Solutions to Handout
EEP/IAS 118 Privatization of Water Utilities in Argentina
April 14, 2014
Galiani et al. (2005) use municipality level data from 1990 to 1999 and a difference-indifferences research design to estimate the effect of water utility privatization on childhood
mortality. Assume privatization occurred in some of the municipalities in 1995. Suppose that
that the true effect of privatization on infant mortality was negative, discuss whether each of the
following [hypothetical] scenarios would introduce any bias relative to the true effect. If there
would be some bias, then indicate if the bias would be towards or away from zero [relative to the
true effect]. That is, under the given hypothetical scenario would 𝐸[𝛽̂3 ] be more negative than
the true [negative] effect (biased away from zero) or would the 𝐸[𝛽̂3 ] be less negative than the
true [negative] effect (biased towards zero)? [Note: In absolute terms being biased away from
zero means the estimated effect is larger than the true effect, while being biased towards zero
means the estimated effect is smaller than the true effect.]
̂𝟎 + 𝜷
̂ 𝟏 𝒕𝒓𝒆𝒂𝒕 + 𝜷
̂ 𝟐 𝒑𝒐𝒔𝒕 + 𝜷
̂ 𝟑 𝒑𝒐𝒍𝒊𝒄𝒚 + 𝒖
̂
DID Estimating equation: 𝒎𝒐𝒓𝒕𝒂𝒍𝒊𝒕𝒚 = 𝜷
-treat denotes the municipalities that eventually privatized their water utilities
-post denotes the period after which privatization occurred
-policy (=treat*post) denotes the treatment areas in the post period when privatization of
water utilities was in place.
Hypothetical scenarios
A) Municipalities that privatized also received aid from the World Bank to invest in hospital
infrastructure coincident with privatization.
RE: Assume investment in hospital infrastructure coincides with on-set of
Privatization (corr(policy, hospital infrastructure) >0) and that hospital infrastructure has negative
affect on childhood mortality (ie, reduces childhood mortality). This generates a negative bias using
the difference in differences approach. The estimated effect is too large because it includes the effect
of enhanced hospital infrastructure on reducing childhood mortality. The true effect is negative so
̂ ] < 𝜷𝒕𝒓𝒖𝒆 < 𝟎.
that the estimated effect would be biased away from zero: 𝐸[𝜷
𝟑
𝟑
B) Municipalities that privatized had populations that were on average wealthier and better
educated than municipalities that did not privatize, and these demographics did not change in any
significant way over the study period.
RE: No bias. Unless these characteristics are changing over time differently in the treatment
municipalities versus the control municipalities, there would be no bias
under the diff-in-diff research design. That is, the treatment group indicator (not to be confused with
the policy indicator) controls for these baseline differences of fixed factors between the control and
treatment groups.
Solutions to Handout
C) Multiple factors in Argentina changed in 1994 including a considerable devaluation of the
Argentine Peso, a five year drought began and a national health insurance program was
implemented.
RE: No bias. Unless these factors were affected differently in municipalities that privatized
versus those that did not, there would be no bias under the diff-in-diff research design. That is, [we
assume] devaluation, drought and national health insurance affected all municipalities in similar
ways.
D) Municipalities that did not privatize had politicians who won their elections by championing
social insurance for elderly, while municipalities that privatized had politicians who won their
elections by championing investments in public works projects such as expanding access to
piped water.
RE: Politicians in municipalities that privatized also prioritized expanding access to piped
water while politicians in municipalities that did not privatize prioritized other policies which were
seemingly unrelated to childhood mortality. Even if privatization wasn’t an option, we would have
observed similar patterns of expanded access in the “treatment” relative to the “control”
municipalities. One could think of the omitted variable as “desire to expand access to piped water”,
which would be positively correlated with on-set of privatization and negatively correlated with
childhood mortality. This would generate a negative bias. Once again, the true effect is negative so
that the estimated effect would be biased away from zero. That is, the difference in differences
approach would be pick up any effect of privatization plus the effect that politicians in the
̂ ] < 𝜷𝒕𝒓𝒖𝒆 < 𝟎.
municipalities that privatized had different priorities than those that did not. 𝐸 [𝜷
𝟑
𝟑