Agricultural transition and the adoption of primitive technology.
Ang, James B.
This paper tests Jared Diamond's influential theory that an
earlier transition from a hunter-gatherer society to agricultural
production induces higher levels of technology adoption. Using a proxy
for the geographic diffusion barriers of Neolithic technology and an
index of biogeographic endowments to isolate the exogenous component of
the timing of agricultural transition, the findings indicate that
countries that experienced earlier transitions to agriculture were
subsequently more capable of adopting new technologies in 1000 BC, 1 AD,
and 1500 AD. These results lend strong support to Diamond's
hypothesis. (JEL 030, 040)
I. INTRODUCTION
Technology is the centerpiece of the literature on economic growth
(Aghion and Howitt 1992, 2009; Hsieh and Klenow 2010; Jones 1995; Mokyr
1990, 2005; Nelson and Phelps 1966). Surprisingly, little is known about
what causes the wide disparity in the adoption levels of technology
across the globe. The importance of historical technology adoption for
long-run comparative economic development has recently been highlighted
in the seminal contribution of Comin et al. (2010). They construct
indicators to capture the ancient state of technology adoption in 1000
BC, 1 AD, and 1500 AD, and show that the historical levels of technology
adoption of a country crucially determines its current technology
adoption levels and per capita income. Comin and Hobijn (2010) provide
further evidence that technology adoption lags explain more than 25% of
per capita income differences across countries, thus suggesting that a
longer lag significantly lowers the present-day standards of living (see
also Comin et al. 2008). However, the issue of what accounts for
differences in technology adoption levels across the world has so far
not been addressed in the literature. Without a proper understanding of
the sources of technology adoption, the history of economic development
will remain incomplete.
A prominent explanation for the cross-country variations in
technology adoption levels has been provided by Diamond (1997), who
links early agrarian development to the capability of adopting new
technology. The transition from a hunter-gatherer society to
agricultural production, also known as the Neolithic Revolution that
first occurred about 11 millennia ago, is one of the most significant
events in human history (Putterman 2008). The first Neolithic transition
occurred in the Middle East, where there was a shift from hunting and
gathering to the cultivation of crops and animals. Diamond (1997) argues
that following this transition, food production was focused on
domesticating rather than gathering wild plants and hunting animals. The
capacity of the agriculture sector to yield more and better food,
provide nourishment for more people, and produce storable food led to
the creation of a non-food production class. These specialists were
responsible for the early development of writing, education,
technology-based military expertise, and social and political
structures, which subsequently played a pivotal role in technological
development.
Along with this development, agricultural settlements also
facilitated more sedentary living, which underlies the historical
development of technology as it enabled the accumulation of technical
know-how. This developmental head start, built upon the foundations
established during the transition, enabled an economy to enter into the
path of endogenous knowledge creation and lowered costs of adopting
technologies, which subsequently translated into greater technological
sophistication. Furthermore, significant improvements in farming
techniques and the ensuing increases in agricultural productivity then
partially contributed to the onset of the Industrial Revolution. Thus,
Diamond's thesis proposes a fundamental link between an early start
in agriculture and the subsequent sophistication in technology.
Against this backdrop, our aim is to shed some light on how the
timing of agricultural transition affects technology adoption in
different periods of human history. In order to provide a direct
assessment for the hypothesis that the Neolithic transition triggered
the emergence of a long-lasting endogenous knowledge creation process,
leading to increased levels of technological development, variations in
the onset of agricultural transition across countries are used to
capture the extent of the developmental head start during the
agricultural stage of development (see Ashraf and Galor 2011; Ashraf et
al. 2010).
A major challenge in estimating the causal influence of
agricultural transition is that it may be endogenous with respect to
technology adoption. That is, the estimates may be biased due to reverse
causation, measurement errors or omitted variables, and thus cannot be
interpreted as reflecting a causal effect of agricultural transition on
technology adoption. In order to isolate the exogenous variation in the
timing of agricultural transition, geographic distance to the Neolithic
point of origin and an index of biogeographic endowments are used as
instruments. The choice of the first instrument is based on the
reasoning that countries located in close proximity to the Neolithic
cores tend to have similar cultural, ecological, and geographic
conditions, and thus enjoy a lower imitation cost. Faced with lower
adoption barriers, this enabled them to absorb the diffusions of the
point of origin's technology more effectively. Initial conditions
of biogeography may serve as another appropriate instrument for the
timing of transition to agriculture on the grounds that countries
endowed with a greater variety of prehistoric wild plants and animals
suitable for domestication were able to experience agricultural
settlements earlier than others.
In line with common practice in the literature, we control for
various effects of geography. Regional dummies, which are classified
according to how Neolithic farming techniques spread across borders
within the same agricultural spread zones, are also included in the
regressions to remove the effects of regional specific unobserved
heterogeneity bias. Using the data sets of Comin et al. (2010) for the
levels of technology adoption and the agricultural transition timing
estimates of Putterman (2006), our results indicate that countries that
experienced an earlier transition to sedentary agriculture were
technologically more sophisticated in 1000 BC. The economic effect of
the timing of agricultural transition is found to be very large, and
this variable turns out to be the most significant contributor to the
levels of technology adoption among all variables included in the
regressions. The timing of transitions to sedentary agriculture is also
found to have a significant influence on technology adoption in 1 AD and
1500 AD, although the size of this impact is much smaller. These results
prevail even if we control for the extent of state presence, genetic
distance relative to the technological frontier and demographic
pressure. Overall, the results provide significant evidence supporting
the hypothesis of Diamond (1997).
This paper is closely related to a growing literature on how early
development affects various economic outcomes. For instance, Chanda and
Putterman (2007) and Putterman (2008) show that an earlier start of
agriculture helps predict the variations in income in 1500 AD, a result
that lends support to the prediction of Diamond (1997). Others have
found that such an effect persists until today. In particular, the
recent empirical work of Ashraf et al. (2010), Putterman and Weil
(2010), and Bleaney and Dimico (2011) corroborate the above findings by
uncovering a statistically robust positive effect of the onset of the
Neolithic Revolution on current levels of income. In line with this, a
highly cited contribution by Olsson and Hibbs (2005) demonstrates that
countries with favorable prehistoric biogeographic endowments, which
subsequently induced an earlier transition to agriculture, tend to have
a higher level of income today. In this connection, it is worth
mentioning that a recent study by Olsson and Paik (2013) documents a
negative correlation between years since agricultural transition and
current income levels in the Western countries. The underlying reason
for this reversal pattern, however, remains unclear.
Some authors have also examined the impact of the Neolithic
transition on other economic outcomes such as income inequality
(Putterman and Weil 2010), population density (Ozak 2011), institutions
(Ang 2013), and the length of state history (Ang 2015). Nevertheless,
despite significant efforts having been made to understand the impact of
agricultural transition, a key premise underlying the theory that an
earlier transition directly contributes to subsequent technological
development has remained untested.
The paper proceeds as follows. Section II sets out the empirical
framework and explains the identification strategy. It also describes
the data and construction of variables. A list of variables with its
definitions and sources are given in the Appendix. Section III presents
and analyzes the empirical findings and provides several robustness
checks. Some additional analyses are performed in Section IV and Section
V concludes.
II. EMPIRICAL STRATEGY AND DATA
A. Regression Model
The following regression model is considered to evaluate the impact
of the timing of agricultural transition on the levels of technology
adoption in the ancient and pre-modern times:
(1) Tech. [adop..sub.i] = [alpha] + [beta] Yrs since agr.
[tran..sub.i] + [gamma]' [Controls.sub.i] + [[epsilon].sub.i]
where Tech. adop. is the levels of technology adoption representing
the state of technological development in 1000 BC, 1 AD, and 1500 AD;
Yrs since agr. tran. refers to the number of years elapsed since the
transition to agriculture was estimated to have occurred; Controls is a
vector of variables controlling for various geographic effects, as
described below; and e is the error term. We are mainly interested in
the sign, size, and significance of p. Equation (1) will be estimated
with and without control variables to check whether the results are
sensitive to their inclusion. Our sample consists of 103 countries,
covering the following five macro-regions: Africa, Eurasia, Oceania,
North America, and South America. As the timing of agricultural
transition could reflect some regional effects, region dummies based on
this classification are also included in all regressions to ensure that
the results are not distorted by the potential influence of some
unobserved regional specific heterogeneous effects.
Diamond's (1997) hypothesis emphasizes that several
geographical antecedents, in particular, climate conditions, the
orientation of axis, and the size of landmasses are fundamental for
agricultural transition to occur. First, a temperate climate is
conducive for agricultural development. The Mediterranean climate of
Eurasia, for example, is particularly suitable for the cultivation of
certain crops, providing favorable conditions for agricultural
settlements. Second, greater East-West rather than North-South
orientation facilitates the diffusion and adoption of farming techniques
across regions. Areas along the same latitude tend to have certain
ecological conditions that are similar, which enable newly arrived
domesticated plants and animals to adapt easily to the new environments.
Third, a bigger landmass implies greater biogeographic diversity, and
this provides more potential domesticates for foragers enabling them to
settle. Controlling for these geographical antecedents is not only
consistent with the prediction of Diamond's thesis but also allows
for the possibility that they may continue to exert an influence on
technology adoption, even after the occurrence of agricultural
transition.
Following standard practice in the literature, the regressions also
include latitude, landlocked dummy, island dummy, and terrain ruggedness
as additional control variables as they may be potentially important in
explaining the heterogeneity of technology adoption levels across the
world in the ancient and pre-modern times, given that poor and
technologically backward countries tend to share certain geographic
characteristics. More specifically, these geographic features may
determine the quality of soil, stability of rainfall, disease
environment, endowment of natural resources, transport costs, and
ability to diffuse technology, all of which tend to influence
technological development via agricultural productivity (see, for
example, Rodrik et al. 2004; Sachs 2001).
B. Identifying the Causal Effect of Agricultural Transition
Equation (1) can be estimated using the ordinary least squares
(OLS) estimator. However, we cannot rule out the possibilities that the
timing of transition is subject to reverse causality; the association
between technology adoption and agricultural transition is spurious due
to the failure to account for some unobserved channels such as human
capital, language, climate change, and depletion of natural resources
which are related to both variables; or that the estimated transition
dates are subject to some measurement errors, all of which will violate
the standard OLS assumptions. Accordingly, in order to estimate the
causal effect of agricultural transition on technology adoption, we use
a proxy for the diffusion barriers of Neolithic technology and an index
of biogeographic endowments as the instruments for the timing of
agricultural transition.
The diffusion barriers of Neolithic technology are captured by the
geographic distance between a society and its closest regional Neolithic
point of origin. Technology diffusions often occur through the channels
of trade, espionage, emigration, war, and subjugation, and their
intensity is strongly influenced by the proximity between societies
(Cavalli-Sforza 2000). Societies located closer to the agricultural
pristine sources are likely to face less cultural, ecological, or
geographic barriers to diffusion (Ozak 2010), thus facilitating the
spread and adoption of the Neolithic technologies. For instance, the
earliest Neolithic period began in the Middle East more than 10,000
years ago. Agricultural innovation spread relatively easily from the
Middle East to its neighboring countries in Europe, Egypt, India, Iran,
and Pakistan (Cavalli-Sforza 2000). Archaeological data also provide
evidence that early farming and adoption of Neolithic tools in Southeast
Asia were strongly connected with its regional source, China (Bellwood
2005). New crops and farming techniques from China reached the Southeast
countries easily due to their geographic and ecological similarity. That
proximity matters for Neolithic diffusion is also evidenced by the fact
that it took only 200 years for farming to spread from Italy to
Portugal, but 1,000 years from the Philippines to Samoa (Bellwood 2005,
276).
Findings by two recent studies further support the use of diffusion
barriers of Neolithic technology as an instrument for the timing of
agricultural transition. In particular, Baker (2008) develops a model of
agricultural transition and provides evidence showing that societies
located further away from the pioneer of agricultural settlement, the
Fertile Crescent, tend to experience a later date of farming transition.
Along similar lines, the empirical results of Ashraf and Michalopoulos
(2015) demonstrate that distance from the nearest Neolithic point of
origin, a proxy we use for the diffusion barriers of Neolithic
technology, exerts a negative influence on the timing of transition to
agriculture, both across countries and across Neolithic sites.
Additionally, the occurrence of agricultural transition may be
precipitated by other factors apart from the spread of farming
techniques. Ammerman and Cavalli-Sforza (1984) and Diamond (1997) argue
that countries endowed with more prehistoric wild plants suitable for
cultivation, and wild animals suitable for domestication were able to
transit from the hunter-gatherer lifestyle to agriculture earlier than
others and hence the initial conditions of biogeography influenced the
timing of the transition. To the extent that biogeographic endowments
triggered the onset of agricultural transition and yet are unlikely to
be directly related to technology adoption, the first principal
component of the number of wild animals and plants suitable for
domestication prior to the onset of the Neolithic Revolution is an
appropriate instrument to obtain the exogenous sources of variation for
agricultural transition. This approach is consistent with the empirical
strategy of Ashraf and Galor (2011).
Accordingly, in the instrumental variable regressions, the timing
of Neolithic transition is treated as endogenous and the equation of
agricultural transition is specified as follows:
(2) Yrs since agr. [tran..sub.i] = [pi] + [[rho].sub.1]
[DTNO.sub.i] + [[rho].sub.2] [BIOGEO.sub.i] + [sigma]'
[Controls.sub.i] + [[mu].sub.i],
where DTNO is a measure of the distance to the Neolithic point of
origin (i.e., "as the crow flies" distance between a
particular country and the nearest country located within its original
Neolithic site), BIOGEO is an index of biogeographic endowments, and p
is the residual. In this case, the variation in agricultural transition
timing that is exogenous due to the diffusion barriers of Neolithic
technology and the initial biogeographic conditions will be isolated by
DTNO and BIOGEO, respectively, from the endogenous variation in
agricultural transition timing due to the unobserved error term. Our
identification strategy will be valid so long as DTNO and BIOGEO are
uncorrelated with the residuals. In other words, under the assumption
that the diffusion barriers of agricultural know-how and biogeographic
conditions do not affect technology adoption directly, other than
through the timing of the occurrence of the Neolithic transition,
conditional on the controls included in the regressions, this exclusion
restriction is an appropriate strategy for identifying the channel of
influence. In econometric terms, DTNO and BIOGEO are strong instruments,
as demonstrated by the satisfactory first-stage partial [R.sup.2] values
and F-statistics for the excluded instruments (see the instrumental
variable estimates reported in Table 3).
C. Data
Technology Adoption. Comin et al. (2010) provide data on technology
adoption levels, which reflect whether a particular technology was in
use at different points in time, i.e., 1000 BC, 1 AD, and 1500 AD. The
data sets cover technologies in use in the following five sectors: (1)
agriculture, (2) transportation, (3) communications, (4) industry, and
(5) military. The total number of state-of-the-art technologies covered
in the above sectors is 12 for 1000 BC and 1 AD, and 24 for 1500 AD.
Equal weights have been assigned to all sectors so that technologies in
use in any particular sector do not dominate the others. The average
level of adoption is first calculated for each sector, and the overall
adoption level is the average level of adoption for all sectors. The
resulting indices, with values ranging between 0 and 1, provide an
indication of the overall level of technology adoption in 1000 BC, 1 AD,
and 1500 AD. Specifically, a value of 1 indicates full adoption of all
technologies considered by Comin et al. (2010) whereas 0 means no
adoption of any of the technologies.
The summary statistics presented in Table 1 indicate that the
average level of technology adoption was significantly higher in 1 AD
(0.72) compared to that in 1000 BC (0.43). The average adoption level in
1500 AD was only 0.48. It should be noted, however, that the estimates
for 1 AD and 1000 BC are not directly comparable to those of 1500 AD due
to the use of different coding procedures for different sources which
involve different types of technologies used. Military, for instance,
indicates the adoption of stone, bronze, or iron tools in the 1000 BC
and 1 AD data sets but refers to the presence of standing armies,
cavalry, firearms, warfare capable ships, etc. in the 1500 AD data set.
If the additional technologies considered were relatively new at the
time when they were introduced in 1500 AD, it is not surprising that the
data show a lower level of adoption. This issue is not particularly
concerning, however, given the fact that our estimation focuses on
cross-country differences rather than variations in the time series of
the data. Nevertheless, to facilitate the comparison of estimates, we
also report the beta coefficients of agricultural transition in all the
main tables.
Agricultural Transition. Data for the timing of agricultural
transition are obtained from Putterman (2006). The years of agricultural
transition reflect the estimated number of years since the transition
has occurred. Therefore, a higher value implies an earlier transition.
These estimates cover a time span of more than 10 millennia, starting
from 8500 BC to the present day, ca. 2000 AD. According to Putterman
(2006), the transition years are estimated based on the first year in
which more than half of a human's calorific needs were obtained
from cultivated plants and domesticated animals. In our sample of 103
countries, the transition to agriculture is estimated to have first
occurred in Israel, Jordan, and Syria (10,500 years ago) and finally
occurred in Australia (400 years ago). Figure 1 presents the
distribution of the estimated agricultural transition dates across the
globe using data for all available countries from Putterman (2006).
[FIGURE 1 OMITTED]
Distance to the Neolithic Point of Origin. We construct the
geographical distance between a particular country and its original
source of Neolithic technology as a proxy for the geographical diffusion
barriers of Neolithic technology. Using details provided by Diamond
(1997), Diamond and Bellwood (2003), and Bellwood (2005), the following
six major centers of agricultural origin are considered (the approximate
date in which farming was spread is indicated in the bracket): (1)
Fertile Crescent (11000 BP), (2) Yangzi and Yellow River Basins (9000
BP), (3) New Guinea Highlands (9000-6000 BP), (4) Central Mexico
(5000-4000 BP), (5) Northern South America (5000-4000 BP), and (6) West
Africa, the Sahel, and Ethiopian highland (5000-4000 BP) (see Table A2
and Figure 1A in the Appendix for more details).
Specifically, we first classify all countries in our sample into
the following agricultural spread zones with their respective Neolithic
centers indicated in brackets: North America (Central Mexico), South
America (Northern South America), Sub-Saharan Africa (West Africa and
Ethiopian Highland), Middle East and North Africa (Fertile Crescent),
South Asia (Fertile Crescent), Europe and Central Asia (Fertile
Crescent), East Asia (Yangzi and Yellow River Basins), Oceania excluding
Papua New Guinea (Yangzi and Yellow River Basins), and Papua New Guinea
(New Guinea Highlands). Figure A1 provides the details. For example,
Belgium is assumed to have received its agriculture from the Fertile
Crescent, which is defined to include the modern-day territories of
Iraq, Syria, and Turkey (see Table A2). Then Belgium's distance to
its Neolithic point of origin is taken to be "as the crow
flies" distance between the center points of Belgium and the
country nearest to Belgium within the group of countries comprising the
Fertile Crescent, that is, Turkey. The distance is calculated using the
"Haversine" formula, which measures the shortest distance
between two countries on the surface of the globe using the longitudes
and latitudes of their center points.
[FIGURE 2 OMITTED]
The partial regression line in Figure 2A shows the effect of
distance to the Neolithic core on agricultural transition after removing
the influence of geographic controls and region dummies. As expected,
the diagram depicts a negative relationship between these variables.
This is consistent with the notion that countries in the neighborhood of
a Neolithic center could better adopt and assimilate the technologies
from the pristine source, thus experiencing an earlier transition.
Conversely, countries located far from their Neolithic center tend to
have relatively late transitions to agriculture.
Biogeography. The extent of prehistoric biogeographic endowments is
measured by the first principal component of the standardized numbers of
locally available domesticable wild animals (14 species in total) and
plants (33 species in total) about 12,000 years ago, which are edible to
humans or carry economic values, based on the data of Olsson and Hibbs
(2005). Domesticable plants refer to the number of annual or perennial
prehistoric wild grasses with a mean kernel weight >10 mg (e.g., the
ancestors of barley, rice, corn, wheat); domesticable animals denote the
number of prehistoric mammals with weights exceeding 45 kg. They are the
ancient ancestors of the following 14 domesticable animals: sheep,
goats, cows, pigs, horses, Arabian camels, Bactrian camels, llama,
donkeys, reindeer, water buffalo, yak, Bali cattle, and Mithan (Olsson
and Hibbs 2005).
Archaeological evidence suggests that the ancestors of wheat, peas,
olives, sheep, and goats, for instance, were domesticated in the Fertile
Crescent as early as 8500 BC. Ancestors of rice, millet, and pigs were
domesticated nearly as early, in China in 7500 BC (Diamond 1997). The
distribution of plant and animal domesticates was very uneven across
regions. Compared to the Pacific Islands which had no species suitable
for domestication ca. 10000 BC, Europe had a superior initial
biogeographic condition with all the 33 plant species and 9 out of 14
animal species considered in the data set of Olsson and Hibbs (2005).
Sub-Saharan Africa had access to four species of plants but had no
access to any animals suitable for domestication, whereas America had
access to 11 and 1 species of domesticable plants and animals,
respectively. Consistent with our prediction, the partial regression
line shown in Figure 2B confirms that the timing of agricultural
transition and the index of biogeographic endowments are strongly and
positively connected.
III. EMPIRICAL ESTIMATES
A. Least Squares Estimates
The regression results of Equation (1) are presented in Table 2.
Consider first the regressions for the basic model without the inclusion
of control variables. The results indicate that agricultural transition
is a significant determinant of the levels of technology adoption in
1000 BC, 1 AD and 1500 AD (columns (la), (2a), and (3a)). This
relationship is significant at the 1% level in all cases. The [R.sup.2]
values imply that agricultural transition alone is able to explain
between 57% and 83% of the variation in the levels of technology
adoption across countries. Using the results in column 1(a) as an
illustration, if a country transits to agriculture 1,000 years earlier,
the level of technology adoption is expected to be about 0.1 index
points higher. Measured in standardized form, a one standard deviation
change in agricultural transition is associated with 74.6% of a standard
deviation change (beta coefficients) in the level of technology adoption
in 1000 BC. These results imply a large economic effect of agricultural
transition on technological development.
Interestingly, the size of the beta coefficients diminishes with
the dates of technology adoption, implying that the effect of
agricultural transition on technology adoption wane over time. In
particular. the effect of agricultural transition on technology adoption
reduces by more than 50% from 1000 BC to 1 AD. However, the reduction in
the effect is only marginal from 1 AD to 1500 AD. The results prevail
when all control variables are included in the unrestricted regressions
(columns (1b), (2b) and (3b)). Graphical inspection on the partial
regression lines for the effects of agricultural transition on
technology adoption levels shown in Figures 3A-3C are largely consistent
with these findings.
B. Two-Stage Least Squares Estimates
As highlighted before, to counteract potential bias from
simultaneity, omitted variables and measurement error, Equation (1) is
also estimated using a two-stage least squares (2SLS) estimator with
robust standard errors. The results presented in Table 3 show that the
2SLS estimates are qualitatively very similar to those that are based on
the OLS estimator. Sizes of the coefficients of Yrs since agr. tran. are
in all cases considerably larger than those found earlier. This finding
is consistent with the notion that the timing of agricultural transition
is measured with errors rather than subject to simultaneity bias. The
sign and size of the coefficients is quite stable across columns in each
time period, implying that the relationship uncovered is not sensitive
to the inclusion of control variables. Moreover, the explanatory power
of the regressions involving control variables is similar to those that
exclude them, suggesting that technology adoption levels are
significantly influenced only by the dates of agricultural transition.
[FIGURE 3 OMITTED]
Using the estimates under column (la) as a reference, an earlier
transition to sedentary agriculture by 1,000 years improves the state of
technological development in 1000 BC by 0.142 index points. This
magnitude suggests that agricultural transition has a rather significant
economic impact on technological development in ancient times. For
example, El Salvador had a rather late transition to agriculture which
occurred ~3,000 years ago and had a relatively low level of technology
adoption of 0.3 in 1000 BC. If El Salvador had a more favorable
environment that induced an earlier transition time, similar to that
experienced in the Netherlands 6,000 years ago, then El Salvador would
become at least twice technologically more developed in 1000 BC. Its
state of technological development would be 0.73, a level that exceeded
the one that was enjoyed by the Netherlands in 1000 BC, that is, 0.6.
The first-stage partial [R.sup.2] statistics measure the
correlation between agricultural transition and its instruments, that
is, distance to the Neolithic point of origin and biogeographic
endowments. A higher value indicates stronger instruments, implying that
the estimates are less biased. These statistics, along with the large
first-stage T-test statistics for the excluded instruments, which have
their null hypothesis that the instruments do not explain cross-country
variations in agricultural transition, provide credence that our
instruments are strong in all cases. Furthermore, the robust score tests
indicate that the null of agricultural transition being exogenous is
rejected at conventional levels of significance for all models. Given
these findings, the instrumental variable results reported in Table 3
will be used as the baseline estimates, and all subsequent analyses that
follow will be benchmarked against them.
C. Sensitivity Checks
This subsection performs several robustness checks for the
instruments used. Following the format of the baseline estimates in
Table 3, we provide results for technology adoption levels in 1000 BC, 1
AD, and 1500 AD (columns (1), (2), and (3), respectively). Estimates
without control variables are first presented (all columns (a)),
followed by those with the inclusion of all control variables (all
columns (b)). Region dummies are included in all regressions.
First, we consider an alternative approach to measuring the
diffusion barriers of farming technology in which the Neolithic points
of origin are determined by the countries in our sample that had the
earliest dates of agricultural transition in each continent, as follows:
Egypt and Libya (Africa); Mexico and Peru (America); Israel, Jordan,
Lebanon and Syria (Asia); Cyprus and Greece (Europe); and New Zealand
(Oceania). This classification of regions is based on the conventional
approach rather than the macro spread zones used throughout the paper.
The transition dates are based on the data set of Putterman (2006).
Panel A of Table 4 presents the results based on these alternative
estimates for distance to the Neolithic pristine sources. Consistent
with the baseline results in Table 3, the timing of agricultural
transition is found to have a positive impact on technology adoption
levels in 1000 BC, 1 AD, and 1500 AD, irrespective of whether control
variables are included. In all cases, the parameter estimates of
agricultural transition are very precisely estimated at the 1% level of
significance. It should be noted that the regression results here are
based on the use of spread zone dummies. The results are similar if the
conventional continent dummies were used (unreported). Hence, the
results are not sensitive to this alternative way of determining the
agricultural cores.
Panels B to D consider alternative instruments but follow the
baseline approach in assigning distance to agricultural cores. In panel
B we report the estimates which use only distance to the Neolithic point
of origin as the instrument. We repeat this exercise in panel C by
considering only biogeography as the instrument. In both cases, the
qualitative aspect of the results is very similar to those reported
previously, suggesting that using only either one of these instruments
does not bias our estimates. In panel D, we use the number of
domesticable plants available and the number of domesticable animals
available as instruments. The results, again, are largely invariant to
this consideration. Overall, the results in Table 4 provide some
evidence that our estimates are not sensitive to the consideration of
these alternative instrumental variable strategies.
Next, we perform some additional robustness checks and report their
results in Table 5. The technology adoption data set used in this paper
considers agriculture as one of the components of the overall level of
technology adoption. To the extent that the agricultural component
captures similar information to the timing of agricultural transition,
its inclusion in the computation of the technology adoption level
measures may generate some artificial correlations between the outcome
variables and the agricultural transition timing measure. Consequently,
in panel A, we exclude agriculture in the technology adoption measures
but do not find any substantial qualitative variation in the results
compared to the baseline estimates. Specifically, the parameter
estimates of agricultural transition remain statistically highly
significant in all cases.
Furthermore, the classification of the region dummies used
throughout the paper is based on the agricultural spread zones. In other
words, countries are grouped based on the relevant spread regions of
agriculture that they belong to, which results in the following five
macro-regions: Africa, Eurasia, Oceania, North America, and South
America. The conventional approach, however, is to consider the
following five landmasses as continents: Africa, America, Asia, Europe,
and Oceania. In panel B, we control for the region effects using the
conventional classification to check whether our results are distorted
by this consideration. As is evident, the significance of the parameter
estimates of agricultural transition is not driven by how we control for
region effects.
Finally, the estimations so far do not distinguish between
countries that experienced the transition in the very distant past from
those which transit to agriculture more recently. This consideration is
relevant since in the event that the significance of agriculture
disappears after dropping countries with late transition dates, this
would suggest that agricultural transition does not have a long-term
effect on comparative technological development. We choose 1000 BC as
the cut-off point as this is the date for which data of technology
adoptions levels are first available. By including countries that
transit to agriculture only after 1000 BC in our previous analyses, we
have implicitly embraced the idea that how many more years would be
needed to transpire before these countries underwent transition is
relevant to determining their technological levels in 1000 BC. This may
be a strong assumption. Hence, it is necessary to exclude countries that
transit to agriculture after 1000 BC in the estimations to check whether
our results still prevail. Panel C reports the findings. As is evident,
except for the fact that the coefficients of agricultural transition now
become statistically significant only at the 10% level in two out of six
cases, overall, the estimates are largely robust.
IV. FURTHER ANALYSES
A. Are There Persistent Effects of Technology Adoption?
This section analyses whether the effect of technology adoption is
persistent, and if so, whether controlling for this persistent effect
would render the effect of agricultural transition insignificant. Table
6 reports the findings. First, we include technology adoption in 1 AD in
columns (la) and (lb) (without and with control variables, respectively)
to examine whether it has a significant effect on the subsequent
adoption level in 1500 AD. Its coefficients are found to be highly
significant at the 1% level, suggesting that the technology adoption has
a persistent effect. This finding is consistent with the results of
Comin et al. (2010). Interestingly, despite controlling for the initial
adoption level of technology, agricultural transition continues to exert
a statistically significant influence on technology adoption in 1500 AD.
In columns (2a) and (2b), we repeat this exercise by using
technology adoption in 1000 BC as the variable that captures the initial
condition. In this case, we do not find any persistent effect of
technology while the coefficients of agricultural transition dates
remain statistically significant. In columns (3a) and (3b), we examine
how the timing of agricultural transition affects technology adoption in
1 AD while controlling for the effect of initial adoption in 1000 BC.
The evidence suggests a weak persistent effect of technology adoption,
and the timing of agricultural transition is not statistically
significant. Overall, our results are mixed in terms of whether initial
technological development or the timing of agricultural transition
matter more for the subsequent adoption levels of technology.
In Table 7, we carry out some mediation analyses to decompose the
effect of the timing of agricultural transition while controlling for
all geographic and regional effects. Specifically, we use the Sobel test
to investigate whether the indirect effect of agricultural transition
dates on subsequent technology adoption via influencing initial
technology adoption is statistically different from zero (see MacKinnon
et al. 1995 for details). Considering the mediation effect of technology
adoption in 1 AD in column (1), the Sobel test statistic is estimated to
be 0.018 with a p value of .011. Thus, the null of no mediation is
rejected at the 5% level of significance. Moreover, the mediation effect
is quite material, with approximately 49.1% of the total effect of
agricultural transition timing on technology adoption in 1500 AD being
partially mediated by the initial technological development.
The mediation effect is found to be much weaker in column (2) where
the initial condition is measured as of 1000 BC. In this case,
approximately 64.4% of the total effect comes directly from the timing
of agricultural transition. Consistent with the findings of Table 6,
there is clear evidence supporting the notion that the effect of the
timing of agricultural transition on technology adoption in 1 AD is
significantly mediated by the initial condition of technological
development in 1000 BC (column (3)). Overall, the analyses performed in
Tables 6 and 7 suggest that the direct role of agricultural transition
is more significant for the 1500 AD estimates, but the reverse is found
for the 1 AD estimates.
B. Controlling for the Effects of Other Early Development
While the above results show that technology adoption levels in the
pre-modern (up to 1 AD) and early modern period to 1500 AD are
critically influenced by the timing of agricultural transition, we
cannot rule out the possibility that the effects of a transition that
occurred in the very distant past may have evolved into other forms,
which continue to exert an influence on technology adoption
subsequently. Alternatively, our estimates may be biased due to the
failure to account for some omitted channels through which agricultural
transition affects technology adoption. For instance, the effect of
agricultural transition can potentially affect technology adoption
indirectly through influencing other developments. These concerns invite
some additional analyses, and the results are presented in Table 8.
First, Diamond's (1997) theory proposes that the Neolithic
transition not only precipitated higher adoption levels of technology,
but also led to the formation of state polities. The transition to fully
fledged agricultural production gave rise to rapid population growth
where more extensive, complex, and settled forms of agricultural
societies gradually emerged out of the initial hunter-gatherer base.
Settled agricultural villages with small-scale political entities
governed by supratribal authorities subsequently compounded into larger
polities and thereby fully fledged states emerged (see also Ang 2015;
Childe 1950). Consistent with this proposition, empirical evidence of
Putterman (2008) shows that state history is a significant determinant
of economic development in 1500 AD. Accordingly, we control for state
antiquity in column (1) using the data of Putterman (2012). If this
channel is operative, including a proxy of state history in the
estimations may render the effect of agricultural transition
insignificant. Despite the fact that state polities were present before
1 AD, we consider state history only from 1 AD to 1500 AD since the data
of Putterman (2012) only go back to 1 AD. This should not be a major
concern since measuring state history for 1,500 years is sufficient to
provide an indication of its strength in 1500 AD, assuming that state
history prior to 1 AD is relatively unimportant for development in 1500
AD.
Another possible conduit through which agriculture transition
affects technology adoption is cultural diffusion barriers. According to
the Neolithic demic diffusion model of Ammerman and Cavalli-Sforza
(1984), genetic exchange, which reduces cultural barriers to diffusion,
is an outcome of agricultural transition. Demic diffusion embodies
technology as farmers brought agriculture along with them when they
migrated. Increased food production following agricultural settlements
led to tremendous increases in population density. Demographic
pressures, however, triggered competition for resources, and forced
farmers to migrate into other areas with lower population densities.
This often caused displacing, replacing, or intermixing of populations,
and resulted in lower genetic distance between the population of a
country and those living at the frontier (Cavalli-Sforza et al. 1996,
105). The European migration to North America is a relevant historical
example (Cavalli-Sforza 2000, 93). Given that technology adoption may be
correlated with diffusion barriers of technology across borders, the
causal influence of agricultural transition that we have found so far
may disappear once we control for genetic distance to the frontier. Thus
we also control for genetic distance to the frontier in column (2) to
allow for the possibility that it may also have an independent effect on
the adoption levels of technology.
Barriers to cultural diffusion are captured by the degree of
genealogical unrelatedness between two populations using the genetic
distance to the global frontier data of Spolaore and Wacziarg (2009).
Spolaore and Wacziarg (2009) argue that genetic distance provides a
summary measure for the long-run divergence in a number of human traits
such as cultures, customs, beliefs, habits, etc., which are transmitted
from one generation to another over a long period of time. Such a
divergence underlies the existence of some development barriers which
prevent the diffusion of innovations from the world technological
leader. That is, countries with genetic traits that are very different
from the frontier due to a longer duration of historical non-relatedness
face greater barriers to technology adoption. Genetic distance reflects
the degree of genealogical dissimilarities or historical unrelatedness
between two populations. The data are obtained from Spolaore and
Wacziarg (2009). Following their approach, genetic distance from the
frontier is defined as the genetic distance for a particular country
relative to the technological frontier in 1500 AD. Spolaore and Wacziarg
(2009) define England as the global frontier in 1500 AD whereas we
choose Italy as it was one of the most technologically sophisticated
countries several hundred years before 1500 AD, and England only became
a leader ca. 1500 AD.
The above discussions also suggest that agricultural transition can
affect technological development through affecting its effect on
population density. To control for the effect of demographic pressure,
we therefore also include population density in 1500 AD in column (3)
separately and jointly with state history and genetic distance to the
world technological frontier in column (4).
Considering first the results with the inclusion control variables
for 1500 AD (panel A, all columns (b)), the results in columns (lb) and
(2b) of panel A, respectively, show that while the coefficients of state
history and cultural diffusion barriers are statistically significant
and have the expected signs, their effects are only significant at the
10% level. When population density is added to column (3b), the
coefficient of agricultural transition is still statistically
significant at the 1 % level, but the effect of population density is
insignificant. (1) When these three additional early development
measures are jointly included in column (4b), only the coefficient of
agricultural transition is found to be statistically significant. The
beta coefficients reported in all columns suggest that variations in the
timing of agricultural transition has a much larger economic effect than
all other indicators of early development, and hence is much more
powerful in explaining the variation in technology adoption levels in
1500 AD across countries. The results are similar when control variables
are excluded (all columns (a) in panel A). Taken together, the results
here suggest that the effect of early agrarian development on technology
adoption in 1500 AD is unlikely to work through state history, genetic
mix, or population density.
A similar exercise, however, cannot be readily performed for the
estimates of 1000 BC and 1 AD since, except for population density for
the 1 AD estimates, data on their state history, population composition,
and population levels are not currently available. However, when we
repeat the regressions in panel A using some indirect measures (i.e.,
population density in 1 AD, state presence in 1-50 AD, and genetic
distance to Italy mapped by population composition in 1500 AD), the
estimates for 1 AD (panel B) and 1000 BC (panel C) are not overturned.
Except for the coefficient of genetic distance in the estimates of 1 AD
(panel B, column (2b)), which is statistically significant only at the
10% level, coefficients of all additional early development indicators
are not precisely estimated. The coefficients of agricultural
transition, however, are found to be statistically and economically
significant in all cases. Under the assumption that state presence in
1-50 AD and population density in 1 AD were similar to what they were
1,000 years ago, and population mixes were largely similar in 1500 AD, 1
AD, and 1000 BC, these results imply that an early exposure to
agriculture induces an early head start in technological development in
1000 BC and 1 AD, and this effect does not work through the channels
considered above. These analyses are, of course, crude and caution
should be exercised when interpreting the results.
Summing up, the results in this subsection suggest that
agricultural transition directly matters for an early start in
technological development. The mediation analyses reports in Table 9 are
consistent with the above findings in the sense that the mediation
effects of genetic distance and population density are rather weak. The
evidence, however, suggests that the effect of agricultural transition
on technology adoption since pre-modern times is significantly mediated
by state antiquity, although the impact of state antiquity is found to
be negligible in Table 8. Overall, the results here are still largely
consistent with the above findings that the timing of agricultural
transition has a significant direct impact on technology adoption, and
such a relationship holds even if we control for other effects of early
development.
C. How Agricultural Transition Affects Technology Adoption in Each
Sector
The data of Comin et al. (2010) are available at the sectoral
level, which enables us to investigate how technology adoption levels in
each sector respond to the transition to agriculture. Thus, to gain some
insight into how the effect of agricultural transition works through
technology adoption, we replace the dependent variables at each time
period using the sectoral estimates of technology adoption.
The results reported in Table 10 show that the effect of
agricultural transition on technology adoption works through all sectors
in 1000 BC, with agriculture, communication, and transport being the
sectors that benefit most from an early exposure to agriculture. Similar
results are obtained for the estimates in 1 AD. That an earlier
transition to agriculture induces a high level of agricultural
technology adoption is not surprising. The importance of agricultural
transition for technological development in the other two sectors during
the pre-modern period perhaps reflect the fact that the development and
adoption of communication and transport technologies benefit most from
the domestication of pack and draft animals following the transition to
agriculture, which facilitated the spread of writing and records, along
with breeding and husbandry techniques. In the case of 1500 AD, the
results are also similar, except for the finding that the effect of
agricultural transition on the military sector is more significant than
that on agriculture and transport. The estimates without control
variables are largely similar and hence are not reported to conserve
space.
V. CONCLUSIONS
While significant progress has been made in improving our
understanding of the role of agricultural transition in explaining
variations in income across countries, the way that early technology
adoption reacts to historical agrarian development is still not known.
The central theme of this paper is that an early transition to sedentary
agriculture provides a developmental head start, which enables a country
to adopt technologies in its early stages of development.
This premise is built on the influential hypothesis of Diamond
(1997) that countries which made the transition to agriculture earlier
are able to maintain their lead and continue to enjoy a higher level of
technological development than others. The underlying premise of this
hypothesis is that technological progress has continued to build upon
the foundations laid down during the transition, through an endogenous
process of knowledge creation. Differences in the stock of technology
accumulated over the course of economic development give rise to
differentials in the costs of its adoption which explain variations in
the levels of technological development.
Potential endogeneity, spurious regressions, and specification
problems are dealt with using geographic distance to the Neolithic
center and an index of biogeographic endowments as instruments for the
timing of agricultural transition. Exploiting the exogenous sources of
the cross-country variations in the dates of agricultural transition
based on this identification strategy, the analysis demonstrates that
the locations in which agrarian innovations first took place, by way of
an earlier transition to agriculture, have a far reaching positive
influence on the subsequent levels of technological development in 1000
BC and, to a lesser extent, 1 AD and 1500 AD. Thus, our results lend
credence to the influential Diamond hypothesis.
Further investigations were conducted to uncover the mechanisms
through which agricultural transition affects technology adoption
levels. In particular, we find that the significance of the timing of
agricultural transition does not disappear even after controlling for
the effects of state history, cultural diffusion barriers, and
population density. These results imply that an early exposure to
agriculture directly enhances technology adoption during the ancient,
premodern, and early modern periods. This effect does not work through
either facilitating the establishment of social structures, small
polities and early political institutions, increasing the intensity of
population admixing, or expanding the density of populations. However,
these findings could be driven by the fact that the channel indicators
considered here are measured with errors or early agriculture affects
subsequent technological development via other channels.
ABBREVIATIONS
2SLS: Two-Stage Least Squares
BIOGEO: Biogeographic Endowments
DTNO: Distance to the Neolithic Center
OLS: Ordinary Least Squares
PNG: Papua New Guinea
doi: 10.1111/ecin.12210
APPENDIX
TABLE A1
Definition of Variables and Data Sources
Variable Description Source
Technology The average adoption Comin et al. (2010)
adoption levels of technology
in 1000 BC, 1 AD,
and 1500 AD. It
covers the following
sectors:
agriculture,
transportation,
communications,
industry, and
military.
Years since The number of years Putterman (2006)
agricultural elapsed, in 2000 AD,
transition since the transition
to agriculture was
estimated to occur
(in thousand years)
Neolithic This measure Diamond (1997),
distance to the captures the Diamond and Bellwood
regional center geographic distance (2003), Bellwood
between a country (2005), and author's
and its nearest own calculation
Neolithic center in
the same
agricultural spread
zone. The Neolithic
points of origins
are chosen based on
the estimated
archaeological sites
for the centers of
origin of
agriculture reported
in Diamond (1997),
Diamond and Bellwood
(2003) and Bellwood
(2005). Geographical
distance is
calculated using the
"Haversine formula,
which calculates the
shortest distance
between two points
on the surface of a
sphere based on
their longitudes and
latitudes. See
Section II.C for
details.
Biogeographic The first principal Hibbs and Olsson
endowments component of the (2004); Olsson and
standardized numbers Hibbs (2005)
of domesticable wild
plants and animals
Climate Climate Hibbs and Olsson
classification of 1 (2004); Olsson and
-4 based on the Hibbs (2005)
Koppen's approach. A
higher value
indicates more
favorable climate
conditions for
agriculture.
Axis The east-west Hibbs and Olsson
orientation of the (2004); Olsson and
axis, calculated as Hibbs (2005) and
the longitudinal author's own
distance between the estimation using the
furthest eastern and horizontal width and
western points in vertical length data
each continent from http://www.
divided by worldatlas.com/
latitudinal
distance. Unlike
Hibbs and Olsson
(2004) and Olsson
and Hibbs (2005) who
use the axis values
of the nearest
continents for some
island countries, we
directly measure the
axes for these
countries.
Landmass size Size of landmass to Olsson and Hibbs
which a country (2005)
belongs (in millions
of square
kilometers)
Latitude Absolute value of CIA World Fact Book
the latitude of each
country
Landlocked A dummy variable CIA World Fact Book
that equals 1 if a
country is fully
enclosed by land and
0 otherwise
Island A dummy variable CIA World Fact Book
that equals 1 if a
country is an island
and 0 otherwise
Terrain An index that Nunn and Puga (2012)
ruggedness quantifies
small-scale terrain
irregularities in
each country
State An index of state Putterman (2012)
antiquity history covering the
period from 1 AD to
1500 AD, scaled to
take values between
0 and 1. The data
set was originally
introduced by
Bockstette et al.
(2002), but the
current paper uses
its latest version,
v3.1.
Genetic The degree of Spolaore and
distance to genealogical Wacziarg (2009)
the technology similarities or
frontier historical
relatedness for the
population of a
particular country
relative to that of
the technological
frontier up to 1500
AD, that is, Italy.
Data on population
are matched to
countries based on
their ethnic
composition as of
1500 AD.
Population The population McEvedy and Jones
density divided by land area (1978) and World
Bank (2012)
CIA, Centra] Intelligence Agency.
TABLE A2
Estimated Archaeological Sites of the Centers of
Agricultural Origin for Each Region and the Modern-Day
Countries with Significant Territory within the Sites
Region/Country Neolithic Center Present-Day
(Date of Farming Country
Spread)
North America Central Mexico Mexico
(5000-4000 BP)
South America Northern South Colombia, Ecuador,
America (5000-4000 Peru and Panama
BP)
Sub-Saharan West Africa, the Cote D'Ivoire,
Africa Sahel, and Ethiopian Ethiopia and Ghana
highland (5000-4000
BP)
Middle East and Fertile Crescent Iraq, Syria and
North Africa (11000 BP) Turkey
South Asia Fertile Crescent Iraq, Syria and
(11000 BP) Turkey
Europe and Central Fertile Crescent Iraq, Syria and
Asia (11000 BP) Turkey
East Asia Yangzi and Yellow China
River Basins (9000
BP)
Oceania (excluding Yangzi and Yellow China
PNG) River Basins (9000
BP)
Papua New Guinea New Guinea Highlands Papua New Guinea
(PNG) (9000-6000 BP)
Notes: The Neolithic sites and the years during which
farming was spread are taken from the estimates of Diamond
(1997), Diamond and Bellwood (2003), and Bellwood (2005).
The present-day countries are chosen by the author.
[FIGURE A1 OMITTED]
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(1.) When population density is regressed on agricultural
transition, where the latter is instrumented by distance to the
Neolithic core and biogeography, parameter estimates of agricultural
transition are not precisely estimated at any conventional levels of
significance in all cases. Hence, the results consistently suggest that
agricultural transition directly affects technology adoption,
independent of its effect on population density.
JAMES B. ANG *
* Comments received from Jan F. Kiviet, three referees of this
journal, and seminar participants at Nanyang Technological University,
National University of Singapore, and Singapore Management University
are greatly appreciated.
Ang: Associate Professor, Division of Economics, School of
Humanities & Social Sciences, Nanyang Technological University,
Singapore, Singapore. Phone +65 65927534, Fax +65 67955797, E-mail
james.ang@ntu.edu.sg
TABLE 1
Descriptive Statistics
Variable Observed Mean SD
Technology adoption in 1000 82 0.43 0.29
BC (index)
Technology adoption in 1 AD 101 0.72 0.29
(index)
Technology adoption in 1500 89 0.48 0.32
AD (index)
Years of agricultural 103 4.42 2.33
transition (1,000 years)
Distance to the Neolithic 103 21.21 16.58
point of origin (100 km)
Biogeography (standardized 103 0.04 1.01
values)
Climate classification 103 2.60 1.05
Axis (ratio) 103 1.55 0.68
Landmass size (millions of 103 30.08 14.21
square)
Absolute latitude 103 0.29 0.19
Landlocked (dummy) 103 0.23 0.42
Island (dummy) 103 0.08 0.27
Terrain ruggedness (index) 103 1.24 1.10
Variable Minimum Maximum
Technology adoption in 1000 0.00 1.00
BC (index)
Technology adoption in 1 AD 0.00 1.00
(index)
Technology adoption in 1500 0.00 1.00
AD (index)
Years of agricultural 0.40 10.50
transition (1,000 years)
Distance to the Neolithic 0.00 111.77
point of origin (100 km)
Biogeography (standardized -0.95 1.48
values)
Climate classification 1.00 4.00
Axis (ratio) 0.50 3.00
Landmass size (millions of 0.00 44.61
square)
Absolute latitude 0.01 0.71
Landlocked (dummy) 0.00 1.00
Island (dummy) 0.00 1.00
Terrain ruggedness (index) 0.02 6.20
Notes: Refer to the text or Table A1 in the Appendix
for descriptions of all variables.
TABLE 2
Ordinary Least Squares Estimates
1000 BC 1 AD
Dep. Var. =
Tech. adop. (la) (lb) (2a) (2b)
Yrs since 0.102 *** 0.096 *** 0.045 *** 0.047 ***
agr. tran. (0.015) (0.021) (0.008) (0.011)
[beta] [74.6] [69.8] [36.1] [38.3]
coefficients
[%]
Climate -0.007 -0.006
(0.049) (0.029)
Axis 0.033 -0.010
(0.060) (0.028)
Landmass size -0.000 -0.001
(0.002) (0.001)
Latitude 0.101 0.127
(0.197) (0.160)
Landlocked -0.090 -0.009
(0.064) (0.053)
Island -0.180 -0.039
(0.162) (0.060)
Terrain 0.003 0.005
ruggedness (0.022) (0.017)
[R.sup.2] 0.572 0.594 0.698 0.703
Observations 82 82 101 101
Region dummies Yes Yes Yes Yes
1500 AD
Dep. Var. =
Tech. adop. (3a) (3b)
Yrs since 0.043 *** 0.035 ***
agr. tran. (0.014) (0.013)
[beta] [28.4] [23.6]
coefficients
[%]
Climate 0.055 **
(0.026)
Axis -0.002
(0.037)
Landmass size -0.001
(0.001)
Latitude 0.075
(0.121)
Landlocked 0.013
(0.040)
Island -0.005
(0.048)
Terrain -0.017
ruggedness (0.016)
[R.sup.2] 0.832 0.862
Observations 89 89
Region dummies Yes Yes
Notes: The dependent variable is the levels of technology
adoption in 1000 BC, 1 AD, or 1500 AD. Figures in the
parentheses are robust standard errors. The region dummies
are Africa, Eurasia, Oceania, North America and South
America. An intercept is included in the regressions but is
not reported to conserve space.
***, **, and * denote significance at the 1%, 5%, and 10%
levels, respectively.
TABLE 3
Instrumental Variable Regressions (Baseline Results)
1000 BC 1 AD
Dep. Var. =
Tech. adop. (la) (lb) (2a) (2b)
Panel A: Second-stage regressions
Yrs since 0.142 *** 0.150 *** 0.071 *** 0.089 ***
agr. tran. (0.024) (0.035) (0.014) (0.022)
[beta] [103.6] [109.3] [57.5] [71.7]
coefficients
[%]
Climate -0.040 -0.037
(0.045) (0.032)
Axis 0.070 -0.008
(0.089) (0.027)
Landmass size -0.002 -0.002 *
(0.004) (0.001)
Latitude 0.268 0.269
(0.242) (0.181)
Landlocked -0.076 0.003
(0.063) (0.054)
Island -0.063 0.016
(0.216) (0.063)
Terrain -0.001 0.004
ruggedness (0.022) (0.017)
[R.sup.2] 0.538 0.546 0.680 0.671
Panel B: First-stage regressions
DTNO -0.049 *** -0.040 *** -0.051 *** -0.043 ***
(0.008) (0.012) (0.009) (0.010)
BIOGEO 0.827 *** 0.906 * 0.897 *** 0.818**
(0.297) (0.462) (0.222) (0.367)
[R.sup.2] 0.757 0.786 0.770 0.803
Partial 0.397 0.311 0.426 0.314
[R.sup.2]
F-test for 24.936 13.181 25.025 17.949
excluding
instruments
Robust score 4.714** 4.678 ** 4.750 ** 4.969 **
test for [p = .029] [p = .031] [p = .029] [p = .026]
endogeneity
Observations 82 82 101 101
Region dummies Yes Yes Yes Yes
1500 AD
Dep. Var. =
Tech. adop. (3a) (3b)
Panel A: Second-stage regressions
Yrs since 0.080 *** 0.076 ***
agr. tran. (0.020) (0.022)
[beta] [53.0] [50.7]
coefficients
[%]
Climate 0.027
(0.029)
Axis 0.005
(0.047)
Landmass size -0.002
(0.001)
Latitude 0.188
(0.156)
Landlocked 0.020
(0.040)
Island 0.043
(0.064)
Terrain -0.018
ruggedness (0.017)
[R.sup.2] 0.808 0.839
Panel B: First-stage regressions
DTNO -0.047 *** -0.043 ***
(0.008) (0.011)
BIOGEO 0.881 *** 0.788 **
(0.219) (0.365)
[R.sup.2] 0.776 0.799
Partial 0.450 0.366
[R.sup.2]
F-test for 23.835 18.536
excluding
instruments
Robust score 8.516 *** 9.475 ***
test for [p = .003] [p = .002]
endogeneity
Observations 89 89
Region dummies Yes Yes
Notes: The dependent variable is the levels of technology
adoption in 1000 BC, 1 AD, or 1500 AD. The timing of
agricultural transition is instrumented by distance to the
Neolithic center (DTNO) and an index of biogeographic
endowments (BIOGEO). The region dummies are Africa, Eurasia,
Oceania, North America, and South America. An intercept is
included in the regressions but is not reported to conserve
space. In the full specifications (all columns (b)), all
control variables and region dummies are also included in
the first-stage regressions. The F-test for excluded
instruments tests the null hypothesis that the coefficients
on the instruments equal zero in the first stage of the
regressions. An F-statistic <10 indicates that the
instruments are weak. The null for the robust score tests is
that the timing of agricultural transition is exogenous. The
results show Chi-square statistics and p values (in square
brackets). Figures in the round parentheses are robust
standard errors.
***, **, and * denote significance at the 1%, 5%,
and 10% levels, respectively.
TABLE 4
Robustness Checks Using Alternative Instruments (IV-2SLS Estimates)
1000 BC 1 AD
Dep. Var. =
Tech. adop. (la) (lb) (2a) (2b)
Panel A: Choosing the agricultural cores by continent
Yrs since 0.155 *** 0.168 *** 0.085 *** 0.109 ***
agr. tran. (0.035) (0.045) (0.017) (0.027)
[R.sup.2] 0.525 0.530 0.663 0.648
Observations 82 82 101 101
Instrument(s) (i) Geographical distance to one of the
countries that first transit to agriculture
in each continent; (ii) biogeography index
(BIOGEO)
Panel B: Using only distance to the Neolithic center as the IV
Yrs since 0.125 *** 0.125 *** 0.052 *** 0.065 ***
agr. tran. (0.028) (0.037) (0.016) (0.022)
[R.sup.2] 0.561 0.580 0.696 0.698
Observations 82 82 101 101
Instrument(s) (i) Distance to the Neolithic point
of origin (DTNO)
Panel C: Using only biogeography as the IV
Years since 0.178 *** 0.199** 0.108 *** 0.150 ***
agricultural (0.071) (0.080) (0.030) (0.049)
transition
[R.sup.2] 0.450 0.417 0.593 0.507
Observations 82 82 101 101
Instrument(s) (i) Biogeography index (BIOGEO)
Panel D: Using the availability of plants and animals as IVs
Yrs since 0.182 ** 0.206 ** 0.107 *** 0.149 ***
agr. tran. (0.076) (0.087) (0.029) (0.051)
[R.sup.2] 0.436 0.394 0.598 0.512
[R.sup.2] 82 82 101 101
Instrument(s) (i) Number of domesticable plants available;
(ii) number of domesticable animals available
Region dummies Yes Yes Yes Yes
Geographic No Yes No Yes
controls
1500 AD
Dep. Var. =
Tech. adop. (3a) (3b)
Panel A: Choosing the agricultural cores by continent
Yrs since 0.083 *** 0.063 ***
agr. tran. (0.019) (0.021)
[R.sup.2] 0.822 0.859
Observations 89 89
Instrument(s) (i) Geographical distance to one of the
countries that first transit to agriculture
in each continent; (ii) biogeography index
(BIOGEO)
Panel B: Using only distance to the Neolithic center as the IV
Yrs since 0.040 ** 0.059 ***
agr. tran. (0.017) (0.021)
[R.sup.2] 0.832 0.855
Observations 89 89
Instrument(s) (i) Distance to the Neolithic point
of origin (DTNO)
Panel C: Using only biogeography as the IV
Years since 0.144 *** 0.122** *
agricultural (0.039) (0.040)
transition
[R.sup.2] 0.645 0.756
Observations 89 89
Instrument(s) (i) Biogeography index (BIOGEO)
Panel D: Using the availability of plants and animals as IVs
Yrs since 0.144** * 0.118 ***
agr. tran. (0.036) (0.041)
[R.sup.2] 0.647 0.766
[R.sup.2] 89 89
Instrument(s) (i) Number of domesticable plants available;
(ii) number of domesticable animals available
Region dummies Yes Yes
Geographic No Yes
controls
Notes: The dependent variable is the levels of technology
adoption in 1000 BC, 1 AD, or 1500 AD. The region dummies
are Africa, Eurasia, Oceania, North America, and South
America. The geographic controls are climate, latitude, axis
(the orientation of continent), size of landmass, landlocked
dummy, island dummy, and terrain ruggedness. An intercept is
included in the regressions but is not reported to conserve
space. Figures in the parentheses are robust standard
errors.
***, **, and * denote significance at the 1%, 5%, and 10%
levels, respectively.
TABLE 5
Other Robustness Checks (IV-2SLS Estimates)
1000 BC 1 AD
Dep. Var. =
Tech. adop. (1a) (1b) (2a) (2b)
Panel A: Considering non-agriculture
technology adoption
Yrs since 0.123 *** 0.136 *** 0.069 *** 0.088 ***
agr. tran. (0.024) (0.032) (0.017) (0.025)
[R.sup.2] 0.557 0.586 0.658 0.672
Observations 82 82 95 95
Panel B: Using conventional continent dummies
Yrs since 0.152 *** 0.162 *** 0.063 *** 0.081 ***
agr. tran. (0.027) (0.034) (0.010) (0.017)
[R.sup.2] 0.488 0.515 0.688 0.697
Observations 82 82 101 101
Panel C: Excluding countries experiencing late
transition (within the last 3,000years)
Years since 0.137 *** 0.138 0.060 0.056
agricultural (0.037) (0.047) (0.019) (0.032)
transition
[R.sup.2] 0.487 0.602 0.627 0.667
Observations 67 67 79 79
Region dummies Yes Yes Yes Yes
Geographic controls No Yes No Yes
1500 AD
Dep. Var. =
Tech. adop. (3a) (3b)
Panel A: Considering non-agriculture
technology adoption
Yrs since 0.081 *** 0.077 ***
agr. tran. (0.023) (0.024)
[R.sup.2] 0.801 0.854
Observations 88 88
Panel B: Using conventional continent dummies
Yrs since 0.064 *** 0.068 ***
agr. tran. (0.020) (0.021)
[R.sup.2] 0.844 0.853
Observations 89 89
Panel C: Excluding countries experiencing late
transition (within the last 3,000years)
Years since 0.088 *** 0.067 *
agricultural (0.031) (0.038)
transition
[R.sup.2] 0.764 0.826
Observations 74 74
Region dummies Yes Yes
Geographic controls No Yes
Notes: The dependent variable is the levels of technology
adoption in 1000 BC, 1 AD, or 1500 AD. The region dummies
are Africa, Eurasia, Oceania, North America, and South
America. The geographic controls are climate, latitude, axis
(the orientation of continent), size of landmass, landlocked
dummy, island dummy, and terrain ruggedness. An intercept is
included in the regressions but is not reported to conserve
space. Figures in the parentheses are robust standard
errors.
***, **, and * denote significance at the 1%, 5%, and 10%
levels, respectively.
TABLE 6
Analyzing the Persistent Effects of Technology Adoption
X = 1500 AD X = 1500 AD
Dep. Var. =
Tech. adop. in X (1a) (1b) (2a) (2b)
Yrs since 0.049 ** 0.048 ** 0.098 *** 0.099 **
agr. tran. (0.021) (0.024) (0.034) (0.041)
P coefficients [%] [32.7] [32.0] [62.0] [62.4]
Tech. adop. 0.337 *** 0.316 ***
in 1 AD (0.090) (0.086)
P coefficients [%] [31.0] [29.0]
Tech. adop. in -0.081 -0.048
1000 BC (0.136) (0.138)
p coefficients [%] [-7.1] [-4.2]
[R.sup.2] 0.862 0.886 0.817 0.843
Observations 87 87 77 77
Region dummies Yes Yes Yes Yes
Geographic controls No Yes No Yes
X = 1 AD
Dep. Var. =
Tech. adop. in X (3a) (3b)
Yrs since 0.054 0.060
agr. tran. (0.034) (0.044)
P coefficients [%] [39.7] [43.7]
Tech. adop.
in 1 AD
P coefficients [%]
Tech. adop. in 0.231 * 0.237 *
1000 BC (0.123) (0.133)
p coefficients [%] [23.3] [23.9]
[R.sup.2] 0.727 0.727
Observations 81 81
Region dummies Yes Yes
Geographic controls No Yes
Notes: The dependent variables are the levels of technology
adoption in 1500 AD (columns (1) and (2)) and 1 AD (column
(3)). The timing of agricultural transition is instrumented
by distance to the Neolithic center and biogeography. The
region dummies are Africa, Eurasia, Oceania, North America,
and South America. The geographic controls are climate,
latitude, axis (the orientation of continent), size of
landmass, landlocked dummy, island dummy, and terrain
ruggedness. An intercept is included in the regressions but
is not reported to conserve space. Figures in the
parentheses are robust standard errors.
***, **, and * denote significance at the 1%, 5%, and 10%
levels, respectively.
TABLE 7
Analysis of the Mediation Tests
(1) (2) (3)
X = 1500 AD X = 1500 AD X = 1 AD (the
(the Mediation (the Mediation Mediation
Dep. Var. = Effect of Effect of Tech. Effect of Tech.
Tech. adop. Tech. adop. adop. in 1000 adop. in 1000
in X in 1 AD) BC) BC)
Direct effect 0.018 * 0.027 * 0.007
[p = .087] [p = .052] [p = .663]
Indirect effect 0.018 ** 0.015 * 0.037 ***
(Sobel test) [p = .010] [p = .059] [p = .001]
Total effect 0.036 *** 0.042 *** 0.044 ***
[p = .002] [p = .001] [p = .006]
Total effect
mediated (%) 49.1 35.6 83.2
Observations 87 77 81
Region dummies Yes Yes Yes
Geographic
controls Yes Yes Yes
Notes: The Sobel test statistics are calculated using the
approach described in MacKinnon (2008). This method tests
the null hypothesis that there is no indirect effect from
the timing of agricultural transition (years since
agricultural transition) via the channels considered
(technology adoption in 1 AD for column (1) and technology
adoption in 1000 BC for columns (2) and (3)). The approach
involves estimating two regression equations. Take column
(1) as an example, first we estimate the parameter ([[beta].sub.1])
describing the effect of years since agricultural transition
on the mediator (technology adoption in 1 AD) (Model 1).
Next, the direct effect is estimated by regressing
technology adoption in 1500 AD on years since agricultural
transition while controlling for the mediator (Model 2). The
coefficient of years since agricultural transition provides
the magnitude of this effect ([[beta].sub.2]). The indirect
effect is given by the product of [[beta].sub.1] and
[[beta].sub.3] where [[beta].sub.3] measures the strength of
the correlation between technology adoption in 1500 AD and
technology adoption in 1 AD in Model 2. This term also
reflects the size of the mediation, which essentially
depends upon the extent to which years since agricultural
transition influences the mediator (Pj) and the extent to
which the mediator affects technology adoption in 1500 AD
([[beta].sub.3]).
***, **, and * indicate significance at the 1%, 5%, and 10%
levels, respectively.
TABLE 8
Controlling for the Effects of Other Early
Development (IV-2SLS Estimates)
(la) (lb) (2a)
Dep. Var. = Tech. adop.
in 1500 AD
Panel A: 1500 AD estimates
Yrs since agr. tran. 0.061 ** 0.063 ** 0.083 ***
(0.024) (0.027) (0.021)
[beta] coefficients [%] [40.2] [41.7] [54.8]
State history up 0.156 0.164 *
to 1500 AD (0.095) (0.083)
[beta] coefficients [%] [15.6] [8.3]
Genetic distance -0.090 **
in 1500 AD (0.038)
[beta] coefficients [%] [-14.7]
Population density
in 1500 AD
[beta] coefficients [%]
[R.sup.2] 0.832 0.859 0.806
Observations 84 84 84
Dep. Var. = Tech. adop.
in 1 AD
Panel B: 1 AD estimates
Yrs since agr. tran. 0.052 ** 0.054 * 0.063 ***
(0.023) (0.032) (0.016)
[beta] coefficients [%] [43.3] [44.3] [52.1]
State history in 1 -50 AD 0.067 0.099
(0.093) (0.106)
[beta] coefficients [%] [7.7] [11.5]
Genetic distance in 1 AD -0.071
(0.045)
[beta] coefficients [%] [-13.7]
Population density in 1 AD
[beta] coefficients [%]
[R.sup.2] 0.706 0.727 0.703
Observations 84 84 84
Dep. Var. = Tech. adop.
in 1000 BC
Panel C: WOO BC estimates
Yrs since agr. tran. 0.128 *** 0.128 ** 0.134 ***
(0.034) (0.049) (0.027)
[beta] coefficients [%] [95.7] [95.7] [100.2]
State history 0.020 0.029
in 1-50 AD (0.122) (0.139)
[beta] coefficients [%] [2.4] [3.5]
Genetic distance 0.052
in 1 AD (0.080)
[beta] coefficients [%] [10.2]
Population density
in 1 AD
[beta] coefficients [%]
[R.sup.2] 0.528 0.557 0.526
Observations 71 71 71
Region dummies Yes Yes Yes
Geographic controls No Yes No
(2b) (3a) (3b)
Dep. Var. = Tech. adop.
in 1500 AD
Panel A: 1500 AD estimates
Yrs since agr. tran. 0.088 *** 0.075 *** 0.081 ***
(0.027) (0.022) (0.027)
[beta] coefficients [%] [58.2] [49.4] [53.5]
State history up
to 1500 AD
[beta] coefficients [%]
Genetic distance -0.080 *
in 1500 AD (0.043)
[beta] coefficients [%] [-13.0]
Population density 0.002 -0.001
in 1500 AD (0.002) (0.002)
[beta] coefficients [%] [5.3] [0.4]
[R.sup.2] 0.826 0.806 0.826
Observations 84 84 84
Dep. Var. = Tech. adop.
in 1 AD
Panel B: 1 AD estimates
Yrs since agr. tran. 0.075 *** 0.057 ** 0.070 **
(0.024) (0.022) (0.029)
[beta] coefficients [%] [61.9] [47.1] [57.6]
State history in 1 -50 AD
[beta] coefficients [%]
Genetic distance in 1 AD -0.086 *
(0.050)
[beta] coefficients [%] [-16.6]
Population density in 1 AD 0.000 0.001
(0.007) (0.009)
[beta] coefficients [%] [0.6] [1.9]
[R.sup.2] 0.717 0.698 0.709
Observations 84 84 84
Dep. Var. = Tech. adop.
in 1000 BC
Panel C: WOO BC estimates
Yrs since agr. tran. 0.149 *** 0.130 ** 0.140 ***
(0.038) (0.032) (0.042)
[beta] coefficients [%] [111.6] [96.8] [104.4]
State history
in 1-50 AD
[beta] coefficients [%]
Genetic distance 0.049
in 1 AD (0.073)
[beta] coefficients [%] [9.6]
Population density -0.003 -0.003
in 1 AD (0.010) (0.012)
[beta] coefficients [%] [-4.0] [-4.0]
[R.sup.2] 0.530 0.527 0.542
Observations 71 71 71
Region dummies Yes Yes Yes
Geographic controls Yes No Yes
(4a) (4b)
Dep. Var. = Tech. adop.
in 1500 AD
Panel A: 1500 AD estimates
Yrs since agr. tran. 0.069 ** 0.074 **
(0.027) (0.031)
[beta] coefficients [%] [45.6] [49.0]
State history up 0.116 0.147
to 1500 AD (0.096) (0.089)
[beta] coefficients [%] [11.7] [14.8]
Genetic distance -0.079 * -0.068
in 1500 AD (0.040) (0.044)
[beta] coefficients [%] [-12.9] [-11.1]
Population density 0.001 -0.001
in 1500 AD (0.002) (0.002)
[beta] coefficients [%] [1.9] [3.3]
[R.sup.2] 0.832 0.852
Observations 84 84
Dep. Var. = Tech. adop.
in 1 AD
Panel B: 1 AD estimates
Yrs since agr. tran. 0.056 ** 0.060 *
(0.028) (0.036)
[beta] coefficients [%] [46.3] [49.5]
State history in 1 -50 AD 0.056 0.072
(0.090) (0.103)
[beta] coefficients [%] [6.5] [8.4]
Genetic distance in 1 AD -0.068 -0.080
(0.048) (0.051)
[beta] coefficients [%] [-13.2] [-15.5]
Population density in 1 AD -0.003 0.001
(0.007) (0.008)
[beta] coefficients [%] [-4.4] [0.3]
[R.sup.2] 0.712 0.733
Observations 84 84
Dep. Var. = Tech. adop.
in 1000 BC
Panel C: WOO BC estimates
Yrs since agr. tran. 0.120 *** 0.133 **
(0.038) (0.052)
[beta] coefficients [%] [89.7] [99.4]
State history 0.058 0.031
in 1-50 AD (0.114) (0.131)
[beta] coefficients [%] [7.0] [3.8]
Genetic distance 0.055 0.053
in 1 AD (0.081) (0.072)
[beta] coefficients [%] [10.9] [10.5]
Population density -0.002 -0.002
in 1 AD (0.010) (0.012)
[beta] coefficients [%] [-2.9] [-3.5]
[R.sup.2] 0.545 0.556
Observations 71 71
Region dummies Yes Yes
Geographic controls No Yes
Notes: The dependent variables are the levels of technology
adoption in 1500 AD (panel A), 1 AD (panel B), and 1000 BC
(panel C). The timing of agricultural transition is
instrumented by distance to the Neolithic center and
biogeography. The region dummies are Africa, Eurasia,
Oceania, North America, and South America. The geographic
controls are climate, latitude, axis (the orientation of
continent), size of landmass, landlocked dummy, island
dummy, and terrain ruggedness. An intercept is included in
the regressions but is not reported to conserve space.
Figures in the parentheses are robust standard errors.
***, **, and * denote significance at the 1%, 5%, and 10%
levels, respectively.
TABLE 9
Mediation Analyses for the Effect of Other Early Development
(2)
(1) The Mediation
The Mediation Effect of
Effect Cultural
of State Diffusion
History Barriers
Panel A:
1500AD estimates
Indirect effect 0.019 *** -0.002
(Sobel test) (p = .008] [p = .366]
Total effect mediated (%) 54.3 7.2
Observations 84 84
Panel B:
1 AD estimates
Indirect effect 0.020 ** -0.001
(Sobel test) [p = .040] [p = .986]
Total effect mediated (%) 41.5 0.1
Observations 84 84
Panel C:
1000 BC estimates
Indirect effect 0.021 0.001
(Sobel test) [p = .148] [p = .641]
Total effect mediated (%) 21.8 1.4
Observations 71 71
Region dummies Yes Yes
Geographic controls Yes Yes
(3)
The Mediation
Effect of
Population
Density
Panel A:
1500AD estimates
Indirect effect 0.001
(Sobel test) [p = .570]
Total effect mediated (%) 3.9
Observations 84
Panel B:
1 AD estimates
Indirect effect 0.007
(Sobel test) [p = .347]
Total effect mediated (%) 13.9
Observations 84
Panel C:
1000 BC estimates
Indirect effect 0.007
(Sobel test) [p = .495]
Total effect mediated (%) 7.6
Observations 71
Region dummies Yes
Geographic controls Yes
Notes: The Sobel test statistics are calculated using the
approach described in MacKinnon (2008). This method tests
the null hypothesis that there is no indirect effect from
the timing of agricultural transition via the channels
considered (state history, cultural diffusion barriers, or
population density). The approach involves estimating two
regression equations. Take column (1) as an example, first
we estimate the parameter ([[beta].sub.1]) describing the
effect of years since agricultural transition on the
mediator (state history) (Model 1). Next, the direct effect
is estimated by regressing technology adoption on years
since agricultural transition while controlling for the
mediator (Model 2). The coefficient of years since
agricultural transition provides the magnitude of this
effect ([[beta].sub.2]). The indirect effect is given by the
product of p, and p3 where p3 measures the strength of the
correlation between technology adoption and state history in
Model 2. This term also reflects the size of the mediation,
which essentially depends upon the extent to which years
since agricultural transition influences the mediator (p3)
and the extent to which the mediator affects technology
adoption in 1500 AD (P3).
*** and ** indicate significance at the 1% and 5% levels,
respectively.
TABLE 10
Effects of Agricultural Transition on Adoption
in Each Sector (IV-2SLS Estimates)
Dep. Var. = (1) Y = (2) Y =
Tech. adop. Agriculture Communication
in Sector Y
Panel A: Technology adoption in 1000 BC
Yrs since agr. tran. 0.205 (0.074) 0.139 *** (0.042)
[R.sup.2] 0.208 0.379
Observations 82 82
Panel B: Technology adoption in 1 AD
Yrs since agr. tran. 0.093 (0.036) 0.171 *** (0.058)
[R.sup.2] 0.287 0.325
Observations 95 95
Panel C: Technology adoption in 1500 AD
Yrs since agr. tran. 0.081 (0.032) 0.115 *** (0.038)
[R.sup.2] 0.593 0.671
Observations 88 88
Region dummies Yes Yes
Geographic controls Yes Yes
Dep. Var. = (3) Y = (4) Y =
Tech. adop. Transport Industry
in Sector Y
Panel A: Technology adoption in 1000 BC
Yrs since agr. tran. 0.193 *** (0.041) 0.113 ** (0.046)
[R.sup.2] 0.582 0.643
Observations 82 82
Panel B: Technology adoption in 1 AD
Yrs since agr. tran. 0.137 *** (0.039) 0.045 ** (0.021)
[R.sup.2] 0.530 0.578
Observations 95 95
Panel C: Technology adoption in 1500 AD
Yrs since agr. tran. 0.053 *** (0.019) 0.045 * (0.025)
[R.sup.2] 0.795 0.916
Observations 88 88
Region dummies Yes Yes
Geographic controls Yes Yes
Dep. Var. = (5) Y =
Tech. adop. Military
in Sector Y
Panel A: Technology adoption in 1000 BC
Yrs since agr. tran. 0.099 *** (0.034)
[R.sup.2] 0.625
Observations 82
Panel B: Technology adoption in 1 AD
Yrs since agr. tran. 0.045 ** (0.021)
[R.sup.2] 0.578
Observations 95
Panel C: Technology adoption in 1500 AD
Yrs since agr. tran. 0.094 ** (0.040)
[R.sup.2] 0.702
Observations 88
Region dummies Yes
Geographic controls Yes
Notes: The dependent variable is the adoption levels of
technology in sector Y for 1000 BC, 1 AD, or 1500 AD, where
Y = agriculture, communication, transport, industry or
military. The timing of agricultural transition is
instrumented by distance to the Neolithic center and
biogeography. The region dummies are Africa, Eurasia,
Oceania, North America, and South America. The geographic
controls are climate, latitude, axis (the orientation of
continent), size of landmass, landlocked dummy, island
dummy, and terrain ruggedness. An intercept is included in
the regressions but is not reported to conserve space.
Figures in the parentheses are robust standard errors.
***, **, and * denote significance at the 1%, 5%,
and 10% levels, respectively.
