on one country rather than looking across
countries, and two results that resonate are better than one. Second,
the data were collected in a systematic and uniform way, which generally
is not done at the cross-country level (few surveys which systematically
ask the same question in a large number of countries). Third, it was
possible to do a number of reliability checks on the data, which can be
done at the cross-country level, as Transparency International does, but
only at the costs of truncated samples and selection bias. Fourth, they
were able to clean the corruption data of respondent bias, which cannot
easily be done at the cross-national level. Finally, for at least some
of the analysis, they were able to run regressions on almost the entire
sample, which mitigates concerns about selection bias.

Thus sub-national analyses, if properly conducted, can respond to
questions about the quality and comparability of the data which plague
cross-national analyses. Furthermore, if there are serious concerns
about omitted variables in cross-country regressions, it may be possible
to collect data on these variables at the sub-national level. As
decentralization proceeds around the world, an increasing number of
countries have meaningful cross-jurisdictional variation in
institutions, and this method can be effectively used to evaluate the
effects of institutions on performance in many countries. Indeed, a
number of economists are now conducting analyses using
cross-jurisdictional data at the sub-national level in Indonesia and
other countries.

**Table 4 The effect of corruption on health outcomes**

+-----------------+------------------------------------------+---------+
| **Authors and   | **Results**                              | **      |
| data**          |                                          | Signifi |
|                 |                                          | cance** |
+-----------------+------------------------------------------+---------+
| Gupta, Davoodi  | Clear univariate relationships between   | Sig.    |
| and Tiongson    | corruption and various health outcomes:  | OLS     |
|                 | child mortality, infant mortality,       | results |
| Cross country   | attended births, immunizations and low   |         |
| data 89-116     | birthweight babies (t-stats\>5.6).       | Some    |
| observations    |                                          | insig.  |
|                 | Results become weaker but generally      | R       |
| ICRG data but   | remain significant after controlling for | esults. |
| also repeat     | income.                                  | Signs   |
| with Kaufmann   |                                          | unr     |
| et al. Graft    | Results for child mortality remain       | eported |
| index           | significant after controlling for        |         |
|                 | various other variables including female | Sig.    |
| Panel data      | education, public spending on health and | panel   |
| 204-468         | urbanization. Result holds in IV         | results |
| country-year    | estimation using Treisman's equation to  |         |
| observations    | predict corruption. They state that the  |         |
| for 1985-1997   | results also hold for infant mortality   |         |
|                 | and low birth weight babies but don't    |         |
|                 | present the results.                     |         |
|                 |                                          |         |
|                 | Panel data results show that corruption  |         |
|                 | affects child mortality (both random     |         |
|                 | effects and fixed effects) and infant    |         |
|                 | mortality (only random effects) but not  |         |
|                 | infant mortality.                        |         |
+-----------------+------------------------------------------+---------+
| Rajkumar and    | Controlling for several variables        | Sig.    |
| Swaroop         | including female education, income,      | OLS     |
|                 | urbanization, and distance from the      | results |
| ICRG data.      | equator they show that public health     | (But    |
|                 | spending has a greater effect on child   | Azfar   |
| Two year (1990  | and infant mortality the higher is the   | and Lee |
| and 1997) panel | quality of government --measured both as | could   |
| data            | the absence of corruption and the        | not     |
|                 | quality of the bureaucracy. Results hold | rep     |
| 148-169         | in a IV estimation using neighbouring    | licate) |
| observations    | countries' expenditures and governance   |         |
|                 | quality as instruments.                  |         |
+-----------------+------------------------------------------+---------+
| Azfar, Kähkönen | Controlling for income, voting, media    | Sig.    |
| and Meagher     | exposure, and other variables find that  | result  |
| 2001, Azfar and | corruption is significantly related to   |         |
| Gurgur 2001     | knowledge of required immunizations and  | Insign  |
|                 | that knowledge of required immunizations | ificant |
| Cross municipal | is strongly related to immunizations and |         |
| and cross       | disease incidence.                       | /mar    |
| clinic data     |                                          | ginally |
| from the        | Effect of corruption on satisfaction     |         |
| Philippines.    | with health services, waiting times is   | sig.    |
| Corruption data | of the expected sign but not quite       | result  |
| based on        | significant (t-stats from 1.05-1.88).    |         |
| household and   |                                          | Insign  |
| public          | Direct effect on immunizations and       | ificant |
| officials       | disease incidence is also of the         |         |
| responses.      | expected sign but not quite significant  | /mar    |
|                 | (t-stats from 1.35-1.90).                | ginally |
| 127-133 clinics |                                          |         |
| (160 sampled    |                                          | sig.    |
| some lost dues  |                                          | result  |
| to missing      |                                          |         |
| variables) from |                                          |         |
| 78              |                                          |         |
| municipalities  |                                          |         |
| (80 sampled)    |                                          |         |
+-----------------+------------------------------------------+---------+

Summary: Six results have the right sign; of these, four are significant
at the 5% level.

## 

## 

## 

## 

## Table 4 B

The effect of corruption on health outcomes

+-----------+----------+---------+---------+--------+--------+--------+
|           | Authors  | G, D &  | G, D &  | G, D & | R & S  | A & L  |
|           |          | T       | T       | T      |        |        |
+-----------+----------+---------+---------+--------+--------+--------+
|           | Es       | Uni     | Con     | Cont   | Cont   | Cont   |
|           | timation | variate | trolled | rolled | rolled | rolled |
|           | method   | reg     | for     | for    | for    | for    |
|           |          | ression | income  | i      | i      | i      |
|           |          |         |         | ncome, | ncome, | ncome, |
|           |          |         |         | female | female | female |
|           |          |         |         | ed.    | ed.    | ed.    |
|           |          |         |         | etc.   | etc.   | etc.   |
+-----------+----------+---------+---------+--------+--------+--------+
| Dependant | Ind      | 4.1     | 4.2     | 4.3    | 4.4    | 4.5    |
| variable  | ependent |         |         |        |        |        |
|           | variable |         |         |        |        |        |
+-----------+----------+---------+---------+--------+--------+--------+
| Child     | Co       | 0.37    | 0.13    | 0.07   | 0.04   | 0.08   |
| Mortality | rruption |         |         |        |        |        |
|           |          | (12.26) | 4.30    | 2.59   | 1.03   | 2.10   |
+-----------+----------+---------+---------+--------+--------+--------+
|           | Corr     |         |         |        | 0.07   | 0.002  |
|           | \*health |         |         |        |        |        |
|           | exp      |         |         |        | 2.36   | 0.22   |
+-----------+----------+---------+---------+--------+--------+--------+
| Infant    | Co       | 0.35    | 0.14    |        | 0.03   | 0.08   |
| Mortality | rruption |         |         |        |        |        |
|           |          | 12.72   | 5.14    |        | 0.91   | 1.45   |
+-----------+----------+---------+---------+--------+--------+--------+
|           | Corr     |         |         |        | 0.08   | 0.003  |
|           | \*health |         |         |        |        |        |
|           | exp      |         |         |        | 2.92   | 0.25   |
+-----------+----------+---------+---------+--------+--------+--------+
| Attended  | Co       | -0.13   | -0.05   |        |        |        |
| births    | rruption |         |         |        |        |        |
|           |          | 6.06    | 2.01    |        |        |        |
+-----------+----------+---------+---------+--------+--------+--------+
| Immu      | Co       | -0.06   | -0.02   |        |        |        |
| nizations | rruption |         |         |        |        |        |
|           |          | 5.61    | 1.40    |        |        |        |
+-----------+----------+---------+---------+--------+--------+--------+
| Low-bi    | Co       | 0.14    | 0.06    |        |        |        |
| rthweight | rruption |         |         |        |        |        |
| babies    |          | 7.20    | 2.87    |        |        |        |
+-----------+----------+---------+---------+--------+--------+--------+
| Life      | Co       |         |         |        |        | 0.018  |
| e         | rruption |         |         |        |        |        |
| xpectancy |          |         |         |        |        | 3.60   |
+-----------+----------+---------+---------+--------+--------+--------+
|           | Corr     |         |         |        |        | -0.004 |
|           | \*health |         |         |        |        |        |
|           | exp      |         |         |        |        | 2.00   |
+-----------+----------+---------+---------+--------+--------+--------+

Regressions reported in columns 4.4 and 4.5 were run with both
corruption and the interaction of corruption and public expenditure as
right hand side variables.

G,D&T Gupta, Davoodi and Tiongson

R&S Rajkumar and Swaroop

A&L Azfar and Lee

# **5. The causes of corruption in the health and education sectors**

Several studies including Azfar et al., Kaufmann, Mehrez and Gurgur
(2002) and five papers on hospitals in Latin America have examined the
causes of corruption in the health sector. The literature on the
determinants of corruption in the education sector is far more sparse.
According to Klitgaard's formula, the causes of corruption may be broken
down as follows:

Corruption = Monopoly + Discretion - Accountability[^8]

We will discuss the findings of the various studies according to this
framework.

Azfar and Gurgur and Azfar, Kähkönen and Meagher find that the level of
corruption in Filipino municipal governments is lower in municipalities
where voting rates are higher. Thus political accountability does appear
to reduce corruption. Using exposure to the media as a proxy for
transparency, the authors find that only exposure to national newspapers
has an effect. Managerial accountability or transparency measured by the
frequency of audits and evaluations appears to have no effect. Monopoly
(or rather competition), proxied by the willingness of the population to
migrate if health services are poor, has no perceptible effect.
Urbanization does appear to have an effect, which may be due to
competition from other providers, but certainly other explanations are
possible and even likely. Discretion does have an effect, though it is
only significant in one regression and only at the 10% level. Wage
effects measured by delays in salary payments are also insignificant.

Kaufmann, Mehrez and Gurgur (hence KMG) conduct a micro-analytic
investigation of corruption and integrity-vulnerability in Bolivia. They
collect data on corruption, service delivery, transparency, meritocracy,
voice and other variables from 110 public institutions in Bolivia. The
institutions include the top executive agencies, ministries, line
agencies (e.g. tax and customs), autonomous agencies (e.g. central
bank), departmental institutions and (16) municipal governments. They
lose 21 observations due to missing variables and run their regressions
with 89 observations.

Their data consists of the following variables: service performance,
bribery, corruption, transparency, enforcement, meritocracy,
politicization, autonomous agency dummy, resource adequacy, values,
voice, education, wage satisfaction -the paper also includes the various
questions on which the answers to each variable are based.

Their empirical analysis has two sets of results on most interest to us:

First, bribery and a broader measure of corruption each appear to have a
negative effect on service delivery

Second transparency and meritocracy appear to reduce corruption, while
politicization appears to increase it. Government wages have a
marginally significant effect (at 15%) on reducing corruption.

One important concern with such an analysis is that different ministries
and agencies fulfill very different functions and therefore there can be
serious omitted variable biases in the estimation of the causes and
consequences of corruption.

There are of course important causality issues relating almost any
governance variable to almost any other. KMG try to resolve this concern
by performing instrumental variable analysis (they try both 2 stage and
3 stage least squares). As usual the believability of such instrumental
variable results depends on the validity of the instruments (i.e.,
identifying restrictions). For instance they appear to assume that the
education levels of public officials could only affect bribery,
corruption or the quality of service delivery by increasing the level of
transparency. If --as is possible- education levels had a direct effect
on corruption or service delivery, their instrumental variable results
would not be valid.

One useful extension of their work would be a repetition of this
estimation methodology but with a merged data set from many countries.
This exercise which would allow controls for various agencies would
reduce concerns about omitted variables and the larger data sets may
allow reasonable tests to be conducted on the identifying restrictions.

A recent book edited by Di Tella and Savedoff represents an important
effort in the study of corruption in the health sector. The book has
several advantages: First, it examines the incidence of corruption at
the micro level---i.e., the level of the hospital. This is relatively
innovative, though as discussed above, it has also been attempted by
Azfar et al. Second, it tries to examine "hard" data on corruption like
hospital prices and excessive Caesarian deliveries. Such "hard" data on
corruption like prices is useful for several reasons. First, it allows
researchers to check the data they get from subjective responses---which
is subject to different forms of biases. If the two variables are
correlated, this provides some reassurance that each is measuring
corruption---though there remains the concern that knowledge of higher
prices being paid by the hospital might be affecting the subjective
responses on corruption. Second, if similar results are found on the
causes or consequences of corruption using both hard and soft data, this
would provide the reader with some reassurance that a real rather than a
spurious relationship has been uncovered.

The paper on Bolivia by Gray-Molina, Perez de Rada and Yanez (2001)
examines corruption in 30 Bolivian hospitals. Gray-Molina et al. collect
data on corruption perceptions; informal payments from patients, nurses
and doctors; and procurement prices. They find that the data on informal
payments and corruption perceptions are highly correlated. However, data
on corruption perceptions are only weakly correlated with price data.
Looking at their graph it appears that there would be a relatively
strong correlation but for one outlier---Copacabana. This highlights one
drawback of the various studies in the book. The sample of hospitals
ranging from 20-35 is simply too small in each country to allow for any
reasonable econometrics to be conducted and is small enough that one
outlier can negate or create statistically significant effects. The
other drawback in this particular comparison of hard and soft data is
that the soft data on perceptions did not ask about corruption
perceptions in that particular hospital; rather, it asked about
corruption perception in public hospitals. While a person's perceptions
about corruption in public hospitals are likely to be influenced by
experience in his own hospital, they may also be influenced by other
factors which would reduce the correlation between the perception
questions and the procurement prices. In fact, the best questions may
focus on perceptions about corruption in the procurement department of
the hospital and compare the responses to the procurement prices.

There may also be other ways to collect information on procurement. If
researchers find evidence that certain suppliers do not supply
pharmaceuticals to the more corrupt hospitals, they can ask these firms
why they do not bid. It may be informative if these firms
respond---without being prompted!---that kickbacks are routinely
required at these hospitals. Performing such analyses is likely to shed
more light on the value of collecting "hard" data on corruption. Such
work might be attempted in the IRIS-USAID project on corruption in East
Europe.

Gray-Molina go on to analyze the causes of corruption using two
corruption variables, informal payments and input prices. For both input
prices and informal payments they find the activism of the DILOS (a
participatory planning and monitoring board consisting of community
members, municipal officials and prefectural health officials) reduces
corruption. This can be thought of as an accountability variable, which
they call "voice". The presence of a personnel supervision system also
has the expected sign, but the effect is not quite significant---as
noted before, the small sample sizes make meaningful statistical
analyses difficult. They also find that the presence of a private
hospital is related to lower levels of informal payments, indicating
that competition does reduce corruption.

Jaen and Paravinski (2001) look at the effect of wages and penalties on
corruption in Venezuelan hospitals. They also use hard and soft data for
their analysis but do not present a comparison of hard and soft data
like Gray-Molina et al.

Jaen and Paravinski find that wages have a perverse effect on
corruption---higher wages are correlated with more corruption
(procurement prices are higher in hospitals with higher wages). They
interpret this as evidence of capture of wage setting by corrupt wage
earners. The mechanisms by which corruption would raise prices and wages
are quite similar. The wage earner or the seller would bribe---or
perhaps influence---the buyer into paying a higher price for goods or
services. Hospitals susceptible to one practice may well be susceptible
to the other. In fact, the sale of jobs is a widespread practice in
developing countries and reformers attempting to reduce corruption by
raising wages should be aware that this practice might undermine the
effectiveness of reform. Higher wages can reduce corruption for two
reasons: first, a public servant with a higher wage can more easily
afford to be honest (because the marginal utility of money is lower),
and second, dismissal carries larger costs in terms of foregone wages
(this has clear parallels in the economic theory of crime, Becker 1968,
Ehrlich and Becker 1973). The sale of jobs would undermine the first
effect as the wage would get capitalized in the price of the job. We do
not know of any empirical study of this point, but it seems intuitively
plausible. (Such an empirical study may be possible soon after wage
reform.) The second effect depends on there being a non-trivial---though
not necessarily high---probability of detection and dismissal. Thus,
especially in places where corruption has become entrenched in the
practice of the sale of jobs, wage reform is only likely to be effective
if combined with a realistic probability of detention and dismissal.

In fact Jaen and Paravinski do find that greater accountability reduces
two forms of corruption: theft and unjustified absenteeism. Their
results on absenteeism, however, suggest an important caution. In
hospitals with higher detection rates there was less unjustified
absenteeism but more justified absenteeism. Raising the probability of
detection for unjustified absences may simply induce people to find
better justifications for being absent. In general terms, giving
incentives based on one outcome is likely to improve performance on that
particular outcome without necessarily improving overall service
delivery; this is a broad implication of the multi-tasking literature.
See Holmstrom and Milgrom 1991, and see Azfar 2002a for a survey. It
may, therefore, be more effective to base incentives on the ultimate
objective like improvements in service delivery than exclusively on
inputs like unjustified absences.

Schargrodsky, Mera and Weinschelbaum (2001) in a study of Argentinian
hospitals find that prices did fall following enactment of a law on
sending price information to the center but that prices rose after
agents realized that the price information was not being used for
sanctions. This reminds us of a theoretically clear postulate: raising
the probability of detection, or the size of salaries is only going to
deter corruption if sanctions are actually implemented.

Alcazar and Andrande (2001) find interesting if disturbing evidence of
another form of corruption in Peruvian hospitals---induced demand for
Caesarian operations. The medically required number of Caesarians is
around 10% in an average population, but all four hospitals they
examined conducted significantly more Caesarians than 10% (the numbers
varied from 21 to 78%). Alcazar and Andrande also found significant
evidence of increases in Caesarian deliveries before weekends and
holidays, signaling that doctor's convenience rather than patient's need
was driving medical decisions. In fact, most of these Caesarian
deliveries took place in private hospitals, raising some cautions about
the possibility of greater corruption in the private sector. Private
ownership and the appropriability of profits creates incentives, and
these incentives may well create some more effective treatments---which
patients can easily evaluate---and a better bedside manner, but they
also create incentives for doctors over-prescribing their own or the
hospital's services. As a general point this is another example of the
possible perverse effects of incentives that we encountered above in the
discussion of absenteeism in Venezuelan hospitals.

Like procurement prices, this paper demonstrates the possibility of
detecting corruption---or gross negligence and malpractice---by looking
at large numbers of events when any one event would allow no inference.
Since no one piece of information implicates anyone the information may
be relatively easy to get---this would be especially true if even the
aggregate information could not implicate any one person. Still, the
aggregation of the information can allow relatively clear inferences of
the incidence of corruption. This principle could reasonably be applied
to detecting misgovernance in other spheres. For instance, it may be
worthwhile examining if the performance of students worsens when they
get a particular teacher. Of course, this does not necessarily imply
that there is "an abuse of office" taking place, but if the performance
is bad enough it may make sense to replace the teacher whether or not
she is abusing her office. Civil punishments starting from censures and
small fines all the way up to dismissal can fairly be used to improve
performance and will probably reduce corruption too. Of course, no
criminal charges are either possible or desirable without clear evidence
of the abuse of office (Azfar 2002b).

Alcazar and Andrande also found that doctors on fixed terms who can be
disciplined more easily than permanent staff are more careful about
prescribing Caesarians. This is further evidence that accountability
matters for reducing corruption.

Gideon, Morales and Acosta (2001) conduct an analysis of variations in
purchase prices across 32 Bogota hospitals. Like Alcazar and Andrande,
they find a relationship between nonpermanent staff and integrity. They
find hospitals with more non-permanent staff pay lower prices. The
mechanism is frankly not that clear: it would be better to examine the
effect on prices of the purchasing officer having non-permanent
employment status. They do find that hospitals with female purchase
managers pay lower prices for purchases. This resonates with the results
of Swamy et al. (2001) and Knack and Azfar (2002), who show that women
disapprove more of corruption than men in most countries and that
countries with more women in parliament have lower levels of corruption.

Gideon et al. find that the existence of formal hierarchical controls
increases purchase prices. This is not entirely surprising. Hierarchical
controls can create a vertical chain where each member must be paid a
bribe, which can increase prices. In fact this is one off the reasons
cited for the harmfulness of corruption in Shleifer and Vishny's (1993)
classic article on the subject.

I found one study which examined the effect of corruption on the
education sector -- An evaluation of El Salvador's EDUCO program by
Jimenez and Sawada. Jimenez and Sawada find that being in a community
program appears to reduce teachers absences but the effect becomes
insignificant when school effects are controlled for. The number of
visits to the school however does appear to reduce absences.

**\
**

**Table 5. The causes of corruption:
Corruption=Monopoly+Discretion-Accountability**

  ------------------------ ------------------- ---------------------------
  **Monopoly**             **Discretion**      **Accountability**

  **Cross-country                              
  results**                                    

                                               Treisman finds that
                                               continuous democracy deters
                                               corruption across
                                               countries. Lederman et al.
                                               Corroborate this finding
                                               for a panel data analysis.

                                               Weder and van Rijkehem find
                                               that higher wages are
                                               correlated with less
                                               corruption. Rauch and Evans
                                               and Lederman et al find
                                               this result is not robust

  Ades and Di Tella find                       
  that more open                               
  economies---which may                        
  face more                                    
  competition---are less                       
  corrupt. Knack and Azfar                     
  show these results may                       
  be driven by sample                          
  selection.                                   

  **Cross-municipality                         
  results from the                             
  Philippines**                                

  Azfar et al. find that   Azfar et al. find   Azfar et al. find that
  citizens' willingness to that discretion is  voting rates in local
  leave the municipality   weakly correlated   elections are correlated
  has no effect on         with corruption     with less corruption.
  corruption               across Philippine   However the frequency of
                           municipalities      audits and evaluations has
                                               no perceptible effect.

  **Results from Latin                         
  American hospitals**                         

  Gray-Molina et al. show                      Gray-Molina et al. show
  that the presence of a                       that the activism of the
  private hospital in the                      oversight board (DILOS) is
  city is correlated with                      correlated with lower
  lower corruption in                          corruption in Bolivian
  Bolivian hospitals.                          hospitals

                           Jaen and Paravinski Jaen and Paravinski show
                           find more           that higher probabilities
                           corruption in       of detection are correlated
                           larger hospitals.   with lower corruption
                                               (theft and absenteeism) in
                                               Venezuelan hospitals

                                               Andrande and Alcazar find
                                               that physicians without
                                               permanent contracts (who
                                               can be disciplined more
                                               easily) overprescribe
                                               Caesarians less often in
                                               Peruvian hospitals.

                                               Gideon et al. find that
                                               hospitals with more
                                               non-permanent staff pay
                                               less for purchases.

  **Experimental results**                     

                           Azfar and Nelson    Azfar and Nelson find that
                           find that in        in an experimental setting
                           experimental        increases in wages and the
                           settings higher     probability of detection do
                           public revenues are significantly reduce
                           correlated with     corruption.
                           more corruption.    
  ------------------------ ------------------- ---------------------------

#  

# **6. Emerging data sets and ongoing studies.**

As mentioned earlier the most promising avenue of new research on the
effect of corruption (and more generally misgovernance and management
practices) on service delivery is the collection of primary data at the
local government or service delivery (school, clinic, university,
hospital) level and the analysis of this data. The World Bank is
undertaking a number of such studies in several countries[^9].

In Indonesia the World Bank sponsored a study that spanned across
approximately 150 of the 360+ kabupaten approximately one year after the
large scale devolution of authority to the kabupaten in January 2001.
The surveys covered households (\~50/kabupaten), firms (\~20 per
kabupaten) and various branches of government including the health and
education departments. The surveys include explicit questions about
corruption. The data has only recently been collected and no results are
available for review, but preliminary results are expected soon.

*Quantitative Service Delivery Surveys (QSDS)*

A World Bank team led by Ritva Reinikka is leading a set of
"Quantitative Service Delivery Surveys" (QSDS) in Chad, Madagascar,
Mozambique, Nigeria and Uganda. Several other people at the World Bank
are working on a number of other countries (listed in table 7 below).
The Bank and collaborating institutions will produce survey instruments,
data sets, diagnostic reports and research and policy-oriented results.
These are surveys of frontline service providers (i.e., schools and
clinics). The surveys have been completed in Uganda, Zambia and Papua
New Guinea but results are not yet available. In the other countries the
data are still being collected.

The architects of these studies have various aims: first, they hope to
uncover evidence of actual management practices in the frontline
service-providing institutions, including information on some form of
corruption**.** Second, they hope to link these management practices to
"upstream" administrative and political structure, in order to
understand the causes of poor management practices. This may allow some
analysis of the causes of corruption, as carried out by Azfar et al. and
the various chapters in Di Tella and Savedoff. Third, they want to
examine the impact of these management practices on corruption and the
quality of service delivery. This they hope to do by merging data from
users and providers at the clinic level, along the lines of work by
Azfar et al. and some of the authors of the papers in Di Tella and
Savedoff. One set of users will be surveyed as they exit the service
center. There are potentially important selection issues here, as those
with the best information about shortfalls in service delivery might not
use the services. This concern may be mitigated by the surveys of
private and not-for-profit service providers, where the surveyors may
find users avoiding public services. In some countries, including Chad
and Zambia, the various authors will merge the facility data with
household surveys. This seems the most promising way of getting
selection-free data.

These studies would thus, in principle, be able to examine the causes
and consequences of corruption in the health and education sectors.
However, we should raise an important caution. Azfar et al. conducted
their surveys in Uganda and the Philippines. In both countries they
performed a number of reliability tests on the data. They were only able
to conduct their analysis of the causes and consequences of corruption
in the Philippines, because the data on corruption did not pass some of
the reliability checks in Uganda. Data on corruption is presumptively
suspect and should only be used after it passes reliability checks. For
example, consider a country where respondents are so afraid in the most
misgoverned districts that they feign ignorance of corruption.
Corruption rankings of the various districts in this country based on
these responses would rank the most misgoverned districts as the most
honest. Any subsequent analysis of the data could easily find that
corruption improved health outcomes when in fact it worsened outcomes.
In fact, the reliability tests conducted by Azfar et al. would not
reject use of the data if all respondents were misreporting in identical
ways, and there is an on-going IRIS-USAID effort to improve both the
quality of corruption measures and the strength of the reliability
tests.

Reinikka and colleagues also hope to compare management practices in the
public, private and not-for-profit sectors. This is a welcome step and
could allow comparisons of management practices and misgovernance across
sectors. Perhaps subsequent to the study, policy prescriptions on
privatization and other policy approaches will be made based on
(admittedly imperfect) evidence rather than on presumptive beliefs.
Still, cautions are in order. Even if less corruption is uncovered in
private schools, this does not mean that there will be less corruption
in [publicly financed]{.underline} private schools. Corruption, which
often occurs on the boundaries of the public and private spheres, might
emerge in the form of ghost schools that process vouchers without
providing education.

*Public Expenditure Tracking Surveys (PETS)*

A related effort being undertaken by the World Bank is on public
expenditure tracking surveys (PETS). These were pioneered by Ritva
Reinikka and Jakob Svensson in Uganda and are now being implemented in
several countries. Public Expenditure Tracking Surveys track public
expenditures down the chain from the central government, to the
provincial government, to the district government, to the subdistrict
governments all the way to delivery points (i.e., schools and
clinics---though there is no fundamental reason why the surveys should
be restricted to health and education services). If one level of
government reports distributing more funds than the level below
receives, it may be possible to pinpoint the leakage (by theft or
diversion) of public funds.

Reinikka and Svensson found that 87% of non-wage expenditures allocated
to education in Uganda were leaked in the transfer from districts to
schools over the period 1991-1995 (though the leakage steadily declined
over the period). In Tanzania, Price-Waterhouse-Coopers found that 57%
of non-wage expenditure in education and 41% in health was leaked before
reaching delivery points. In Ghana 50% of non-wage expenditures in
education and 80% in education were similarly lost. While reported
leakage was lower in wage expenditures (20% in both Uganda and Ghana),
this may simply be an artifact of accounting. Leakage in "wage"
expenditures may consist of people getting paid but not working, which
would not necessarily show up in a purely expenditure tracking survey. A
study to track such absenteeism was conducted in Honduras and found that
there was substantial absenteeism---14% in education and 27% in health.
Adding these figures to the observed leakage of around 20% in wage
expenditures brings the total leakage closer to that in non-wage
expenditures.

**Table 6: Results from Public Expenditure Tracking Surveys.**

**Percentage leakage of public funds.**

+-----------+-----------+-----------+-----------+-----------+-----------+
| **        | **Year**  | *         | *         | **Ed      | **Ed      |
| Country** |           | *Health** | *Health** | ucation** | ucation** |
|           |           |           |           |           |           |
|           |           | **Wage**  | **non     | **wage**  | **non     |
|           |           |           | wage**    |           | wage**    |
+-----------+-----------+-----------+-----------+-----------+-----------+
| Uganda    | 1993      | \-        | \-        | 20        | 85        |
+-----------+-----------+-----------+-----------+-----------+-----------+
| Tanzania  | 1999      | \-        | 41        | \-        | 57        |
+-----------+-----------+-----------+-----------+-----------+-----------+
| Ghana     | 2000      | 20        | 80        | 20        | 50        |
+-----------+-----------+-----------+-----------+-----------+-----------+

Summarized from Reinikka and Svensson (Original sources Reinikka and
Svensson, Price-Waterhouse Coopers and World Bank)

One interesting question for reformers is whether PETS can reduce the
leakage of public funds. As with accounting systems, if the flow of
money is better tracked, it may make it more difficult for agents to
steal from principals.

Nevertheless, in our conversations about public expenditure tracking
with civil servants, we have been reminded that accounting systems can
also be rigged to hide theft---it just takes some effort to do so. As a
rule of thumb, one should expect the agent to be craftier than the
principal. In the immediate aftermath of implementing a public
expenditure tracking survey, theft may well decline. Business might,
however, return to usual if bureaucrats observe that sanctions are not
applied when discrepancies are found---or only applied selectively.
Recall the study of Argentinian hospitals by Schardorsky et al. (2001)
which found that procurement prices did drop in the immediate aftermath
of implementing the reporting system but reverted to near their original
level over time.

Over time, it is also likely that corrupt officials will find other ways
of siphoning off money, like paying more for procurement and getting
kickbacks. Money that was once stolen easily will now be stolen in
cleverer and subtler ways. (As a parallel case, one may recall Jaen and
Paravinski's study of absenteeism in Venezuela, where increasing the
probability of detection for unjustified absences reduced unjustified
absences but increased "justified" absences by almost as much.) A recent
study of Pre-Shipment Inspections on customs revenue in the Philippines
has also found that smugglers find other ways of avoiding duties and
tariff revenues do not increase.

For a PETS to be effective, it must be followed by a tracking of
procurement practices and other dimensions of public administration to
cover other avenues for corruption that become more lucrative when
expenditure tracking is introduced. Ultimately, it is unclear whether
all such avenues can be identified and blocked. The most effective
reform of the system may require a measurement of ultimate
outcomes---literacy rates and exam scores directly measured by
outsiders, vaccination rates measured by collecting and testing blood
samples from randomly selected individuals who have reportedly been
vaccinated, etc. Incentives based on such measures, which are difficult
to manipulate, may be more effective at reducing corruption than
measures that seek to reduce specific instances of corruption.

There may be some synergies from implementing PETS and QSDS at the same
time. PETS produces "hard" data on corruption. QSDS produces data on
management practices, outcomes and soft data on corruption. Taken
together, these surveys would provide reliability tests and examinations
of the causes and consequences of corruption. Indeed, they are being
undertaken together in many countries. Table 7 provides an incomplete
listing of ongoing studies.

Table 7. Ongoing quantitative service delivery surveys

+----------------+-------------+---------+-------------+-------------+
| **Country**    | **Sectors   | *       | **Merged    | **Expected  |
|                | covered     | *Number | with        | date for    |
|                | health      | of      | households  | circulation |
|                | and/or      | fac     | and         | of research |
|                | education** | ilities | upstream    | outputs**   |
|                |             | sur     | ins         |             |
|                |             | veyed** | titutions** |             |
+----------------+-------------+---------+-------------+-------------+
| Uganda         | Both        | 100-420 | Yes, some   | Some        |
|                |             |         |             | reports     |
| PETS and QSDS  |             |         |             | complete    |
+----------------+-------------+---------+-------------+-------------+
| Honduras PETS  | Both        | 25-35   | No          | 2003        |
+----------------+-------------+---------+-------------+-------------+
| Ghana PETS     | Both        | 200     | Yes         | Draft       |
|                |             |         |             | available   |
+----------------+-------------+---------+-------------+-------------+
| Rwanda PETS    | Both        | 351     | No          | Draft       |
|                |             |         |             | available   |
+----------------+-------------+---------+-------------+-------------+
| Madagascar     | Health      | \-      | No          | 2003        |
| QSDS           |             |         |             |             |
+----------------+-------------+---------+-------------+-------------+
| Tanzania PETS  | Both        | 35-45   | No          | Report      |
|                |             |         |             | available   |
+----------------+-------------+---------+-------------+-------------+
| Mozambique     | Health      | 120     | No          | 2003/4      |
| QSDS           |             |         |             |             |
+----------------+-------------+---------+-------------+-------------+
| Pakistan QSDS  | Education   | \-      | \-          | 2003/4      |
|                | (pvt.)      |         |             |             |
+----------------+-------------+---------+-------------+-------------+
| Laos QSDS      | Education   | \-      | \-          | 2003/4      |
+----------------+-------------+---------+-------------+-------------+
| Chad QSDS      | Health      | \-      | \-          | 2003/4      |
+----------------+-------------+---------+-------------+-------------+
| Nigeria QSDS   | Health      | \-      | \-          | 2003/4      |
+----------------+-------------+---------+-------------+-------------+
| Papua New      | Education   | \-      | \-          | 2003/4      |
| Guinea QSDS    |             |         |             |             |
+----------------+-------------+---------+-------------+-------------+
| Zambia         | Education   | \-      | \-          | 2003/4      |
+----------------+-------------+---------+-------------+-------------+

Adapted from Dehn, Reinikka and Svensson 2002 "Survey tools for
assessing service delivery"

The following two web-sites are useful for readers who want to find out
more about the quantitative service delivery surveys and the public
expenditure tracking surveys

<http://www.worldbank.org/research/projects/publicspending/tools/survey.tools.for.assessing.sd.june2002.pdf>

<http://www.worldbank.org/research/projects/publicspending/tools/newtools.htm>

# 

# **7. Recommendations**

What does our imperfect state of knowledge allow us to recommend to the
policy maker? While not persuasive on either account, the evidence does
suggest that various forms of accountability might reduce corruption and
that corruption undermines health and education outcomes. Therefore,
better accountability may indirectly improve health and education
outcomes.

Starting with reforms that reduce corruption itself, we recommend that
policy makers make educated guesses about various reforms and try these
out in pilot studies and then adopt the more successful ones. For
instance if the aim is to reduce teachers' absenteeism and improve
student performance, policy makers may want to try out two reforms:
fines for unjustified absences and rewards for good performance of
students on tests. A pilot study may divide 200 schools into four groups
50 who get no reforms, 50 where fines are implemented, 50 where rewards
for good performance are implemented, and 50 where both reforms are
tried. Such prospective random evaluations are described in more detail
in Kremer (2002) and Azfar and Zinnes (2002).

Data should be collected on several outcome variables. Unjustified
absences by teachers, total absences by teachers, number of times
teachers are late for school, students' performance on the tests being
used for the rewards, students' performance on other tests, students'
satisfaction with teachers, parents' satisfaction with the school. The
tests should be carried out by external evaluators not the teachers
themselves. An evaluation of these outcome variables before and after
the trial period in the four groups of schools would allow an assessment
of which of the reforms was more successful and whether there was any
synergy between the reforms (i.e., whether fines for absences were more
or less effective if rewards for student performance were also being
given). In general, it is likely that incentives based on outcomes (like
test scores) would do better than punishments for narrowly defined
misdemeanors like unjustified absences (which might just lead to more
resources being expended on finding justifications for absences).

A certain amount of unpredictability in the evaluation procedures may
also be helpful in improving performance. If the evaluation protocol is
too predictable, it can probably be manipulated by the service provider
to increase scores without actually improving service delivery. However,
too much unpredictability might just dishearten providers, who may stop
trying to do well. Designing an evaluation procedure is a bit like
setting an exam: the evaluator must be predictable enough to provide
incentives for providers, but unpredictable enough that the incentives
are for genuine improvements, not artificial ones.

Several different kinds of reforms can be tried in different contexts,
these include:

1: increasing media coverage of service delivery by training journalists
etc.

2: making public officials aware of preferences---this is likely to be
more effective for education which appears to be more widely demanded
than health services (Azfar et al.)

3: improving the transparency of financial flows (e.g. PETS)

4: requiring public declarations of the assets of public officials

5: imposing fines and suspensions for consequences which probably result
from corruption or mismanagement

6: surveying users about the quality of service delivery and rewarding
service providers on the basis of these reports

7: more ambitiously users could be scientifically sampled and
re-examined---in the case of education by asking students to take new
tests, and in the case of vaccinations by collecting blood sampled to
examine whether the antigens were in fact present (it may be easier
though a little less effective to sample vaccines randomly which might
show if the vaccines had been diluted).

8: setting up competitions between different delivery points and local
governments for improved performance

9: privatization or "NGOisation" of service delivery

10: reduction in staff and an increase in wages

11: requiring detailed documentations of public procurements, how bids
were solicited, which ones were received and the basis on which the
award was made

12: implementing an IT system to track multiple prescriptions, shadow
patients etc.

13: implementing a system of statistical forensics that examines exam
scores to detect cheating and collusion of students with teachers

14: introducing standardized treatment guidelines

15: increasing community oversight of schools and clinics

In all cases it is important for there to be a setup to evaluate the
reforms, best done with foreknowledge of the reform even if
randomization is politically or administratively infeasible. In none of
the cases above can we be confident *a priori* that the reform will
reduce corruption or improve performance. For instance, standardized
treatment guidelines could result in inappropriate and inflexible
treatments; private or NGO suppliers could be as venal as public
officials; increased wages may be dissipated in higher prices paid for
jobs; and fines and suspensions might be politically applied and result
in the outflow of some of the best practitioners.

The widespread decentralization of authority in many countries might
allow policy makers to learn which reforms work. In the United States,
one of the main arguments for decentralization is experimental
federalism, the idea that states try "experiments" which if successful
are emulated by other states. This relies on the ability of local
governments to learn from each other, which is sadly absent in many
developing countries, either because there is insufficient analytical
capacity, or because there is insufficient media attentiveness and
coverage of the programs. Creating the capacity to do such evaluations
of reforms in developing countries will be an important step in learning
about which reforms actually work.

Identifying the impact of corruption on health and education outcomes
requires larger and more rigorous studies of the kind attempted by Azfar
et al. Establishing the negative effects of corruption on health and
education outcomes is important not only as a scientific question, but
also important for policy makers to widen and solidify the political
support for anti-corruption reforms. Azfar et al. were hampered by
budgetary restrictions which forced them to interview only 14 households
in each municipality, and only one public official in each department,
school or clinic. In addition they had to rely on survey based data and
could not conduct interviews etc. in the 80 municipalities to collect
other kinds of data. The consequence of these restrictions was that
there was a fair amount of noise in the data. If such a study was
replicated with larger samples interviewed in each municipality, more
detailed questions about corruption in the various questionnaires, and
perhaps more importantly the use of non-survey methods like in-depth
interviews, and data on prices paid and vaccinations received, then it
is possible that clearer results could be found on the links between
corruption and service delivery. An IRIS/USAID project in formerly
communist countries may attempt such a protocol.

# **Conclusions**

This paper reviewed evidence on the nature of corruption in the health
and education sectors, evidence on the extent to which corruption
undermines the delivery of health and education, and, finally, evidence
on the determinants of corruption in these sectors. The evidence seems
to suggest that corruption does in fact undermine the delivery of health
and education services and that increasing accountability reduces
corruption. The first of these conclusions can be stated with more
confidence than the second.

Where does this leave the policy maker? My basic recommendation is that
various reforms be tried out in scientifically rigorous pilot projects
and the more successful of those reforms be widely implemented. If
advice must be given without the benefit of such experience, I would
propose the alignment of incentives of those delivering services with
broad measures of outcomes, like test scores and vaccination rates
(which should be measured by people other than those delivering
services). Evidence from private sector studies in other contexts
suggests that such measures might improve outcomes. They are likely to
improve the effectiveness of service delivery. They may well reduce
corruption too.

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# **Appendix A. Empirical Methods for the Analysis of Corruption and Governance**

This section is adapted from "The NIE approach to economic development:
An analytical primer" by Omar Azfar (2002).

There are four main categories of empirical methods

1: cross-country empirics

2: micro-empirics

3: experiments

4: narratives

*A.1 Cross-Country Regressions*

The first of these methods cross country empirics is important in
identifying the impact of institutional variation on socio-economic
outcomes. Many important institutions only vary across countries; or,
equally problematically, data is available only at the cross-national
level. Hence cross-country analysis, which allows the identifying
patterns between institutions and economic performance, is an important
component of evaluating any hypothesis.

Cross-country empirics is replete with methodological problems and some
eminent economists like Abhijit Bannerjee believe it is of very limited
value. The most important of these problems are poor data, selection
biases and inferring causality, which we will discuss below.

But before turning to the problems and limitations of cross-country
econometrics, it is worth noting its advantages. We have already
mentioned the first advantage above---that it allows the identification
of patterns between institutions and economic performance. Second, for
many relationships it is possible to use data covering the majority of
the world's population. Hence the relevance of a finding is not limited
to any one country. A finding that across the globe, countries with
higher levels of corruption have worse health outcomes even after
filtering out the effects of income, education etc. has greater
generality than the finding that corruption affects health outcomes in
any one country. If the finding is to be believed, i.e., issues of
causality etc. have been resolved, the finding would suggest that
corruption in general affects health outcomes in most countries
throughout the world. It does not show that corruption undermines health
outcomes in every country, yet it has more relevance to, say, Kenya than
a finding that corruption undermines health outcomes in the Philippines.
The advantage to studying countries in detail, which we will describe
below, is that the analysis can be done with fewer methodological
problems.

Second, in cross-country regressions we have a fair amount of general
knowledge about each observation. If we find regression results are
driven by one or two observations, our general knowledge about them can
offer some guidance about whether or not they should be discounted. So
if a regression on the effects of inequality on crime is driven by
Brazil and the USA, our general knowledge on the links between
inequality and crime in these two countries may lead us to not discount
these observations and to place some faith in the results. On the other
hand if the relationship between commercial law and growth is driven by
the observation on South Korea, whose impressive growth performance is
not clearly caused by the quality of its commercial law, then we may be
skeptical of this result. In this case we should re-estimate the
equation without South Korea before placing any faith in the result (see
Azfar, Matheson and Olson 1999 for a demonstration). A more technical
statement of this argument is: "Regression results are often driven by
one or two observations, these observations either carry a lot of
information or a lot of noise. In cross country regressions our general
knowledge allows us to judge whether these observations are informative
or noisy and we can use this information to evaluate our results".

However, cross-country comparisons are not always informative on the
direction of causality between any two variables. At the level of
countries it is difficult to tell whether finance causes growth or
growth drives financial development; whether inequality causes poor
educational outcomes or poor educational outcomes cause inequality; or
whether corruption delays trade reform or trade restrictions lead to
corruption. Questions about causality are perhaps best resolved by
micro-level analyses, but there are two techniques which are often used
in cross-country regressions, "Granger Causality" and "Instrumental
Variables," which we review below.

One way of resolving concerns about whether A causes B or B causes A is
to look at the effect of past values of A on future values of B and vice
versa. If past values of A affect subsequent changes in B but past
values of B do not predict subsequent changes in A, then it might be
possible to infer that A causes B but B does not cause A. It is
important to point out that this is not a general rule. For instance if
anticipated future changes in B (economic growth) can effect present
values of A (financial development), then demonstrating that past values
of financial development predict subsequent economic growth does not
show that financial development drives growth.

Another way of resolving the question of whether the causality flows
from corruption to trade or vice versa is to use "Instrumental
Variables" or "Two Stage Least Squares" (sometimes referred to by the
acronym 2SLS). The idea behind the method of instrumental variables is
to use some instrumental variable (C) closely related with the
independent variable of interest (A), which is presumptively unlikely to
be caused by the dependant variable of interest (B) and examine whether
that variable (C) is correlated with the dependant variable of interest
(B). If it is, then under a set of quite restrictive conditions, it can
be inferred that A causes B. For instance if we are examining the
hypothesis that financial development causes economic development, we
might look for empirical links between commercial laws and financial
development, and commercial laws and economic growth. If commercial laws
are clearly correlated with economic growth, and with financial
development and there is no other plausible mechanism by which
commercial laws can affect economic growth then we might reasonably
infer that financial development causes economic development (Azfar,
Matheson and Olson 1999, Levine 1999). This is pretty much as good as it
gets with instrumental variables. Even in such a case there is the
worrying possibility that some other unmeasured variable, which is
closely empirically related to commercial law might be driving the
results---for instance some unmeasured aspect of legal culture could be
closely related to commercial law and directly affect economic growth.
Many applications of instrumental variables are more questionable.

To summarize: An instrumental variable C is a valid instrument for
examining whether variable A causes variable B if

1: C is strongly correlated with A

2: C does not have a plausible direct effect on B

3: B does not have a plausible effect on C

4: There in no D correlated with C and unmeasured which can plausibly
cause B

It is generally very difficult to find an instrument that satisfies all
four requirements. Many economists use instruments while acknowledging
they fail on one of these four conditions. Economists often cite tests
for "over-identifying restrictions", however as some econometricians
have pointed out, these tests are not persuasive in the relatively small
samples used in cross-country regressions (Nakamura and Nakamura 1985
and Newey 1985). While they have become mainstreamed in the profession,
the value of instrumental variable analyses, where the instruments do
not pass presumptive tests for validity, and samples are too small for
persuasive statistical tests for the validity of the instruments, is
unclear. Economists would do better by looking for other kinds of
information to resolve questions about causality from micro-data and
detailed narratives. It is to these methods we now turn.

*A.2: Microempirics*

Given the problems with inferring causality from cross-country
regressions, how might social scientists answer important questions
about the impact of institutions on socio-economic outcomes and the
determinants of good institutions?

It is instructional to ask how scientists would resolve this question.
The answers to many interesting and important scientific questions about
whether humans are descended from other animals or smoking causes cancer
in humans, are not directly observable. In neither case is there a piece
of evidence a "smoking gun" that answers these questions conclusively in
favor of one hypothesis or another.[^10] Rather it's the painstaking
accumulation of a large number of facts that is used to make the case.
The effects of Carbon Monoxide (CO) on lung cells in vitro, the
identification of similar damage near cancerous growth in smokers lungs,
the effect of exposure to CO to mice in controlled experiments, and
finally the demonstration of clear statistical patterns between the
propensity to smoke and contracting cancer, have placed the proposition
that smoking causes cancer in humans beyond reasonable doubt. Note that
this demonstration has been made in a world where it
is---thankfully---impossible to run controlled experiments on human
subjects (much like the impossibility of randomly choosing countries for
institutional reforms).

Economists would do well to learn from this. Cross-country empirics is
analogous to the mere demonstration that there is a statistical
correlation between the propensity to smoke and the proclivity of
contracting cancer. Instrumental variable analysis is little better: an
analogous instrumental variable resolution to the question of whether
smoking causes cancer would be to argue that people who went to poor
schools smoke more, and therefore if a link can be found between being
born in a poor school district and lung cancer then smoking must be
culprit. It that were the sum of evidence for the proposition that
smoking causes lung cancer then we would have found it difficult to
generate the breadth of scientific consensus on the issue that made even
cigarette manufacturers admit the causal link. Many beneficial reforms
have formidable opponents who, at least in democratic societies, have to
disguise their opposition to reform in imperfect scientific knowledge.
Clear and persuasive evidence needs to be accumulated to counter these
claims. How is this evidence to be accumulated?

Micro-level evidence on the behavior of firms and households can be used
to examine questions about causality. Micro-level evidence can be
produced and used in several ways:

-   Surveys can be used to study behavior at the level of the household
    or firm

-   Surveys can also be used to create data sets at the level of
    sub-national jurisdictions as done by Azfar et al. in the
    Philippines and described in the section on corruption and health
    outcomes

-   In depth interviews can be used to investigate reasons for actions,
    and for reliability checks on surveys

-   Historical narratives can also be used to investigate reasons for
    actions

-   Experiments can be used to create artificial institutional variation
    to investigate the effects of institutional innovations that don't
    occur naturally

*A.3 Surveys*

Surveys can be used to ask close-ended (typically multiple choice)
questions of a large number of randomly selected people. These samples
can be designed so they are representative of all or most of the
districts in the country being studied. The information collected can be
used both at the individual level and for cross-district comparisons.

In addition to cross-jurisdictional analysis, surveys can also provide
information at the level of the household or firm about the effect of
institutions on socio-economic outcomes. Although institutions only vary
across jurisdictions, they are used by private actors. These private
actors can provide us with information about the impact of institutions
on outcomes. For instance the following questions could be asked in a
household survey

Does you eldest child go to school?

> If yes When you took your eldest to be admitted to school, did you
> have to pay a bribe?
>
> If yes How much did you have to pay?
>
> Do you have to pay bribes every year to the principal or teachers of
> the school?
>
> If yes How much do have to pay every year
>
> If no Why doesn't your eldest child go to school?

1\. we need him to work at home

2\. we can't afford the fees

3\. we can't afford the bribes

4\. he doesn't like school

5\. the school is very bad

6\. Other specify \_\_\_\_\_\_\_\_\_

Does you second child go to school?

If yes...........

If the household reports spending a non-trivial proportion of its income
on bribe payments to the eldest child's school and states that the
reason the second child does not go to school is that they cannot afford
the bribe payments, then this information would indicate that corruption
does lead to lower enrollment. If this is systematically found for a
large number of households, and jurisdictions with high corruption
ratings have low enrollments then the conjunction of these pieces of
evidence would strongly indicate that corruption leads to low enrollment
rates.

The conjunction of micro and macro level evidence can produce convincing
arguments where any one piece might not be persuasive. An example we
have already discussed in the work of Swamy et al. who found that at the
micro level women disapproved more of corruption than men and firms
managed by women paid fewer bribes, and at the macro level that
countries where women were more involved in public life had less
corruption. A final piece of evidence which could settle the argument is
direct evidence on the behavior of women in parliament with respect to
corruption---how they vote on campaign finance reform, what bills they
introduce and what committees are they part of. Similarly cross country
results linking a large middle class to better governance would be
helped by micro-evidence that the middle class does in fact vote more
often, is better informed and votes for better reasons. Links between
inequality and crime would be helped by micro-level evidence linking
extreme poverty to crime, and links between trade reform and broad based
growth would be helped by micro-level evidence on the access of the poor
to liberalized markets.

*A.4 In Depth Interviews*

Surveys have several limitations. Questions have to be phrased in a way
that the answers can sensibly be listed as a set of options, and the
survey instrument has to be kept below a certain length (typically 150
close-ended questions).

Complex issues of causality may therefore have to be investigated using
in depth interviews. We can easily think of ways we could follow
responses to the questions above by more detailed questions about the
effect of corruption on the decision to send the child to school. The
survey might even have missed out on corruption as the culprit even if
the respondent had sufficient information to allow us to infer that
corruption was the root cause of the child missing school. For instance
the reason may be that the teacher is illiterate---this happens in
Pakistan---and the reason for the teacher being illiterate is that he
was appointed as the repayment of a favor by the Chief Minister. Or
there are no text-books because they have all been stolen. Or .....

In depth interviews are however more costly as they involve the lead
researcher's time, and conducting enough to get a statistically
defensible sample can be exhausting. The best strategy may be both to
conduct a randomized survey and a few detailed interviews where
statistical patterns can be presented from survey data, with
corroborating anecdotes from detailed interviews.

*A.5 Historical Analyses and Analytic Narratives*

Analytic narratives are one of the most powerful tools for understanding
the links between different levels of institutions and economic
performance. The term refers to a method that combines neat theoretical
predictions (analytic) with thick historical description (narrative). A
good researcher can read documents and archival material, interview
other scholars and---or modern day problems---olitical actors and "seek
to understand the actors' references, their perceptions, their
evaluation of alternatives, the information they possess, the
expectations they form, the strategies they adopt and the constraints
that limit their action". As an empirical method for uncovering
causality in social science such research and analysis can be a powerful
tool.

One of the first and most impressive analytic narratives for the student
of NIE is Mancur Olson's "Logic of collective action". One of the great
strengths of the "logic" is the breadth and depth of discussion of real
world examples that support its basic hypothesis. The "logic" starts
with a mathematical statement of the theory of public goods which states
that private individuals would under-supply public goods. But it was the
breadth of applications outside economics to theories of pressure groups
and class struggles that greatly influenced later analyses in political
science. The analysis of extra-rational and organizational factors that
reinforced the predictions of orthodox economic theory served to answer
the frequent criticism that economists rely on an unrealistic model of
human behavior that makes unrealistic predictions. And the analysis of
trade unions and farm lobbies persuasively demonstrates the empirical
relevance of the central hypothesis of *The Logic* that large groups can
only organize collective action if they have access to selective
incentives.

Another impressive analytic narrative for the student of NIE is Douglas
North's "Structure and change in economic history". While lacking a
mathematical theoretical framework (like Bates' article in *Analytic
Narratives*) there is a clear story that North tells on the links
between democracy, legal institutions, financial development and
economic performance. The rich detail of the narrative leaves the reader
with little doubt that democracy laid the foundation of secure property
rights and the development of commercial law, and that this led to
financial development and economic growth.

Another excellent analytic narrative is Avner Grief's account of
governance in Genoa. Genoa was unfortunately beset with several clans
vying and warring for power. The existence of an external threat
motivated them to adopt a political system where they hired an outside
"podesta" to run their government. The podesta was not only well paid
but was given clear incentives to run Genoa well. He had to stay in
Genoa and be audited following his reign and was fined for any
misconduct. Excellent performance on the other hand would improve his
chances of being appointed podesta somewhere else. These incentives in
Genoa led to good governance, military expansion and prosperity. This is
another example of how a society may mitigate the consequences of its
poor social structure (warring tribes) by cleverly designed political
institutions.

Much as economics has done well to colonize the other social sciences
and made the analytic, economics would benefit from making room for
narratives in its own method. Increasingly narratives are seen in
economic articles even outside history. An excellent example is Hart,
Shleifer and Vishny's new classic on the "Proper scope of government:
Theory and an application to prisons", which combines the theory of
contracts with a detailed narrative examination of the incentives in the
prison system.

*A.6 Experiments*

Another empirical method which has now entered the mainstream of
economic analysis is experiments. An economic experiment typically
involves a number of subjects --often University students---who play
"games" with each other for real stakes (i.e., money). The results of
these experiments have been important in casting doubt on long cherished
assumptions of rationality in economics. Two economists Daniel Kahneman
and Vernon Smith got the Nobel Prize in 2002 for work on experimental
economics. The handbook by Kagel and Roth (1995) is an excellent
compendium of the methods and results of experimental economics.

One of the best known experiments is the ultimatum game. Two subjects A
and B play this game to divide \$10. A moves first, offering B a portion
of the \$10. He can offer \$0, \$1, \$2....all the way to \$10. Subject
B can accept or reject the offer. If he rejects the offer both parties
get nothing. The "hyper rational" (everybody is rational and everybody
knows everybody else is rational) equilibrium is A keeping \$9 for
himself and only offering \$1 to B, knowing that B will accept it. In
fact, however, such offers are rarely made and often rejected if they
are. Most first movers offer \$3 or more, many offering \$5. Many second
movers reject offers of less than \$5. The game has been played in
developing countries for stakes that are a non-trivial fraction of
annual income and similar results are found. Human beings have deep set
notions of fairness and if they feel wronged are likely to want revenge.
Other humans knowing this "play fair."

The steady accumulation of such facts and corroborative evidence from
other games like "the dictator game" and the "Allais paradox" have
called into question the rationality postulate as a description of human
behavior. Most economists today think that "rational actors" with well
defined utility functions are analytically convenient for many purposes
but not accurate descriptions of human behavior.

One important advantage of experiments for the institutional economist
is that institutions can be varied in clean and systematic ways in a
classroom experiment. The cost of paying subjects is often less than the
cost of collecting survey data (\$10,000 in payments can generate
sufficient data for a meaningful analysis). The main disadvantage with
experiments is the suspicion that players act differently in
experimental settings than they would in real life. This concern is
partially grounded in the expectation that undergraduates especially
economics undergraduates are not representative of the population.
Certainly lessons from the behavior of American undergraduates are
treated with suspicion when used to predict behavior in developing
countries. This concerns can be resolved by actually conducting
experiments with representative people in developing countries. The
deeper concern is that people act differently in experimental settings.
Here emerging evidence from the analysis of questions like choice over
time, where experimental results correspond more closely than the
predictions of economic theory with observed real world behavior have
given more credence to experimental economics (Laibson).

Some recent experiments have direct relevance to NIE. Marco Casari and
Charles Plott designed an experiment that harnesses human vices such as
spite to the production of efficient outcomes. They replicated a
centuries old institution called "carte de regola" used to protect
common property resources in the Italian Alps, in a classroom
laboratory. This system rewards the discoverer of a violation with part
of the fine charged to the violator. This effectively harnesses the
behavior of spiteful agents, who spend resources identifying violators
rather than over-consuming which they would otherwise do. Casari and
Plott find that there are spiteful agents who willingly engage in
finding and punishing violators even when incentives to do so are not
strong enough for strictly rational agents to do so. When incentives are
sharpened to the point where even strictly rational agents would spend
resources identifying violators the efficiency of outcomes sharply
increases.

One important lesson from this experiment is that rather than try to
change human nature, smart institutional design should harness both
selfishness and other morally questionable human qualities, such as
ambition and spite for socially useful purposes.

Other experiments relevant to corruption and governance are also being
conducted. Jean-Robert Tyran and Lars Feld have shown that rule
obedience is higher among groups that make rules democratically. Azfar
and Nelson are investigating the effects of government wages, the
seperation of powers and the difficulty of detection on corruption
levels and probability of exposing corruption. Experiments are a
promising new method that can be used in NIE, and are uniquely capable
of analyzing novel institutions that do not naturally occur.

[^1]: ^\*^ IRIS Center, University of Maryland, College Park,
    Omar@iris.econ.umd.edu.

[^2]: Even in this case informal payments may not be efficient for
    several reasons as pointed out by Lewis (2000). Bribes for doctors
    rather than payments to hospitals may lead to underinvestment in
    equipment (which is shared by other doctors). Bribes are often given
    in the form of gifts rather than money in some countries which can
    lead to deadweight losses. The presence of lucrative opportunities
    for corruption can delay reform (as shown in another context by Lee
    and Azfar 2001).

[^3]: As a general point, this raises the issue of perverse effects of
    incentives on behavior: paying doctors for each procedure may give
    them incentives to improve both their performance and their bed-side
    manner, but also induces them to recommend unnecesary procedures.

[^4]: Some examples may help clarify the impact that a two-point gain in
    integrity represents. In Uganda, the replacement of Obote by
    Museveni was accompanied by a two-point gain; in the Philippines,
    the movement from Marcos to Aquino brought the same; Bolivia's
    market-oriented reforms of the 1980s and 1990s also brought about a
    two-point improvement in integrity, as did the lifting of military
    rule and the introduction of a market economy in Poland in the late
    1980s and early 1990s.

[^5]: A recent paper by Easterly and Levine (2002) makes the point that
    differences in institutions explain the vast majority of differences
    in incomes

[^6]: Section 6 contains a discussion of public expenditure tracking
    surveys that find significant leakages in the delivery of education
    and health.

[^7]: A survey of this magnitude costs around \$100,000 to conduct and
    it costs as much again to analyse the data and write up the results.
    Such studies are probably the most effective way of uncovering the
    relationship between corruption and service delivery.

[^8]: Klitgaard offers Corruption=Monopoly+Discretion-Accountability
    using a broad definition of accountability. Azfar (2003) found it
    useful to divide accountability into transparency, wages and a
    narrower definition of accountability.

[^9]: The interested reader can contact Kai Kaiser for more information
    on the Indonesian study. The contact person for various other
    studies is listed in Dehn, Reinikka and Svensson 2002.

[^10]: Or, at least, the answers were placed beyond reasonable doubt
    before a smoking gun like a DNA sequence became available.