Abstract
This research advances the hypothesis that natural land productivity in the past, and its effect on the desirable level of cooperation in the agricultural sector, had a persistent effect on the evolution of social capital, the process of industrialization and comparative economic development across the globe. Exploiting exogenous sources of variations in land productivity across (a) countries; (b) individuals within a country, (c) migrants of different ancestry within a country, and (d) individuals residing in regions within a country, the research establishes that lower level of land productivity in the past is associated with more intense cooperation and higher levels of contemporary social capital and development.
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Notes
Appendix A in supplementary material provides a number of sources on which the suggested mechanism builds upon.
The suggested changing effect captures the partial effect of natural land productivity without necessarily implying that this is the dominating effect. In particular, as established in Ashraf and Galor (2011a), consistently with the predictions of a long-run Malthusian equilibrium, productivity in the preindustrial era, as captured by overall land quality and the timing of the Neolithic Revolution, had a significant positive effect on population density and a negligible impact on income per capita. For the contemporary era, the relevant variable that captures aggregate productivity is income per capita. Thus, establishing that countries that had high population density in the preindustrial era also have low per-capita incomes in the contemporary era, is tantamount to establishing a reversal in terms of aggregate productivity.
Unlike the proposed mechanism that focuses on the effect of unfavorable natural land productivity on cooperation in the construction of physical agricultural infrastructure, Durante (2010) explores the role of climatic variability and thus the insurance motive in the emergence of trust. Moreover, in contrast to Durante who establishes empirically only the reduced form relationship between climatic variability in the past and contemporary level of trust, the current paper explores empirically the channel through which unfavorable land productivity affected the contemporary level of trust, establishing the intermediate effect on cooperation in the agricultural stage of development. Furthermore, the current research also focuses primarily on comparative development, whereas the emergence of trust is an intermediate element of the mechanism.
The substitutability between natural land productivity and agricultural infrastructure is further explored in the empirical section of the paper. In particular, it will be established that higher land suitability for agriculture is associated with lower incentives to invest in agricultural infrastructure.
The structure of the dynamical system is inspired by Ashraf and Galor (2011b).
\(\omega \in (0,1)\) captures the fact that only a fraction of the population contributes to the creation of new knowledge in the industrial sector. While it can be argued that people employed in the industrial sector can contribute to the creation of new knowledge in the industrial sector, indirectly, it would be less plausible to argue that all people employed in the agricultural sector can positively influence knowledge creation in the industrial sector. It is therefore assumed that a constant fraction of the total workforce is positively affecting knowledge creation in industry.
The online Appendix in supplementary material elaborates more on this point.
The initial size of the adult population, \(L_{0}>0\), is given.
The tables for the years 1000 CE and 1 CE can be found in the Appendix C in supplementary material.
Other institutional controls have been explored as well, such as constraints on the executive or expropriation risk, without affecting the results (results not available in the paper).
The advantage of this approach has also been highlighted by Bentzen et al. (2012) who have constructed an alternative measure for irrigation potential.
It could be plausibly argued though that since early industrialized countries are excluded from the sample, migration is unlikely to be a major factor in the analysis.
The type of cooperation that is explored in the paper is not institutions driven. It is more of a norm rather than a centralized institution such as religion. For this reason other potential proxies of cooperation such as e.g., organized religions are not employed as proxies of cooperation.
All the baseline regressions are repeated in the Appendix C in supplementary material while controlling for the trade channel.
The result is robust to other thresholds as well.
For a more extensive discussion on this data see Ashraf and Galor (2011a). Moreover the concerns associated with measurement errors are mitigated by the introduction of continental fixed effects.
The argument for adopting such an approach is based upon the observation that at the global scale, climate and soil factors form the major constraints on cultivation, and adequately describe the major patterns of agricultural land (Ramankutty et al. 2002),
It should be noted that it is not the ranking of countries that is used as the measure of the explanatory variable, instead it is the actual measure of land suitability. The argument about the ranking of countries aims to highlight that changes in land productivity, as captured by the index, are hardly so drastic to change the ranking of countries.
Durante (2010) has examined at the relationship between climatic conditions for the years 1900–2000 and 1500–1900. In particular he looks at the relationship separately for average precipitation, average temperature, precipitation variability and temperature variability. His findings confirm that regions with more variable climate in the present years were also characterized by more variate climate in the past, thereby reassuringly implying that climatic conditions have not significantly changed over time. A similar argument has been made by Ashraf and Michalopoulos (2013).
The robustness section addresses these concerns by employing as the explanatory variable climatic and soil suitability respectively.
The values are provided in 1000 ha units.
Similar results are established for the effect of log land on log population density in the years 1 CE and 1000 CE. and can be found in the Appendix C in supplementary material.
One concern would be the case of some countries which are uninhabited due to being non-suitable for agriculture (e.g. Egypt which is largely uninhabited due to the desert). In this case, population density would be underestimated and this could lead to a spurious positive correlation between land suitability and population density. To mitigate this concern, the regression in Column (4) has been repeated using a measure of population density defined as population in 1500 CE divided by arable land. The results remain intact. The same concern could be raised for the measure of land suitability as well. In this case however, the index of land suitability is underestimated thereby attenuating the coefficient on land suitability towards zero. Constructing the corresponding index of suitability while taking into account only the fraction of arable land would strengthen the results.
The sample is extended to the maximum number of countries available for the industrial era. These countries already contain the 130 countries that are available in the Malthusian era.
The threshold level of the native population is chosen in a way that minimizes the trade-off between the reduced observations and a sufficiently high fraction of the native people that allows to infer that the portable component of land suitability is present within the population. As a robustness different thresholds have been employed as well and the results remain qualitatively the same.
It should be noted that the sample is restricted to the countries with a fraction of native population higher than 80 %. Since the migration matrix is referring to the ancestors of the population in the year 2000 CE, it is not possible to calculate the ancestry adjusted land suitability. Similarly, certain controls such as schooling are not relevant for this analysis.
The timing of industrialization is determined as the year in which the share of agricultural sector became less than 30 % of the aggregate economic activity. The measure used is provided by Oded Galor. Bentzen et al. (2013) have also constructed and provided a measure of industrialization, where the timing of industrialization is determined as the year in which the share of agricultural sector became less than 50 % of the aggregate economic activity.
One potential concern may be that the adverse effect of land productivity on current economic outcomes is reflecting the effect of the “natural resource curse”. Reassuringly though, the negative correlation between the index of land productivity and income from natural resources as a fraction of GDP (-0.4), implies that the adverse effect of land productivity on contemporary economic outcomes does not capture the resource curse. Controlling though for OPEC countries as an additional robustness check, does not qualitatively affect the results (results are reported in the Appendix C in supplementary material).
If coordination problems among members of the community dictate a suboptimal level of investment in infrastructure, the qualitative results would be enhanced. Since the complexity of coordination increases with the size of the community, less favorably endowed places, and therefore more sparsely populated places (according to the Malthusian mechanism) would coordinate more easily than more densely populated places. Hence, the sub-optimally level of investment in infrastructure will be larger in favorably endowed places, enhancing the hypothesis that less favorably endowed places invest more in infrastructure.
The analysis does not employ the potential for irrigation as the main explanatory variable due to the fact that such an approach would limit the scope of the analysis. Irrigation is only one form of cooperation whereas the measure of land suitability for agriculture can capture a wide range of cooperation-intensive activities.
The full set of controls is sustained for the shake of symmetry with the baseline regressions.
The argument that irrigation as well as any other type of infrastructure can be associated with autocratic regimes and the use of slaves is extensively discussed in the robustness section and empirically addressed in the Appendix C in supplementary material.
Results are reported in the Appendix C in supplementary material.
Results are reported in the Appendix C in supplementary material.
It could be argued that the threat of war and the fear of being invaded could enforce cooperation in the past. However, the presence of this plausible effect would suggest that the identified adverse effect of land productivity on cooperation represents an upper bound of the actual effect. First, if one plausibly assumes that the more fertile places faced an increased risk to be invaded then land productivity would generate a positive effect on cooperation via this channel, mitigating the actual adverse effect identified in the regression analysis. Moreover, even if implausibly, less fertile places were faced with an increased probability of being invaded, it would only constitute a complementary channel through which land productivity is affecting cooperation and trust, since as the established effect of low land productivity on cooperation, via irrigation, medium of exchange, and communication technologies are tangential to cooperation for defensive purposes.
As already argued, adjusted land suitability is the appropriate measure of land suitability since vast migration has taken place in current years.
Further analysis in the robustness section explores the channel of slavery by controlling for a measure of stratification ancestry adjusted. Reassuringly the results are unaffected, thereby suggesting that despite the fact that in some cases infrastructure may have been developed by slaves, nevertheless non fertile land is associated with more incentives for cooperation and higher levels of trust today.
One concern that may arise is that land productivity is correlated with the degree of land diversity and high land diversity may generate conflict and therefore hinder cooperation and ultimately trust. To address this concern, one could capture this channel by controlling for land diversity. Reassuringly, as established in the Appendix in supplementary material, controlling for land diversity does not affect the qualitative results. In addition, the coefficient of land diversity is positive thereby implying that if indeed conflict emerges, it is not the dominating effect. In particular, it is plausibly suggested by the positive coefficient, that unequal land productivity fostered cooperation and trade among regions, generating positive effects on economic outcomes in the past and the present as well as on the current levels of trust.
There are three reasons for focusing on irrigation potential. First it is more precisely measured than all other variables, second it is available for a larger number of countries and last it is more exogenous control than actual irrigation.
The repetition of the results is to make the two samples comparable.
Soil suitability is a geospatial index of the suitability of land for agriculture based on ecological indicators of soil suitability for cultivation, such as soil carbon density and soil pH whereas climatic suitability is a geospatial index of the suitability of land for agriculture based on ecological indicators of climate suitability for cultivation such as growing degree days and the ratio of actual to potential evapotranspiration.
Durante (2010) has examined at the relationship between climatic conditions for the years 1900–2000 and 1500–1900. His findings confirm that regions with more variable climate in the present years were also characterized by more variate climate in the past, thereby reassuringly implying that climatic conditions have not significantly changed over time.
A measure of adjusted climatic (soil) suitability is constructed using the weighted average of the climatic (soil) suitability of the ancestral population of each country today. The adjustment of the land suitability index is based on the migration matrix constructed by Putterman and Weil (2010), which provides estimates of the proportion of the ancestors in the year 1500 of one country’s population today that were living within what are now the borders of that and each of the other countries. The adjustment captures the portable component associated with land suitability, namely the social capital that emerged as the outcome of cooperation.
A non-linear specification (quadratic) has been tested as well, which is however not supported by the data (results not reported in the text).
http://www.geerthofstede.nl/dimension-data-matrix (accessed on 23/09/2015).
The WVS sample is not sufficiently detailed to trace all migrants and their country of origin (except for the Vth wave in which case the sample size is dramatically reduced) Therefore the analysis in this section, employs the same measure of ancestry adjusted land suitability for each individual in the country. Importantly though recall that the results are robust to the use of the measure of unadjusted land suitability in the sample of countries with a high fraction of native population as Table 5 suggests.
An analysis exploiting variations in land suitability associated with migrants, is conducted in the next section of the paper, using a much more extensive sample from the ESS.
In principle it would be preferable to use ethnic group instead of religious groups. However, responses on ethnic groups are much more limited and therefore significantly reduce the sample size. Yet, religious group are so detailed (90 religious groups are reported) that can be viewed as a good proxy for ethnic groups.
The questionnaire covers 90 categories of religious denominations. As to education attained, the questionnaire distinguishes seven different levels of education (inadequately completed elementary education, completed (compulsory) elementary education, (compulsory) elementary education and basic vocational qualification, secondary, intermediate vocational qualification, secondary, intermediate general qualification, full secondary, maturity level certificate, higher education - lower-level tertiary certificate, higher education - upper-level tertiary certificate).
The information provided from the WVS is not sufficient to exclude migrants and to repeat the same analysis for the natives only. This approach will be adopted in the next section of the paper, where this information is available for the full sample.
The rational behind these robustness tests is analytically described in the robustness part of the cross country section.
In the previous two sections, in order to indirectly capture the cultural component associated with natural land productivity, the analysis employed the measure of adjusted land productivity.
As already discussed on Luttmer and Singhal (2011) though, who also use the European Social Survey dataset, the fact that migrants from many different countries move to a number of European countries, makes it less likely that selection is a major concern. Moreover, selective migration would attenuate the coefficients, thereby biasing the estimates downward. In the extreme case where all migrants would select their destination country, it would not be feasible to trace any effect of culture.
The questionnaire covers 8 broad categories of religious denominations (Roman Catholic, Protestant, Eastern Orthodox, Other Christian denomination, Jewish, Islamic, Eastern Religions, Other non-Christian Religions) and a category of non-religious people.
As to education attained the questionnaire distinguishes seven different levels of education (less than lower secondary, lower secondary, lower tier upper secondary, upper tier upper secondary, advanced vocational, lower tertiary BA level, higher tertiary \(> \hbox {MA}\) level).
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Acknowledgments
I would like to thank the editor and two anonymous referees for useful comments and suggestions. I would also like to thank participants at the NBER Summer Institute, the CAGE/CEPR Long Run Growth Conference at Warwick, the “Long-Run Factors in Comparative Development and International Interactions” conference at Brown, the AMSE workshop, 8th BETA-Workshop, the “Intergenerational Transmission of Entrepreneurship Occupations and Cultural Traits in the Process of Long-run Economic Growth” conference at Naples, the CRETE conference, the RES conference, and seminar participants at Brown, Copenhagen, CREA, Gothenburg, Groningen, IRES, Luxembourg, Namur, and Porto, for their comments.
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Litina, A. Natural land productivity, cooperation and comparative development. J Econ Growth 21, 351–408 (2016). https://doi.org/10.1007/s10887-016-9134-7
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DOI: https://doi.org/10.1007/s10887-016-9134-7