The impact of the Betuweroute freight railway on
the prices of surrounding real estate properties
Course: RESEARCH PROJECT: REAL ESTATE ECONOMICS &
FINANCE
By:
Thijs Postema / 2703420
Sebastian Domenici / 2791028
Janick Schütz / 2791578
Noah Kordić / 2712026
February, 2023
Amsterdam, The Netherlands
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1. Introduction
Transportation services play a vital role in the process of delivering raw and intermediate materials to
producers and delivering final products to retailers and final customers (Meitzen et al., 2012). A firm that
can provide its customers with the most reliable, affordable, and timely service gains a competitive
advantage over rivals. By constructing The Betuweroute freight railway, Netherlands aims, among other
objectives, to ensure that the port Rotterdam maintains its lead over Hamburg, Le Havre and Antwerp
over the long run (Koetse & Rouwendal, 2008). It will do so by providing the companies in the German
mainland and the rest of Europe with an ecologically friendly, compared to transport by trucks or inland
waterways, reliable and fast way of receiving the materials and products.
The Betuweroute is a double-track Dutch freight railway line. It runs from the Maasvlakte near Rotterdam
to the Zevenaar , where it connects to the German railway network via the existing Arnhem – Emmerich
connection. The line is 160 kilometers long, of which 95 kilometers are bundled with the A15
motorway. There are 160 kilometers (single-sided) of noise barriers; 7.5 kilometers of the line is sunken,
and 12 kilometers is a total of 130 viaducts and bridges. The line also contains 5 tunnels, 190 fauna
passages, 155 points, and 4 movable flood defenses (Wikipedia-bijdragers, 2022). Its name is taken from
a district in central Netherlands through which the line passes. This route was opened in 2007 at a cost of
€4.7bn.
Freight transportation projects result in unequal cost and benefit distribution across regions. Furthermore,
they have both direct and indirect economic consequences. We will be focusing on the impact that the
construction of The Betuweroute freight railway had on the value of residential real estate properties in
nearby areas. The project's explicit cost is measured in billions of euros, and the implicit gain or cost
realized from potential changes in property prices is critical in evaluating the project's financial viability.
Determining the scale of the effect caused by externalities on housing prices will carry, as well, large
implications for future considerations of similar projects (Levkovich, Rouwendal, & van Marwijk, 2015).
New or upgraded infrastructure may have a negative impact on the rental appeal or capital growth
potential of a prospective property along the route (Clark, 2005). The reasoning behind it is that the most
important advantages of improved passenger transportation, such as improved mobility of the residents,
are lost with cargo transportation, as the access points are not directly used by residential property owners
(Simons & El Jaouhari, 2004). However, the negative effects of such transport routes are still present
along the route. Railroad tracks increase noise pollution, hinder mobility in direction opposite of the track
and may have negative aesthetic effects for the residents in close proximity, leading to a decrease in
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prices of bordering properties (Strand & Vågnes, 2001). Overall, the impact on house prices that the
construction of freight transport projects brings is unclear.
The scarcity of literature covering the impact of construction of freight railway on nearby real estate
prices only adds to the importance of our findings. Therefore, this paper aims to bring more clarity by
answering the research question: How did the construction of The Betuweroute freight railway impact the
real estate prices of surrounding properties? We use the hedonic price model, the most commonly used
method to determine the price of a property as a function of its attributes in the housing market literature
(Rosen, 1974). We test the hypothesis: The construction of The Betuweroute freight railway had a small
but negative effect on the value of properties located along the route. By using this model, we control for
different house characteristics because it treats a specific property as a composite of characteristics to
which value can be attached.
This paper is structured as follows. Section 2 summarizes the existing literature on this subject. Section 3
describes data sources and datasets utilized in the research and the constructed method. Section 4 presents
the results of our analysis. Finally, section 5 brings the conclusion, including possible limitations and
extensions to our work.
2. Literature review
Impacts of rail transit on property values
As stated in the introduction, there is little literature on the dominant group of externalities and their
effects on house prices. We will discuss the literature that is relevant for our research question here. The
main focus of this literature review will be on the impact of a (freight) railroad on real estate prices near
the train track.
The effect of freight railroad tracks and train activity on residential property values
This paper examines the effect of freight railway infrastructure projects on the value of residential
properties. Freight railroads can basically be viewed as regular railroads without access to transit
infrastructure. Therefore, freight railways do not affect the transportation cost, nor the transportation time
of property owners. Consequently, the main driver for the increase in property value disappears whereas
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all the negative externalities such as noise and pollution nuisances, fear of accidents and a less idyllic
scenery remain (Simons & El Jaouhari, 2004). The paper of Simons and El Jaouhari (2004) analyses the
“before” and “after” effect of reconfiguring the freight carrying railroad lines in Cuyahoga County, Ohio,
and its effect on the value of single-family homes. Their study concludes that rail traffic, not just
proximity to tracks, impacts the value of residential properties.
Besides the railroad tracks, there are other linear land uses such as roads, high-voltage overhead electrical
transmission lines and pipelines. Hughes and Sirmans (1992) conducted a study in Baton Rouge,
Louisiana on the effect of roads with an annual average daily traffic of 1000 cars (AADT) on home
prices. They found that an AADT in the city center decreases the residential property value by 1%
whereas an AADT in a suburban area decreases the residential property value by 0.5%. A similar study
conducted by Colwell (1990) found out that high-voltage overhead electrical transmission lines (HVOTL)
in proximity to a residential property will decrease the value of it by 5%-8%. Last but not least, Simons
(1999) examined the impact of a pipeline rupture on the value of uncontaminated residential properties in
a study conducted in Fairfax County, Virginia. The finding of the study is that a rupture in a pipeline
decreases the uncontaminated residential property value by 4% to 5%. This is relevant to the research
question in this paper as it shows that real estate prices are sensitive to other (linear) infrastructure
investments, besides freight railroads, where the added value of the infrastructure does not benefit the
residence immediately. Therefore, proximity to such sites has a negative effect on the value of the
property.
The spatial effect of infrastructure development on the real estate price
To investigate the effect the freight railway Betuweroute had on the development of real estate prices in
the municipalities through which the railway line runs, we will compare this project with the analysis of
Chen et al. (2022) on the spatial effect of infrastructure development on the real estate price in the
Yangtze river delta. They explored how urban infrastructure development affects real estate prices. The
researchers used a spatial panel model to calculate this effect. The main variables that were used to
examine this spatial effect were the real estate prices (RSP), infrastructure development level (IDL),
industrial structure (IS), city size (CZ), land transfer price (LTP), per capita disposable income of urban
residents (INC) and the credit scale (CS) (Chen et al., 2022).
According to Liu, Chen, Huang, and Deng (2022) the spatial panel model suggests that the closer the
geographic distance between cities, the stronger the spatial correlation of urban real estate prices when
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considering the integration of the regional economic development and geographic proximity between
cities (Liu et al., 2022). As found out by Chen et al. (2022), urban infrastructure construction can
significantly affect real estate prices as the demand form real estate increases as infrastructure
development increase. This this effect is influenced by geographical location. Therefore, we have decided
to investigate the effect of transportation infrastructure, specifically The Betuweroute on the real estate
prices in the Netherlands.
When analysing the indirect effect of the infrastructure investment Chen et al. (2022) concluded that:
“The rapid construction of urban infrastructure can promote the increase of real estate prices in other
regions when considering the spatial correlation of urban geography, economy and other factors” (Chen et
al., 2022, p.15). This is an important factor that should be taken into account when comparing the rise of
real estate prices in regions close to The Betuweroute with the rise of real estate prices in regions that are
further away from this route. It might be the case that the construction of The Betuweroute also influences
the real estate prices of the regions that do not directly connect to The Betuweroute. The increase of real
estate prices in other regions can be caused by the “feedback effect” and the “diffusion effect”. The
feedback effect, also called direct effect, is the implications of infrastructure projects on house prices in
other (close by) municipalities, which then effects the investments and real estate values in the own
municipality. Whereas the diffusion effect describes the scenario where investments in a central
municipality also benefits the surrounding municipalities due to increased economic conditions (Chen et
al., 2022).
Chen et al. conclude that urban infrastructure constructions can have a significant positive impact on the
real estate prices when comparing the results of the panel and spatial panel model. However, among the
control variables, city size and disposable income have an economical significant strong influence on the
real estate prices (Chen et al., 2022). Both, an increase in city size and in disposable income, will increase
the demand for real estate. As the supply of real estate typically only adjust over time an increased
demand leads therefore to higher prices in (at least) the short-term.
It is important to discuss that transit infrastructure has a fundamentally different effect on real estate
prices than freight rail infrastructure. Economic theory suggests that accessibility to an efficient
transportation system increases residential property value. The efficiency of a transportation system is
defined by the transportation cost and the commuting time. This relationship was first noted by Thünen
(1826) as the compensation principal. As the transportation cost falls it allows the economic agent to
spend more money on housing, therefore, bidding up the rents/prices of properties which are located in
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low transportation cost areas. This principal is the underlying assumption for the land value/density
gradient. Several studies on the matter confirm that an improved (railway) infrastructure and its
accessibility improve the value of residential properties (Damm, Lerman, & Lerner-Lam, 1980; Duncan,
2008). These studies do not examine the proximity to a railway itself but the proximity to transit
infrastructure. However, not only residential properties benefit from an improved transportation system,
office and industrial properties profit from it as well. Those benefits arise from the increased labour
market to which the businesses have access to. The increase in value for office buildings is larger
compared to industrial properties as office buildings cluster a larger amount of labour (Diaz & Mclean,
1999). Infrastructure projects often take several years until construction is finalized. Therefore, the paper
by Yiu and Wong (2005) examined the effect of expected infrastructure improvements and discovered
that on the basis of the Rational Expectation Theory a rational investor will take all the available
information, including future improvements into consideration when valuing a property. Therefore, also
expected infrastructure increments will lead to an appreciation in property value.
The effect of the noise of trains on real estate prices.
The Rapoza, Rickley, and Raslear (1999) study found that train horn noise causes the greatest annoyance
to residents within about 300 meters of a freight train line. However, to simply forbid the train horns
might not be a good idea after all as a study carried out by Aurelius and Korobow (1971) found out that
the use of railroad horns in combination with wayside signal horns may reduce accidents by 69%. This
might not be the case for a freight railroad in the Netherlands as level crossings are guarded and
automated. Therefore, the use of the horn can be limited.
Noise of train tracks influences real estate prices (Rapoza et al., 1999; Simons & El Jaouhari, 2004). Some
other studies discuss the impact that the development of infrastructure, location of the dwellings and the
urban rail noise has on the rail estate prices. Chen, Zhang, Zhou, and Ding (2022) argued that
infrastructure development leads to an increase in economic activities and therefore to an increase in
annual GDP. This increase in activity and GDP leads to a appreciation of real estate prices. However,
Szczepańska, Senetra, and Wasilewicz-Pszczółkowska (2020) stated in their article that this development
has a negative effect. The reason for this negative effect is the noise that is caused by the use of the
infrastructure. In their study they analysed three residential districts in the city of Olsztyn in north-eastern
Poland. By using a linear correlation analysis, they showed that there is a negative correlation between
apartment price and traffic noise. However, other infrastructure developments, like the construction of a
ring road and subways, could have a positive effect (Szczepańska et al., 2020).
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This conclusion is supported by Le Boennec and Salladarre (2021). They found that noise can influence
real estate prices significantly and is dependent on the location of the residence. In their paper they used a
simultaneous equation model to tested if and how environmental variables, such as air pollution and
noise, could affect housing prices in Nantes. They stated that noise is directly perceptible, but do not
provide evidence that supports this statement. In order to get this information, they proposed a study that
takes those environmental elements into account (Le Boennec & Salladarre, 2021).
A study that estimates the influence of noise on real estate prices more accurately is done by Chang and
Kim (2013). They studied the effects of urban rail noise in Seoul (South Korea). By using a hedonic
pricing model, they estimated that the property value decreases with 0.53% per unit increase in dB(A).
The main goal of this study was to improve the guidelines of transport valuation. These guidelines advise
to calculate the maintenance cost and construction of special walls that reduce noise (Chang & Kim,
2013).
3. Methodology
In order to investigate the effect of the Betuweroute on the real estate prices in its vicinity, a hedonic
pricing model is used. Hedonic pricing model allows for the identification of price factors based on the
premise that the price is both determined by internal and external characteristics. This approach is suitable
for our investigation as real estate prices are determined by a large number of both internal and external
factors.
Data
The statistical analysis for this paper uses the (WoON (Woononderzoek Nederland), 2022) housing
research in the Netherlands. This dataset is ideal for a hedonic pricing model as it contains a large number
of measured variables for each observation (specific housing characteristics, municipality data, multiple
years of observation), which allows for the implementation of a large number of control variables, which
is of a large benefit as determining causal relationships in the real estate sector can be rather inaccurate as
real estate prices have a large number of internal and external price factors. (For a complete list of
variables, see Appendix)
The dataset contains observations from the years 2002-2015 excluding years 2004, 2007, 2010, 2013. For
a yearly breakdown of observations, see Appendix.
Sorting through data/Data manipulation
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The dataset used has got 347 532 unique observations, however only about half of the datapoints are
usable as only 176 166 unique observations contain price data. Therefore, a decision has been made to
drop all the observations that do not contain price data, as those datapoints are irrelevant for the purpose
of our investigation. Also, there were 2 observations with negative values for price, which were dropped
as well, making the total unique observations total 176 164.
Transformation of the dependent variable
The dependent variable price is not approximately normally distributed, hence a new variable lnprice has
been created. The logarithmic transformation of the dependent variable reduces skewness, reduces the
relative weight of outliers, and makes the distribution of the dependent variable more symmetrical
(approximately normally distributed). Having the dependent variable be in a logarithmic form also allows
for the interpretation of the coefficients of the regression as elasticities.
The distribution of the level and log prices can be found below in figures A and B respectively:
Fig. A
Fig. B
Separation of treatment and control groups/Variable of interest creation
A new dummy variable Betuweroute is generated which acts as an indicator for the municipalities that the
Betuweroute railway goes through. The mapping of the municipalities has been done manually, with the
use of Google Maps and information on the railway’s route from (Wikipedia-bijdragers, 2022). Also, the
CBS municipality codes serve as a reference in the dataset to distinguish between municipalities, which
enabled for the generation of the dummy variable, allowing for the separation of the dataset into treatment
and control categories.
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Summary Statistics
Fig. C
Variable
Observations
Mean
Std. Dev.
Min.
Max.
lnprice | Betuweroute = 0
168,163
12.44095
0.5037436
9.218807
14.75497
lnprice | Betuweroute = 1
8,001
12.33019
.5101833
9.386998
14.50915
Comparing the two sample groups of interest, we can notice a large disproportion in the number of
observations between the two groups, which is logical as the ‘treatment’ group only consists of 15
municipalities. The mean ln (house price) is 0.89% bigger in the ‘control’ group municipalities. The
control group consists of all the other municipalities in the Netherlands. A graphical distribution of the
ln(house price) for the control and treatment groups is depicted below in Fig. D.
Fig. D
The control and treatment sample have a very similar distribution of ln (house prices) over all the
available years of observation.
Hedonic price regression
To estimate the effect of the Betuweroute on real estate prices in the municipalities it goes through, a
hedonic price regression (Regression 1) is constructed. Where lnprice is the dependent variable,
Betuweroute is the independent variable of interest and the regression also contains 30 other control
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variables controlling for year of observation, housing characteristics and municipality differences, which
are depicted by the vector X in the equation 1 below.
Equation 1: ln(𝑃𝑟𝑖𝑐𝑒𝑖 ) = 𝛽0 + 𝛽1 𝐵𝑒𝑡𝑤𝑒𝑟𝑜𝑢𝑡𝑒𝑖 + 𝜃𝑿𝒊 + 𝜀𝑖
Regression 1
(1)
VARIABLES
lnprice
Betuweroute
-0.0618***
(0.00423)
mun
1.35e-05***
(1.87e-06)
unsafe
0.916***
(0.0281)
size
0.00107***
(2.04e-05)
floor
-0.0124***
(0.00127)
elevator
0.176***
(0.00571)
garage
0.144***
(0.00242)
centralheating
0.0647***
(0.00503)
rooms
0.0755***
(0.00201)
maintgood
0.108***
(0.00374)
Constant
11.37***
10
(0.0228)
Observations
R-squared
176,164
0.388
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
**** A complete list of estimators from Regression 1 can be found in the Appendix
4. Results
From Regression 1, it can be concluded that house prices in the Betuweroute municipalities are on average
6.18% smaller than in the municipalities in the rest of the Netherlands. To elaborate, municipalities that the
Betuweroute goes through on average experience a 6.18% loss in residential real estate prices ceterisparibus, this result is statistically significant at 1% α.
One of the most important assumptions for this model, for the result to be interpreted as causality, is that
the two samples (treatment and control) are both to be representative of the population and each other. This
assumption in reality is however questionable. Fig. D supports this assumption, showing that the ln(price)
distribution of real estate prices over years of observations seem to be very similar for both groups.
However, given the heterogeneity of real estate and the limited data available, this assumption can cause
internal validity problems.
Omitted variable bias is also a very probable source of internal validity issues. As already mentioned,
Regression 1 is based on an imperfect data source, which only accounts for some real estate price factors
such as housing characteristics, however real estate prices have a large number of both internal and external
price factors which are not accounted for in Regression 1, which could be correlated with some of the
regressors, leading to biased estimates.
The integrity of a hedonic pricing model can be questioned as well. Although it is suitable for the
determination of price of products with multiple price factors, as it considers both internal and external
factors, it also has a number of significant drawbacks. Hedonic pricing is only able to capture the
consumers’ willingness to pay for what are regarded as environmental factors and their subsequent
outcomes. This is further dependent on the consumers’ ability to perceive these environmental factors in
order for the hedonic pricing model to pick up on them, which in the case of Betuweroute is questionable.
5. Conclusion
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The statistical analysis comes to a conclusion that the construction and use of Betuweroute freight railway
has got a negative effect on real estate prices in the proximity of the railway on a municipality-based scale.
The negative effect is of 6.18%. This conclusion coincides with our hypothesis established in the
introduction. The causality of this effect is rather questionable as mentioned before due to a number of
potential sources for endogeneity in Regression 1.
This finding coincides with our hypothesis that the railway track would negatively affect the real estate
prices in its proximity due to the negative external effects its produces, such as noise pollution, aesthetics
pollution and other forms of negative externalities. This proves as a helpful indicator for policy makers and
the transportation practitioners in general.
Based on the findings in this paper, there should be an incentive to construct freight railway networks away
from residentially occupied land to minimize the negative externalities it produces. This however is easier
said than done, as accommodating for this advice can lead to other unforeseen consequences which could,
due to the construction of railway lines away from populated areas, lead to other negative external effects
and other inefficiencies.
Avoiding populated areas for the construction of freight transportation networks could result in a rise in the
construction costs of infrastructure projects, as it is typically easier and less costly to build new
infrastructure as an addition to already existing infrastructure from a logistical standpoint, hence building
transportation (non-passenger) infrastructure along populated areas is quite common.
Relation of conclusion to the literature review.
Overall, the conclusions is supported by most literature. The negative effect of the fright railway, of
6.18%, can partially be explained by the paper of Chang and Kim (2013). However, that Chang and Kim
measured a decrease of 0.53% of the property value per unit increase in dB (A) does not mean that the
increase in noise is 6.18% / 0.53% = 11.66 dB(A). Other variables that could explain the negative effect
are the city size and disposable income, since they have an economical significant strong influence on the
real estate prices (Chen et al., 2022). The feedback effect and the diffusion effect are less likely to explain
this decrease, since these effects should cause a rise of real estate prices. This means that our decrease in
property value is not in line with all the literature. Several other studies suggest that the construction of
train tracks will increase the prices of residences as stated in the literature review.
Limitations
The real estate market is one of the most complex economical markets to exist, which certainly puts into
question the predictive power of predictive models. Real estate prices have a large number of price
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determinants, which makes accurate predictions extremely hard to create. Even though the regression
analysis in this paper contains a fairly large number of control variables, internal validity of the regression
is not certain. This makes it hard to establish any concrete conclusions about causality of the Betuweroute
on real estate prices.
The use of municipality data as a proxy for geographical location data in relation to the Betuweroute is
also rather imprecise. It would be more suitable to use a higher degree of precision via for example postal
code data, which would allow for a more accurate distance measurements of the real estate in question in
relation to the freight railway line.
The Betuweroute has been open for operation in 2007, which is also a period in which the world real
estate market experienced some trouble due to the real estate market and financial market crash in the
United States. This adds another layer of complexity to real estate price movements, which is a possible
source of further inaccuracy in our regression analysis. This fact also highlights the issue of omitted
variable bias, as Regression 1 does not fully account for macroeconomic price factors. This further
undermines the validity of our findings.
Expanding on research
One of the main weaknesses of this research is the imprecision of the geographical data used in the
hedonic pricing model. Therefore, it is also one of the areas that could be improved upon the most. We
suggest that using postal code data would be ideal, as it would allow for a more precise distance
calculations of the real estate in question to the railway line. A much more precise variable measuring the
geographical distance of an individual real estate property to the railway line could then be created with a
much smaller measurement error, as postal codes in the Netherlands tend to be much more spatially
tightly packed, when compared to the scale of municipalities. This would then allow for the regression of
real estate prices on the distance of real estate properties from the Betuweroute, allowing us to estimate
the effect of distance from the Betuweroute on real estate prices. This would provide much more of a
concrete conclusion, along with higher degree of precision and accuracy of the results.
Distinguishing between how industrial transportation affects different real estate sectors (residential,
industrial, commercial) would be a nice expansion on our research as well. It would be a helpful
expansion, as it would inform policy makers on the overall picture of how transportation infrastructure
affects different real estate sectors, allowing for a more efficient planning of future projects. We could
hypothesize that commercial and industrial real estate would internalize the negative externality from
freight railway less than residential real estate, because we would expect noise pollution for example to be
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of a lesser importance to industrial and commercial real estate owners than for residential real estate
owners.
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7. Appendix
List of variables in used dataset (WoON (Woononderzoek Nederland), 2022):
Variable
id
mun
Employed
Hoursworked
satisfaction
move
unsafe
income
kids
single
singlewkids
foreign
age
price
pricesqm
woz
wozsqm
size
type_appartment
type_terraced
type_semidetached
type_detached
floor
floorsbuilding
elevator
garage
centralheating
rooms
maintgood
constryear
constrlt1945
constr19451959
constr19601970
constr19711980
constr19811990
constr19912000
constrgt2000
Description
unique respondent id
municipality code
employed
hours worked per week
satisfaction with living environment (0=bad,
1=good)
individual intends to move (=1)
share of people in neighbourhood that feels
unsafe
gross yearly income of individual (in €)
household has kids
person is single
individual is single with kids
person is foreign-born
age of individual
price of the house (sales place) (in €)
house price per m2 (in €)
assessed 'woz' value (in €)
assessed value per m2 (in €)
size of the property
house type - apartment
house type - terraced
house type - semi-detached
house type - detached
floor of the property
number of floors of the building
building has elevator
property has garage
property has central heating
number of rooms in property
maintenance quality is good
construction year of the property
construction year of the property, 1945
construction year of the property, 1945-1959
construction year of the property, 1960-1970
construction year of the property, 1970-1980
construction year of the property, 1981-1990
construction year of the property, 1991-2000
construction year of the property, />2000
18
year
year_2002
year_2003
year_2005
year_2006
year_2008
year_2009
year_2011
year_2012
year_2014
year_2015
publhousing
xcoord
ycoord
munname
shhhwkids
areaha
shluresidential
shlucommercial
shluindustrial
shluopenspace
shluwater
shluinfrastructure
unsafe_count
year of observation
year of observation, 2002
year of observation, 2003
year of observation, 2005
year of observation, 2006
year of observation, 2008
year of observation, 2009
year of observation, 2011
year of observation, 2012
year of observation, 2014
year of observation, 2015
property is part of public housing
x-coordinate of centroid
y-coordinate of centroid
name of municipality
share household with kids
areaha
share residential land
share commercial land
share industrial land
share open space
share water
share land used for infrastructure
Number of observations in each year 2002-2015 (excluding 2004, 2007, 2010, 2013)
Year
2002
2003
2005
2006
2008
2009
2011
2012
2014
2015
Number of observations
32215
3736
21,778
8474
12240
26984
16434
20428
16730
17145
19
List of predictor variables used in Regression 1
Dependent variable: lnprice
Independent variable of interest: Betuweroute
Control variables: year_2002, year_2003, year_2005, year_2006, year_2008, year_2009, year_2011,
year_2012, year_2014, year_2015, mun, unsafe, size, type_appartment, type_terraced,
type_semidetached, type_detached, floor, elevator, garage, centralheating, rooms, maintgood,
constrlt1945, constr19451959, constr19601970, constr19711980, constr19811990, constr19912000,
constrgt2000
Stata Regression 1 output
20