The Contribution of Migration to EconomicDevelopment in Holland 1570–1800

8/14/2019 The Contribution of Migration to EconomicDevelopment in Holland 15701800

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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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2 P. Foldvari et al.

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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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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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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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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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


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


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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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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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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-.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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-.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


.000

.004

.008

.012

.016

.020

2 4 6 8 10 12 14 16 18 20


-.08

-.04

.00

.04

.08

.12

.16

.20

.24

2 4 6 8 10 12 14 16 18 20


.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


.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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-.04

-.02

.00

.02

.04

2 4 6 8 10 12 14 16 18 20


-.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


-.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


-.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


-.06

-.04

-.02

.00

.02

.04

2 4 6 8 10 12 14 16 18 20


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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-.03

-.02

-.01

.00

.01

.02

.03

2 4 6 8 10 12 14 16 18 20


-.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


-.002

-.001

.000

.001

.002

.003

2 4 6 8 10 12 14 16 18 20


-.20

-.15

-.10

-.05

.00

.05

.10

2 4 6 8 10 12 14 16 18 20


-.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


-.02

-.01

.00

.01

.02

.03

2 4 6 8 10 12 14 16 18 20


Fig.7 Impulse response function 17001800, responses to one SD (1,191 immigrants) impulse in migration

(2 SE confidence intervals)

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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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The Contribution of Migration to EconomicDevelopment in Holland 1570–1800