Evaluating Urban Planning: Evidence from Dar es Salaam
Vernon Henderson (LSE)
Francisco Libano-Monteiro (LSE)
Martina Manara (Sheffield)
Guy Michaels (LSE)
Tanner Regan (GWU)

May 2024

Urban Planning▶ Developed country cities: urban planning plays central role
▶ Public use (esp. roads) 40-50% of developed land; private land zoned or regulated

1. Intro 2. History & Lit 3. Context 4. Model 5. Data & Methodology 6. Empirical Results 7. Conclusions

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Urban Planning▶ Developed country cities: urban planning plays central role
▶ Public use (esp. roads) 40-50% of developed land; private land zoned or regulated▶ Developing country cities: planning often absent or ineffective▶ Informality may lower private investments and tax bases and worsen disamenities▶ Large cities, where growing share of humanity lives, face proliferation of slums

1. Intro 2. History & Lit 3. Context 4. Model 5. Data & Methodology 6. Empirical Results 7. Conclusions

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‘De novo’ Urban Planning in Developing Countries▶ ‘De novo’ urban planning is important policy tool to address informality problem▶ Purchase cheap (greenfields) agricultural land on urban fringe▶ Partition into formal plots with minimal services - mostly unpaved roads▶ People buy plots and build their own homes

1. Intro 2. History & Lit 3. Context 4. Model 5. Data & Methodology 6. Empirical Results 7. Conclusions

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‘De novo’ Urban Planning in Developing Countries▶ ‘De novo’ urban planning is important policy tool to address informality problem▶ Purchase cheap (greenfields) agricultural land on urban fringe▶ Partition into formal plots with minimal services - mostly unpaved roads▶ People buy plots and build their own homes▶ World Bank financed such ‘Sites and Services’ in many countries (1970s-80s)▶ Low repayment rates and exclusion of poorest stopped policy in late 80s▶ But in long-run: cost effective, raises land values, attracts private investment▶ In recent decades, some African governments picked up policy▶ Likely a response to rise of middle-class demand for better housing

1. Intro 2. History & Lit 3. Context 4. Model 5. Data & Methodology 6. Empirical Results 7. Conclusions

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‘De novo’ Urban Planning in Developing Countries▶ ‘De novo’ urban planning is important policy tool to address informality problem▶ Purchase cheap (greenfields) agricultural land on urban fringe▶ Partition into formal plots with minimal services - mostly unpaved roads▶ People buy plots and build their own homes▶ World Bank financed such ‘Sites and Services’ in many countries (1970s-80s)▶ Low repayment rates and exclusion of poorest stopped policy in late 80s▶ But in long-run: cost effective, raises land values, attracts private investment▶ In recent decades, some African governments picked up policy▶ Likely a response to rise of middle-class demand for better housing▶ But scant evidence on effects of how de-novo neighborhoods are laid out
1. Intro 2. History & Lit 3. Context 4. Model 5. Data & Methodology 6. Empirical Results 7. Conclusions

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This paper▶ Research question:▶ How does layout within de novo neighbourhoods affect outcomes?

1. Intro 2. History & Lit 3. Context 4. Model 5. Data & Methodology 6. Empirical Results 7. Conclusions

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This paper▶ Research question:▶ How does layout within de novo neighbourhoods affect outcomes?

▶ Context: de novo government projects in Dar es Salaam (Tanzania) ∼2000-2010

1. Intro 2. History & Lit 3. Context 4. Model 5. Data & Methodology 6. Empirical Results 7. Conclusions

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This paper▶ Research question:▶ How does layout within de novo neighbourhoods affect outcomes?

▶ Context: de novo government projects in Dar es Salaam (Tanzania) ∼2000-2010▶ Planning choices:▶ Residential: size of own plot and neighbouring plots, regularity of layout▶ Non-residential: road access and planned public and commercial uses

1. Intro 2. History & Lit 3. Context 4. Model 5. Data & Methodology 6. Empirical Results 7. Conclusions

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This paper▶ Research question:▶ How does layout within de novo neighbourhoods affect outcomes?

▶ Context: de novo government projects in Dar es Salaam (Tanzania) ∼2000-2010▶ Planning choices:▶ Residential: size of own plot and neighbouring plots, regularity of layout▶ Non-residential: road access and planned public and commercial uses

▶ Outcomes measured in ∼2020: bare land values (market transactions); housing
investment (satellite imagery); educational attainment (household questionnaires)

1. Intro 2. History & Lit 3. Context 4. Model 5. Data & Methodology 6. Empirical Results 7. Conclusions

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This paper▶ Research question:▶ How does layout within de novo neighbourhoods affect outcomes?

▶ Context: de novo government projects in Dar es Salaam (Tanzania) ∼2000-2010▶ Planning choices:▶ Residential: size of own plot and neighbouring plots, regularity of layout▶ Non-residential: road access and planned public and commercial uses

▶ Outcomes measured in ∼2020: bare land values (market transactions); housing
investment (satellite imagery); educational attainment (household questionnaires)▶ Research designs: within neighbourhood variation; spatial regression discontinuity

1. Intro 2. History & Lit 3. Context 4. Model 5. Data & Methodology 6. Empirical Results 7. Conclusions

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Greenfield Site

1. Intro 2. History & Lit 3. Context 4. Model 5. Data & Methodology 6. Empirical Results 7. Conclusions

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Greenfield Site, land is parcelled and sold1. Intro 2. History & Lit 3. Context 4. Model 5. Data & Methodology 6. Empirical Results 7. Conclusions

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20 years on, owners develop plots themselves

1. Intro 2. History & Lit 3. Context 4. Model 5. Data & Methodology 6. Empirical Results 7. Conclusions

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Main findings (∼10-20 years after project implementation)
1. Descriptive: the plots sold, covered project cost (∼1 USD 2021 per sqm)
▶ Bare land values increased and are now double those in nearby unplanned areas▶ But only half of ∼36k residential plots currently built

1. Intro 2. History & Lit 3. Context 4. Model 5. Data & Methodology 6. Empirical Results 7. Conclusions

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Main findings (∼10-20 years after project implementation)
1. Descriptive: the plots sold, covered project cost (∼1 USD 2021 per sqm)
▶ Bare land values increased and are now double those in nearby unplanned areas▶ But only half of ∼36k residential plots currently built

2. Focusing on within-project variation:

1. Intro 2. History & Lit 3. Context 4. Model 5. Data & Methodology 6. Empirical Results 7. Conclusions

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Main findings (∼10-20 years after project implementation)
1. Descriptive: the plots sold, covered project cost (∼1 USD 2021 per sqm)
▶ Bare land values increased and are now double those in nearby unplanned areas▶ But only half of ∼36k residential plots currently built

2. Focusing on within-project variation:▶ Plot layout:▶ Relatively too few small plots were supplied (price elasticity of plot size around -0.5;
larger plots have more open space and about 1/3 population density)

1. Intro 2. History & Lit 3. Context 4. Model 5. Data & Methodology 6. Empirical Results 7. Conclusions

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Main findings (∼10-20 years after project implementation)
1. Descriptive: the plots sold, covered project cost (∼1 USD 2021 per sqm)
▶ Bare land values increased and are now double those in nearby unplanned areas▶ But only half of ∼36k residential plots currently built

2. Focusing on within-project variation:▶ Plot layout:▶ Relatively too few small plots were supplied (price elasticity of plot size around -0.5;
larger plots have more open space and about 1/3 population density)▶ Regular plot layout and proximity to similarly small plots spur plot development

1. Intro 2. History & Lit 3. Context 4. Model 5. Data & Methodology 6. Empirical Results 7. Conclusions

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Main findings (∼10-20 years after project implementation)
1. Descriptive: the plots sold, covered project cost (∼1 USD 2021 per sqm)
▶ Bare land values increased and are now double those in nearby unplanned areas▶ But only half of ∼36k residential plots currently built

2. Focusing on within-project variation:▶ Plot layout:▶ Relatively too few small plots were supplied (price elasticity of plot size around -0.5;
larger plots have more open space and about 1/3 population density)▶ Regular plot layout and proximity to similarly small plots spur plot development▶ Amenities:▶ Proximity to main paved roads is prized (higher land values and plot development)

1. Intro 2. History & Lit 3. Context 4. Model 5. Data & Methodology 6. Empirical Results 7. Conclusions

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Main findings (∼10-20 years after project implementation)
1. Descriptive: the plots sold, covered project cost (∼1 USD 2021 per sqm)
▶ Bare land values increased and are now double those in nearby unplanned areas▶ But only half of ∼36k residential plots currently built

2. Focusing on within-project variation:▶ Plot layout:▶ Relatively too few small plots were supplied (price elasticity of plot size around -0.5;
larger plots have more open space and about 1/3 population density)▶ Regular plot layout and proximity to similarly small plots spur plot development▶ Amenities:▶ Proximity to main paved roads is prized (higher land values and plot development)▶ Natural amenities (higher elevation, low ruggedness, distance from water) are valued

1. Intro 2. History & Lit 3. Context 4. Model 5. Data & Methodology 6. Empirical Results 7. Conclusions

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Main findings (∼10-20 years after project implementation)
1. Descriptive: the plots sold, covered project cost (∼1 USD 2021 per sqm)
▶ Bare land values increased and are now double those in nearby unplanned areas▶ But only half of ∼36k residential plots currently built

2. Focusing on within-project variation:▶ Plot layout:▶ Relatively too few small plots were supplied (price elasticity of plot size around -0.5;
larger plots have more open space and about 1/3 population density)▶ Regular plot layout and proximity to similarly small plots spur plot development▶ Amenities:▶ Proximity to main paved roads is prized (higher land values and plot development)▶ Natural amenities (higher elevation, low ruggedness, distance from water) are valued▶ Proximity to planned non-residential amenities is not valued (low implementation)

1. Intro 2. History & Lit 3. Context 4. Model 5. Data & Methodology 6. Empirical Results 7. Conclusions

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Main findings (∼10-20 years after project implementation)
1. Descriptive: the plots sold, covered project cost (∼1 USD 2021 per sqm)
▶ Bare land values increased and are now double those in nearby unplanned areas▶ But only half of ∼36k residential plots currently built

2. Focusing on within-project variation:▶ Plot layout:▶ Relatively too few small plots were supplied (price elasticity of plot size around -0.5;
larger plots have more open space and about 1/3 population density)▶ Regular plot layout and proximity to similarly small plots spur plot development▶ Amenities:▶ Proximity to main paved roads is prized (higher land values and plot development)▶ Natural amenities (higher elevation, low ruggedness, distance from water) are valued▶ Proximity to planned non-residential amenities is not valued (low implementation)

3. The owners, especially of large plots, are highly educated (poor mostly excluded)
1. Intro 2. History & Lit 3. Context 4. Model 5. Data & Methodology 6. Empirical Results 7. Conclusions

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Ancient history of urban planning▶ Some urban gridding in Indus Valley,
Mesopotamia, Assyria, and Egypt▶ Greek towns initially developed organically▶ But ∼ 479 BCE: Miletus planned (Paden 2001)▶ Two sizes of grids▶ Two agoras (public spaces)▶ Spaces for public buildings▶ Gridded cities spread around ancient world
via Alexander the Great & Roman Empire
1. Intro 2. History & Lit 3. Context 4. Model 5. Data & Methodology 6. Empirical Results 7. Conclusions

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“Garden Cities of To-Morrow” heavily influenced suburban planning(Ebenezer Howard 1902)▶ Exclusionary zoning iscommon (e.g., in US
cities)▶ Hundreds of graduate
urban planning programs
worldwide (QS ranking)▶ But very little systematic
evaluation of de novo
urban planning

1. Intro 2. History & Lit 3. Context 4. Model 5. Data & Methodology 6. Empirical Results 7. Conclusions

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The economic problem: planners and markets▶ Tradition of criticizing planners for ignoring individual agency▶ Adam Smith: “man of system” organizes lives as “pieces upon a chess board”▶ Jane Jacobs: critiques strict urban planning of Le Corbusier and Robert Moses

1. Intro 2. History & Lit 3. Context 4. Model 5. Data & Methodology 6. Empirical Results 7. Conclusions

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The economic problem: planners and markets▶ Tradition of criticizing planners for ignoring individual agency▶ Adam Smith: “man of system” organizes lives as “pieces upon a chess board”▶ Jane Jacobs: critiques strict urban planning of Le Corbusier and Robert Moses▶ Some early recognition by economists that planning is key to city development▶ Zoning mitigates externalities (Davis and Whinston 1962, 1964)▶ Roads reduce congestion (Solow and Vickrey 1971, Dixit 1973)

1. Intro 2. History & Lit 3. Context 4. Model 5. Data & Methodology 6. Empirical Results 7. Conclusions

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The economic problem: planners and markets▶ Tradition of criticizing planners for ignoring individual agency▶ Adam Smith: “man of system” organizes lives as “pieces upon a chess board”▶ Jane Jacobs: critiques strict urban planning of Le Corbusier and Robert Moses▶ Some early recognition by economists that planning is key to city development▶ Zoning mitigates externalities (Davis and Whinston 1962, 1964)▶ Roads reduce congestion (Solow and Vickrey 1971, Dixit 1973)▶ Urban planners & economists should communicate more (Bertaud 2018, Duranton 2017)▶ Market-based development - reflects people’s preferences and information▶ Planning – property rights, public goods, externalities, coordination, distribution

1. Intro 2. History & Lit 3. Context 4. Model 5. Data & Methodology 6. Empirical Results 7. Conclusions

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Related Literature▶ Land-use regulation and land markets(Turner et al. 2014 general restrictions; Kulka 2019, Kulka et al. 2022 density; Shertzer et al 2018 zoning; Gyourko & Molloy 2015 review)▶ We look at plot size and configuration explicitly, designated on greenfield land

▶ Urban housing policy in developing countries(Angel 2012, Romer 2012 urban expansion; Harari & Wong 2021 slum upgrading; Franklin 2020 housing estates; Owens et al. 2018 ‘Sites’)▶ We study the impacts of ex-ante planning on greenfield, within minimal investment▶ Colonial origins of institutions (e.g., Acemoglu et al. 2001, Baruah et al. 2021)▶ We study impact of planning regulations, which originated from British colonial rules▶ Value of formal planned areas with property rights protection(De Soto 1989 property rights, Liebcap & Lueck 2011 orthogonal demarcation, Michaels et al. 2021 planning bundle)▶ We look within formal areas – study consequences of specific planning decisions▶ Valuation of local amenities with sorting(e.g., Epple and Sieg 1999, Bayer, Ferreira and McMillan 2007)▶ We study planned amenities of different types and shed light on sorting
1. Intro 2. History & Lit 3. Context 4. Model 5. Data & Methodology 6. Empirical Results 7. Conclusions

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Context: the “20,000 Plot” project in Tanzania▶ Project implemented circa 2000-2010, ‘de novo’ (empty greenfield)▶ Initiated in response to perceived demand for formal plots in late 1990s▶ Initial plan for around 20,000 plots in Dar es Salaam and other cities▶ We refer to it informally as “20k plots” or “20k” for short▶ Eventually, 12 program areas in Dar es Salaam with ∼36,000 residential plots▶ This is the focus area of our study▶ We ignore a few thousand additional plots in other cities in Tanzania (no data)

1. Intro 2. History & Lit 3. Context 4. Model 5. Data & Methodology 6. Empirical Results 7. Conclusions

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Locations of 20k areas in
Dar es Salaam.
Bold lines: preexisting.
main paved roads
Dashed lines: city edge.

1. Intro 2. History & Lit 3. Context 4. Model 5. Data & Methodology 6. Empirical Results 7. Conclusions

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Initial log land prices set
by government circa 2001
to cover costs

1. Intro 2. History & Lit 3. Context 4. Model 5. Data & Methodology 6. Empirical Results 7. Conclusions

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Log land prices rose
steeply everywhere,
but heterogeneously

1. Intro 2. History & Lit 3. Context 4. Model 5. Data & Methodology 6. Empirical Results 7. Conclusions

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Project specifications▶ 20k plot project provided:▶ Residential plots (planned, surveyed, and titled) sized from 400-4000 sqm▶ Non-residential plots where public and commercial services could be provided▶ In practice there was very little gov’t provision of services or utilities▶ Roads of different widths, mostly unpaved and without roadside ditches

1. Intro 2. History & Lit 3. Context 4. Model 5. Data & Methodology 6. Empirical Results 7. Conclusions

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Project specifications▶ 20k plot project provided:▶ Residential plots (planned, surveyed, and titled) sized from 400-4000 sqm▶ Non-residential plots where public and commercial services could be provided▶ In practice there was very little gov’t provision of services or utilities▶ Roads of different widths, mostly unpaved and without roadside ditches▶ Although government sale price (mean ∼1 USD in 2021 prices per sqm) wasaffordable, it was difficult for poor people to purchase plots because:▶ Minimum plot size, which under British rule facilitated segregation, was large▶ Retained after independence, only recently reduced to 300 sqm (Kironde 2006)▶ Process of plot sale was rushed to repay internal gov’t loan

1. Intro 2. History & Lit 3. Context 4. Model 5. Data & Methodology 6. Empirical Results 7. Conclusions

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Example: plots of different sizes in Tuangoma

1. Intro 2. History & Lit 3. Context 4. Model 5. Data & Methodology 6. Empirical Results 7. Conclusions

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Example: plots of different sizes in Tuangoma

1. Intro 2. History & Lit 3. Context 4. Model 5. Data & Methodology 6. Empirical Results 7. Conclusions

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Distribution of planned plot sizes

1. Intro 2. History & Lit 3. Context 4. Model 5. Data & Methodology 6. Empirical Results 7. Conclusions

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Planned non-residential use and road reserves in Mwbeni Mpiji

1. Intro 2. History & Lit 3. Context 4. Model 5. Data & Methodology 6. Empirical Results 7. Conclusions

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Planned non-residential use and road reserves in Mwbeni Mpiji
Plot

1. Intro 2. History & Lit 3. Context 4. Model 5. Data & Methodology 6. Empirical Results 7. Conclusions

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Planned non-residential use and road reserves in Mwbeni Mpiji
Plot
Insula(pluralinsulae)

1. Intro 2. History & Lit 3. Context 4. Model 5. Data & Methodology 6. Empirical Results 7. Conclusions

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Planned non-residential use and road reserves in Mwbeni Mpiji
Plot
Insula(pluralinsulae)“Super insula”(adjacent insulaewith same mean
size category)

1. Intro 2. History & Lit 3. Context 4. Model 5. Data & Methodology 6. Empirical Results 7. Conclusions

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Planned non-residential use and road reserves in Mwbeni Mpiji

1. Intro 2. History & Lit 3. Context 4. Model 5. Data & Methodology 6. Empirical Results 7. Conclusions

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What we examine using a model▶ What types of people end up buying the limited supply of plots in 20k areas▶ Who buys plots with different sizes and amenities▶ How plot sizes and amenities affect land prices, capital investment, and timing of
construction (we observe whether plot is built in cross-section)

1. Intro 2. History & Lit 3. Context 4. Model 5. Data & Methodology 6. Empirical Results 7. Conclusions

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Model assumptions on preferences▶ Residents live in city (center). At time 0 (early 2000s, after gov’t sale) they can
buy a 20k plot and move there at time τ and build a house using land and capital▶ Utility in city at time t (h1 city housing, z1 city consumption, A city amenity):u1 (t) = φlnh1 + βlnz1 + Ae−θt▶ Utility in 20K suburbs (l plot size, k capital, z2 other consumption):u2 (t) = φln(lα k 1−α ) + βlnz2 + B
where φ, β < 1 and A, B, θ > 0 and B < A
1▶ City amenity deteriorates at a rate dudt /u1 = −θ relative to 20k (congestion)

1. Intro 2. History & Lit 3. Context 4. Model 5. Data & Methodology 6. Empirical Results 7. Conclusions

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Objective function
People maximise lifetime utility subject to a budget constraint
Z ∞[φlnh1 + βlnz1 + Ae−θt ]e−ρt dt +[φln(lα k 1−α ) + βlnz2 + B]e−ρt dt
h1 ,z1 ,k,z2 ,τ
0
τ
Z ∞

Z τ
Z ∞
−δt
−δt
+ω
we dt −
(ph1 + z1 )e dt −z2 e−δt dt − rke−δτ − R(B, l)
Z τ

max

0

0

τ

where:▶ p is rental price (or opportunity cost) of housing in city
▶ w is annual income of resident (can be any income stream)▶ r is price of capital (ignore capital market imperfections for the well-off)▶ R(B, l) is price of a 20K plot of type (B, l) at time 0
1. Intro 2. History & Lit 3. Context 4. Model 5. Data & Methodology 6. Empirical Results 7. Conclusions

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Optimization, comparative statics, parameterization▶ Assume ρ = δ so z constant over time
▶ Use first order conditions to characterize comparative statics on τ and k, holding
income constant, but we have limited data on income▶ Equilibrium cannot be solved in closed form
▶ Take parameters from literature where possible (list in a subsequent slide)▶ Calibrate A to fit typical plot sizes, prices, and time of exit.

1. Intro 2. History & Lit 3. Context 4. Model 5. Data & Methodology 6. Empirical Results 7. Conclusions

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Example 1: Nash equilibrium with 2 plot sizes and a range of incomes▶ Assume N plots in total: N1 are small (800 sq m) and N2 large (1600 sq m)▶ Generalizes to many (countable number) of plots of different sizes▶ Assume incomes are between 1 and 21000▶ In equilibrium plots go to highest income people▶ We assume that income distribution is such that:▶ The N people who buy in 20k plots have incomes 4000 to 21000▶ Person with 4000 indifferent between moving and staying in city (rest get surplus)▶ Those with incomes above w(N2 ) = 7000 buy large plots (the rest buy small)▶ Person with 7000 indifferent between the two plot sizes

1. Intro 2. History & Lit 3. Context 4. Model 5. Data & Methodology 6. Empirical Results 7. Conclusions

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Example 1: Plot lifetime utilities (net of city utility) and kτ = 9.5 at w = 4000. τ rises to 15.1 as w approaches 7000 (rich can consume more housing
in city, whereas formal 20k land rationed). τ falls to 8.3 on switch to larger plots
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Example 1: Oversupply of large plots in equilibrium▶ With marginal consumer of large plots at w = 7000, price per square meter on asmall plot (6.3) greater than a large plot (5.4)▶ To equalize price per sq m, supply more small plots and fewer large plots▶ Or equivalently, increase marginal consumer’s income to 20,000

1. Intro 2. History & Lit 3. Context 4. Model 5. Data & Methodology 6. Empirical Results 7. Conclusions

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Example 2: Differential amenities (B)Now assume each plot size (small and large) has four types: B4 > B3 > B2 > B1
Assume that differences in amenities (Bs) are small relative to plot size diffs▶ In equilibrium, plot prices increase in B within plot size category▶ For same plot size, higher B plots go to the richer people▶ Given prices, marginal consumers indifferent between their plot and next higher B▶ As B jumps up:▶ τ drops, so people leave center city earlier (plots likelier to be built)▶ k declines slightly (with the price increase).

1. Intro 2. History & Lit 3. Context 4. Model 5. Data & Methodology 6. Empirical Results 7. Conclusions

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Example 2: Differential B’s (Switch point to large plots now 12000)
Optimal τ in equilibrium

Plot of lifetime utilities

1. Intro 2. History & Lit 3. Context 4. Model 5. Data & Methodology 6. Empirical Results 7. Conclusions

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Taking model predictions to the data
▶ Rich people sort into 20k (lifetime earnings proxied by education)▶ Within 20k richer owners sort into larger plots▶ Plot size (l)▶ Increases plot price but may reduce price per sq m (if large plots oversupplied)▶ Conditional on income, decreases k and increases build rate (inverse of τ )▶ But large plots attract richer people, which increases k and decreases build rate
▶ In city (not in 20k) land is optimized - in simulations this matters more for rich, whostay long in city. So unconditional effect of l on k and build rate ambiguous▶ Amenities (e.g., proximity to main paved road)▶ Increase total plot price and price per square meter▶ Have ambiguous effect on on k and build rate (similar to plot size)

1. Intro 2. History & Lit 3. Context 4. Model 5. Data & Methodology 6. Empirical Results 7. Conclusions

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Data Sources▶ Data on planning treatments from three Tanzanian gov’t sources:▶ Survey Maps, Town Planning Drawings and Cadaster data

▶ Questionnaires we administered:▶ Local real estate agents (dalali) for plot transactions prices▶ Residents (∼3,200 households) - augment prices, educational attainment▶ Local (mitaa) leaders▶ Enumeration of non-residential plots and public transport access▶ Very high-resolution satellite imagery▶ From 2019-2021 (and going back to 2000) - traced building outlines (and more)▶ Others: Historical paved main roads, Digital elevation map, OpenStreetMap
1. Intro 2. History & Lit 3. Context 4. Model 5. Data & Methodology 6. Empirical Results 7. Conclusions

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Dataset construction▶ We classify residential plots as those which:▶ Are not designated for non-residential use▶ And have an area of no more than 4,000sqm (formal maximum)▶ We define 20m x 20m gridcells (observations)▶ Corresponds to size of minimum formal plot (400sqm)▶ Focus on cells whose centroid is within residential plot
▶ Each gridcell can be treated by planners (e.g., assigned to small vs. large plot)▶ Twelve 20k areas in Dar es Salaam (total of ∼75sqkm)▶ About half of area (38sqkm) taken up by ∼95,000 residential gridcells▶ Other half: non-residential plots, roads, and hazardous areas (e.g., streams)
1. Intro 2. History & Lit 3. Context 4. Model 5. Data & Methodology 6. Empirical Results 7. Conclusions

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Dataset construction
Plots as defined in
the survey plans
Residential (white),and non-residential(“NR” grey) insulae
Each residential
insula given unique
ID (A, B, C)

1. Intro 2. History & Lit 3. Context 4. Model 5. Data & Methodology 6. Empirical Results 7. Conclusions

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Dataset construction
Gridcell (“cells”)
centroids spaced
20m apart
Take cells withcentroids that fallin plots (dots)
Ignore cells thatfall between (‘x’s)

1. Intro 2. History & Lit 3. Context 4. Model 5. Data & Methodology 6. Empirical Results 7. Conclusions

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Dataset construction• Boundaries fallequidistant between
insulae• Use residentialresidential
boundaries (blue andred)• Ignore nonresidential
boundaries (black)

1. Intro 2. History & Lit 3. Context 4. Model 5. Data & Methodology 6. Empirical Results 7. Conclusions

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Dataset construction• Assign grid squares
based on theboundary that thatare nearest to (blueand red)

1. Intro 2. History & Lit 3. Context 4. Model 5. Data & Methodology 6. Empirical Results 7. Conclusions

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Dataset construction• Assign grid squares
based on theboundary that thatare nearest to (blueand red)This gridcell is on
small side of B-Cboundary segment,
since insula B has
smaller mean plotsize

1. Intro 2. History & Lit 3. Context 4. Model 5. Data & Methodology 6. Empirical Results 7. Conclusions

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Methodology overview▶ OLS using variation in planning variables within 20k areas▶ Semi-parametric RD for estimating effects of plot size
▶ Restrict to 100m from insulae boundary, linear distance controls on both sides▶ For RD regressions we use mean plot size of gridcell’s insula’s▶ Inference: cluster s.e. by insula (main units of plot size assignment)▶ Outcomes: price (∼3% of plots), quantity (proxy build rate and k for all plots)▶ Ln market transaction price of plots sold as bare land
▶ Share gridcell built; Plot built; Ln(sum footprints up to 3 largest plot buildings);
Multiple buildings indicator (backyarding)
1. Intro 2. History & Lit 3. Context 4. Model 5. Data & Methodology 6. Empirical Results 7. Conclusions

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Methodology overview (continued)▶ Controls:▶ (F.e. for 20k project areas) X (F.e. for mitaa, which are small admin areas)▶ For price regs: (F.e. time period of sale) X (Source: Real estate agents, Residents)▶ Amenities (including disamenities):▶ Elevation, ruggedness, indicators for within 100m of river/stream or water/wetland▶ Indicator for gridcell within 100m of planned amenities▶ Open space, nursery school, religious site, education (primary or secondary school),
service trade, housing estate, public buildings, cemetery, other▶ Distance to preexisting paved main road
▶ Insula Regularity Z-index

1. Intro 2. History & Lit 3. Context 4. Model 5. Data & Methodology 6. Empirical Results 7. Conclusions

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Insula Regularity Z-index combines three measures

Insula Homogeneity:
1 ‐ coefficient of variationof plot sizes within insula

1. Intro 2. History & Lit 3. Context 4. Model 5. Data & Methodology 6. Empirical Results 7. Conclusions

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Insula Regularity Z-index combines three measures

Insula Homogeneity:
1 ‐ coefficient of variationof plot sizes within insula

Insula Rectangularity:(Size of insula) / (Size ofminimal containing rectangle)

1. Intro 2. History & Lit 3. Context 4. Model 5. Data & Methodology 6. Empirical Results 7. Conclusions

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Insula Regularity Z-index combines three measures

Insula Homogeneity:
1 ‐ coefficient of variationof plot sizes within insula

Insula Rectangularity:(Size of insula) / (Size ofminimal containing rectangle)

Insula Alignment: 1 ‐ difference in alignment of insula’s
minimal containing rectangle (modulo 90 degrees) andalignment of nearest insula’s containing rectangle

1. Intro 2. History & Lit 3. Context 4. Model 5. Data & Methodology 6. Empirical Results 7. Conclusions

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Empirical Results Overview

1. Descriptive: price appreciation in 20k areas
2. Residential plotsa) Own plot size effect on land pricesb) Own plot size effect on housing outcomesc) Neighbouring plot effects

3. Natural and planned amenities (and implementation rates)
4. Sorting (into 20k areas, between 20k areas, and within them)

1. Intro 2. History & Lit 3. Context 4. Model 5. Data & Methodology 6. Empirical Results 7. Conclusions

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Descriptive: price appreciation in 20k areas▶ Initial plots all sold in early 2000s Plot acquisition process▶ Yet, only ∼50% of plots built upon by 2020

▶ Large price increases in every area
▶ ∼Sixfold mean real appreciation of prices compared to gov’t sale price1. Intro 2. History & Lit 3. Context 4. Model 5. Data & Methodology 6. Empirical Results 7. Conclusions

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Descriptive: price in and outside of 20K areas (dalali data only)
ln pricep(i),t(p) = β ln sizep(i) + γNon20kSp(i) + δNon20kUp(i) + ηt(p) + µl(i) + εi
Note: gridcell i falls in plot p sold at time period t(p) in neighborhood location l(i)1. Intro 2. History & Lit 3. Context 4. Model 5. Data & Methodology 6. Empirical Results 7. Conclusions

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Descriptive: price in and outside of 20K areas (dalali data only)
ln pricep(i),t(p) = β ln sizep(i) + γNon20kSp(i) + δNon20kUp(i) + ηt(p) + µl(i) + εi
Note: gridcell i falls in plot p sold at time period t(p) in neighborhood location l(i)▶ Land prices in 20k roughly
twice in non-20k unsurveyed▶ Broadly consistent withinterviews of local leaders▶ Non-20k surveyed are rare

1. Intro 2. History & Lit 3. Context 4. Model 5. Data & Methodology 6. Empirical Results 7. Conclusions

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Descriptive evidence on price mechanisms from Leader interviews▶ We surveyed Mtaa leaders intersecting 20k areas. Key question:▶ What factors or characteristics do you think determine the difference in the price ofland in 20k versus non-20k areas?

▶ 31 (of 34) leaders answered this question:▶ 24 mentioned property rights▶ Reasons mentioned: reductions in boundary conflicts, increased tenure security,
access to financial credit (titles can serve as collateral)▶ 23 mentioned access▶ Non-20k areas tend to clog up▶ Other explanations much less common

1. Intro 2. History & Lit 3. Context 4. Model 5. Data & Methodology 6. Empirical Results 7. Conclusions

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Own plot size effect on land price (OLS, dalali + household data)ln pricep(i) = β ln sizep(i) + ηt(p) + µl(i) + εi

1. Intro 2. History & Lit 3. Context 4. Model 5. Data & Methodology 6. Empirical Results 7. Conclusions

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Own plot size effect on land price (OLS, dalali + household data)ln pricep(i) = β ln sizep(i) + ηt(p) + µl(i) + εi

1. Intro 2. History & Lit 3. Context 4. Model 5. Data & Methodology 6. Empirical Results 7. Conclusions

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Own plot size effect on land price (OLS, dalali + household data)ln pricep(i) = β ln sizep(i) + ηt(p) + µl(i) + εi
▶ Bigger plots sell at a unit
price discount on land
▶ Suggests misallocation

1. Intro 2. History & Lit 3. Context 4. Model 5. Data & Methodology 6. Empirical Results 7. Conclusions

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Own plot size effect on land price (OLS, dalali + household data)ln pricep(i) = β ln sizep(i) + ηt(p) + µl(i) + εi
▶ Bigger plots sell at a unit
price discount on land
▶ Suggests misallocation▶ Results similar using:▶ Plot-level regressions▶ 2SLS using dalali estimatesof ln plot size to address
measurement error

1. Intro 2. History & Lit 3. Context 4. Model 5. Data & Methodology 6. Empirical Results 7. Conclusions

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Own plot size effect on land price/sqm (OLS)
ln(price/sqm)p(i) = β ln sizep(i) + ηt(p) + µl(i) + εi

1. Intro 2. History & Lit 3. Context 4. Model 5. Data & Methodology 6. Empirical Results 7. Conclusions

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Own plot size effect on land price/sqm (OLS, bins)ln(price/sqm)p(i) =Pb βb I(sizep(i) ∈ b) + ηt(p) + µl(i) + εi

1. Intro 2. History & Lit 3. Context 4. Model 5. Data & Methodology 6. Empirical Results 7. Conclusions

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Own plot size effect on land price/sqm (OLS, bins)ln(price/sqm)p(i) =Pb βb I(sizep(i) ∈ b) + ηt(p) + µl(i) + εi

1. Intro 2. History & Lit 3. Context 4. Model 5. Data & Methodology 6. Empirical Results 7. Conclusions

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Own plot size effect on land price/sqm (Spatial RD)
ln price/sqmp(i) = βlargerI(i) + δ0 disti ∗ largerI(i) + δ1 disti ∗ smallerI(i) + ηt(p) + µl(i) + εi
Note: gridcell i belongs to insula I which is paired with i’s nearest other insula.

1. Intro 2. History & Lit 3. Context 4. Model 5. Data & Methodology 6. Empirical Results 7. Conclusions

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Own plot size effect on land price/sqm (Spatial RD)
ln price/sqmp(i) = βlargerI(i) + δ0 disti ∗ largerI(i) + δ1 disti ∗ smallerI(i) + ηt(p) + µl(i) + εi
Note: gridcell i belongs to insula I which is paired with i’s nearest other insula.

1. Intro 2. History & Lit 3. Context 4. Model 5. Data & Methodology 6. Empirical Results 7. Conclusions

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Back of the envelope: net gains from splitting large plots▶ Could splitting (marginal) large plot in initial plan have increased current value?
▶ At planning phase, average cost per plot : <US$157▶ e.g. surveying, road construction, valuation, etc▶ Take one 1600 sqm plot: worth US$16.7k in 2021
▶ Split it into four 400 sqm plots, each worth: US$6.3k in 2021
▶ Assume splitting involves mean cost of plot creation (conservative)▶ Net gain: US$8k, or ∼50%
▶ 8000 ∼ 6300 ∗ 4 − 16700 − (4 − 1) ∗ 157▶ Even if some further cost of allocating more land to roads, gain still substantial▶ But nowadays splitting plots is difficult (legal and procedural barriers)

1. Intro 2. History & Lit 3. Context 4. Model 5. Data & Methodology 6. Empirical Results 7. Conclusions

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Own plot size effect on housing outcomes (OLS)

1. Intro 2. History & Lit 3. Context 4. Model 5. Data & Methodology 6. Empirical Results 7. Conclusions

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Own plot size effect on Plot Built (OLS, bins)
Plot is Builtp(i) =Pb βb I(sizep(i) ∈ b) + µl(i) + εi

1. Intro 2. History & Lit 3. Context 4. Model 5. Data & Methodology 6. Empirical Results 7. Conclusions

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Own plot size effect on Plot Built (OLS, bins)
Plot is Builtp(i) =Pb βb I(sizep(i) ∈ b) + µl(i) + εi

Larger plots(perhaps) less likely
to be built upon

1. Intro 2. History & Lit 3. Context 4. Model 5. Data & Methodology 6. Empirical Results 7. Conclusions

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Own plot size effect on Share Gridcell Built (OLS, bins)
Share Builti =Pb βb I(sizep(i) ∈ b) + µl(i) + εi

larger plots havemore outdoor space(wasted vs preference)

1. Intro 2. History & Lit 3. Context 4. Model 5. Data & Methodology 6. Empirical Results 7. Conclusions

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Own plot size effect on Log size 3 largest buildings (OLS, bins)
Build Sizep(i) =Pb βb I(sizep(i) ∈ b) + µl(i) + εi

Ln total footprint of
up to three largest
buildings on plot increases with plot size

1. Intro 2. History & Lit 3. Context 4. Model 5. Data & Methodology 6. Empirical Results 7. Conclusions

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Own plot size effect on Multiple Buildings (OLS, bins)
Multi Buildsp(i) =Pb βb I(sizep(i) ∈ b) + µl(i) + εi

Share of plots withmultiple buildings
increases in plot size(backyarding vs outbuildings)

1. Intro 2. History & Lit 3. Context 4. Model 5. Data & Methodology 6. Empirical Results 7. Conclusions

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Own plot size effects on housing outcomes (Spatial RD)

1. Intro 2. History & Lit 3. Context 4. Model 5. Data & Methodology 6. Empirical Results 7. Conclusions

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Own plot size effects (Interacted RD magnitudes similar to OLS)

1. Intro 2. History & Lit 3. Context 4. Model 5. Data & Methodology 6. Empirical Results 7. Conclusions

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Neighbouring (super-insula) plot size effects (Spatial RD)

1. Intro 2. History & Lit 3. Context 4. Model 5. Data & Methodology 6. Empirical Results 7. Conclusions

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Amenities part I: proximity to main roads prized (both p and q)

1. Intro 2. History & Lit 3. Context 4. Model 5. Data & Methodology 6. Empirical Results 7. Conclusions

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Amenities part I: elevation valued (spurs development)

1. Intro 2. History & Lit 3. Context 4. Model 5. Data & Methodology 6. Empirical Results 7. Conclusions

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Amenities part I: ruggedness, rivers/streams, water/wetlands avoided

1. Intro 2. History & Lit 3. Context 4. Model 5. Data & Methodology 6. Empirical Results 7. Conclusions

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Amenities part I: Insula Regularity (rectangularity, alignment) valued

1. Intro 2. History & Lit 3. Context 4. Model 5. Data & Methodology 6. Empirical Results 7. Conclusions

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Amenities part II: planned non-residential ignored

1. Intro 2. History & Lit 3. Context 4. Model 5. Data & Methodology 6. Empirical Results 7. Conclusions

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Implementation rates for non-res amenities are low

1. Intro 2. History & Lit 3. Context 4. Model 5. Data & Methodology 6. Empirical Results 7. Conclusions

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Implementation rates for non-res amenities are low

Note: implemented not always where planned. Main unplanned uses: farming and residential
1. Intro 2. History & Lit 3. Context 4. Model 5. Data & Methodology 6. Empirical Results 7. Conclusions

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Implementation rates vary by planned use

1. Intro 2. History & Lit 3. Context 4. Model 5. Data & Methodology 6. Empirical Results 7. Conclusions

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When non-res implementation does happen, locations mostly follow plan
For non-res plots with known use and implementation, calculate:P(implemented as use j | planned as use j)/P(implemented as use j)

1. Intro 2. History & Lit 3. Context 4. Model 5. Data & Methodology 6. Empirical Results 7. Conclusions

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Implemented non-residential amenities positively correlated with housing,while unused-unkept negatively correlated

1. Intro 2. History & Lit 3. Context 4. Model 5. Data & Methodology 6. Empirical Results 7. Conclusions

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Sorting (mean years of schooling) into 20k areas▶ We measure sorting by mean years of schooling▶ Strong sorting into 20k ownership▶ Heads of households in Dar es Salaam as a whole: 8.7 years (LSMS Dar es Salaam 2014)▶ Heads of households in 20k: 11.5 years (our survey)▶ N=3230, median=11 years▶ About half are owners: 13.8 years (our survey)▶ N=1662, median=16 years▶ Other half non-owners (our survey): 9.1 years

1. Intro 2. History & Lit 3. Context 4. Model 5. Data & Methodology 6. Empirical Results 7. Conclusions

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Sorting of owners by mean years of schooling across 20k areas
Sorting of owners across 20k areas

1. Intro 2. History & Lit 3. Context 4. Model 5. Data & Methodology 6. Empirical Results 7. Conclusions

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Sorting of owners by years of schooling within 20k areas

1. Intro 2. History & Lit 3. Context 4. Model 5. Data & Methodology 6. Empirical Results 7. Conclusions

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Settlement dates of owners within 20k areas - as in model

1. Intro 2. History & Lit 3. Context 4. Model 5. Data & Methodology 6. Empirical Results 7. Conclusions

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Conclusions (∼10-20 years after project implementation)
1. Descriptive: the plots sold, covered project cost (∼1 USD 2021 per sqm)
▶ Bare land values increased and are now double those in nearby unplanned areas▶ But only half of ∼36k residential plots currently built

2. Focusing on within-project variation:▶ Plot layout:▶ Relatively too few small plots were supplied (price elasticity of plot size around -0.5;
larger plots have more open space and about 1/3 population density)▶ Regular plot layout & proximity to similarly small plots spur plot development▶ Amenities:▶ Proximity to main paved roads is prized (higher land values and plot development)▶ Natural amenities (higher elevation, low ruggedness, distance from water) are valued▶ Proximity to planned non-residential amenities is not valued (low implementation)

3. The owners, especially of large plots, are highly educated (poor mostly excluded)
1. Intro 2. History & Lit 3. Context 4. Model 5. Data & Methodology 6. Empirical Results 7. Conclusions

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Suggestions for improving ‘de novo’ planning, based on our findings fromthis specific context
1. Ensure that property rights are secure
2. Make neighborhoods physically accessible both locally and globally
3. Make plot allocation process more transparent and less rushed
4. Allow for relatively more small plots
5. Focus the planning on formal plots and roads rather than on other amenities
6. Where possible, make plot layout regular
7. Allow for the fact that physical hazards influence buildup rate

1. Intro 2. History & Lit 3. Context 4. Model 5. Data & Methodology 6. Empirical Results 7. Conclusions

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Thank you!

▶ Feedback very welcome after the talk or over email▶ Guy Michaels (G.Michaels@lse.ac.uk)

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Plot acquisition process for a prospective buyer back1. Collect application forms from municipality or Ministry of Lands
2. Submit completed form to municipal land office
3. Receive confirmation of successful application:▶ Priority given to those who: had owned land in this specific area, could pay for plot
type, and met gender and disability criteria.

4. Collect acceptance form and start making the payment within 14 days
▶ Failure to complete the payment and finalize the transaction within 60 days resulted
in reallocation of plot to another potential buyer

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Standard error estimation back▶ Cluster by insulae - the main units of plot size assignment (Abadie et al. QJE 2023)▶ Insulae fixed effects have high r-squared in explaining variation in plot size
assignment, though there is a bit of variation in plot size within insulae▶ There is also a bit of correlation between insulae▶ Results broadly similar when clustering on:▶ (Smaller) plot identifiers▶ (Larger)▶ Interactions of program areas with enumeration areas in the 2012 census▶ mitaa (34 local administrative units)▶ The 12 project areas

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