the contribution of migration to economicdevelopment in holland 1570–1800
TRANSCRIPT
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De Economist (2013) 161:118
DOI 10.1007/s10645-012-9197-6
The Contribution of Migration to Economic
Development in Holland 15701800
Peter Foldvari Bas van Leeuwen
Jan Luiten van Zanden
Received: 20 December 2011 / Accepted: 31 August 2012 / Published online: 12 September 2012 Springer Science+Business Media New York 2012
Abstract Migration always played an important role in Dutch society. However,
little quantitative evidence on its effect on economic development is known for the
period before the twentieth century even though some stories exist about their effect on
the Golden Age. Applying a VAR analysis on a new dataset on migration and growth
for the period 15701800, we find that migration had a positive effect on factor accu-
mulation during the whole period, and a positive direct effect on the per capita income
during the Golden Age. This seems to confirm those studies that claim that the Dutcheconomy during its Golden Age at least partially benefitted from immigration.
Keywords Economic growth Immigration Holland Endogenous development
Human capital
JEL Classification J15 N13 N33
P. Foldvari B. van Leeuwen (B) J. L. van Zanden
Economic and Social History Department, Utrecht University, Drift 17, Utrecht 3512 BS,
The Netherlands
e-mail: [email protected]
P. Foldvari
Debrecen University, Debrecen, Hungary
B. van Leeuwen
Warwick University, Coventry, UK
J. L. van Zanden
Groningen University, Groningen, The Netherlands
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1 Introduction
Migration is a hot topic. Historians, not entirely insensitive for those kinds of societal
debates, have also turned to examples of large migration flows to study their long term
impact on society and economy. The Dutch Republic is one of those cases that havebeen discussed extensively in this literature. There is consensus that a lot of migra-
tion occurred during its Golden Age (15801670). It has also been concluded that
those large migration flows did not have negative effects on society and economy (for
exampleLucassen and Lucassen 2011). The debate we engage with in this paper is
about the question how important immigration was for economic success. Here we
can distinguish two views.
One view, argued most forcefully byIsrael (1989) and echoed until today (e.g.
Esser 2007), is that the spectacular success of the Dutch republic after 1580 was to
a very large extent due to the immigration of highly schooled and relatively wealthyentrepreneurs and skilled labourers from the southFlanders and Brabant. They fled
for the Spanish forces, relocated in the cities of Holland and Zeeland, and brought
with them the high-valued added activities that created a big economic boost. In other
words, the Golden Age mainly consisted of the relocation of the economic centre of
the Low Countries from Antwerp and the surrounding areas to Amsterdama process
resulting from the Spanish reconquest of the south. In this scenario, the migration flow
of the period between the 1580s and the 1620s is the decisive link between Flemish
and Dutch prosperity.
Other authors (van Zanden 1993; de Vries and van der Woude 1997) have, incontrast, argued that the growth of the Holland economy was first of all based on
indigenous developments: the emergence of an efficient set of institutions there, set
in motion a process of autonomous economic growth, which already started between
1350 and 1500 when, for example, the share of urbanisation rose from 23 to 40% mak-
ing Holland one of the most urbanised (and non-agricultural) regions in the world. In
this view, the growth spurt of the Golden Age was the continuation of a process of
economic growth that began much earlier. It was also logical that the expelled mer-
chants and craftsmen of Flanders after 1585 moved to Holland and Zeeland, because
this region offered by far the most attractive opportunities for themsuch as an effi-
cient set of institutions. In the endogenous-growth-model the immigration wave of
15801620 is a relevant and important development, but its contribution to long term
economic growth is limited. What is perhaps more important in this approach (as for-
mulated byde Vries and van der Woude 1997and byvan Zanden 1993), is that the
Holland labour market was a very open one, which, when the economy accelerated
after 1580, was able to attract increasingly large numbers of labourers from the rest
of the Netherlands (Brabant, Overijssel, Friesland) and from parts of Germany and
Scandinavia. The VOC, for example, became an employer of thousands of sailors and
soldiers recruited from all parts of the North Sea area. It has been argued that this
very flexible supply of unskilled and semi-skilled labour, which continued during the
seventeenth and eighteenth centuries, was a key to the long-term economic success of
the region.
The discussion on the links between economic development and migration so far
has concentrated on these themes (there are no contributions which approached this
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The Contribution of Migration 3
subject from another perspectivelooking at HeckscherOhlin forces, for example).
The debate has mostly centred on measuring the numbers of migrants and on their
impactbut only few have attempted to quantify that impact (but see Gelderblom
2000). It has fortunately become possible to bring more sophisticated statistical meth-
ods into the debate because we have just finished a large research project constructingthe national accounts of Holland on an annual basis between 1514 and 1807 (in fact,
the series goes back to 1347, but the pre 1514 estimates are very tentative). Moreover,
we are now also able to estimate the inflow of migrants into Holland somewhat better,
thanks to new estimates of the demographic development of Holland between 1514
and 1807, a spinoff of the project on reconstructing the national accounts. As a result,
it is now, for the first time, possible to test the ideas on the relation between migration
and economic growth more rigorously.
2 Data
The main datasets on the economy of Holland used here have been introduced and
explained in detail in other papers. The focus is on Holland, the biggest province in the
Netherlands in the early modern period, approximately equal to the current provinces
of Northern -and Southern Holland. It was also the most dynamic and richest part of
the early modern period and, hence, the region that profited most from the Golden
Age.
The main results of the project on the reconstruction of the national accounts ofHolland in the period before 1800 have been presented in van Zanden and van Leeuwen
(2012), where it is explained how the estimates of GDP, GDP per capita and popu-
lation have been constructed. For the period after 1514, estimates of total GDP were
the result of putting together value added series for 27 branches of the economy (from
agriculture to banking); the evidence for the 13471514 period is much weaker, but we
will not include this period into the analysis of this paper. Moreover, using a method
developed byFeinstein and Thomas(2001), we were also able to estimate the margins
of error of the GDP figures. Figures1and2printed below report the main findings.
The estimates demonstrate that the period 15801670the classical Golden
Agewas a period of rapid growth of total GDP and of the population of Hol-
land, but in terms of intensive growththe growth of GDP per capitait was not
exceptional. As Fig. 1 shows, there was already strong growth of GDP per capita in the
late medieval period (but the margins of error of these estimates are quite large). It also
is clear that this trade-oriented economy was characterized by a relatively high level of
instability of GDPmainly due to exogenous shocks (wars, harvest failures etc.). Our
estimates are also rather positive about growth during the eighteenth century, which
has often been portrayed as a period of economic stagnation. We find continuous per
capita growth during that century, albeit that total GDP and total population is growing
at a much slower pace. The Golden Age is therefore in the first place a period of very
rapid population growth, whereas the pace of intensive growth seems to be rather
stableboth before and after the seventeenth century.
It is thus clear that, the period 15741650 saw considerable growth in both per capita
output and population. Immigration was a main factor behind this sharp increase in
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4 P. Foldvari et al.
Fig. 1 Per capita GDP (1,800 constant guilders, including error margins).Source van Zanden and van
Leeuwen(2012)
Fig. 2 GDP (million 1990 GK dollars), population (*1,000) in Holland, 13471807.Sourcevan Zanden
and van Leeuwen(2012)
population growth during the post 1580 period. In another paper we have presented
estimates of the main demographic features of the Holland, including estimates of the
minimum level of immigration to the region. The total population of Holland increased
from 275,000 in 1514 to 400,000 in 1572an increase that was almost entirely the
result of its own natural increase. After 1572 there was first a small dip, followed by
very rapid growth resulting in a peak level of about 880,000 in 1672. This was followed
by a moderate decline to about 783,000 in 1750, after which the population stabilized
at this level for about 50 years. This stabilization remained until the mid-nineteenth
century. Afterwards, we saw larger number of migrants entering the Netherlands, but
never in those magnitudes as recorded in the seventeenth century.
Figure3 presents the estimates of the population curve of Holland, including our
estimates of net immigration. In the final decades of the sixteenth century net immi-
gration (from outside of Holland) was about 3,800 per year, to increase to on average
5,200 during the seventeenth century; the peak of around 10,000 immigrants occurred
about 1650. Total immigration in Holland between 1574 and 1650 is estimated at
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The Contribution of Migration 5
Fig. 3 Population (in 1,000, right-hand scale); Annual number of immigrants (in 1,000, Left-hand scale)
for Holland and the Netherlands, 15101800.SourceThis paper,Oomens(1989), and 200 jaar statistiek in
tijdreeksen
480,000so larger, for example, than the original population of Holland in 1570.
These are lower bound estimates; they are based on the difference between the natural
increase of the population and its actual growth, and therefore do not include people
who emigrated from Holland (for example, left on the ships of the East Indies Com-
pany); their total number is roughly estimated at about 200250,000 (Lucassen 2002),bringing total immigration to about 700,000. We also ignore in our estimates tem-
porary migratory flows, such as the seasonal workers analysed byLucassen(1987).
Clearly, immigration was huge in the late sixteenth and seventeenth century.
In the late seventeenth and eighteenth century the number of migrants fell to on
average 1,300 migrants per year while in nineteenth century the number of migrants
increased from ca. 7,000 per annum in the 1860s to roughly 17,000 in the 1890s.
Even though these latter migrants were bigger in number than in the Golden Age, we
have to be aware that they made up a far smaller proportion of the total population.
Lucassen(2002) andOomens(1989), for example, calculated that, whereas the share
of migrants in the population in the 1890s was around 1.6 %, in 1600 it was no less
than 10 %. And these numbers are for the Netherlands, while most migrants would
have travelled to Holland.
Migration can have a direct on economic growth (for example via technological
development) but it may also work via the factors of production such as physical- or
human capital if the migrants brought these two assets along with them to Holland/the
Netherlands. Therefore, in our following analysis we also include series of physical-
and human capital. We will use both the human capital (i.e. average years of education
in the population aged 15 and older) and non-residential physical capital for Holland
(van Zanden and van Leeuwen 2012).
In Table 1 we report the unit root test of our 4 variables: log of real GDP per
capita (lny), physical capital per capita (lnk), average years of education (avyears),
and number of migrants per 1000 inhabitants (migration) for the sub-periods 1572
1650, 16501700 and 17001800. The sub-periods follow the standard periodization
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6 P. Foldvari et al.
Table 1 Unit root tests
15721650 16501700 17001800
ADF KPSS ADF KPSS ADF KPSS
lny 5.051 0.130 3.889 0.169 2.772 0.109
lnk 4.930 0.069 2.034 0.135 2.078 0.155
migration 3.712 0.167 1.767 0.188 2.600 0.093
avyears 2.345 0.251 2.398 0.175 1.055 0.093
lny 9.701 0.166 8.902 0.500 11.734 0.055
lnk 5.184 0.066 5.001 0.162 2.997 0.217
migration 5.930 0.029 9.222 0.479 8.886 0.035
avyears 2.767 0.157 3.410 0.180 2.861 0.447
For the levels we used a test specification with constant and trend, for the differences we used a specificationwith constant only. The null-hypotheses of the ADF and KPSS tests are non-stationarity and stationarity
respectively. For the ADF tests we used an automatic lag selection (max. lag = 20 with the SBC as model
selection criterion). For the KPSS test we used Bartlett kernel with automatic NeweyWest bandwith choice
of the economic development of the Dutch Republic with 15721650 being the Golden
Age, 16501700 a period of crisis and 17001800 a period of stagnation. We adopt the
same periodization for the VAR analysis as well. We intentionally used two unit-root
tests with different null-hypotheses: while the ADF has the null of non-stationarity,
the KPSS tests stationarity against the alternative of non-stationarity. The two testsometimes lead to contradicting results: the log of GDP per capita is usually found
to be I(1) but for the period of 16501700 the KPSS suggest trend-stationarity while
the ADF indicates I(1). Similarly the type of stationarity is not easy to determine for
migration: depending on which test we prefer it can be trend-stationary for 15721650
but based on the KPSS test it is rather I(1). In the next section we carry out Johansen
cointegration tests and also discuss the effect of migration on per capita real GDP.
3 Empirical Analysis
In this paper we aim at estimating the effect of migration on economic growth. In the
literature, it is argued that migration can have a direct impact on economic growth,
or indirect via the factors of production (e.g. Dolado et al. 1994;Walz 1995).1 Like-
wise,Morley(2006) argues for a reverse causality between migration and growth. We
will rely on VAR system to draw conclusions about the direction of causality and the
existence of a long-run relationship (cointegration) and use impulse response func-
tions (IRF) to obtain a picture of the dynamics of the relationships and to estimate its
long-run effect.
1 Just to mention some examples: direct effects may rise due to an increased demand for goods and housing,
while indirect effects may result from different propensities to save or different attitudes toward education
that affect physical- and human capital accumulation in the long-run.
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The Contribution of Migration 7
3.1 Granger Causality Tests
In order to identify the causal relation between the different variables, we start with a
Granger causality test. VariableXis said to Granger-cause variable Yif the past values
ofXcontain useful information on the current value ofY (Granger 1969). The stan-dard procedure involves fitting the best possible autoregressive model on the values of
Yand introducing past values ofXas additional explanatory variables to the specifi-
cation. If a joint significance test of the coefficients of the lags ofXsuggests that they
actually improved the fit of the model, we can reject the null-hypothesis of the lack
of Granger-causality. Possible pitfalls of this methodology may include omitted vari-
able bias, incorrect choice of the number of lags, and also the effect of non-stationary
variables. Obviously, a VAR-system is ideally suited for a Granger-test on stationary
variables, but asToda and Yamamoto(1995) claim, standard Granger-causality tests
can be misleading in the presence of integrated series. Using a Granger-causality teston a VEC (Vector Error Correction) system or on a VAR on differenced variables,
however, would also be misleading since taking first differences would remove the
possible long-run relationship among the endogenous variables. Additionally, unnec-
essary differencing may increase the error to signal variance ratio in the presence of
measurement errors (Plosser and Schwert 1978).
Therefore, Toda and Yamamoto suggest a procedure that enables the test for
Granger-causality even in the presence of integrated variables and cointegration.2
First, one identifies the highest order of integration (denoted as m) in the endoge-
nous variables and estimates the best possible VAR(p) model. The second step is aGranger-causality test that should be carried out on the first p lags of a VAR(p + m)
system. Since we found that the highest order of integration was one for all periods
we use the m = 1 assumption. Our decision regarding the lag length of the VAR system
(p) is not solely based on model selection criteria; if at the suggested lag length we
still find residual autocorrelation significant at 5% we add further lags as long as it
disappears. Also if the stability conditions were not fulfilled for the VAR system, we
increase the order of the VAR as long as we obtain no characteristic roots outside the
unit circle.3 This strategy sometimes leads to high order VAR systems. The results of
the specification process are summarized in Table2while Table3contains the results
from the TodaYamamoto Granger-causality test.
For all the periods we find evidence that migration Granger caused some mac-
roeconomic variable of interest: for the pre-1700 period we find no direct effect of
migration on GDP per capita, but we do find a causality running from migration toward
physical capital stock. For the period 15721650 we find that physical capital Granger
caused per capita GDP which suggests the existence of an indirect link through which
migration may have affected per capita income.
2 As unit-root tests have generally low power and the results from the cointegration tests may be sensitive
to the choice of lags or affected by the measurement error in our data, we decide to follow Toda and Ya-
mamotos method as it allows carrying out a Granger test without transforming the model based on possibly
biased test results.
3 Thereby we assure that the VAR is invertible to a VMA representation and we can obtain meaningful
impulse response functions.
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Table 2 VAR specifications
15721650 16501700 17001800
Lag length preferred by FPE 3 10+ 3
Lag length preferred by AIC 10+ 10+ 3
Lag length preferred by SBC 2 2 2
Our choice (p) 11 7 10
FPEfinal prediction error,AICakaike information criterion,SBCSchwarz Bayesian information criterion,
we choose the lag length (p) so that no residual autocorrelation significant at 5 % remains and the system
fulfils that stability conditions
Table 3 Results of the TodaYamamoto (1995) Granger test (only pvalues are reported)
Period Explanatory variables Dependent variables
log GDP p.c. log capital Migration Av. years of
p.c. schooling
15721650 log GDP p.c. 0.335 0.838 0.152
log capital p.c. 0.008 0.799 0.698
Migration 0.181 0.070 0.059
Av. years of schooling 0.638 0.345 0.010
16501700 log GDP p.c. 0.481 0.012 0.025
log capital p.c. 0.268 0.024 0.119
Migration 0.723 0.000 0.390
Av. years of schooling 0.179 0.122 0.000
17001800 log GDP p.c. 0.031 0.840 0.024
log capital p.c. 0.020 0.802 0.268
Migration 0.008 0.212 0.684
Av. years of schooling 0.265 0.033 0.438
Bold values are causality significant at 5 %, i.e. a pvalue less than 0.1 (0.05) means that the variable in the
respective row Granger caused the variable in the respective column at 10 % (5 %) level of significance
3.2 Cointegration Analysis
For a possible existence of long-run relationship among the variables we apply Johan-
sen cointegration tests on the above estimated VAR specifications. This test is based
on a Vector Error-Correction representation of the processes.
Yt= +
p1
i=1
iYti +Yt1 + et
where the rank of matrix (r()) is indicative of the existence of cointegration and the
number of cointegrating vectors. If there are k endogenous variables, if 0< r ()
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The Contribution of Migration 9
Table 4 Results from the trace and maximum eigenvalue tests (onlypvalues are reported)
Rank of matrix 15721650 16501700 17001800
Trace test
At most 0 0.000 0.000 0.000
At most 1 0.000 0.035 0.000
At most 2 0.074 0.012 0.004
At most 3 0.431 0.068 0.013
Maximum eigenvalue test
At most 0 0.000 0.000 0.000
At most 1 0.000 0.089 0.026
At most 2 0.060 0.024 0.028
At most 3 0.431 0.068 0.013
Suggested rank 3 4 4
(at 10 % sign.)
Trace test for rank k: H0 rank is at most k, H1 rank is 4 Maximum eigenvalue test for rank k: H0 rank is at
most k, H1 rank is k+1
by definition cannot be cointegrated. The standard testing methods include the trace
and the maximum eigenvalue test. Table4has the outcomes:
We find that with the exception of 15721650 matrix is of full rank, indicating
that the variables were stationary even though this contradicts some of the unit-root
tests results reported in Table1.Still, unit-root tests generally have low power so we
prefer the results from the Johansen-test. It should be noted that the results from the
Johansen test of cointegration is sensitive to the choice of lag. Generally if the order
of the VAR system is chosen too low, the test has the tendency to find spurious coin-
tegration(Cheung and Lai 1993).4 At 10 % level of significance we find evidence for
three cointegrating vectors for the Golden Age and find the matrix of full rank for
the rest of the periods meaning that the variables should be I(0).5
4 In other words, if we had accepted the suggestion by the Schwarz Information Criterion and had estimated
VAR(2) or VAR(3) systems for all sub-periods, we would have found one cointegrating vector for 1572
1650, and no cointegrating vectors for 16501700 and 17001800 with the variables being non-stationary
(that is the Johansen test could not reject that the rank of matrix was zero. The presence of residual
autocorrelation in those models, however, is a clear warning that these specifications could not completely
capture the dynamic of the variables. Furthermore, Cheung and Lai (1993) find that the Johansen test is sen-
sitive to underparametrization (choosing too few lags) and the results can be biased toward finding spurious
cointegrating vectors. They claim that Johansen test is robust to the overparametrization, however, hence
we rather take the risk of overfitting the model at the price of losing some efficiency than underfitting it. The
obtained CIRFs would only be qualitatively different in case of 15721650 where the CRIFs from a VEC(1)
specification would reflect a permanent effect of migration on all endogenous variable. This result does not
make any sense as with the observed inflow of immigrant we should observe an accelerating growth of per
capita income in the period which is obviously not found in the data.
5 We opted to decide based on a 10 % level of significance because due to the limited sample sizes (79, 51
and 101 years respectively) and the presence of measurement error in historical estimates. With 1 % level
of significance we would find 3 cointegrating vectors for all the periods.
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Table 5 Restricted cointegrating vector for 15721650
CV 1 CV2 CV3
Constant 6.129 2.506 0.774
lny 1 0.312 0
(3.056)
lnk 0.861 1 0
(1.296)
migration 0 0.012 0.044
(4.03) (4.88)
avyears 0 0 1
The LR test for binding restrictions pvalue 0.144
Table 6 Restricted adjustment coefficients for 15721650
CV 1 CV2 CV3
lny 0 2.783 0
(4.055)
lnk 0.121 0.272 0
(2.081) (2.676)
migration 0 0 6.656
(2.596)
avyears 0 0.026 0.035
(1.734) (4.314)
For the 15721650 period we tested different restrictions on the cointegrating vector
so that we get a better insight to the long-run relationships. The restricted cointegrating
vector, with the adjustment coefficients are reported in Tables 5and6.
We choose the coefficients of the log GDP per capita, log capital per capita and the
average years of education to be normalized to unit respectively. Neither migration
nor average years of education were found to yield a significant long-run coefficient
in the first cointegrating vector, so they were omitted. This means that there was no
direct long-run relationship among the per capita GDP and the migration. On the other
hand we find evidence for indirect relationships: first, migration seems to have had
a positive relationship with per capita physical capital stock (second cointegrating
vector) and also with average years of education (third cointegrating vector). This
leads to the conclusion that immigrants during the Golden Age did not necessarily
contribute to higher productivity in Holland, but rather brought a different attitude to
factor accumulation, with higher propensity to save and invest and higher likelihood
to follow some formal education. These attitudes are expected to have been beneficial
for the rise of commercial capitalism.
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The Contribution of Migration 11
3.3 Impulse-Response Functions
The estimation of impulse-response functions can be a way to obtain a better under-
standing of the dynamic relationship among the endogenous variables, and to have an
estimate on the long-run effect as well. Still, simply estimating IRFs on the baselineVAR model would be likely to lead to biased estimates and wrong conclusions if the
variables are in simultaneous relation (contemporaneously correlated). In order to see
why this is the case, let us have the following VAR(p) system:
AYt= 0 +
p
i=1
iYti + ut,YTt = (lnyt, ln kt,migrationt, avyear st)
Where matrix A has the coefficients of the simultaneous relationship among the Y
variables. Obviously if A is an identity matrix then the variables are not correlated
contemporaneously and the IRFs on the baseline model can be trusted. If this is not
the case, however, the residuals form the VAR will contain not only the shocks to a
given variable, but also the effect of innovations in other variables, or in other words,
the residuals will be correlated:
Yt= A10 +
p
i=1
A1iYti + A1ut
So before any meaningful IRF can be estimated from this model, one needs to have cer-
tain assumptions about matrix A, which involves a structural factorization (estimation
of a Structural VAR or SVAR).
A useful check for the existence of a simultaneous relationship among our endoge-
nous variables is to check if there is some linear correlation among the VAR residuals.
The results are included as Table7.
As for 15721650 and 16501700 we find only two possible simultaneous rela-
tionships: one is between average years of education and log of GDP per capita, the
other is between average years of education and migration. For 17001800 we obtain
a significant correlation coefficient for the residuals of the log capital stock and log of
GDP per capita, and the average years of education and migration. The identification
of the matrix A requires that the correlation is attributed to only one of the variables.
We operate on the assumption that it is more likely that the GDP per capita was affected
by average years of education, and not vice versa, and migration affected education,
so the observed correlation can be attributed fully to migration. For the period 1700
1800 we assume that a shock in GDP per capita had an immediate impact on capital
stock, but not vice versa.6
The IRFs and the cumulated IRFs are reported as Figs.4,5,6,and7.
The impulse response functions reveal that migration had a positive level effecton GDP per capita during the Golden Age, while we find a negative effect for
6 The obtained IRFs are not much different without a structural factorization either.
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The Contribution of Migration 13
-.08
-.04
.00
.04
.08
.12
2 4 6 8 10 12 14 16 18 20
Response of log GDP p. c
-.02
-.01
.00
.01
.02
.03
2 4 6 8 10 12 14 16 18 20
Response of log capital stock p. c
-1.5
-1.0
-0.5
0.0
0.5
1.0
2 4 6 8 10 12 14 16 18 20
Response of migration
-.005
.000
.005
.010
.015
.020
2 4 6 8 10 12 14 16 18 20
Response of av. years of education
-.10
-.05
.00
.05
.10
.15
.20
2 4 6 8 10 12 14 16 18 20
Accumulated Response of log GDP p.c.
-.04
.00
.04
.08
.12
.16
2 4 6 8 10 12 14 16 18 20
Accumulated Response of log capital stock p.c
-2
-1
0
1
2
3
4
5
2 4 6 8 10 12 14 16 18 20
Accumulated Response of migration
-.05
.00
.05
.10
.15
.20
2 4 6 8 10 12 14 16 18 20
Accumulated Response of av. years of schooling
Fig. 4 Impulse response function 15721650 based on SVAR(11), responses to one SD (3,040 immigrants)
impulse in migration (2 SE confidence intervals)
We applied a VAR system on a newly available dataset to draw conclusions about
the causality and long-run relationships of migration and other macro-economic vari-
ables. Interestingly during the Golden Age migration had a positive long-run direct
effect on GDP per capita. It also positively affected capital accumulation and the level
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14 P. Foldvari et al.
-.08
-.04
.00
.04
.08
.12
2 4 6 8 10 12 14 16 18 20
Response of log GDP p.c.
-.03
-.02
-.01
.00
.01
.02
.03
2 4 6 8 10 12 14 16 18 20
Response of log capital stock p.c.
-1.0
-0.5
0.0
0.5
1.0
1.5
2 4 6 8 10 12 14 16 18 20
Response of migration
.000
.004
.008
.012
.016
.020
2 4 6 8 10 12 14 16 18 20
Response of av. years of education
-.08
-.04
.00
.04
.08
.12
.16
.20
.24
2 4 6 8 10 12 14 16 18 20
Accumulated Response of log GDP p.c.
.00
.05
.10
.15
.20
.25
2 4 6 8 10 12 14 16 18 20
Accumulated Response of log capital stock p.c.
1
2
3
4
5
2 4 6 8 10 12 14 16 18 20
Accumulated Response of migration
.00
.04
.08
.12
.16
2 4 6 8 10 12 14 16 18 20
Accumulated Response of av. years of education
Fig. 5 Impulse response function 15721650 based on VEC(10), responses to one SD (3,040 immi-
grants)impulse in migration (no confidence intervals are available)
of education in the population. This changed after 1650 when the effect of migrants
on economic development either directly or via the factors of production became
insignificant. After 1700 the positive effect on physical capital went up again, but the
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The Contribution of Migration 15
-.04
-.02
.00
.02
.04
2 4 6 8 10 12 14 16 18 20
Response of log GDP p.c.
-.008
-.004
.000
.004
.008
.012
2 4 6 8 10 12 14 16 18 20
Response of log of capital stock p.c.
-2
-1
0
1
2
2 4 6 8 10 12 14 16 18 20
Response of migration
-.008
-.006
-.004
-.002
.000
.002
.004
.006
2 4 6 8 10 12 14 16 18 20
Response of av. years of schooling
-.15
-.10
-.05
.00
.05
.10
2 4 6 8 10 12 14 16 18 20
Accumulated Response of log GDP p.c.
-.02
-.01
.00
.01
.02
.03
.04
.05
2 4 6 8 10 12 14 16 18 20
Accumulated Response of log of capital stock p.c.
-4
-2
0
2
4
6
8
2 4 6 8 10 12 14 16 18 20
Accumulated Response of migration
-.06
-.04
-.02
.00
.02
.04
2 4 6 8 10 12 14 16 18 20
Accumulated Response of av. years of education
Fig. 6 Impulse response functions 16501700, responses to one SD (5,270 immigrants) impulse in migra-
tion (2 SE confidence intervals)
direct effect on GDP per capita became negative, hence, cancelling each other out to
a certain extent. This means that only during the Golden Age the net effect of migra-
tion on per capita GDP was positive and significant which confirms those studies that
claim that, for example, rich merchants went to Amsterdam and brought their capital
and networks along(van Dillen 1958;Brulez 1960). Altogether, the positive effect
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16 P. Foldvari et al.
-.03
-.02
-.01
.00
.01
.02
.03
2 4 6 8 10 12 14 16 18 20
Response of log GDP p.c.
-.008
-.004
.000
.004
.008
.012
2 4 6 8 10 12 14 16 18 20
Response of log of capital stock p.c.
-.4
-.2
.0
.2
.4
.6
2 4 6 8 10 12 14 16 18 20
Response of migration
-.002
-.001
.000
.001
.002
.003
2 4 6 8 10 12 14 16 18 20
Response of av. years of education
-.20
-.15
-.10
-.05
.00
.05
.10
2 4 6 8 10 12 14 16 18 20
Accumulated Response of log GDP p.c.
-.04
.00
.04
.08
.12
.16
2 4 6 8 10 12 14 16 18 20
Accumulated Response of log capital stock p.c.
0
1
2
3
4
2 4 6 8 10 12 14 16 18 20
Accumulated Response of migration
-.02
-.01
.00
.01
.02
.03
2 4 6 8 10 12 14 16 18 20
Accumulated Response of av. years of education
Fig.7 Impulse response function 17001800, responses to one SD (1,191 immigrants) impulse in migration
(2 SE confidence intervals)
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The Contribution of Migration 17
Table 8 Estimated long-run effect of 1,000 new immigrants after 20 years (based on the cumulated IRFs)
15721650 15721650 16501700 17001800
SVAR(11) VEC(10) SVAR(7) SVAR(10)
On per capita GDP (%) 2.1 3.0 0.5 4.1
On per capita capital stock (%) 2.0 2.8 0.4 5.4
On migration 621 913 224 1,517
On average years of education (years) 0.028 0.003 0.001 0.005
For the period 15721650 we report the long-run effects from a VEC(10) specification as well, but the
results are subject to the assumption of no contemporary correlation of the variables. This can cause a bias
of migrants on factor accumulation is strong indication of their significant role in the
success of commercial capitalism in Holland during the Golden Age.
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