Lima Urbanization Process (2013)

8/10/2019 Lima Urbanization Process (2013)

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Limas urbanization process

Enver Figueroa

Economist, MPP, MPA (c)

Contents

Introduction........................................................................................................................................... 2

Literature review and theorybased diagnosis....................................................................................... 3

Empirical estimation of the demand for housing................................................................................ 11

References............................................................................................................................................ 17

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Introduction

Lima, the capital city of Peru, has been one of the fastest growing cities in the world for decades.

Lima is currently among the 30 world largest cities, in area as in population.

But that is not good news. In 2012, the Economist Intelligence Unit ranked Lima in the spot 88

out of 120. Among the main difficulties that hinder a good life in Lima we can mention the

transport system. It may take 1 hour to cover 6 km in rush hour on weekdays. Also, there a lot of

families yet without basic public services such as drinking water and sewer, mostly settled in the

edges of the city, a kind of sorting that closely follows the income distribution.

Growing crime, low educational standards, pollution, an others, have added to those mentioned

above. It has been so long that Lima has borne those problems that citizens have got accustomed

to them and policymakers as well as analysts call them structural.

But being as enduring as there are, is somehow surprising not having a pile of studies about, at

least speculating on possible solutions. Maybe such a shortage is due to a lack of scholars that had

produce analyses sharp enough to enlighten policymakers so as to try potentially successful

alternatives.

According to Caldern (1998), in a span of 53 years (1940 to 1993), Limas population increased

10 times, meaning that roughly every 10 years it doubled its population. In approximately the same

period (1940 to 1995) the urban area of Lima grew 11 times, from 42 km2to 507km2. In the last

20 years (1993 to 2013), however, Limas growth had a sharp braking, because its population and

its area grew almost equally little 34% and 39%, respectively.

Those broad facts let us draw an early conclusion: it seems that the structural problems of Lima

generated from 1940 to 1993, and the preliminary evidence indicates that the culprit is the

combination of a superfast population growth and a disorganized territorial expansion.

Therefore, the present document seeks to offers reasonable answers to two basic questions: why

Lima grew that much, and what led to the sorting Lima exhibits. At doing that, we would help us

with the insights and predictions from the Basic Urban Model (Alonso, Yinger, Glaeser, and other

authors).

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The document is organized as follows. After this introduction, we develop a literature review

intended to come up with a theory-based diagnosis. In the following section we outline what might

be a theoretical model for Lima, which may be used to make predictions about the further moves

of this metropolis. In the last section, we would try to support our propositions with data collected

from Lima.

Literature review and theorybased diagnosis.

In 1940 Lima city had 650 mil inhabitants (Rodriguez, 1968) and occupied an urban area of 42

km2, some 10,379 acres (Caldern, 1998). In 1967, the urban area of Lima extended to 190 km2

and with a population of 2.39 million people, meaning larger by 352% in extension and 268% in

population.

Caldern (1998) shows that in a span of 53 years (1940 to 1993), Limas population increased 10

times, meaning that roughly every 10 years it doubled its population. In approximately the same

period (1940 to 1995) the urban area of Lima grew 11 times, from 42 km2to 507km2. In the last

20 years (1993 to 2013), however, Limas growth had a sharp braking, because its population and

its area grew almost equally little 34% and 39%, respectively.

Those facts let us advance that the structural problems of Lima generated from 1940 to 1993, and

the preliminary evidence indicates that the culprit is the combination of a superfast population

growth and a disorganized territorial expansion. Consequently, it is important to try to answerwhy

Lima grew that much, and what led to the sorting Lima exhibits.

Griffin (1980) offers an interesting view of the evolution of the Latin American cities in contrast

with the common pattern followed by AngloAmerican cities. The author brings up that LA cities

were not supply places of services for industry and trade, and for that, working places of their own,

but instead as administrative and political centers. They never played the role Central Business

Districts (CBD), in the first place.

The typical configuration of those cities was a downtown around a plaza, with the most important

city buildings around: the catholic temple, the municipality building, and the very first conquerors

or founders houses, while the houses of all residents were placed in the blocks surrounding the

plaza in a way that the nearest ones were for the wealthiest and so going farther to reflect social

and economic status. Businesses scattered across residences, run by artisans and shopkeepers in the

first floor of their houses, all in strictly comply with the Laws if Indies.

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Figures 1 and 2.

As Figures 1 and 2 show, kept a certain distance from the coast in order to protect it from theattacks of pirates, for what Lima also held for long a city wall (demolished as late as 1869). Around

the city there was agricultural land, filling the gap between it and the shore.

Lima like other LA cities made the transit from the colony to the Republic in the 19thcentury

without many changes in their economic functions as well as their configuration. On the contrary,

while the Industrial Revolution would take place in Europe and masses of labor workers were

displaced from the farms to the new factories, the shortage in food supply was covered by a

growing agricultural production. That pattern dominated during the 19thcentury and the first

third of the 20th

century and so it reinforced the role of the cities as administrative centers, notproductive locations that would attract labor force.

It wasnt until well advanced the 20th century that the industrialization in Europe and the US

started to affect Limas cityscape. As foreign enterprises began to arrive, the demand for space and

services started also to put pressure on the citys capacities. Since urban services and amenities were

concentrated in the downtown, it was natural that the incoming businesses took on progressively

many of the residences, increasing the land values and so displacing first the upper and after the

middle class families out of the city center, who did not either whish live any longer in downtown

due the new congestive dynamics going on there.

As Griffin points out, elites started to make their way out of the city center but in a pattern that

differs from that predicted by the Basic Urban Model (Yinger, 2006; Glaeser, 2008), meaning, not

uniformly around the CBD, but along a mall (or alameda, as we call it in Spanish) that extend

outward from the downtown to their favorite resort (lower right parts in Figures 3 and 4). In Lima

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that mall was the Arequipa Avenue, created in 1919 by President Augusto Legua. It had sidewalks

adorned with palm trees and gardens, scattered with benches, and illuminated by streetlights. Of

course there other malls or walkscreated shortly before or after, such as the Nicolas de Pirola Ave,

the Salaverry Ave, or the Arica walk, but there were all close to each other while it was not clear yet

where the elites were to head farther.

Figures 3 and 4.

But, since expansion in the center was limited by the presence of the house of government, the

Congress building, the Cathedral, the City Hall, and other important structures either by political

or religious grounds, also business started to move outwards the downtown. Before the 30s, fouravenues were made to ease the transit from the haciendason the way to El Callao and vice versa:

the ProgresoAve (today Venezuela Ave), the road to El Callao (named after Colonial Ave and

currently Benavides Ave), the Arica Ave, as an upgrade of the Arica walk, and the Argentina Ave

(see the upper-left part of Figures 3 and 4). That last one would became the main industrial of the

city.

At this point the expansion of Lima was ordered and organized and the proximity of the nascent

industries to the city center was explained by the limited scope of public services and amenities

such as drinking water and electricity, but also by their need to stay close to governmental officesand the elites, who were not only power holders but also the educated and bilingual labor force to

manage the foreign companies.

When Lima entered the 40s it had begun to follow the Anglo-American city growing pattern and

transform its city center in a CBD, but then the context changed dramatically.

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There were three main trends that caused the overflowing growth of Lima dwellers. By that time

vaccination spread over the countryside dramatically reducing mortality rates. Also a concentration

of land property process reached a high point, starting to drive out peasants towards the big coastal

ranches (haciendas) first, and then to the coastal cities, especially Lima (CIRED & ONEC, 1974),

where the government would promote a nascent national industry (Industry Promotion Law of1959 and the General Law of Industries of 1974; Abugattas, 1999).

The population Lima increased from 1940 to 1972 in a context in which the countrywide

population doubled and the urban population growth rate averaged 5% per year over that period.

Now we remember the figures offered by Rodriguez (1968) that from 1940 to 1967 Lima grew

38% in extension and 268% in population. Provided that the average population growth rate in

Peru was around 2.2% annually, then we may impute 2.8% to immigration.

While in 1940 only 17.4% of the Limas population were immigrants, by 1960 they were 27.9%,and 43.5% by 1970. It has been proved that from 1961 to 1970, Lima received its largest migratory

wave (CIRED & ONEC, 1974). By that time, Lima already had 25.6% of its population living in

slums outside the urban boundaries of the city, all of them immigrants (CIRED & ONEC, 1974).

The Figure 5 below shows what Lima looked like after receiving successive waves of immigrants.

Figure 5.

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How migration affected housing and land prices? How these factors intertwined? How was their dynamics?

What can the BUM tell us about?

Lets start by responding the last question: a lot.

We describe now the way in which migration, land market and housing are linked, based on theinformation provided by Rodrguez (1968), Caldern (1998), and INEI (2007).

To Rodrguez (1968), the immigration brought about by itself a dramatic change in the residential

structure of Lima. The result was a sorting based on the income distribution. That sorting worked

through highly speculative rental and housing markets and was heavily influenced by social

prejudices and ethnic discrimination against the immigrants who came from the highlands.

He grounds his argument by pointing out the skyrocket increase in houses and apartments prices

as well as rents during the period 19401967. The average housing index price calculated by thegovernment for that period was 484, (an increase of 384%), but then Rodriguez collects more than

4500 newspaper advertisements and records the prices asked for new houses and apartments sales

and rents. He calculates the price and rent indices for new houses and apartments comparing their

data to the 1940 official levels. Thus he comes up with a rent index of 7,524 for new houses and

7,220 for new apartments.

The rents for houses ranged from 1,223 soles1to 7,975 soles (3 rooms, on average) and from 916

soles to 4,794 soles for apartments (2 rooms, on average), while 72% of the households had

monthly incomes lower than 7,000 soles, a range that included most immigrants. That means that

around 72% of the population, most likely immigrants were excluded of the rental market. The

only option left for them was to rent small rooms in the city center and the neighborhoods that

the middle class had started to abandon in their move outwards behind the upper class (a process

that Griffin called filtering-down).

Then the immigrants, at the time almost half of Lima peoples, faced this dilemma: living in city

center or nearby, enjoying amenities but in crowding versus relinquishing amenities and going for

space beyond the very edges of the city.

What may have been an immigrants decision in that case? The distribution of immigrants in the

economic active population by activities gives us a hint. Roughly 60% of immigrants did a manual

activity for a living and 53% of them worked in the tertiary sector. Although we do not know the

1The Peruvian currency at that time.

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conditional distributions of those processes, we can still say that, roughly speaking, a third of

immigrants could decide where to live not based on labor considerations just because they could

perform the same kind of activities anywhere and, therefore, able to move away from the city

center.

The decision of an immigrant family of moving out the city center may be consistent with the

assumption that the services from the house and the amenities could be perfect substitutes (see

Figure 8), the opposite of what Zabel (2003) assumes to estimate the demand for housing.

Many years after Fernndez (2012) provides evidence for the sorting predicted by Rodriguez. The

Figure 6 below shows the concentration of socioeconomic strata by districts in Lima towards 2004.

The green stands for high and middle-high income families, the orange is for middle class, and the

red for mid-low and low income. The further a family goes away from the city center, the lower

tends to be its income.

Figure 6.

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Caldern (1998) has given a perspective that goes in the same direction, but provides mores

elements that enrich the analysis. He jumps up directly to an advanced stage in the sorting process,

but stresses the role played by capital restrictions, the informal land market, and the inadequate

public policies.

For him the sorting had two moments. First, the immigrants were pushed out of the city, towards

the barren land beyond the green belt that since the colony surrounded Lima (here is where a

driver lacks). Lima was at that time somewhere thee 40s and the 70s and agricultural rents were

still larger than urban.

But then two things happened. In 1968 the military government decreed the Agrarian Reform by

which every agricultural lot with irrigation larger than 150 hectares were to be expropriated. The

policy exogenously reduced the expected value of agricultural land, meaning a reduction in of

the BUM (movement from A to B in Figure 7). In the meantime, more immigrants had beenarriving to Lima, placing in the city center first, but as the bid for room and services pushed the

prices of housing beyond the reach of their little incomes, immigrants started to move in the

barren land beyond the green belt that was twice as big as the urban area (Caldern, 1998). That

was the inflection point for the sorting that has characterized Lima for decades.

In a second moment, several agents (lawyers, notary publics, financial agents, even banks) collide to

covertly sell agricultural land to groups of immigrants gathered in pro-housing associations or

cooperatives, so as to avoid expropriation and that the government takes over the transactions.

Among those agents were some landowners themselves, who this way abandoned farming forrealty.

In a first moment, the shock by the Agrarian reform caused an exogenous decrease in along with

an exogenous increase in land supply for urban use L. One of the predictions of the BUM is

skyline in the CBD due to the bidding for housing, given a fixed amount of land. But because the

land supply exogenously rose, the land rent drawn from the urban uses of land dropped:

where

The fall in caused an expansion in the city, as the BUM predicts. But in a second moment, as

immigrants kept coming over to the city, they provoked a movement outwards in the BF for

{ }{ } { }

K u

qR uL u =

( ) 1

1a

A a

q C r

=

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Figure 82

By the early 90s, a series of reforms freed land and housing markets, and allowed private

investment in urban infrastructure. It was also a time when the immigrants and their descendants

started to consolidated economically, socially, and politically.

Empirical estimation of the demand for housing.

Many things have changed since the 90s in Lima. The economic growth spread out throughout

Lima converting the ancient barriadas in new urban centers plentiful of economic activity. This

new dynamics, closer to those of the cities in industrial countries, may have had the effect of

changing the sorting.

In order to assess if Lima behaves in line with the main predictions of the BUM, we decided to

estimate the price for housing in Lima using an hedonic approach, as well as the demand for

housing, following Zabel (2003).

2http://www.amigosdevilla.it/historia/antecedentes02.html

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For that, we have got the National Household Survey from where have extracted a set of variables

including: households income and spending; house ownership; rents effectively paid and

imputed; number of family members; schooling, age, gender, and employment status of the

household heads; spending in transportation (as a proxy for distance); physical characteristics of

the houses; and, availability or access to amenities (telephone, internet, cable, electricity, drinkingwater, and sewage).

We have collected the information for the years 2004 to 2012 and for the 43 districts (or towns)

within Lima, although at this initial stage we have only worked with the 2012 data.

In order to comply with the theoretical insights of the BUM, the coordinates of two city centers

have been calculated: the historical city center that comes from the colony times, and the modern

financial center. Using the geocode and timetravel Stata commands, as well as manual

calculations, the distances from every district to the financial center are reported, and subsequently

used as an explanatory variable in the housing demand function.

Hedonic estimation of the price for housing.

Having data of rents, and assuming an infinite time horizon (which is reasonable since a house

normally surpasses an individuals lifetime) the following equivalence has been used:

Rent (in each period t) = Price per unit of housing . Units of housing = P. H

And:

House price = Rents NPV = P. H / r

First of all, a series of dummies were created to account for house characteristics such as the

number of rooms, the materials of which the houses floor, ceiling and walls are made, and

whether the house has an independent access. Also a series of dummies to reflect the number of

amenities available (basic services such as drinking water and sewage) were generated.

Then, an hedonic housing price model is estimated taking the rents (either declared or imputed) as

proxies of the houses prices. The model estimated is that of the equation (14) in Zabel (2003):

=0 + 1 + 2 +

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The equation estimated in the second column of the Table 1 shows the houses value

decomposition into neighborhood value and structure value [Zabels eq. (14)].

Table 1.lnrenta lnrenta

habitac 0.0807*** 0.1372***(0.0036) (0.0101)[22.2810] [13.5989][0.0000] [0.0000]

habitac2 -0.0050***(0.0008)[-6.0533][0.0000]

pared_adec 0.2875*** 0.2804***(0.0236) (0.0237)[12.1860] [11.8433][0.0000] [0.0000]

piso_adec 0.2735*** 0.2687***(0.0313) (0.0312)[8.7471] [8.6248][0.0000] [0.0000]

techo_adec 0.4034*** 0.3988***(0.0191) (0.0192)[21.0720] [20.7348]

[0.0000] [0.0000]

agua_red 0.1669*** 0.1955***(0.0336) (0.0220)[4.9647] [8.8826][0.0000] [0.0000]

alcant_red 0.0609*(0.0336)[1.8136][0.0698]

telef 0.2488*** 0.2430***

(0.0155) (0.0155)[16.0174] [15.6327][0.0000] [0.0000]

cable 0.1497*** 0.1540***(0.0146) (0.0146)[10.2449] [10.5443][0.0000] [0.0000]

internet 0.2277*** 0.2261***

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(0.0161) (0.0161)[14.1781] [14.0639][0.0000] [0.0000]

Constant 6.6104*** 6.5253***(0.0312) (0.0344)

[211.9280] [189.8793][0.0000] [0.0000]

Observations 8,621 8,621R-squared 0.4739 0.4755AdjR2 0.473 0.475F 861.1 873.9RSE 0.382 0.382

Standard errors in brackets*** p


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Table 2.lnhousing

lnprice -0.1015***(0.0104)[-9.7728]

[0.0000]

lngasto 0.5319***(0.0226)[23.5024][0.0000]

viv_indep 3.3776***(0.1376)[24.5497][0.0000]

sin_ing 0.1221***

(0.0378)[3.2256][0.0013]

escolares -0.1614***(0.0388)[-4.1596][0.0000]

dist_car_time 0.0031**(0.0013)[2.3703][0.0178]

Constant -3.1936***(0.2753)

[-11.5995][0.0000]

Observations 8,920R-squared 0.4353AdjR2 0.435F 227.8RSE 0.382

Standard errors in brackets*** p


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Sorting

Finally, building upon the results just obtained, we explored if in recent years the sorting in Lima

was the same observed in the past decades. For that, the data have been ordered by income

quintiles.

Figure 9 shows that middle-high and high income families have started to move outwards the city

center.

Figure 9.

Figure 10.

Likewise, Figure 10 shows that housing demand increases by income. Together the two trends just

looked over manifest that Lima now exhibits the normal sorting predicted by the BUM: highest

income people live away from the BDC in order to afford more housing.

0

10

20

30

4050

60

70

1 2 3 4 5

distance (km)

200

250

300

350

400

450

1 2 3 4 5

housing demand

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References

Abugattas, Luis (1999). Estabilizacin macroeconmica, reforma estructural y comportamiento

industrial: la experiencia peruana. CEPAL, Santiago, Chile.

Caldern, Julio (1998). Mercado de tierras en reas agrcolas y periurbanas de Lima. DEBATE

AGRARIO N 28. Centro Peruano de Estudios Sociales CEPES, Lima Per.

Comit Internacional para la Coordinacin de las Investigaciones en Demografa CIRED and

Oficina Nacional de Estadsticas y Censos ONEC (1974). La poblacin del Per. Lima, Per.

Fernndez de Crdova Gutirrez, Graciela (2012). Nuevos patrones de segregacin socio-espacial

en Lima y Callao 1990-2007. Pontificia Universidad Catlica del Per, Lima, Per.

Glaeser, Edward (2008). Cities, agglomeration and spatial equilibrium. The Oxford University

Press, New York.

Griffin, Ernst (1980). A model of Latin American City Structure. Geographical Review, Vol. 70,

No. 4, pp. 397422. Got from: http://www.jstor.org/stable/214076

INEI (2007). Per: migracin interna reciente y el sistema de ciudades. Lima, Per.

Quispe, Arl (2012). Una aplicacin del modelo de precios hednicos al mercado de viviendas enLima Metropolitana. Revista de Economa y Derecho, Vol. 9, No. 36. Universidad Peruana de

Ciencias Aplicadas, Lima Per.

Rodrguez, Alfredo (1968). Oferta de vivienda y terrenos en Lima Metropolitana 1940 1967.

Desco, Lima Per.

Yinger, John (2006). Housing and Commuting: The Theory of Urban Residential Structure. E-

book, version 1.4.

Zabel, Jeffrey (2003). The demand for housing services. Journal of Housing Economics 13 (2004)16-35.

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