Impact of property rights on poor households investment decisions: a treatment evaluation of a titli

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Impact of property rights on poor households
investment decisions a treatment evaluation of a
titling programme in Peru

• Oswaldo Molina
• July 1, 2008

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Contents

• Motivation
• Data
• Methodology of impact evaluation
• Defining control groups and potential bias
  problems
• Empirical estimation
• Empirical results
• Baseline results
• Robustness to functional form
• Dynamic response
• Final remarks

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Why this topic can be interesting? (1)

• Motivation

• Protection of property rights has long been
  emphasized as an essential precondition
  development (North and Thomas, 1973 Demsetz,
  1967 Johnson et al., 2002).
• Fragile property rights not only tend to reduce
  total investment but also have significant
  effects on its composition. Tenure insecurity
  hinders long-term investments (Dercon et al
  2005).
• Nowadays, millions of people in urban areas of
  developing countries occupy dwellings (31.6 of
  the global urban population). A large proportion
  without a title.
• This topic has become primordial on the
  policymakers agenda (Baharoglu, 2002 Field,
  2003).

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Why this topic can be interesting? (2)

• Motivation

• Many governments have started land-titling
  programmes in urban areas (such as Colombia,
  Mexico, Peru, Angola, Senegal, South Africa,
  India).
• Even though a considerable empirical literature
  explores the effects of property rights on
  investment, it has been principally focused on
  rural areas.
• The Peruvian experience is one of the largest
  government titling programmes targeted to urban
  areas (more than 1.5 million property titles were
  recorded by governmental agency Cofopri)
  (Cofopri, 2006).

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Objective of this paper

• Motivation

• To evaluate the impact of the Peruvian
  large-scale titling programme on housing
  investment.
• The Peruvian experience was previously analyzed
  by Field (2005) impact of titling is limited
  only to short-run investment.
• Some of her findings contrast those of this
  paper we find not only a positive relationship
  with short-run investments, but also with
  long-run ones.
• This analysis considers the methodology suggested
  by Field (2005) as a starting point using. We
  extend the analysis utilizing different
  econometric techniques, employing different
  control groups, dealing with endogeneity problems
  and including a richer set of control variables.

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Data (1)

• Data

• Cross-section data set, collected in June 2003
  from five different regions (includes panel data
  information of eight categories of housing
  investment).
• It includes information of tenure status from
  2331 properties (836 having a Cofopris title).
  51 are communities that were effectively reached
  by Cofopri.
• Ex-post cross-section data can be used to
  evaluate programmes if (Field and Kremer, 2005)
• it incorporates retrospective questions about the
  intervention,
• data cover enough period to estimate the total
  benefits. Fortunately our survey satisfies both
  requirements.

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Data (2)

• Data

• Defining the investment variable.
• Before-programme sum of the number of
  investments undertaken in the two years priors to
  the programme.
• After-programme sum of number of investments
  completed in 2001 and 2002.
• It is also feasible to distinguish between
  short-run and long-run housing investment.
• Investment variable has some specific
  characteristics

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Defining control groups and potential bias (1)

• Methodology of impact evaluation

• Two different control groups are used to provide
  more robustness to our results.
• First control group households in communities
  that were reached by the programme and that did
  not obtain a title, because they did not fulfil
  all the requirements. The selection is at the
  household level.
• Second control group households that, according
  to requirements, were eligible to get a title,
  but did not get one because they lived in areas
  that were not treated yet by Cofopri (potential
  future beneficiaries). The selection is at the
  area level.

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Defining control groups and potential bias (2)

• Methodology of impact evaluation

• Potential biases
• First control group (household level) selection
  bias.
• The analysis incorporated as controls the
  requirements to obtain a title (residency time
  and non-possession of other proper title).
• Second control group (area level) timing in
  which Cofopri reached each community is related
  to any unobservable variable that, at the same
  time, is correlated with investment.
• Not contaminated by the potential selection bias
  of the first control group
• Programme seems to focus first on the easier to
  title lots (average cost of titling increased
  over time Morris, 2004). Timing in the
  implementation was not exogenous.
• Analysis also includes the variables that were
  considered in the selection of the cities
  (distance from commercial centres, city size and
  concentration of informality)

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Empirical estimation

• Methodology of impact evaluation

• The expression for the investment level
• After taking first differences becomes
• This strategy allows us to remove any bias
  produced by time-invariant unobserved
  heterogeneity as it cancels out upon subtraction.
  

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Baseline results (OLS difference-in-difference
models)

• Empirical results

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Baseline results (2)

• Empirical results

• Results using OLS models, similar to the
  methodology employed by Field (2005).
• Large impact of Cofopris title on total housing
  investment. Being treated implies that the
  expected number of investments increases by
  0.20-0.30 (rises by 60 on average). Results are
  similar to those of Field (2005), whose reported
  treatment effects at 68.
• Average treatment effect on long-run investment
  is about 0.08 and highly statistically
  significant (given the low baseline, an increase
  by 0.08 implies an increase by more than 200).

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Baseline results (3)

• Empirical results

• These results differ substantially from those
  obtained by Field (2005). There are (at least)
  two reasons for this
• Our regressions include a richer set of control
  variables.
• Perhaps more importantly, our data span a longer
  period after titling than Fields data.
• Impact of the programme by level of income. To do
  so, we estimate regressions for each quartile of
  income. Results indicate that as the level of
  income increases, the significance and the
  coefficient associated with the impact of titling
  also rises, especially in long-run investment.
• These results suggest that other barriers exist,
  besides risk, which limit investment for the
  poorer households, and can be then attributed to
  persistent market failures.
• These programmes need to be complemented with
  other policy measures.

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Robustness to functional form (count data models)

• Empirical results

• Results tend to be lower than those obtained in
  the OLS models. Impact on total investment is
  between 0.17 and 0.26.

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Robustness to functional form (dif-in-dif
propensity score matching models)

• Empirical results

• Two different propensity score, according to the
  control group (households prob. of being
  selected and communitys prob.)
• Low bias if we incorporate in the participation
  regression the variables which explain selection
  (Heckman et al, 1997)
• Impact on long-run investment an increase in the
  number of housing sizeable additions by 170-200.

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Dynamic response

• Empirical results

• Although we do know that title impact positively
  on investment, we do not recognize if this impact
  tends to be immediate or if it takes time to be
  relevant.
• We construct the temporary investment behaviour
  of each region. Considering as time zero the two
  years prior the treatment, we generate a binary
  variable of any investment in two-year periods
  and compare each of them with the pre-programme
  baseline.
• In the case of total and short-run investments,
  the impact of title on housing renovations is
  significant even in the following two years after
  the programme.
• On the contrary, title enhances the probability
  that a household makes a long-run investment, but
  only four years after of being treated.
  Households appear not to react promptly to the
  incentive provided by the title.

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Dynamic response

• Empirical results

• A considerable horizon of time is required in
  order to measure the complete impact of a titling
  programme.

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Final remarks

• Empirical results

• Title presents a highly significant and larger
  effect on long-run investment. The results from
  the dif-in-dif propensity score matching indicate
  that the estimated average treatment effect
  implies an increase in the number of housing
  sizeable additions by 170-200.
• Impact of the programme is different depending on
  the level of income as the households income
  increases, the significance and the coefficient
  associated with the effect of titling also rises,
  particularly in long-run investment.
• While the effects on housing renovations can be
  significant even in the following two years after
  the implementation, its impact on long-run
  investment requires more than four years. This
  result has serious implications for the
  evaluation of programmes a considerable
  time-horizon is needed to measure its total
  impact.
• Collecting new panel data sets can allow further
  research to produce more accurate estimations.
• Other ideas anticipation bias, differential
  investment behaviour associated to risk aversion
  and split total impact in its components.

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Impact of property rights on poor households
investment decisions a treatment evaluation of a
titling programme in Peru

• Thanks.