DYNAMICS AND STAGNATION IN THE MALTHUSIAN EPOCH PDF

  • DYNAMICS AND STAGNATION IN THE MALTHUSIAN EPOCH PDF

    February 22, 2021 By admin 0 comments

    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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    Similarly, a decline in the population due to an epidemic malthuian as the Black Death — CE would temporarily reduce population, while temporarily increasing income per capita. Notes — Number of observations in parentheses. The Origins of Ethnolinguistic Diversity: While agriculture originated 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, Journal of Economic Growth.

    DYNAMICS AND STAGNATION IN THE MALTHUSIAN EPOCH. – Dimensions

    First, it establishes that the onset of the Neolithic Revolution, 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. In all cases, dynamixs sector-specific indices are normalized to assume values in the [0, 1]-interval.

    Lower resource diversity at higher absolute latitudes would imply lower carrying capacities of these environments due to the greater extinction susceptibility of the resource base under adverse natural shocks such as disease and sudden climatic fluctuations.

    Summary — This figure depicts the partial regression lines for the rhe of technological sophistication on population density in the years CE and 1 CE, respectively, while controlling for the influence of land productivity, absolute latitude, access to waterways, and continental fixed effects.

    The interested tje is also referred to www. Quarterly Journal of Economics. This working paper can be ordered from The price is Free.

    Indeed, the conditional correlation between technology and income per capita is not statistically different from zero at conventional levels of significance.

    The remainder of the analysis in this section is concerned with establishing the causal effect of technology on population density in the years CE and 1 CE. In contrast, Column 3 reveals that the corresponding effect on the change in income per capita over the epch period 1— CE is relatively marginal and not statistically significantly different from zero.

    Ahd level of technology in each sector is indexed as follows. Specifically, the level regression results may be explained by the following non-Malthusian theory.

    Dynamics and Stagnation in the Malthusian Epoch

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

    Summary — This table establishes, consistently with Malthusian predictions, the relatively small effects of land productivity and the level of technological advancement, as proxied by the timing of the Neolithic Revolution, on income per capita in the years CE, CE and 1 CE, but their significantly larger effects on population density in the same time periods, while controlling for access to navigable waterways, absolute latitude, and unobserved continental fixed effects.

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    The quantitative robustness of the results are verified by the fact that, despite the statistical significance of some of the effects in the year CE under the weighted methodology, the transition-timing and land-productivity channels continue to remain economically non-substantial for income per capita in all three periods, as reflected by estimated elasticities that are still about an order of magnitude smaller than those of population density in the corresponding periods.

    As 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 language, science and technology, and for the formation stagnattion cities, technology-based tje powers and nation states.

    Population, Food, and Knowledge: Column 1 of Table 10 reveals the qualitative robustness of the full-sample regression results for population density in the year CE under controls for distance to the closest regional frontier as well as small island and landlocked dummies. To interpret the coefficients of interest, a 1 percent increase in the level of technological sophistication in the years CE and 1 CE corresponds to a rise in population density in the respective time periods by 4.

    Summary — This table collects the first-stage regression results for all IV regressions examined in the text. In particular, the epcoh elasticities of population density with respect to these channels are about an order of magnitude larger than the corresponding elasticities of income per capita regardless of the set of additional controls included in the specification. Ane support from the Watson Institute at Brown University is gratefully acknowledged.

    This section establishes the significant positive effects of land productivity and the level of technological advancement, as proxied by the timing of the Neolithic Revolution, on population density in the year CE. In each period ta generation consisting of L t identical individuals joins the workforce.

    As discussed earlier, maltgusian alternative migration-driven theory predicts that an increase in technology in a given region will not differentially eppoch income per capita in that region due to the cross-regional equalization of wage rates, but will increase income per capita in all regions. As predicted by the Malthusian theory, the slope coefficients in Columns 1 and 2 indicate that the change malthusuan the level of malhusian between the years BCE and 1 CE has a positive and statistically significant effect on the change in population density over the 1— CE time horizon.

    For more details on the underlying data and the aggregation methodology employed to construct this index, the reader is referred to Peregrine and Comin, Easterly, and Gong Specifically, for a given time period, their procedure selects from each continent the two largest cities in that period, belonging to distinct sociopolitical entities. Columns 3—6 reveal the robustness of the results for income per capita as well as population density in the income per capita data-restricted sample, under controls for the technology-diffusion channel and additional geographical factors.

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    This runs contrary to the Malthusian prediction that increases in the level of technology in a given region should ultimately translate into increases in population density in that region, leaving income per capita constant at the subsistence level in all regions.

    Moreover, the level regressions in Table 7, indicating the significant positive relationship between the level of technology and population density but the absence of a systematic relationship with income per capita, could potentially reflect spurious correlations between technology and one or more unobserved time-invariant country fixed effects.

    Finally, the study establishes that the results are not driven by unobserved time-invariant country fixed effects. 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. The population data reported by the authors are based on a wide variety of country and region-specific historical sources, the enumeration of which would be impractical for this appendix.

    Finally, the partial regression lines associated with the period-specific indices of technology in the baseline regressions for population density in CE and 1 CE are depicted in Figures D.

    EconPapers: Dynamics and Stagnation in the Malthusian Epoch

    From Stagnation to Growth: Finally, given the possibility that the disturbance terms in the baseline regression models may be non-spherical in nature, particularly since economic development has been spatially clustered in certain regions of the world, the appendix presents results from repeating the baseline analyses for population density and income per capita in the three historical periods, with the standard errors of the point estimates corrected for spatial autocorrelation following the methodology of Timothy G.

    First, in contrast to the positive relationship between absolute latitude and contemporary income per capita, population density in pre-industrial times was on average higher at latitudinal bands closer to the equator.

    Moreover, the positive impact on economic development of geographical factors capturing better access to waterways is also confirmed for these earlier periods. Brown University Working Paper —7. The results from regressions explaining log population density in the year CE are presented in Table 2.

    Consistent with Malthusian 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. Specifically, it establishes that the results for population density and income per capita in CE are robust under two alternative specifications that relax potential constraints imposed by the baseline regression models, including i the treatment of the Americas as a single entity in accounting for continental fixed effects, and ii the employment of only the common variation in the logs of the percentage of arable land and the index of agricultural suitability when accounting for the effect of the land-productivity channel by way of the first principal component of these two variables.

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