Sona Kalantaryan Housing Market Responses to Immigration Shocks: Evidence from Italy.
Housing Market Responses to Immigration Shocks; Evidence from Italy
Ph.D. Student, University of Turin, Italy
(Preliminary draft)
Abstract
In this paper, I examine empirically the impact of immigration shocks on the dynamics of housing prices across Italian provinces during the period from 1996 until 2007. There is massive debate going on upon the impact of current intensive immigration flow on the well-being of native Italian population and Europeans in general. The ongoing research is mainly focused on the influence of immigration on Italian labor market outcome. However, if the ultimate subject of interest is the changes in real income and wealth, then the possible impact of immigration shocks on prices should be taken into account as well. Such an intensive inflow extra consumers with potentially different preferences in the housing consumption leads to an increase in demand for housing, as well as to the segregation among immigrants and natives in the housing market. Housing expenditures represents a big portion of family expenditures for renter and income for house owners. In addition, due to underdevelopment of Italian financial markets it is one of major direction for savings for many Italian households. Hence, the estimation of influence of immigration on housing prices can significantly improve the understanding of immigrants’ influence on real income and wealth of natives.
I used data on the self-reported housing values estimated using data from the Survey of Households Income Wealth in Italy to measure the changes in housing prices in Italian provinces. Using number of valid residence permits as a measure of immigration stock, I find that the increase in concentration of immigrants in Italian provinces has positive but declining effect on average housing prices in province. The instrumental variable estimation thought confirm the positive affect in all used specifications they do not the resulting coefficients at measure of foreign presence turn to statistically not significant ones. Keywords: Housing market, Immigration, House prices, Italy
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Sona Kalantaryan Housing Market Responses to Immigration Shocks: Evidence from Italy.
1.1 Introduction and Motivation
There is massive flow of economic literature emerged during last two decades
dedicated to investigation of the influence of immigration shocks on the economic position of
host countries. Such a strong interest to the topic is motivated by recent sharp increase in the
intensity of labor force mobility. The scale and intensity of the ongoing research covers many
aspects of influence of immigration shocks on host economies; already now, it is possible to
draw conclusions about many of them. However, there are still active and open debates going
on upon the influence of immigrants on the well-being of native populations. Still, the
prevailing part of economic literature on immigration is dedicated to the labor market
outcomes. The vast majority of the ongoing research is focused on the influence of
immigration on the employment and wages of host country1. However, a comprehensive
picture can be obtained by considering immigrants not only as extra labor force with possibly
different labor force characteristics, but also as extra consumers with potentially different
preferences in the consumption process.
Estimation of changes in employment and wages alone does not give possibility to
evaluate fully the effect of immigrants on the real income and real wealth of population in the
destination country. Despite their insightfulness, these results tell only part of the story.
Actually if the ultimate subject of interest is the effect of immigration on the real income of
resident population, then the impact of immigration shocks on prices should be taken into
account as well. Indeed, changes in prices clearly have an effect on real wages and real
wealth. Moreover, changes in relative prices may have distributional effects in addition to
those arising from changes in wages.
It is worth to mention that despite the intensity of current economic research on
immigration impact on the labor market, there is no evidence that immigration shocks alter
wages much. For example, the meta-analysis carried out in Poots&Cochrane (2005) based on
eighteen papers from international literature suggests that the effect of immigration on local
wages is very mild; one per cent increase in local labor force leads to reduction in wages less
than 0,1 per cent. To explain the absence of strong reaction of wages to immigration shocks
response economic literature proposes three possible explanations. First, natives may choose
the areas being afraid of possible completion they may face because of immigrants’ inflow
(Filer(1992). Second, immigrants may choose the cities with positive shock in productivity 1 See, for example, Brucker and Jahn (2008), Clark and Drinkwater (2008).
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Sona Kalantaryan Housing Market Responses to Immigration Shocks: Evidence from Italy.
and wage growth. Finally, the labor market is more elastic than it is considered (Lewis
(2004)).
Migrating people usually carry with themselves to the country of destination not only
their skills but also their traditions, customs and attitudes; this makes them different from
natives in many dimensions. Among many other fields, the cultural background affects the
behavior of immigrants as consumers. The resulting shift in composition of consumers affects
not only the scale but also the structure of consumption of goods and services in a country or
a region. Those changes in their turn alter the structure of the aggregate demand. The effect is
more vivid once the supply for particular good or service is relatively inelastic. In case of
inelastic supply, the shift in demand results in changes in prices. Housing market is usually
considered as one with inelastic supply in the short run. Hence, the shift of housing demand
due to the inflow of immigrants can alter the housing prices in the area. The resulting changes
cannot leave the real income and wealth of those previously living in the area unaltered. First,
housing represents a considerable share of households’ wealth. Second , the housing-related expenses
represent an important part of overall expenses for majority of households. House-price dynamics are
a key factor in the process of reallocation of household wealth (Davies and Shorrocks, 2000)
interacting with financial asset prices (Sutton, 2002) and conditioning labor mobility (Cannari, Sestito
and Nucci, 2000).
Taking into account the above mentioned arguments, in this paper I evaluate the
influence immigrants may have on the housing market in Italy. The choice of Italian housing
market as the subject for empirical estimation is motivated by several reasons. First, Italy was
traditionally considered as a country facing continues waves of emigration. The situation has
changed dramatically only recently. Immigration became one of the most distinct features of
Italian economic reality during last two decades. The country became a desirable destination
point for hundreds of thousands immigrants with European and non-European origin. The
number of legally registered immigrants increased from 648 to 2.414 thousands during period
from 1992 to 2007. However, the intensity of immigration flow was not homogeneous across
Italian provinces. Such a drastic change could not leave housing market uninfluenced.
Second, the peculiarities of Italian financial markets are such that houses, or real estate in
general, serves as an alternative way of wealth accumulation for many Italian families. Italian
households have very strong preferences towards housing wealth (Brandolini et al., 2004;
Faiella and Neri, 2004). and vivid orientation towards owner occupation (Paiella, 2001; Di Addario,
2002). For the considered period of time dwellings constituted approximately 80 percent of total real
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Sona Kalantaryan Housing Market Responses to Immigration Shocks: Evidence from Italy.
assets of Italian households (Cannari et al., 2008). ( Though financial aspects of housing markets
are out of the scope of this paper, it is worth to mention that changes in real price of such an
important component of households` income and expenditures cannot leave real income or
wealth of natives unaltered. Third, according to Venturini&Del Boca (2003) Italian
population is immobile within country; hence, the inflow of immigrants coupled with
immobility of natives can intensify changes in local demand for housing units and housing
prices. Investigation of the link between international immigration and the housing prices in
Italy can serve as good opportunity first, to extend existing research on housing market
response in European region, and second, to enhance the previously done research on
influence that immigration has on real income and wealth of population in Italy.
To understand better the mechanism through which immigrants can influence prices
in general, let us see what the economic theory proposes. Economic theory suggests that
immigration affects prices through different and opposing mechanisms making the overall
effect ambiguous and difficult to predict. On the supply side, production costs may increase
or decrease depending on the way the changes in the overall composition of labor supply
affect relative wages. In a fully traded economy, it would not translate into changes in output
prices, but would rather result in changes in factor intensity or output mix. Still, part of the
output will typically be non-tradable. In this case, one can expect final prices to decrease for
those goods and services produced at lower cost, and reverse result for those goods and
services that immigration has made relatively more expensive to produce. Similarly, the
effect of immigration on the demand side is ambiguous as well. It depends heavily on the
changes that immigration shocks may cause in the composition of consumers, which in its
turn transforms into changes in demand for goods and services. However, these changes are
not necessarily homogenous across different goods and services.
The above-mentioned theoretical insight refers to the response of prices to
immigration shocks in general. What if, the subject of interest is a specific segment of
market. In this particular case, how can housing market respond to the inflow of immigrants?
The response of both demand and supply sides should be taken into consideration. Housing is
considered as a non-tradable good with relatively inelastic supply in the short term. Hence,
qualitative and quantitative changes in housing demand caused by immigration shocks may
translate into changes in housing prices and rents. However, the direction of these changes is
not easy to predict. Immigrants, as additional consumers, do not only create simple increase
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Sona Kalantaryan Housing Market Responses to Immigration Shocks: Evidence from Italy.
in aggregate demand but also potentially change its composition. In fact, foreign population
may differ from natives in many aspects including tastes. In other words, immigrants can
have preferences different from the ones specific to native population. For example, due to
low income they may prefer to occupy relatively cheap housing units, or choose to live in
overcrowded flats. The price effect depends also on the reaction of natives to the inflow of
foreigners into area. On the one hand, if immigrants and natives are compliments in
production process, then the immigrants as additional consumer can increase the housing
demand, which may translate to increase in housing prices and rents. On the other hand, if
immigrant and natives are substitutes in the labor market, natives may prefer to leave the area
to avoid possible competition. In this case the outflow of natives may neutralize the effect of
positive immigration shock on housing market. As a result, prices decrease or remain
unchanged. Though housing market can be one of the major non-labor market channels
through which immigrants can influence the well-being of natives, the overall demand effects
are not clear a priori The uncertainty about the direction of the final effect leaves a room for
further empirical analysis.
In this paper, I investigate the effects of the growth in the immigrant population on the
housing prices across Italian provinces over the period from 1996 until 2007.
The rest of the paper is organized in the following way. Section 2 presents and
analyzes the related literature Section 3 presents the identification strategy, and the
econometric issues. Section 4 describes the data. Section 5 reports and discusses the results.
Section 6 concludes the paper.
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Sona Kalantaryan Housing Market Responses to Immigration Shocks: Evidence from Italy.
2 Related Literature 2.1 Papers on prices
The literature considering the effect of immigration on prices exists but it is scarce.
There is some limited empirical evidence which is mostly single country analysis focused on
the influence of unskilled immigrants on prices of different goods and services. Besides the
scarcity of research in this particular direction, the prevailing part of it is focused on countries
considered as traditional destinations for immigrants` flows, such as the USA, Canada, New
Zealand.
To my best knowledge, there are three main recent articles, which consider the
influence of immigration shocks on the dynamics of prices. The problem was first elucidated
in Cortes (2008) and then Frattini (2008), who investigate the effect of immigration shocks
on prices in the UK and in the USA respectively. According to Cortes (2008) and
Frattini(2008) immigration shocks have significant however quantitatively limited effects on
prices, and that those effects are different for services and tradable goods. The question about
the influence of low-skilled immigration waves to the price dynamics in the USA was first
taken up in Cortes(2008). The empirical results state that the increase of immigrant to native
ratio by 1 per cent leads up to 0.2 per cent decrease in prices of services. These results are
confirmed also in Frattini (2008) where changes in price dynamic as a result of immigration
shocks in the UK from 1996 till 2006 are considered. The empirical study states that
immigration flow had dual effect on prices in the UK during the considered period. On the
one hand, immigration contributed to the reduction of price growth of services in sectors
where the concentration of low-wage workers is high. The effect is stronger when prices of
such services as restaurants, bars, and take-away food are considered. The inflow of relatively
cheap labor force lead to reduction in production costs of these services during the considered
period. Plus, the inflow of immigrants could increase competition in the sectors providing
these services; very often immigrants run bars or small restraints. In other words, the
observed negative effect on the prices of such services is probably achieved through labor
supply channel. On the other hand, the opposite effect is documented once the prices of low-
value grocery goods are considered. The inflow of immigrants could cause increase in
demand of such goods, which later could be translated into changes in their prices. Hence, in
this case prices were probably influenced through demand channel.
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Sona Kalantaryan Housing Market Responses to Immigration Shocks: Evidence from Italy.
Some opposite effect is documented in the empirical work by Lach (2007), who
shows reduction of grocery prices as a result of immigration shock. It is necessary to mention
that this paper examines the behavior of prices following the unexpected arrival of a large
number of immigrants from the former Soviet Union to Israel during 1990. Once the size of
native population, city and time effects are controlled the estimations show that a 1
percentage point increase in the ratio of immigrants to natives in a city decreases prices by
0.5 percentage point on average. However, the documented negative effect can be explained
by the fact that Former Soviet Union immigrants had higher price elasticity and lower search
costs than the native population. Actually, most of them were not active in the labor market.
The market economy reality where they suddenly appeared could motivate the grocery
shopkeeper to attract new potential customers by temporary price decrease.
As it is mentioned above, the changes in prices due to immigration shocks cannot be
considered as simple change in scale. The response of prices is not homogenous across
various goods and services. Such a change in the distribution of prices may cause
distributional consequences of real income in the country of destination. For example,
according to the estimation presented in Cortes(2008) the low-skilled immigration wave
during the period from 1980 to 2000 had the following effects. It increased the purchasing
power of high-skilled workers living in the 30 largest cities of the USA by in average of 0.32
per cent and decreased the purchasing power of native high school dropouts by a maximum
of 1 per cent and by 4.2 per cent of Hispanic low-skilled natives. Frattini (2008) reports
similar evidence. The author states that the types of items that experience the highest price
reductions (food and drinks out of home, dry cleaning, hairdressing) are those that tend to be
relatively less consumed by low-income households. At the same time, the share of
expenditure for food and drinks which are consumed at home and for which positive price
effects is found, tends to be inversely proportional to the household's income. Coupled with
the distributional effects on wages for the same period of time highlighted by Dustmann et
al.(2008), it can be stated that there indeed was income distributional effect in the UK due to
recent immigration shocks.
2.2 Papers on housing prices and rents
The influence that immigration shocks have on housing prices and rents can be
considered as a particular case of immigrants’ influence on prices in general. However,
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Sona Kalantaryan Housing Market Responses to Immigration Shocks: Evidence from Italy.
relying only on the existing economic literature it is quite complicated to find common
judgment upon the influence of immigration shocks on the housing market in the countries of
destination.
There are number of reasons that make this task non-trivial. First, the existing
economic literature on responses of housing markets to immigration shocks is scarce.
Research on the determinants of the price of low quality housing was focused mostly on the
effects of zoning and land use regulation (Malpezzi&Green (1996)) or profitability of
constructing low quality housing (Ohls (1975)) Second, most of scientific works are case
studies, so it is not easy to draw common conclusion. Finally, the existing scientific works
examine mostly the housing markets of countries considered as traditional destinations for
immigrants, such as the USA, Canada, New Zealand and Australia. The question becomes
even more complex once the task is the application of the existing results to the countries for
which inflow of immigrants is just recent experience; to Italy in this particular case.
The first attempts to document the response of American housing market to
immigration shocks were made in the 80s. For example, Muller&Espenshade (1985),
Burnley, Murphy&Fagan(1997) as well as Ley&Tuchener (1999) find strong between inflow
of immigrants and housing prices. However, these early studies have rather descriptive
character. The first attempt to measure empirically the influence of immigrants on the US
housing market was made by Susin(2001) and Saiz(2003), which consider the changes in
rental prices in Miami after the Mariel boatlift, when the exogenous immigration shock added
extra 9 per cent to Miami’s renter population during 1980. Saiz (2003) found that the
unexpected immigration shock led to an increase in rents in Miami from 8 to 11 per cent
more than in the comparison groups between 1979 and 1981. By 1983, the rent differential
was still significantly positive. The change in rents was mainly for dwellings occupied by
low-income Hispanic residents, probably because of the tendency of immigrants to settle
initially in the districts populated by Hispanic residents. The paper states that the rental price
for units of higher quality was not affected by the immigration shock. For the same period,
the relative housing prices moved in the opposite direction. Despite the relative increase of in
rents in Miami, the immigration flow did not alter the rent to income ratio or so called “rent
burden”2 in Miami (Gleulich, Quigley and Raphael,2005). The effect is robust with respect
to rental units less probable to be occupied by immigrants. A similar conclusion is drown in
2 Rent burden is rent to income ratio. (To give more detailed explanation to seem more serioud )
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Sona Kalantaryan Housing Market Responses to Immigration Shocks: Evidence from Italy.
Card(2007). Estimating the influence of immigrants on the US cities the author finds that the
magnitude of effect on average wages he estimates is very similar to the one found by
Saiz(2006) for housing market. Hence, the so called “rent burden” remains roughly constants
“Mariel boatlift” case described in Saiz (2003) and Susin (2001) is very special (in particular
point of time in particular city) and could hardly be generalized. However, this study together
with the previously mentioned ones suggests that the labor market is not the only area where
the consequences of immigration shocks should be looked for.
An attempt to get more general picture upon the impact of immigration shocks on
housing rents in American cities can be found in papers that are more recent. For example,
Saiz(2006) demonstrates that there is positive association between rents growth and
immigration inflows for all metropolitan areas; 1 per cent immigrants` inflow to a city
population leads to 1 per cent increase in average rents and housing values. These results
confirm the initial expectations of the author about the magnitude of effect on housing
market. Indeed, the order of magnitude of housing market responses to the immigration
shocks is much bigger than the ones observed in the labor market, which can at least partially
explain the fact that wages do not respond strongly to immigration shocks.
The further extension of the research topic was made through consideration of labor
and housing market simultaneously. Ottaviano&Peri(2007) use a general equilibrium
approach to evaluate the effects of immigration flows on skill-segmented labor and housing
market in the USA. According to the model developed by the authors, the inflow of
immigrants is associated with long run higher average wages and higher average rents. The
rental price of units occupied by high-educated residents is much more sensitive to
immigration shocks compared with those occupied by low-educated ones. As regards to
wages, the model predicts the largest positive effect for the most educated ones and small
negative effect on least educated ones. Once the results generated by the model are compared
with the real data, the following predictions were confirmed First, due to the complimentarity
between natives and immigrants, the overall production effect is positive on natives. Second,
immigrants that are more educated increase the competition for the housing in the best areas
and make the prices to increase from 0.6 up to 2.3 per cent. This finding contradicts to the
results of Saiz (2003) who estimates the strongest effect to low quality dwellings occupied by
Hispanic immigrants. Third, even the natives in the lowest skill group have higher house
ownership rate hence each group of education receive positive transfer from immigrants.
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Sona Kalantaryan Housing Market Responses to Immigration Shocks: Evidence from Italy.
Somewhat different results are documented for New Zealand. Stillman&Mare (2008)
empirically estimates the response of housing market to immigration shocks in New Zealand
during period from 1986 till 2006. New Zealand along with the USA, Canada and Australia is
another country traditionally considered as one of the main destinations for immigrants.
Hence, one can expect results similar to ones stated in the papers considering American
housing market responses (for example, Saiz(2006), Ottaviano&Peri(2006)). The estimation
results demonstrate that 1 per cent increase of population in the area is associated with
increase in local housing prices from 0.2 to 0.5 per cent. However, the authors find no
evidence of positive relationship between inflow of foreign-born immigrants to an area and
local housing prices. The only strong positive relationship found is the one between inflows
of New Zealanders previously living abroad into an area and local housing prices, which
however is not robust over time.
With the exception of the work by Gonzales&Ortega (2009), to my best knowledge
there has been no economic research on the link between international immigration and the
price of urban housing in European countries. Gonzales&Ortega (2009) presents empirically
estimated effect of immigration on housing prices and residential construction activity in
Spain over the period from 1998 to 2008. During the mentioned period, Spain was
experiencing both a spectacular immigration flow3 and an impressive housing market boom.
The authors find sizeable causal effect of immigration shocks on both quantities and prices in
the housing market. The inflow of immigrants increased housing prices by about 52 per cent
and caused growth in construction of new housing units equal to 37 per cent; it accounts for
one third of the housing boom in terms of both new construction and prices.
As it can be seen from the reviewed literature the prevailing part of papers are focused
on countries that used to be traditional destination for immigration flows4. In other words,
immigration has always been serving as one of the sources for extension of their labor force.
However, there is no common agreement upon either the existence, magnitude or the
direction of the effect of immigration shocks on housing market.
3 The average Spanish province had an immigrant inflow equal to 17 per cent of its initial working-age population 4 See for example, Saiz(2003), Saiz(2006), Ottaviano& Peri(2007), Card (2007) etc.
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Sona Kalantaryan Housing Market Responses to Immigration Shocks: Evidence from Italy.
2.3 Displacement of natives by immigrants
The magnitude of the effect immigrants have on the local housing prices depends
heavily on the reaction of natives on the presence of immigrants in the area. Absence of
housing price effects does not imply absence of immigrants’ impact on housing opportunities
of formerly residing population. If there is sizable displacement effects for natives, the cross-
province estimation will not capture the full impact on housing prices. In other words, if
immigrants displace natives, local housing price effects of immigration will be attenuated. If
the displacement is one to one, in case of perfect substitution, then the price effect should
vanish completely. In case of modest displacement, the estimates will provide information
upon lower bounds of the full magnitude of the effects.
The effect of immigration shocks on the migration decision of natives depends on
several factors. There are some questions to answer and the answers are not trivial. For
example, are immigrants attracted to a region by the same characteristics as the natives?
Alternatively, what kind of preferences do natives have? On the one hand, immigrants’
decision about settlement pattern may differ from the native one. Often the key factor for
immigrants is the initial settlement of immigrants from the same country of origin. They are
more probable to be supported by their compatriots (Pedersen&Pytlikova&Smith, 2008). On
the other hand, the utility of natives from immigration shocks depends heavily on their
complimentarity in the labor market. Particularly, if immigrants and natives are compliments
in the production process then the areas where immigrants settle will become more attractive
for natives as well. Exactly the opposite is expected, if natives and immigrants are substitutes
in the production process. In this case, natives can consider the areas densely populated by
immigrants as less attractive. Natives, being scared by possible completion created by
immigrants in the labor market, may try to avoid those areas.
The research on the USA regarding the possibility and extent of displacement of
natives conclude that there is no “one-for-one” offsetting outflow of natives caused by inflow
if immigrants5. To my best knowledge, there is no economic paper considering the
displacement of natives by immigrants in Italy. However, some preliminary picture can be
5 For comprehensive research on possible displacement and size in the USA see, for example, Card and DiNardo (2000), Card (2001, 2005), Federman, Harrington, and Krynski (2006), Ottaviano &Peri (2007), Borjas (2006)
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Sona Kalantaryan Housing Market Responses to Immigration Shocks: Evidence from Italy.
drawn based on from the situation in the labor market. If there is competition between natives
and immigrants in the Italian labor market, it can signal about possibility of the displacement
of natives from areas densely populated by immigrants. People usually form their opinion
upon economic situation in the country relying heavily on information they find through
mass media. Recently held social surveys6 show that Italians consider the received large
masses of uneducated workers as negative phenomenon; they depressed wages, worsened
skill-intensity of the economy, hurt native, especially the less educated. However, economic
research does not support this popular opinion. A comprehensive analysis of history of both
Italian emigration and immigration is presented in Venturini&Del Boca(2003). The empirical
analysis (based on repeating cross-section data covering the period from 1989 until 1995)
estimates the influence of immigration shocks on wages and employment of natives by
branches and Italian regions. The share of immigrants flow with respect to natives has
positive impact on the wage growth of natives. The complimentarity effect is even stronger
when the estimation is performed for data referring to Northern regions of Italy, blue collars
or small companies. However, the response of Italian labor market is nor linear. The negative
estimates suggest that once the share of foreign workers reaches 10-14 per cent in regions and
branches, foreigners will turn from compliments to substitutes and will compete with natives
in Italian labor market (Venturini&Del Boca,2003). Similar results are documented in
Venturini&Villosio (2002); the effect of foreigners’ concentration on the employment
opportunities of natives is discussed. The estimation results state that the probability of
moving from employed to unemployed either decreases once the percentage of foreigners
increases, or is statistically not significant. The effect of percentage of foreigners on the
probability to find new job for natives is estimated as positive. Yet, the probability of finding
job for young people looking for their first job seems slightly negative ( Venturini&Villosio,
2002).
Overall, the estimation based on the data referring to legally present immigrants and
regular Italian labor force shows that immigrants and natives are compliments in the labor
market. However, the extent of empirical research is usually constraint by availability of data.
Most of above presented results are obtained using the number of legally present immigrants
as a measure of foreign presence in Italy. Yet, continuous increase of irregularly present
foreigners who are eager to accept the lower wages as well can stimulate enlargement of 6 See for example 1995 ISSP , National Identity Module or “Demoskopia” survey held by Fondazione Rodolfo DEBENEDETTI(fRDB)in 2003
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Sona Kalantaryan Housing Market Responses to Immigration Shocks: Evidence from Italy.
shadow economy and cause flow of capital from legal sector to illegal on. In this way
irregular immigrants can indirectly compete with natives both in irregular labor market and
with regularly employed natives through stimulating the growth of the irregular economy
injuring the regular one(Venturini&Del Boca, 2003). Even if focus is turned to irregular labor
market where immigrants can undermine the legal employment by increasing the scale of
shadow economy, Italian immigrants do not seem be in completion with natives
(Venturini,1999). The general pattern for Italian immigrant workers appears to be a
fragmented career; restricted to seasonal or temporary jobs as well as alternating between
legal and illegal employment (Venturini&Villosio, 2008). Consequently, one can concluded
that there is no direct competition between natives and immigrant workers. The above-
described results, coupled with Italian labor force immobility, allow supposing that at least on
provincial level, there is no displacement of natives cause immigrants through competition in
Italian labor market.
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Sona Kalantaryan Housing Market Responses to Immigration Shocks: Evidence from Italy.
3 Methodological approach
3.1 OLS estimation
In this section, I present the empirical approach used to estimate the impact of
immigration on prices in provincial housing markets. As a dependent variable, I consider the
logarithm of prices per square meter of housing units. The main explanatory variable is the
number of legally present immigrants in province in a given period. Housing data comes
from biennial survey and it is available for every second year. The peculiarities of data on
housing prices are such that the data is available for every second year.
To derive the regression model I first present the simple model in levels.
Equation (1)
ln ln ln
Where,
ln(Pit) is the dependent variable: mean log price of residential unit per square meter in
province i and in time period t.
ln(IMMit) is the main independent variable, which is the stock of immigrants in province
measured as the of number of valid residence permits in province i in time period t.
ln(POPit) measures the population in province i in time period t.
Wit is the set of macroeconomic variables such as employment rate or GDP per capita in
province i in time period t. The set of macroeconomic controls is supposed to capture
differences in housing prices due to differences in economic conditions between provinces.
μt is the set of year dummies which captures national trends in inflation and other economic
variables.
φi is the set of province dummies, which captures time-permanent and province-specific
characteristics.
Finally, εit is the error term. The main estimate of interest is β, which captures the effect of concentration of
immigrants on the price of residential units. The model constructed in this way let β capture
the effect of increased immigrant population net of increase in overall population in the
province, while γ will capture the effect of overall change in population.
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Sona Kalantaryan Housing Market Responses to Immigration Shocks: Evidence from Italy.
To get rid of all province-specific time-invariant factors, which can influence both the
immigration flows and dynamics of housing markets I take the first difference of the model
described in equation (1). After first-difference transformation, the model takes the following
form
Equation (2)
∆ln ∆ ln ∆ln ∆ ∆ ∆
β coefficient have the following interpretation: the percentage change in housing price
as a result of 1 per cent increase of foreign population in province. It can be the case that the
effect of immigration shocks on housing market dynamics has not linear patters. To capture
it, I propose to compare the estimation results from Equation 2 with the estimation that
include the squared term of number of immigrants7.
Equa )tion (3
∆ln ∆ ln ∆ ln ∆ln ∆ ∆ ∆
3.2 Potential problems
Empirical estimation of impact of immigration shocks on housing market contains a
number of challenges. In this section, I am trying to present these problems and possible
remedies to tackle them.
First, the measure of immigrants’ concentration I use in the estimation is based on the
number of valid residence permits issued by Ministry of Interior Affairs of Italy. Hence, this
measure of foreign presence in Italy refers only to legally present immigrant and says nothing
about illegal immigrants which are not necessarily have the same distribution pattern as legal
ones across Italian provinces. However, illegal immigrants influence the housing market as
well. Measuring immigration stock by using the number of officially present foreigners ,
which does not take into account illegal immigrants, can lead to a bias in the estimates of
immigration concentration influence on housing market outcomes.
15
7 As an alternative, I estimate also using as a dependent variable the concentration of immigrants and its squared term. ∆ln ∆ ∆ ∆ ∆ ∆
Sona Kalantaryan Housing Market Responses to Immigration Shocks: Evidence from Italy.
To tackle the this problem it is necessary to understand where the illegal immigration
arises from and whether and how it is correlated to legal one in Italy. Immigration is a quite
recent phenomenon for Italy, which started in 1980s. The initial unplanned immigration flow
and some control problems created numerous illegal immigrants in Italy. The first law
regulating the inflow and the presence of foreigners was approved and implemented in 1990,
with future amendments in 1995, 1998 and 20028. The Italian immigration policy is based on
residence permits issued by Ministry of Internal Affairs.
Bianci et al.(2008) using from regularization in 1995, 1998 and 2002 prove
empirically that the combination of logarithm fixed effects may attenuate measurement
errors related to illegal immigration.9 Particularly, if the number of officially present
immigrants is proportional to total immigrants and the constant proportionality is the product
of province and year specific c nt g ue 10 onsta s, then the followin is tr
Wher
and are respectively the logarithm of total (official plus the number of
application for regularization) and official share of immigrants with respect to total
population in Italian province i at time period t. The above-mentioned arguments allow
assuming that using logarithm of ratio of number of permits over population (once time and
province dummies are included) helps to avoid bias created by measurement error due to
illegally present immigrants. For the reasons described above, I introduce an alternative
specification, where instead of using number of immigrants and population of province I use
the concentration of immigrants in province; that is the logarithm of immigrants over
population ratio. The first differenced model of this specification has the following form.
e,
Equation (4)
∆ln ∆ ln ∆ ∆ ∆
8 Regularizations in 1995, 1998, and 2002 in ed 246, 217 and 700 thousand individuals, respectively (Bianci et al., 2008). volv9 The relationship between actual and official immigration once both the province and year fixed effect are taken into account are the following: the OLS estimated coefficient of is 0.92 and the R2 is 99%.. For further details see Bianci et al.(2008). 10 For more detailed derivation see Bianciet al.(2008).
16
Sona Kalantaryan Housing Market Responses to Immigration Shocks: Evidence from Italy.
Another problem that can undermine the reliability of the estimates is omitted variable
problem. Indeed, the location choice of immigrants can be motivated by unobserved factors
that cause changes in the housing prices as well. Suppose that for some reason some
provinces became more attractive (for example, expectation of future improvement of
economic conditions or amenities). It will lead to more intensive flow of immigrants and
natives; hence to higher housing prices. In this case, the omitted variable would lead to
overestimation of immigration impact on housing prices.
Endogeneity creates additional problems as well. Indeed immigration flow can be
endogenous to housing prices. On the one hand, immigrants may tend to avoid the regions
where, given the similar employment rate and GDP, housing prices are higher. In this case,
the OLS estimation would lead to downward bias. Immigrants might also be heavily
influenced by cheap available housing in depressed areas or may be especially attracted to or
attractive for declining industries (Filer, 1992). On the other hand, the inflow of immigrants
may make natives avoid area or move out because of the competition in the local labor
market. The possibility of native displacement by immigrants is intensively discussed in
Filer(1992), Card(2001), Card&DiNardo(2000) etc. In fact, if immigrants cause “one-to-one”
offsetting outflow of natives there will be no shift in local housing demand, while positive
effect will suggest that even if there is any displacement it is does not have “one-to-one”
nature. It is necessary to mention that immigrants may have different preferences in housing
market. They might evaluate network of their compatriots or other amenities as more
important factors than the housing prices. Immigration shock can lead to a housing demand
shock and as result outflow of natives that are more sensitive to increase in housing rents or
prices. Hence, the outflows of native due to fear to face competition in the labor market can
weaker the effect of immigrants on housing prices; part of the effect would take place
through native displacement. As a result, the OLS estimate will tend to underestimate the
effect of immigration shocks.
The discussion above suggests that the sign of bias depend on many factors and is not
easy to predict; obtained results should be interpreted with caution.
17
Sona Kalantaryan Housing Market Responses to Immigration Shocks: Evidence from Italy.
3.2 Instrumental variable approach
Instrumental variable approach can serve as a plausible strategy to solve the
endogeneity problem described in the previous section. I use the historical settlement pattern
of immigrants to instrument the current inflow of immigrants in Italian provinces. The current
geographical distribution of immigrants across provinces is supposed to be correlated with
the historical one and uncorrelated with the current province specific housing market shocks.
It is well stated in the economic literature that a number of noneconomic factors
determine the decision upon the destination for international immigrants. The prior existence
of enclaves of immigrants from a country is an important magnet for future flow from a
country. It turned out that immigrants rely on social and ethnic factors and do not follow
simple utility maximizing approach based only on the economic conditions of the destination.
Indeed, it is reasonable to expect that network effect plays significant role in for immigrants
in destination decision making process; new immigrants will tend to settle in the areas
relatively more densely populated by people from the same country of origin to be able to
benefit from the support of their compatriots. Economic literature on migration provides rich
evidence of this prediction. The possibility to live among people speaking the same language,
having similar cultural traditions makes particular regions more attractive for new comers
(Pedersen et al.(2008), Carrington et al.(1996)). These arguments frequently motivated
economists to construct instrumental variables for actual immigration flows based on the
historical information (see, for example, Saiz (2007), Cortes (2008), Ottaviano and
Peri(2007), Card (2000), McKenzie and Rapoport, 2007).
The first instrument is bases on the overall changes in immigration flows from one
period to another and the initial distribution of immigrants across Italian provinces. The
instrument is based on two assumptions. First, the initial distribution of immigrants in t=0 is
not correlated with the omitted variables which can have influence on dynamics of housing
market in the future. More explicitly, this assumption imply that based on available
information set immigrants cannot forecast the future of housing market in any province i
better than natives. Housing price is the capitalized discounted value of the stream of future
rents. If one believes that immigrants are able to pick the future “winner” locations based on
the set of information available in the present then one has to explain why natives did not
capitalize on the same set available information (Saiz,2006). Second, changes in immigrants’
18
Sona Kalantaryan Housing Market Responses to Immigration Shocks: Evidence from Italy.
flows on national level are exogenous with respect changes in the province–specific
amenities. The instrument is calculated according to the following formula
, , · ∆ ,
The predicted number of immigrants in province i period t is calculated based on the
initial distribution of immigrants across provinces in year t=0 and the total number of permits
of stay issued during year t. θi,t=0 is the share of immigrants who settled in province i in t=0.
I take year 1990 as t=0 since it is the first year for which the data on residence permits is
available on provincial disaggregation level. This estimate of immigrants’ presence is free
from province and time specific shocks.
The second instrument relies not only on the historical settlement of overall
immigration stock but also on its composition based on the country of origin. Here I rely on
assumption that country of origin-province initial distribution is not correlated with the
demand shocks which the provinces face in the later periods.
, , , · ∆ , ,
W
, , , is the fraction of immigrants from country or area c which settled in province i
in the period t=0. , , is the number of immigrants from country or are c that lives in
Italy at time period t.
here
To construct this instrument I used number of resident permits issued. The
information about the country of origin of immigrants is available on province disaggregation
level. First, I took thirty-seven origin countries separately. The rest of countries were grouped
based on geographic criteria. The resulting nine geographic groups are the following: Central
Europe, Other Europe, Former Soviet Union, Asia, Northern Africa, Southern Africa,
Southern America, Central America, Australia and Oceania. More detailed description can be
found in Table 2 of Appendix.
The result of the univariate regression confirms that the instrument fits the actual
changes of immigrant population.
19
Sona Kalantaryan Housing Market Responses to Immigration Shocks: Evidence from Italy.
∆ 1410,257 0,671∆
The coefficient at the instrument is significant at 1 percent level. The F-statistic meets
the requirements and is equal to 855.74. The result is robust to inclusion of time dummies as
well.
20
Sona Kalantaryan Housing Market Responses to Immigration Shocks: Evidence from Italy.
4. Data description
The data used for estimation come from different sources. The descriptive statistic of
the data used in estimation is presented in Table1, Appendix. In this section, I present in
details the peculiarities of information used in estimation.
4.1 Immigration flow
The information source for immigration flows comes from ISTAT11 , which in
collaboration with Ministry of Internal Affairs develops and delivers data on foreign nationals
legally present in Italy since early 90s. The Ministry of Internal Affairs provides the initial
information; that is the number of existing valid residence permits on January 1 of each year.
The estimated number of permits is based on the information taken at least 6 month after the
reference date. This allows first, to take into account those foreigners whose permit of stay
was expired by January 1, but who had applied for renewal, hence where legally present in
Italy. Second, in addition to residence permits valid on January 1 of a given year, it allows to
include also those foreigners who though were present legally in Italy, but without residence
permit due to long time required to for completion of the practice of the first release.
The statistical data are available from 1992 with yearly frequency with detailed
description of demographic characteristics: gender, age, marital status, country of origin and
reason for presence in Italy. The territorial units are not necessarily limited to the provincial
level since the residence permits are issued by State Police at the Police Headquarters.
However, the inconsistency of number of Italian province through time requires some
work to be done. Particularly, due to the creation of four new provinces (Olbia-Tempo,
Ogliastra, Medio Campidano and Carbonia-Iglesias) in region Sardinia the number of Italian
provinces grew from 103 to 107. At the same time, the housing market data are available in
“103 provinces” format. Hence, to be consistent with geographic units considered in housing
market data I adjusted the data on immigrants to “103 provinces” as well. I added the number
of immigrants reported for new provinces to the provinces which they geographically
belonged before separation. The number reported for Olbia-Tempo was added the one
reported to Sassari and reported as Sassari, Ogliastra was added to Nuoro and reported as
11 ISTAT-“The Italian National Institute of Statistics is a public research organization. It has been present in Italy since 1926, and is the main producer of official statistics in the service of citizens and policy-makers. It operates in complete independence and continuous interaction with the academic and scientific communities.”
21
Sona Kalantaryan Housing Market Responses to Immigration Shocks: Evidence from Italy.
Nuoro , Medio Campidano together with Carbonia-Iglesias were added to Cagliari and
reported as Cagliari.
4.2 Population
The data on population in Italian provinces comes from ISTAT as well. Particularly,
the Demographic balance of yearly resident population provides the results of the monthly
data collection called “Movement and calculation of resident population”12 It is implemented
by ISTAT in collaboration with the Population Register offices (anagrafi) of the Italian
municipalities (Comuni). The information is available from 1992. It provides with data on
population on January 1 of each year. Resident population encompass Italian and foreign
citizens usually living on the national territory, even if temporarily absent.
Each person having usual residence has to register, by law, in the population register
of the municipality where usually lives. The legal population is being determined on the base
of the Population Census. I adjusted the population data for Italian provinces to “103
provinces” format using the same above described methodology used for data for immigrants.
4.3 Housing price
The data on housing value come from the Italian Survey on Household Income and
Wealth (SHIW13). The sample used in the most recent surveys comprises about 8,000
households (24,000 individuals), distributed over about 300 Italian municipalities. The
variable used is the average value of housing value per square meter. It is calculated based on
the answer to the following question asked during the interview:
“In your opinion, what price could you ask for the dwelling in which you live (unoccupied).
In other words, how much is it worth (including any cellar, garage or attic)? Please give
your best estimate”.
It is worth to mention the disadvantages of the data. First, the value of housing is self-
reported by owner of the house or person who occupies it. It is not always the case that
person is aware about current market price of the dwelling. Second, the number of 12 form ISTAT P.2 13 SHIW - began in the 1960s with the aim of gathering data on the incomes and savings of Italian households. Over the years, the scope of the survey has grown and now includes wealth and other aspects of households' economic and financial behavior.
22
Sona Kalantaryan Housing Market Responses to Immigration Shocks: Evidence from Italy.
observations is around eight thousands, while the number of Italian provinces is equal to 103.
Hence, the number of observation is around 80 per province14, which cannot give very
precise estimate of current market price.
4.5 Gross domestic product
The data on Gross Domestic Product come from Regional Statistics provided by
EUROSTAT15 Statistical office of European Communities. Data are available from 1995
with annual periodicity. The data are calculated by EUROSTAT based on data from
European System of Accounts ESA 1995 initially sent by National Statistical Institute. Italian
provinces correspond to NUTS 3 level regional breakdown. Particularly, the provincial GDP
per capita in Euros (EUR_HAB) and in Purchasing Power Standards are used (PPS_HAB).
The data covers 107 Italian provinces and because of already mentioned reasons, the
data was adjusted to “103 provinces” format. Actually, the only problem is related to
Sardinia. The data on NUTS3 level are available only from 2001 and in “107 format”. To
bring the data to “103 format” I performed several steps. First, to obtain GDP data of “old”
provinces I weighted them by population of “new” ones. It was done for years from 2001 to
2006. Second, I tried to fill the missing values of GDP Sardinian provinces from 1995 till
1999 in the following way. I calculated the average ratio of GDP of Sardinian province i to
regional GDP for period when data are available (from 2001 till 2006). The obtained ratio
was used to calculate the GDP for four “old” provinces for years from 1995 till 1999.
Particularly, to obtain GDP estimate for year t the above-mentioned ratio was multiplied by
the regional data of year t.
4.6 Unemployment rate
The data on unemployment come from EUROSTAT. It is based on LFS (Labor Force
Survey, “age 25 and over” is considered) quarterly household sample survey and is available
on NUTS3 geographical disaggregation level. The first data set is Regional labor market data
based on pre-2003methodology (LFS adjusted series) and covers period from 1995 to 2001.
The second data set comes from Regional unemployment LFS series and covers period from
14 The number of observation varies from five to more than five hundreds. 15 Eurostat is the statistical office of the European Union situated in Luxembourg. Its task is to provide the European Union with statistics at European level that enable comparisons between countries and regions.
23
Sona Kalantaryan Housing Market Responses to Immigration Shocks: Evidence from Italy.
1999 to 2008.The data is given in two datasets because of methodological change made at
some point of time. Because of changes made in definitions there is some discrepancy once
the overlapping period from 1999 to 2001 compared, which points on the fact that these two
datasets are not comparable in the raw way. To fix this problem I took the data from Regional
unemployment LFS series which covers period from 1999 to 2008 as a benchmark and
adjusted the remaining period (from 1995 to 1998) to the recent methodology. I used the data
for overlapping years to calculate the average ratio of “new” to “old” unemployment rate for
every province. Then the “old” values (for period beginning from 1995 to 1998) were
multiplies by already calculated average ratio to obtain “new” values for the mentioned
period.
Beginning from 2001 the data for provincial unemployment is presented in “107
provinces” format. I adjusted the unemployment data to “103 provinces” format as well. To
bring the data to “103 format” I used the population of “new” ones as weights. In the next
step, I filled the missing values of Unemployment for Sardinian provinces from 1995 to 1999
in the following way. I have unemployment rate for all eight Sardinian provinces for year
2008 only and regional data for all years. I brought the 2008 data to “103 format” and
calculated the ratio of provincial to regional data for all “old” provinces. After that, I
calculated the estimated provincial unemployment rate for Sardinian provinces multiplying
the above-mentioned ratio by the regional data of corresponding year.
24
Sona Kalantaryan Housing Market Responses to Immigration Shocks: Evidence from Italy.
5 Results OLS estimation results
In this section, I present estimation results. I begin from ones based on OLS
estimations.
Table A reports estimation results based on model described in Equation (2). Column
(1) shows that with no additional controls growth in immigrant population is positively and
statistically significantly correlated with the growth in housing prices. This makes sense,
because increase in population due to the inflow of immigrants leads to an increase in
demand for housing units, which in its turn pushes housing prices up. The population growth
control is essential in this case, because it captures the effect of larger population, which can
be result of the inflow of both natives and immigrants. Neglecting of control for total
population growth would lead to certainly positive coefficient at the number of immigrants,
as immigrants’ inflow results in larger population, therefore higher demand for housing units.
Column (2) reports the results of basic specification with the set of time dummies that are
supposed to capture national trends in inflation and other economic variables. Although
adding time dummies decreases the magnitude of β and makes it statically insignificant, it
notably improves the explanatory power of the model; R2 increases from 0.03 to 0.60.
Column(3) demonstrates results once the estimation includes controls for changes in
economic conditions at provincial level; i.e. unemployment rate and GDP per capita. These
macroeconomic controls are supposed to capture differences in housing prices due to
differences in economic conditions between provinces. The coefficient at immigration is
equal 0.058, which is significant only at 10 per cent level. The results are robust to inclusion
of time-part dummies, which allows controlling for differences in business cycles across
Italian geographic areas. Both Column (3) and Column (5) report very similar coefficient at
the number of immigrants. The results suggest that 1 percent increase in number of
immigrants increases housing prices in province by about 0.06 percent, which indicates quite
modest effect of immigrants on average housing prices on provincial level. While interpreting
the results, it is necessary to keep in mind some facts. The first one is the possibility of
existence of previously discussed displacement effect. In the presence of displacement, the
estimation results give only a lower bound of the full magnitude of the effect. Second, it can
be the case the effect of immigration shocks on housing market dynamics has non-linear
25
Sona Kalantaryan Housing Market Responses to Immigration Shocks: Evidence from Italy.
patters. To capture non-linearity I repeat the estimations of Column (3) and Column (5)
including squared term logarithm of number of immigrants.(Can I do that ??? Bo). The
results are reported in Column (4) and Column (6) respectively. Indeed, the estimation
confirms the initial suspect about non-linear pattern. The coefficient at immigration increases
drastically and become statistically more significant. For example, the coefficient in Column
(4) increased to 0.394 and became statistically significant at 1 per cent level compared to
0.058 significant at only 10 percent significance level in Column (3). In column (4) and (6),
the coefficients at squared term of number of immigrants are negative and statistically
significant at 5 and 10 per cent respectively. This might mean that initial model without
inclusion of squared term of log-change of number of immigrants overlooked some
potentially important nonlinearities. The number of immigrants no longer has a positive
effect on housing prices of province: the relationship between log-change of housing prices
and log-change of number of immigrants is positive up until log number of immigrants is
equal 8.2. This value corresponds to 3640 immigrants in a province. All estimations reported
in columns (3)-(6) include control for changes in density of population in provinces.
I performed estimations using alternative measure of concentration of foreign
population; that is the ratio of number of immigrants to population in province i at time t. The
results are presented in Table B. Column (1) presents the results of simple first-difference
estimation without any additional controls. As in the previous case, the coefficients at
immigrants’ concentration are positive and statistically significant. Obviously, without other
controls the explanatory power of the model is very low. Adding time-dummies and
macroeconomic controls (see column (2) and (3) respectively) significantly increase the
explanatory power of model however turns the coefficients at foreign concentration to
negative and statistically insignificant. Column (5) shows that addition of time-part dummies
leaves coefficient of main interest negative and statistically insignificant. However, following
the same reasoning as in the previous section I add the squared-term of immigrants`
concentration. Column (4) and (6) show that also in this case the non-linearity of housing
price response to immigrant’s inflow is confirmed. The coefficient of main interest turns to
positive and statistically significant at 10 per cent level. Even more, the coefficient at squared
term of immigrants` concentration is negative and statistically highly significant. From the
estimated coefficients it can be concluded that though concentration of immigrants leads to
appreciation of housing prices the effect turns to negative once concentration reaches some
26
Sona Kalantaryan Housing Market Responses to Immigration Shocks: Evidence from Italy.
critical level. The critical value of immigrants’ concentration after which the relationship
turns to negative one is estimates close to 3 percent.
As it was already mentioned before the measure of foreign presence I use is based on
number of residence permits issued by Ministry of Internal Affairs, which accounts only
legally present immigrants. The possible solution to correct mismeasurement was proposed in
Bianci(2008). Following the methodology presented in this paper, I estimated the model
described by in Equation (4). The results are presented in Table C. As in two previous cases
the simple first–difference estimation shows positive association between increase of foreign
concentration growth of housing prices. The addition of time dummies significantly increases
the explanatory power of model, but turns the coefficient of main interest to statistically
insignificant. Inclusion of both macro controls and time-part dummies leaves the magnitude
of β almost unaltered (see Column (3) and (5) respectively). The coefficient at immigrants
concentration is similar in its magnitude to the once presented in Table A; i.e. that 1 percent
increase in number of immigrants increases housing prices in province by about 0.06 percent.
However, the coefficient remains significant only at 10 percent level.
Instrumental variable estimation result (To be done after discussion with prof. Sembenelli)
6 Conclusion
A large body of literature analyzes the impact of immigration on the employment
opportunities of native population. With few exceptions, this literature addresses the issues
related to the labor market outcome or cost or benefits imposed on native taxpayers because
of immigrants’ inflow. If the purpose of economic studies is investigation of economic
processes for precise policy design, then the final judgment can be made only after careful
consideration of a wide range of phenomenon related results. An enriched picture of
immigrants influence on the well-being of natives can be obtained if along with the effect
immigrants have on production process, through altering composition of labor force, the
impact on local prices through consumption process is taken into account as well.
Consideration of price effect can enhance the influence on real income and real wealth of
population.
27
Sona Kalantaryan Housing Market Responses to Immigration Shocks: Evidence from Italy.
Housing market through changes in rents and prices may present one of the main non-
labor channel by which immigrants can influence the well being of natives. However, the
attempts to estimate the influence of immigrants on housing market outcome were made
mostly for the USA. With the exception of Gonzales&Ortega(2009), the influence of
immigrants on European housing markets remains unexplored.
In this paper, I estimate the estimate the impact of immigration flows on Italian
housing market from 199. The paper contributes to the immigration literature in the following
ways. First, it enhances the image of influence of recent intensive immigration flow to Italian
economy by estimating the impact of immigration to Italian housing market. Italian housing
market has never been considered in connection to immigration flows Second, the fact that
estimation is performed on the subject of European housing market makes it remarkable in a
wider context; i.e. it gives chance to estimate the influence of immigration on housing market
in European region in the future. Third, it contributes to recently emerging branch of
literature on influence of immigration on price in general.
The OLS estimation results show that immigration has positive, however, declining
effect on the growth of housing prices in Italian provinces. The estimated results suggest that,
ceteris paribus, as the growth of immigrants concentration in province reaches approximately
3 percent, the further increase of it leads to decrease in the rate of housing price appreciation.
The instrumental variable estimations in all specifications show that immigration presence in
provinces has positive effect on the growth of housing prices. However, the results are
statistically insignificant.
To add some discussion here
28
Sona Kalantaryan Housing Market Responses to Immigration Shocks: Evidence from Italy.
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Venturini A. and C.Villosio (2008), “Labour-market assimilation of foreign workers in Italy”, Oxford Review of Economic Policy, Volume 24, Number 3,517–541
31
Sona Kalantaryan Housing Market Responses to Immigration Shocks: Evidence from Italy.
Appendix A
Table1 Descriptive statistics Variable Population weighted Non-weighted Min Max Mean Std.dev. Mean Std.dev.
Housing value (euro per sq. m) 1996 1800.12 654.27 1666.9 670.07 640.99 4918.5 2007 2039.96 702.8 1855.29 646.78 823.46 4539.81 Number of residence permits issued 1996 22,127 38,313 7,079 16,602 54 142,780 2007 57,945 77,454 23,446 36,681 942 257,779
Population 1,227,823 1,194,315 558,225 611,628 89,043 4,013,057
Immigrants concentration (permits/population) 1996 0.0128 0.0097 0.0104 0.0077 0.0003 0.0434 2007 0.0408 0.0231 0.0389 0.0222 0.0054 0.0981
GDP per capita 21,628 6,650 20,560 5,504 8500 37300
Unemployment rate (%) 8.04 5.8 7.48 5.52 0.7 29
Province area (sq.km) 3296 1769 2845 1600 212 7400 7400 Notes: All variables are defined at provincial level. All variables are defined at the annual level. Province area is time invariant.
32
Sona Kalantaryan Housing Market Responses to Immigration Shocks: Evidence from Italy.
Figure1 0
1020
3040
Freq
uenc
y
0 50000 100000 150000Number of immigrants
Distribution of immigrants across provinces,1996
05
1015
2025
Freq
uenc
y
0 50000 100000 150000 200000 250000Number of immigrants
Distribution of immigrants across provinces,2007
05
1015
20Fr
eque
ncy
0 .01 .02 .03 .04Immigrants/population ratio
Share of immigrants in total population across province,1996
05
10Fr
eque
ncy
0 .02 .04 .06 .08 .1Immigrants/population ratio
Share of immigrants in total population of province,2007
Source: ISTAT
This figure presents the evolution of distribution of immigrants across Italian provinces during period from 1996 to 2007. The number of immigrants is equal to number of valid residence permits issued by Ministry of Internal Affairs in provinces at the beginning of calendar year. The share of immigrants in total population in provinces is measured as ratio of number of valid residence permits over total population at the beginning of calendar year
33
Sona Kalantaryan Housing Market Responses to Immigration Shocks: Evidence from Italy.
Figure 2. Concentration of immigrants vs. Housing prices
010
0020
0030
0040
0050
00H
ousi
ng p
rice
of a
squ
are
met
er
0 .02 .04 .06 .08 .1Immigrants/population ratio
Fitted values Housing Price of a square meter
Note: the unit of observation is an Italian province
Correlation between the Concentration of Immigrants and Housing Prices
This graph presents correlation between the concentration of immigrants and housing prices per square meter. The horizontal axis is the share of immigrants in total population in provinces measured as ratio of number of valid residence permits over total population at the beginning of calendar year. The vertical axis is the average housing price per square meter in Italian provinces at the beginning of calendar year.
34
Sona Kalantaryan Housing Market Responses to Immigration Shocks: Evidence from Italy.
Figure 3. Log-change of actual vs. predicted number of immigrants
-20
24
6Lo
g-ch
ange
of
acua
l num
ber o
f im
mig
rant
s
-3 -2 -1 0 1 2Log-change of predicted number of immigrants
Fitted values Log-change of number of immigrants
Correlation between log-change of actual and predicted number of immigrants
This graph presents correlation between the log-change of actual number of immigrants and predicted number of immigrants. The vertical axis is the log-change of number of immigrants measured as the number of valid residence permits at the beginning of calendar year. The horizontal axis is log-change in predicted number of immigrants based on initial settlement pattern of immigrants by country of origin.
35
Sona Kalantaryan Housing Market Responses to Immigration Shocks: Evidence from Italy.
Results of OLS estimation (based on number of immigrants)
Table A
(1) (2) (3) (4) (5) (6) Δln(Number of Immigrants) 0.204*** 0.051 0.058* 0.394** 0.060* 0.370*
(0.001) (0.111) (0.075) (0.014) (0.080) (0.041)
Δln(Number of Immigrants)2 -0.024** -0.022* (0.041) (0.093)
Δln(Population) 1.139 -0.188 -1.084 0.109 -0.724 0.050 (0.263) (0.768) (0.439) (0.938) ( 0.617) (0.972)
ΔUnemployment rate 0.009 0.010 0.009 0.009 (0.117) (0.091) ( 0.168) (0.154)
Δln (GDP per capita) 0.417 0.523 0.375 0.419 ( 0.297) (0.186) (0.359) (0.307)
Δ Density -80.340 -23.420 -60.561 -19.372 ( 0.394) (0.808) (0.525) (0.840)
Number of obs. 595 686 500 500 500 500 Number of prov. 103 103 103 103 103 103 Constant Yes Yes Yes Yes Yes Yes Year dummy No Yes Yes Yes Yes Yes Time-part dummy No No No No Yes Yes R-squared 0.0343 0.5955 0.6141 0.6172 0.6285 0.6306 F-stat 6.74 83.38 73.78 68.64 34.38 34.01 Notes: The table presents results of OLS estimations on a panel of biennial observations for 103 Italian provinces during the period 1996-2007. The biennial log-change of average housing prices in provinces is the dependent variable. The log change of number of immigrants (i.e. residence permits) is the main explanatory variable of interest. Housing prices data are from 1996, 1999, 2001, 2003, 2005, 2007 and covers 103 Italian provinces. . Regression also controls for biennial changes in log population, log income, unemployment rates and density of population. The robust standard errors are presented in parenthesis (for now p-values). *, ** and *** denote rejection of the null hypothesis of the coefficient being equal to 0 at10%, 5% and 1% significance level, respectively.
36
Sona Kalantaryan Housing Market Responses to Immigration Shocks: Evidence from Italy.
Results of OLS estimation ( based on concentration of immigrants ) Table B
(1) (2) (3) (4) (5) (6) Δ(Concentration of imm.) 12.271*** -2.604 -3.564 7.614* -3.401 7.468*
(0.000) (0.248) (0.117 ) (0.072) (0.203) (0.068)
Δ(Concentration of imm.)2 -127.315*** -127.381*** (0.002) (0.002)
ΔUnemployment 0.010 0.011 0.009 0.009 (0.099) (0.061) (0.186) (0.172)
Δln (GDP per capita) 0.445 0.399 0.414 0.329 (0.262) (0.315) (0.307) (0.421)
ΔDensity -29.837 -29.342 -19.928 -18.809 (0.530) (0.512) (0.655) (0.663)
Number of obs. 595 595 500 500 500 500 Number of prov. 103 103 103 103 103 103 Constant Yes Yes Yes Yes Yes Yes Year dummy No Yes Yes Yes Yes Yes Time-part dummy No No No No Yes Yes R-squared 0.0353 0.5952 0.6142 0.6203 0.6278 0.6332 F-stat 22.07 111.45 84.94 75.50 37.04 35.68 Notes: The table presents results of OLS estimations on a panel of biennial observations for 103 Italianprovinces for the period 1996-2007. The biennial log-change of average housing prices in provinces is the dependent variable. The change in concentration of immigrants (i.e. number of valid residence permits over population) is the main explanatory variable of interest. Housing prices data are from 1996, 1999, 2001, 2003, 2005, 2007 and cover 103 Italian provinces. Regression also controls for biennial changes in log income,unemployment rates and density of population. The robust standard errors are presented in parenthesis (for now p-values). *, ** and *** denote rejection of the null hypothesis of the coefficient being equal to 0 at 10%, 5%and 1% significance level, respectively16.
16 For now (in the draft version )I present p-value instead , but it will be changes for later drafts.
37
Sona Kalantaryan Housing Market Responses to Immigration Shocks: Evidence from Italy.
Results of OLS estimation (based on log-concentration of immigrants)
Table C
(1) (2) (3) (4) (5) (6) Δln(Concentration of imm.) 0.210*** 0.050 0.053* 0.057***
(0.001) (0.116) (0.100) (0.090)
Δ Unemployment 0.009 0.009 (0.138) (0.172)
Δln (GDP per capita) 0.445 0.389 (0.262) (0.339)
Δ Density -12.101 -18.852 (0.783) (0.664)
Number of obs. 585 585 500 500 Number of prov. 103 103 103 103 Constant Yes Yes Yes Yes Year dummy No Yes Yes Yes Time-part dummy No No No Yes R-squared 0.0316 0.5954 0.6137 0.6283 F-stat 11.47 110.94 83.25 36.10 Notes: The table presents results of OLS estimations on a panel of biennial observations for 103 Italian provinces for the period 1996-2007. The biennial log-change of average housing prices in provinces is the dependent variable. The log-change in concentration of immigrants (i.e. log-change of number of valid residence permits over population) is the main explanatory variable of interest. Housing prices data are from 1996, 1999, 2001, 2003, 2005, 2007 and covers 103 Italian provinces. Regression also controls for biennial changes in log income, unemployment rates and density of population. The robust standard errors are presented in parenthesis (for now p-values). *, ** and *** denote rejection of the null hypothesis of the coefficient being equal to 0 at 10%, 5% and 1% significance level, respectively.
38
Sona Kalantaryan Housing Market Responses to Immigration Shocks: Evidence from Italy.
Results of the IV estimation (based on number of immigrants) Table A1
(1) (2) (3) (4) (5) (6) Δln(Number of Immigrants) 0.962 0.166 0.175 1.242 0.180 1.062
(0.000) (0.519) (0.455) (0.461) (0.472) (0.392)
Δln(Number of Immigrants)2 -0.069 -0.059 (0.459) (0.393)
Δln(Population) -0.706 -0.125 -1.088 1.893 -0.450 1.468 (0.545) (0.875) (0.539) (0.578) (0.810) ( 0.510)
ΔUnemployment rate 0.012 0.013 0.013 0.012 (0.080) (0.049) (0.063) (0.112)
Δln (GDP per capita) 0.350 0.521 0.298 0.433 (0.423) (0.216) (0.517) (0.322)
Δ Density -75.950 73.136 -40.904 63.653 (0.495) (0.709) (0.759) (0.636)
Number of obs. 556 556 461 461 461 461 Number of prov. 103 103 103 103 103 103 Constant Yes Yes Yes Yes Yes Yes Year dummy No Yes Yes Yes Yes Yes Time-part dummy No No No No Yes Yes R-squared 0.5940 0.6141 0.6136 0.6289 0.6286 F-stat 6.74 87.35 68.48 72.04 30.98 34.01 Notes: The table presents IV (second-stage) estimates on a panel of biennial observations for 103 Italian provinces for the period 1996-2007. The biennial log-change of average housing prices in provinces is the dependent variable. The log change of number of immigrants (i.e. residence permits) is the main explanatory variable of interest. Housing prices data are from 1996, 1999, 2001, 2003, 2005, 2007 and covers 103 Italian provinces. Regression also controls for biennial changes in log income, unemployment rates and density of population. The bottom panel reports first-stage estimates of IV regressions(to be added , based on what Sembenelli tells). The first stage instrument is the weighted sum of the changes of immigrant population flow by nationality to whole Italy. The weights are the shares of permits held by each nationality over total permits in a province in 1990 (see equation Section 3 in the main text). The F-statistic for excluded instruments refers to the null hypothesis that the coefficient of the excluded instrument is equal to zero in the first stage. Robust standard errors are presented in parenthesis. *, ** and *** denote rejection of the null hypothesis of the coefficient being equal to 0 at 10%, 5% and 1% significance level, respectively.
39
Sona Kalantaryan Housing Market Responses to Immigration Shocks: Evidence from Italy.
Results of the IV estimation (based on concentration of immigrants)
Table B1 (1) (2) (3) (4) (5) (6) Δ(Concentration of imm.) 39.282 7.615 2.211 21.180
(0.000) (0.560) (0.864) (0.529)
Δ(Concentration of imm.)2 -251.235
(0.411)
ΔUnemployment 0.011 0.015 (0.193) (0.035)
Δln (GDP per capita) 0.445 0.311 (0.306) (0.506)
ΔDensity -6.032 -22.848 (0.931) (0.669)
Number of obs. 556 556 461 461 461 461 Number of prov. 103 103 103 103 103 103 Constant Yes Yes Yes Yes Yes Yes Year dummy No Yes Yes Yes Yes Yes Time-part dummy No No No No Yes Yes R-squared . 0.5867 0.6153 0.6206 0.6278 0.6332 F-stat 32.93 101.69 78.95 68.91 37.04 35.68 Notes: The table presents IV (second-stage) estimates on a panel of biennial observations for 103 Italian provinces for the period 1996-2007. The biennial log-change of average housing prices in provinces is the dependent variable. The change in concentration of immigrants (i.e. number of valid residence permits over population) is the main explanatory variable of interest. Housing prices data are from 1996, 1999, 2001, 2003, 2005, 2007 and covers 103 Italian provinces. Regression also controls for biennial changes in log income, unemployment rates and density of population. The bottom panel reports first-stage estimates of IV regressions(to be added , based on what Sembenelli tells). The first stage instrument is the weighted sum of the changes of immigrant population flow by nationality to whole Italy. The weights are the shares of permits held by each nationality over total permits in a province in 1990 (see equation Section 3 in the main text). The F-statistic for excluded instruments refers to the null hypothesis that the coefficient of the excluded instrument is equal to zero in the first stage. Robust standard errors are presented in parenthesis. *, ** and *** denote rejection of the null hypothesis of the coefficient being equal to 0 at 10%, 5% and 1% significance level, respectively.
40
Sona Kalantaryan Housing Market Responses to Immigration Shocks: Evidence from Italy.
Results of IV estimation (based on log-concentration of immigrants)
Table C1 (1) (2) (3) (4) (5) (6) Δln(Concentration of imm.) 0.963 0.162 0.212 0.190
(0.000) (0.559) (0.428) (0.460)
Δ Unemployment 0.012 0.0125 (0.089) (0.089)
Δln (GDP per capita) 0.347 0.295 (0.429) (0.517)
Δ Density -16.067 -24.508 (0.721) (0.579)
Number of obs. 556 556 461 461 461 461 Number of prov. 103 103 103 103 Constant Yes Yes Yes Yes Year dummy No Yes Yes Yes Time-part dummy No No No Yes R-squared 0.0316 0.5941 0.6122 0.6284 F-stat 11.47 102.01 76.80 35.30 Notes: The table presents IV (second-stage) estimates on a panel of biennial observations for 103 Italian provinces for the period 1996-2007. The biennial log-change of average housing prices in provinces is the dependent variable. The log-change in concentration of immigrants (i.e. log-change of number of valid residence permits over population) is the main explanatory variable of interest. Housing prices data are from 1996, 1999, 2001, 2003, 2005, 2007 and covers 103 Italian provinces. Regression also controls for biennial changes in log income, unemployment rates and density of population. The bottom panel reports first-stage estimates of IV regressions(to be added , based on what Sembenelli tells). The first stage instrument is the weighted sum of the changes of immigrant population flow by nationality to whole Italy. The weights are the shares of permits held by each nationality over total permits in a province in 1990 (see equation Section 3 in the main text). The F-statistic for excluded instruments refers to the null hypothesis that the coefficient of the excluded instrument is equal to zero in the first stage. Robust standard errors are presented in parenthesis. *, ** and *** denote rejection of the null hypothesis of the coefficient being equal to 0 at 10%, 5% and 1% significance level, respectively.
41
Sona Kalantaryan Housing Market Responses to Immigration Shocks: Evidence from Italy.
42
Table 2 The list of origin countries and their groups used to construct instrument for actual immigration flows. Individual countries Albania, Algeria, Argentina, Australia, Bangladesh,
Brasilia, China, Columbia, Cost D’Avour, Cuba, Dominican Republic, Ecuador, Egypt, Ethiopia, Philippines, Pakistan, Peru, Poland, Romania, Senegal, Somalia, Sri Lanka, USA, Switzerland, Tunis, Turkey, Canada, Ghana, Japan, Jordan, India, Iran, Lebanon, Morocco, Mauritius, Nigeria, Without citizenship
Central Europe Bulgaria, Czech Republic, Slovakia, Hungary Europe (other) UK, San Marino, Spain, Sweden, Andorra, Austria,
Belgium, Cyprus, Vatican, Denmark, Finland, France, Greece, Ireland, Island, Lichtenstein, Luxemburg, Malta, Monaco, Norway, The Netherlands, Portugal
Former Soviet Union Armenia, Azerbaijan, Belarus, Estonia, Georgia, Kazakhstan, Kirgizia, Lithuania, Latvia, Moldavia, Russia, Tajikistan, Turkmenistan, Ukraine, Uzbekistan
Asia Afghanistan, Saudi Arabia, Bhutan, Northern Korea, Southern Korea, Laos, Syria, Yemen, Bahrain, Cambodia, Un. Arabic Emirates, Iraq, Israel, Kuwait, Mongolia, Myanmar, Nepal, Oman, Palestine, Qatar, Taiwan, Thailand, Vietnam
America (South ) Bolivia, Chile, Guyana, Nicaragua, Paraguay, Suriname ,Uruguay
America (Central ) Antigua and Barbuda, Bahamas, Barbados, Belize, Costa Rica, Dominica, Salvador, Jamaica, Grenada, Haiti, Honduras, Mexico, Panama, Santa Lucia, Trinidad and Tobago, Venezuela, Guatemala
Africa (North) Cameroon, Capo Verde, Central Africa, Chad, Eritrea, Gambia, Gibraltar, Djibouti, Guinea, Guinea Bissau, Equatorial Guinea, Kenya, Liberia, Libya, Madagascar, Mali, Mauritania, Nigeria, Ruanda, San Tome and Principe, Sierra Leone, Sudan, Togo, Uganda, Benin, Burkina Faso
Africa (South) Angola, Botswana, Burundi, Comoro, Congo, Gabon, Lesotho, Malawi, Mozambique, Namibia, Democratic Republic of Congo, South Africa, Swaziland, Tanzania, Zaire, Zambia, Zimbabwe
Australia and Oceania Brunei, Fiji, Indonesia, Kiribati, Malaysia, Maldives, Marshal, Micronesia, New Zealand, Palau, Papua New Guinea, San Vincent and Grenadine, San Christ and Nevis, Salomon, Samoa, Seychelles, Singapore, Timor, Kingdom of Tonga, Tuvalu, Vanuatu