Dynamics and Stagnation in the Malthusian Epoch by Quamrul Ashraf and Oded Galor. Published in volume , issue 5, pages of American Economic. This paper empirically tests the predictions of the Malthusian theory with respect to both population dynamics and income per capita stagnation. This paper examines the central hypothesis of the influential Malthusian theory, according to which improvements in the technological environment during the.

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This paper examines the central hypothesis of the influential Malthusian theory, according to which improvements in the technological environment during the pre-industrial era had generated only temporary gains in income per capita, eventually leading to a larger, but not significantly richer, population. Exploiting exogenous sources of cross-country variations in land productivity and the level of technological advancement the analysis demonstrates that, in accordance with the theory, technological superiority and higher land productivity had significant positive effects on population density but insignificant effects on the standard of living, during the time period 1— CE.

The transition from an epoch of stagnation to an era of sustained economic growth has marked the onset of one of the most remarkable transformations in the course of human history.

While living standards in the world economy stagnated during the millennia preceding the Industrial Revolution, income per capita has encountered an unprecedented ten-fold increase in the past two centuries, profoundly altering the level and the distribution of education, health and wealth across the globe. The Malthusian theory has been a central pillar in the interpretation of the process of development during the pre-industrial era and in the exploration of the forces that brought about the transition from stagnation to growth.

Nevertheless, the underlying premise of the theory, that technological progress and resource thf during this epoch had contributed primarily to the size of the population leaving income per capita relatively unaffected dpoch the long run, has not been tested.

The Malthusian theory, inspired by Thomas R. Malthussuggests that the worldwide stagnation in income per capita during the pre-industrial epoch dynzmics the counterbalancing effect of population growth on the expansion of resources, in an environment characterized by maltuhsian positive effect of the standard of living on population growth along with diminishing labor productivity.

Periods marked by the absence of changes in the level of technology or in the availability of land, were characterized by a stable population size as well as a constant income per capita, whereas periods characterized by improvements in the technological environment or in the availability of land generated only temporary gains in income per capita, eventually leading to a larger but not richer population.

Technologically superior economies ultimately had denser populations but their standard of living did not reflect their technological advancement. This research conducts a cross-country empirical analysis of the predictions of the influential Malthusian theory.

DYNAMICS AND STAGNATION IN THE MALTHUSIAN EPOCH

In light of the potential endogeneity of population and technological progress Boserup,this research develops a novel identification strategy to examine the hypothesized effects of technological advancement on population density and income per capita. It establishes that the onset of the Neolithic Revolution that marked the transition of societies from hunting and gathering to agriculture, as early as 10, years ago, triggered a sequence of technological advancements that had a significant effect on the level of technology in the Middle Ages.

Hte argued by Jared Diamondan earlier onset of the Neolithic Revolution has been associated with a developmental head start that enabled the rise of a non-food-producing class whose members were essential for the advancement of written stagnwtion, science and technology, and for the formation of cities, technology-based military powers and nation states. Thus, variations in favorable biogeographical factors i. Consistent with Malthusian predictions, the analysis uncovers statistically significant positive effects of land productivity and the technological level on population density in the years 1 CE, CE, and CE.

In contrast, the effects of land productivity and technology on income per capita in these periods are not significantly different from zero. Moreover, the estimated elasticities of income per capita with respect to these two channels are about an order of magnitude smaller than the corresponding elasticities of population density.

Importantly, the qualitative results remain robust to controls for the confounding effects of a large number of geographical factors, including dynamixs latitude, access to waterways, distance to the technological frontier, and the share of land in tropical versus temperate climatic zones, which may have had an impact on aggregate productivity either directly, by affecting the productivity of land, or indirectly via the prevalence of trade and the diffusion of technologies.

Furthermore, the results are also qualitatively unaffected when a direct measure of technological sophistication, rather than the timing of sttagnation Neolithic Revolution, is employed as an dynamivs of the level of aggregate productivity. Finally, the study establishes that the results are not driven by unobserved time-invariant country fixed effects. In particular, it demonstrates that, while the change in the level of technology between BCE and 1 CE was indeed associated with a significant change in population density over the 1— CE time stagnstion, the level of income per maltgusian during this time period was relatively unaffected, as suggested by the Malthusian theory.

Consider an overlapping-generations economy in which activity extends over infinite discrete time. In every period, the economy produces a single homogeneous good using land and labor as inputs. Production occurs according to a constant-returns-to-scale technology.

The output produced at time tY tis:. Thus, AX captures the effective resources used in production. In each period ta generation consisting of L t identical individuals joins the workforce. Each individual has a single mslthusian. Members of generation t live for two periods. In the second period of life parenthoodtthey inelastically supply their labor, generating an income that is equal to the output per worker, y twhich they allocate between their own consumption and that of their children.

Individuals generate utility from consumption and the number of their surviving children: Members of generation t allocate their income optimally between consumption and dynamcis rearing, so as to maximize their intertemporal utility function 3 subject to the budget ad 4. Thus, in accordance with the Malthusian paradigm, income has a positive effect on the number of surviving children.

Substituting 2 and 5 into 6the time path of the working population is governed by the first-order difference equation:.

Similarly, a decline in the population due to an epidemic such as the Black Death — Malthusisn would temporarily reduce population, while temporarily increasing income per capita. The evolution of income per worker is determined by the initial level of income per worker and the number of surviving children per adult.

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Substituting 5 into 11the time path of income per worker is governed by the first-order difference equation:. These predictions emerge from a Malthusian model as long as the model is based upon two fundamental features: The empirical examination of the central hypothesis of the Malthusian theory exploits exogenous sources of cross-country variation in land productivity and technological levels to examine their hypothesized differential effects on population density and income per capita during the time period 1— CE.

In light of the potential endogeneity of population and technological progress, this research develops a novel identification strategy to examine the hypothesized effects of technological advancement on population density and income per capita. First, it establishes that the onset of the Neolithic Malthusin, which marked the transition of societies from hunting and gathering to agriculture as early as 10, years ago, triggered a sequence of technological advancements that had a significant effect on the level of technology in the Middle Ages.

As argued by Diamondan earlier onset of the Neolithic Revolution has been associated with a developmental head start that enabled the rise of a non-food-producing class whose members were essential for the advancement of written language, science and technology, and for the formation of cities, technology-based military powers and nation states.

In addition, to address the possibility that the relationship between the timing of the Neolithic transition and population density in the Common Era may itself be spurious, being perhaps co-determined by an unobserved channel such as human capital, the analysis appeals to the role of prehistoric biogeographical endowments in determining the timing of the Neolithic Revolution.

Importantly, the productivity of land for agriculture in the Common Era is largely independent of the epocn geographical and biogeographical endowments that were conducive for the malthksian of the Neolithic Revolution. While agriculture dynqmics in regions of the world to which the most valuable domesticable wild plant and animal species were native, other regions proved more fertile and climatically favorable once the diffusion of agricultural practices brought the domesticated varieties to them Diamond, Thus, the analysis adopts an instrumental variables strategy, exploiting variation in the numbers of prehistoric domesticable species of plants and animals that were native to a region prior to the onset of sedentary agricultural practices as exogenous sources of variation for the number of years elapsed since the Neolithic Revolution to demonstrate its causal effect on population density in the Common Era.

Moreover, a direct, period-specific measure of technological sophistication is also employed as an alternative metric of the level of aggregate productivity to demonstrate the qualitative robustness of the baseline results for the years CE and 1 CE.

Finally, in order to ensure that the results from the level regressions are not driven by unobserved time-invariant country fixed effects, this research also employs a first-difference estimation strategy with a lagged explanatory variable.

In particular, the robustness analysis exploits cross-country variation in the change in the level of technological sophistication between the years BCE and 1 CE to explain the cross-country variations in the change in population density and the change in income per capita over the 1— CE time horizon.

The most comprehensive worldwide cross-country historical estimates of population and income per capita since the an 1 CE have been assembled by Colin McEvedy and Richard Jones and Angus Maddison respectively.

The measure of land productivity epoc is the first principal component of the percentage of arable land and an index reflecting the overall suitability of land for agriculture, based on geospatial soil quality and temperature data, as reported by Navin Ramankutty et al.

In particular, for a given time period and for a given culture in the archaeological record, the Atlas of Cultural Evolution draws on various anthropological and historical sources to report the level of technological advancement, on a 3-point scale, in each of four sectors of the economy, including communications, industry i. The index of technological sophistication is constructed following the aggregation methodology of Diego Comin, William Easterly, and Erick Gong This section establishes that the Neolithic Revolution triggered a cumulative process of economic development, conferring a developmental head start to societies that experienced the agricultural transition earlier.

In line with this assertion, Table 1 reveals preliminary results indicating that an earlier onset of the Neolithic Revolution is indeed positively and significantly correlated with the level of technological sophistication in non-agricultural sectors of the economy in the years CE and 1 CE. For instance, the coefficient estimates for the year CE, all of which are statistically significant at the 1 percent level, indicate that a 1 percent increase in the epoh of years elapsed since the onset of the Neolithic Revolution is associated with an increase in the level of technological advancement in the communications, industrial, and transportation sectors by 0.

Summary — This table demonstrates that the timing of the Neolithic Revolution is positively and significantly correlated with the level of technology in multiple stwgnation sectors of an economy in the years CE and 1 CE.

These findings lend credence to the empirical strategy employed by this research to test the Malthusian theory. Specifically, they provide evidence justifying the use of the exogenous source of cross-country variation in the timing of the Neolithic Revolution as a proxy for the variation malthusina the level of technological advancement across countries during the agricultural stage of development.

Moreover, they serve as an internal consistency check between the cross-country Neolithic transition-timing variable and those on historical levels of technological sophistication, all of which are relatively new in terms of their application in the empirical literature on long-run development. Formally, the baseline specifications adopted to test the Malthusian predictions regarding the effects of land productivity and the level of technological advancement on population density and income per capita are:.

Consistent with the predictions of the Malthusian theory, the results demonstrate highly statistically significant positive effects of land productivity and the number of years elapsed since the Neolithic Revolution on population density in the years CE, CE and 1 CE. The effects of these explanatory channels on income per capita in the corresponding periods, however, are not significantly different from zero, a result that fully complies with Malthusian priors.

These results are shown to be robust to controls for other geographical factors, including absolute latitude, access to waterways, distance to the nearest technological frontier, the percentage of land in tropical versus temperate climatic zones, and small island and landlocked dummies, all of which may have had an impact on aggregate productivity either directly, by affecting the productivity of land, or indirectly by affecting trade and the diffusion of technologies.

This section establishes the significant positive effects of dynmaics productivity and the level of technological advancement, as proxied by the timing of the Stagnaion Revolution, on population density in the year CE.

DYNAMICS AND STAGNATION IN THE MALTHUSIAN EPOCH

The results from regressions explaining log population density in the year CE are presented in Table 2. In particular, a number of specifications comprising different subsets of the explanatory variables in equation 15 are estimated to examine the independent and combined effects of the transition-timing and land-productivity channels, while controlling for other geographical factors and continental fixed effects.

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Summary — This table establishes, consistently with Malthusian predictions, the significant positive effects of land productivity and the level of technological advancement, dynamicd proxied by the timing of the Neolithic Revolution, on population density in the year CE, while controlling for access to navigable waterways, absolute latitude, and unobserved continental fixed effects.

Consistent with Stagnagion predictions, Column 1 reveals the positive relationship between log years since transition and log population density in the year CE, while controlling for continental fixed effects.

The effect of the land-productivity channel, controlling for absolute latitude and continental fixed effects, is reported in Column 2. In line with theoretical predictions, a 1 percent increase in land productivity raises population density in CE by 0. Interestingly, in contrast to the relationship between absolute latitude and contemporary income per stagnationn, the estimated elasticity of population density in CE with respect to absolute latitude suggests matlhusian economic development during this period wtagnation on average higher at latitudinal bands closer to the equator.

The R-squared of the regression indicates that, along with continental fixed effects and absolute latitude, the land-productivity channel explains 60 percent of the cross-country variation in log population density in CE.

Column 3 maltgusian the results from examining the combined explanatory power of the previous two regressions. The estimated coefficients on the transition-timing and land-productivity variables remain highly statistically significant and continue to retain their expected signs, while jn slightly in magnitude in comparison to their estimates in earlier columns. Furthermore, transition timing and land productivity together explain 66 percent of the variation in log population density in CE, along with absolute latitude and continental fixed effects.

The explanatory power of the regression in Column 3 improves by an additional 7 percentage points once controls for access to waterways are accounted for in Column 4, which constitutes the baseline regression specification for population density in CE. In comparison to the estimates reported in Column 3, the effects of the transition-timing and land-productivity variables remain reassuringly stable in both magnitude and statistical significance when subjected to the additional geographical controls.

Moreover, the estimated coefficients on the additional geographical controls indicate significant effects consistent with the assertion that better access to waterways has been historically beneficial for economic development by fostering urbanization, international trade and technology diffusion.

To interpret the baseline effects of the variables of interest, a 1 percent increase in the number of years elapsed since the Neolithic Revolution raises population density stagnatiin CE by 1. Similarly, a 1 percent increase in land productivity generates, ceteris paribusa 0. Summary — This figure depicts the partial regression line for the effect of transition timing land productivity on population density in the year CE, while controlling for the influence of land productivity transition timingabsolute latitude, access to waterways, and continental fixed effects.

Thus, the x- and y-axes plot the residuals obtained from regressing transition timing land productivity and population density, respectively, on the aforementioned set of covariates. The analysis now turns to address issues regarding causality, particularly with respect to the transition-timing variable.

Specifically, while variations in land productivity and other geographical characteristics are inarguably exogenous to the cross-country variation in population density, the onset of the Neolithic Revolution and the outcome variable of interest may in fact be endogenously determined. Specifically, although reverse causality is not a source epochh concern, given that the vast majority of epoh underwent the Neolithic transition prior to the Common Era, the OLS estimates of the effect of the time elapsed since the transition to agriculture may suffer from omitted variable bias, reflecting spurious correlations with the outcome variable being examined.

Accordingly, the emergence and subsequent diffusion of agricultural practices were primarily driven by geographical conditions such as climate, continental size and orientation, as well as the availability of wild plant and animal species amenable to domestication.

However, while geographical factors certainly continued to play a direct role in economic development after the onset of agriculture, it is postulated that the availability of prehistoric domesticable wild plant and animal species did not influence population density in the Common Era other than through the timing stagnafion the Neolithic Revolution.

The analysis consequently adopts the numbers of prehistoric domesticable species of wild plants and animals, obtained from the dataset of Olsson and Hibbsas instruments to establish the causal effect of the timing of the Neolithic transition on population density.

The final two columns in Table 2 report the results thw with a subsample of countries for which data on the biogeographical instruments are available.

To allow meaningful comparisons between IV and OLS coefficient estimates, Column 5 repeats the baseline OLS regression analysis on this particular subsample of countries, revealing that the coefficients on the explanatory variables of interest remain largely stable in terms of both magnitude and significance compared to those estimated using the baseline sample.

This is a reassuring indicator that any additional sampling bias introduced by the restricted sample, particularly with respect to the transition-timing and land-productivity variables, is negligible. Consistent with this assertion, the explanatory powers of the baseline and restricted sample regressions are nearly identical. Column 6 presents the IV regression results from estimating the baseline specification with log years since transition instrumented by the numbers of prehistoric domesticable species of plants and animals.

This pattern is consistent with attenuation bias afflicting the OLS coefficient as a result of measurement error in the transition-timing variable. To interpret the causal impact of the timing of the Neolithic Revolution, a 1 percent increase in years elapsed since the onset of agriculture causes, ceteris paribusa 2. The coefficient on land productivity, which maintains stability in both magnitude and statistical significance across the OLS and IV regressions, indicates that a 1 percent increase in land productivity raises population density by 0.

Finally, the rather strong F-statistic from the first-stage regression provides verification for the significance and explanatory power of the biogeographical instruments employed for the timing of the Neolithic Revolution, while the high p-value associated with the test for overidentifying restrictions is supportive of the claim that these instruments do not exert any independent influence on population density in CE other than through the transition-timing channel.