Agriculture and Structural Change

  1. Adamopoulos, T. et al. (2017) Misallocation, Selection and Productivity: A Quantitative Analysis with Panel Data from China. Working Papers tecipa-574. University of Toronto, Department of Economics. Available at: Link.
    • Abstract

      We use household-level panel data from China and a quantitative framework to document the extent and consequences of factor misallocation in agriculture. We find that there are substantial frictions in both the land and capital markets linked to land institutions in rural China that disproportionately constrain the more productive farmers. These frictions reduce aggregate agricultural productivity in China by affecting two key margins: (1) the allocation of resources across farmers (misallocation) and (2) the allocation of workers across sectors, in particular the type of farmers who operate in agriculture (selection). We show that selection can substantially amplify the static misallocation effect of distortionary policies by affecting occupational choices that worsen the distribution of productive units in agriculture.

  2. Adamopoulos, T. and Restuccia, D. (2014) “The Size Distribution of Farms and International Productivity Differences,” American Economic Review, 104(6), pp. 1667–97. Available at: Link.
    • Abstract

      We study the determinants of di fferences in farm-size across countries and their impact on agricultural and aggregate productivity using a quantitative sectoral model featuring a distribution of farms. Measured aggregate factors (capital, land, economy-wide productivity) account for ? of the observed differences in farm size and productivity. Policies and institutions that misallocate resources across farms have the potential to account for the remaining diff erences. Exploiting within-country variation in crop-specifi c price distortions and their correlation with farm size, we construct a cross-country measure of farm-size distortions which together with aggregate factors accounts for ? of the cross-country diff erences in size and productivity.

  3. Alvarez-Cuadrado, F. and Poschke, M. (2011) “Structural Change Out of Agriculture: Labor Push versus Labor Pull,” American Economic Journal: Macroeconomics, 3, pp. 127–158.
    • Abstract

      A declining agricultural employment share is a key feature of economic development. Its main drivers are: improvements in agricultural technology combined with Engel’s law release resources from agriculture ("labor push"), and improvements in industrial technology attract labor out of agriculture ("labor pull"). We present a model with both channels and evaluate the importance using data on 12 industrialized countries since the nineteenth century. Results suggest that the "pull" channel dominated until 1920 and the "push" channel dominated after 1960. The "pull" channel mattered more in countries in early stages of the structural transformation. This contrasts with modeling choices in recent literature.

  4. Ashraf, Q. and Galor, O. (2011) “Dynamics and stagnation in the malthusian epoch,” American Economic Review, 101(5), pp. 2003–41.
    • Abstract

      This paper examines the central hypothesis of the influential Malthusian theory, according to which improvements in the technological environment during the preindustrial 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 1-1500 CE.

  5. Betts, C., Giri, R. and Verma, R. (2013) Trade, Reform, And Structural Transformation in South Korea. MPRA Paper 49540. University Library of Munich, Germany. Available at: Link.
    • Abstract

      A two country, three sector hybrid model of structural change with distortionary government policies is used to quantify the impact of international trade and trade reform for industrialization. The model features Arming- ton motivated trade in agriculture and industry, and a novel representation of trade reform as a time sequence of import tariffs, export subsidies and lump sum government transfers of net tariff revenue. We calibrate our economy to data on South Korea and the OECD, inputting time series of country and sector specific labor productivity, tariffs and export subsidies which determine evolution of the effective pattern of comparative advantage. The model’s predicted reallocations of Korean labor from agriculture into industry and services from 1963 through 2000 are quantitatively similar to those in the data. Incorporating trade and measured Korean trade reform are both important for the accuracy of this predicted structural change, although interna- tional real income differences under non-homothetic preferences primarily determine trade and specialization patterns rather than comparative advantage. Counterfactually eliminating a) international trade b) interna- tional labor productivity differentials c) post 1967 Korean tariff reform and d) post 1967 industrial export subsidy reform increase the model’s SSE by 91 percent, 56 percent, 27 percent, and 62 percent respectively.

  6. Boserup, E. (1965) The Conditions of Agricultural Growth. Earthscan Publications.
  7. Bray, F. (1994) The Rice Economies, Technology and Development in Asian Societies. Berkeley, CA: University of California Press.
  8. Bustos, P., Caprettini, B. and Ponticelli, J. (2016) “Agricultural Productivity and Structural Transformation: Evidence from Brazil,” American Economic Review, 106(6), pp. 1320–65. Available at: Link.
    • Abstract

      We study the effects of the adoption of new agricultural technologies on structural transformation. To guide empirical work, we present a simple model where the effect of agricultural productivity on industrial development depends on the factor-bias of technical change. We test the predictions of the model by studying the introduction of genetically engineered soybean seeds in Brazil, which had heterogeneous effects on agricultural productivity across areas with different soil and weather characteristics. We find that technical change in soy production was strongly labor-saving and led to industrial growth, as predicted by the model.

  9. Caselli, F. and II, W. J. C. (2001) “The U.S. Structural Transformation and Regional Convergence: A Reinterpretation,” Journal of Political Economy, 109(3), pp. 584–616.
    • Abstract

      We present a joint study of the U.S. structural transformation (the decline of agriculture as the dominating sector) and regional convergence (of southern to northern average wages). We find empirically that most of the regional convergence is attributable to the structural transformation: the nationwide convergence of agricultural wages to nonagricultural wages and the faster rate of transition of the southern labor force from agricultural to nonagricultural jobs. Similar results describe the Midwest’s catch-up to the Northeast (but not the relative experience of the West). To explain these observations, we construct a model in which the South (Midwest) has a comparative advantage in producing unskilled laborintensive agricultural goods. Thus it starts with a disproportionate share of the unskilled labor force and lower per capita incomes. Over time, declining education/training costs induce an increasing proportion of the labor force to move out of the (unskilled) agricultural sector and into the (skilled) nonagricultural sector. The decline in the agricultural labor force leads to an increase in relative agricultural wages. Both effects benefit the South (Midwest) disproportionately since it has more agricultural workers. With the addition of a less than unit income elasticity of demand for farm goods and faster technological progress in farming than outside of farming, this model successfully matches the quantitative features of the U.S. structural transformation and regional convergence, as well as several other stylized facts on U.S. economic growth in the last century. The model does not rely on frictions on interregional labor and capital mobility, since in our empirical work we find this channel to be less important than the compositional effects the model emphasizes.

  10. Clark, G. (2002) “The Agricultural Revolution and the Industrial Revolution.”
  11. Collier, P. and Dercon, S. (2014) “African Agriculture in 50 Years: Smallholders in a Rapidly Changing World?,” World Development, 63, pp. 92–101. doi: Link.
    • Abstract

      Summary For economic development to succeed in Africa in the next 50 years, African agriculture will have to change beyond recognition. Production will have to have increased massively, but also labor productivity, requiring a vast reduction in the proportion of the population engaged in agriculture and a large move out of rural areas. The paper questions how this can be squared with a continuing commitment to smallholder agriculture as the main route for growth in African agriculture and for poverty reduction. We question the evidence base for an exclusive focus on smallholders, and argue for a much more open-minded approach to different modes of production. To allow alternative modes and scale of production to emerge, new institutional and policy frameworks are required. A rush to establish “mega-farms” with government discretionary allocation of vast tracts of land is unlikely to be the answer. Allowing a more dynamic agriculture to develop will require clear institutional frameworks, and not just a narrow focus on smallholders.

  12. Craig, B. J., Pardey, P. G. and Roseboom, J. (1997) “International Productivity Patterns: Accounting for Input Quality, Infrastructure, and Research,” American Journal of Agricultural Economics, 79(4), pp. 1064–1076. Available at: Link.
    • Abstract

      In this paper, we present measures of land and labor productivity for a group of ninety-eight developed and developing countries using an entirely new data set with annual observations spanning the past three decades. The substantial cross-country and intertemporal variation in productivity in our sample is linked to both natural and economic factors. We extend previous work by dealing with multiple sources of systematic measurement error in conventional agricultural inputs. The mix of conventional inputs, indicators of quality of agricultural inputs, and the amount of publicly provided infrastructure are all significant in explaining observed cross-sectional differences in productivity patterns. Copyright 1997, Oxford University Press.

  13. Eberhardt, M. and Vollrath, D. (2018) “The Effect of Agricultural Technology on the Speed of Development,” World Development, 109, pp. 483–496.   Paper
    • Abstract

      We examine heterogeneity in the elasticity of agricultural output with respect to labor across countries and the effect this has on structural change and development. Employing panel data from 128 countries over a forty year period we find distinct heterogeneity in the elasticity of agricultural output with respect to labor, which we refer to as heterogeneity in agricultural technology. To do this we employ panel time-series methods that explicitly allow for parameter heterogeneity, while also controlling for unobserved shocks to productivity using common factors. We find that the elasticity is lowest in countries with temperate and/or cold climate regions (around 0.15), but much higher in countries including tropical or highland regions (around 0.55). The elasticities are not correlated with development levels or with stocks of other agricultural inputs, but reflect differences in agricultural technology in different climate zones. We then use a standard model of a two-sector economy with non-homothetic preferences for agricultural goods to show that this agricultural elasticity with respect to labor determines the speed of structural change following changes in agricultural productivity or population. Calibration shows shifts in labor allocations and welfare will be 2–3 times larger in temperate regions than in tropical or highland regions for any given shock when economies are closed. In open economies the welfare effects are similar across climate zones, but the shift in labor allocations in response to world price or productivity shocks are 2-3 times larger in tropical or highland regions.

  14. Eberhardt, M. and Vollrath, D. (2016) “The Role of Crop Type in Cross-Country Income Differences.” C.E.P.R. Discussion Papers.   Paper
    • Abstract

      The speed at which structural change from agriculture to non-agriculture takes place is a key determinant of successful aggregate growth in developing countries. We show that crop-level differences in agricultural technology – the coefficients on factor inputs in the production function – account for a substantial portion of cross-country differences in agricultural labor productivity, agricultural labor share, and per capita income. Using a sample of 100 countries we document technology differences across major crop types and illustrate their quantitative implications for structural change and development. Counterfactually eliminating technology heterogeneity in our sample results in 25% lower variance in log income per capita, and 60% higher median per capita income.

  15. Evenson, R. E. and Gollin, D. (2003) “Assessing the Impact of the Green Revolution, 1960 to 2000,” Science, 300(5620), pp. 758–762.
  16. FAO (2007) “FAOSTAT.” Available at:
  17. Fouka, V. and Schlaepfer, A. (2015) “Agricultural Labor Intensity and the Origins of Work Ehtics.”
  18. Fuglie, K. (2010) “Total factor productivity in the global agricultural economy: Evidence from FAO Data,” in Julian Alston, P. P., Bruce Babcock (ed.) The shifting patterns of agricultural production and productivity worldwide. Ames, Iowa: Midwest Agribusiness Trade and Research Information Center, pp. 63–95.
  19. Geertz, C. (1963) Agricultural Involution: The Processes of Ecological Change in Indonesia. Berkeley, CA: University of California Press.
  20. Gollin, D. (2010) “Agricultural Productivity and Economic Growth,” in Pingali, P. and Evenson, R. (eds.) Handbook of Agricultural Economics. Elsevier, pp. 3825–3866.
  21. Gollin, D., Lagakos, D. and Waugh, M. (2014) “The Agricultural Productivity Gap,” Quarterly Journal of Economics, 129(2).
    • Abstract

      According to national accounts data for developing countries, value added per worker is on average four times higher in the non-agriculture sector than in agriculture. Taken at face value this “agricultural productivity gap” suggests that labor is greatly misallocated across sectors in the developing world. In this paper we draw on new micro evidence to ask to what extent the gap is still present when better measures of inputs and outputs are taken into consideration. We find that even after considering sector differences in hours worked and human capital per worker, urban-rural cost-of-living differences, and alternative measures of sector income from household survey data, a puzzlingly large agricultural productivity gap remains.

  22. Gollin, D., Parente, S. and Rogerson, R. (2007) “The Food Problem and the Evolution of International Income Levels,” Journal of Monetary Economics, 54, pp. 1230–1255.
    • Abstract

      This paper examines the effect of agricultural development on a country’s overall development and growth experience. In most poor countries, large fractions of land, labor, and other productive resources are devoted to producing food for subsistence needs. This "food problem" can delay a country’s industrial development for a long period of time, causing its per capita income to fall far behind the world leader. Once industrialization begins, this trend is reversed. The extent to which a country catches up to the leader depends primarily on factors that affect productivity in non- agricultural activities: agricultural productivity is thus largely irrelevant in the very long run. But in the short run, a country that experiences large improvements in agricultural productivity (due to, say, a Green Revolution) will experience a rapid increase in its income relative to the leaders.

  23. Gollin, D., Parente, S. and Rogerson, R. (2004) “Farm Work, Home Work, and International Productivity Differences,” Review of Economic Dynamics, 7(4), pp. 827–850.
    • Abstract

      Agriculture’s share of economic activity is known to vary inversely with a country’s level of development. This paper examines whether extensions of the neoclassical growth model can account for some important sectoral patterns observed in a current cross-section of countries and in the time series data for currently rich countries. We find that a straightforward agricultural extension of the neoclassical growth model fails to account for important aspects of the cross-country data. We then introduce a version of the growth model with home production, and we show that this model performs much better.

  24. Grigg, D. (1974) The Agricultural Systems of the World. Cambridge University Press.
  25. Gutierrez, L. and Gutierrez, M. M. (2003) “International R&D spillovers and productivity growth in the agricultural sector. A panel cointegration approach,” European Review of Agricultural Economics, 30(3), pp. 281–303. Available at: Link.
    • Abstract

      We use the new growth theory framework and panel cointegration techniques to analyse the effect of international agricultural technological spillovers on total factor productivity growth for a sample of 47 countries during the period 1970–1992. The analysis shows that total factor productivity is strongly influenced by domestic as well as foreign public research and development (R&D) spending in the agricultural sector. Geographical factors matter, in that countries located in temperate zones benefit from technological spillovers more than countries located in tropical zones. We find that the rate of return to agricultural R&D spending is higher in tropical countries. This could justify new support and an even greater investment in agricultural R&D for these countries. Copyright 2003, Oxford University Press.

  26. Hayami, Y. and Ruttan, V. W. (1985) Agricultural Development: An International Perspective. Baltimore: Johns Hopkins University Press.
  27. Hayami, Y. and Ruttan, V. W. (1970) “Agricultural Productivity Differences among Countries,” American Economic Review, 60(5), pp. 895–911.
  28. Hayami, Y., Ruttan, V. W. and Southworth, H. M. (1979) Agricultural Growth in Japan, Taiwan, Korea, and the Philippines. Honolulu, HI: East-West Center.
  29. Hayashi, F. and Prescott, E. C. (2008) “The Depressing Effect of Agricultural Institutions on the Prewar Japanese Economy,” Journal of Political Economy, 116(4), pp. 573–632.
    • Abstract

      Why didn’t the Japanese miracle take place before World War II? The culprit we identify is a barrier that kept prewar agricultural employment constant. Using a standard neoclassical two-sector growth model, we show that the barrier-induced sectoral distortion and an ensuring lack of capital accumulation account well for the depressed output level. Without the barrier, Japan’s prewar GNP per worker would have been at least about a half of that of the United States, not about a third as in the data. The labor barrier existed because, we argue, the prewar patriarchy forced the son designated as heir to stay in agriculture.

  30. Henderson, J. V. et al. (2016) The Global Spatial Distribution of Economic Activity: Nature, History, and the Role of Trade. NBER Working Papers 22145. National Bureau of Economic Research, Inc. Available at: Link.
    • Abstract

      We study the distribution of economic activity, as proxied by lights at night, across 250,000 grid cells of average area 560 square kilometers. We first document that nearly half of the variation can be explained by a parsimonious set of physical geography attributes. A full set of country indicators only explains a further 10%. When we divide geographic characteristics into two groups, those primarily important for agriculture and those primarily important for trade, we find that the agriculture variables have relatively more explanatory power in countries that developed early and the trade variables have relatively more in countries that developed late, despite the fact that the latter group of countries are far more dependent on agriculture today. We explain this apparent puzzle in a model in which two technological shocks occur, one increasing agricultural productivity and the other decreasing transportation costs, and in which agglomeration economies lead to persistence in urban locations. In countries that developed early, structural transformation due to rising agricultural productivity began at a time when transport costs were still relatively high, so urban agglomerations were localized in agricultural regions. When transport costs fell, these local agglomerations persisted. In late developing countries, transport costs fell well before structural transformation. To exploit urban scale economies, manufacturing agglomerated in relatively few, often coastal, locations. With structural transformation, these initial coastal locations grew, without formation of more cities in the agricultural interior.

  31. Herrendorf, B., Rogerson, R. and Valentinyi, Á. (2014) “Growth and Structural Transformation,” in Handbook of Economic Growth. Elsevier (Handbook of Economic Growth), pp. 855–941. Available at: Link.
    • Abstract

      Structural transformation refers to the reallocation of economic activity across the broad sectors agriculture, manufacturing, and services. This review article synthesizes and evaluates recent advances in the research on structural transformation. We begin by presenting the stylized facts of structural transformation across time and space. We then develop a multi-sector extension of the one-sector growth model that encompasses the main existing theories of structural transformation. We argue that this multi-sector model serves as a natural benchmark to study structural transformation and that it is able to account for many salient features of structural transformation. We also argue that this multi-sector model delivers new and sharper insights for understanding economic development, regional income convergence, aggregate productivity trends, hours worked, business cycles, wage inequality, and greenhouse gas emissions. We conclude by suggesting several directions for future research on structural transformation.

  32. Hicks, J. H. et al. (2017) Reevaluating Agricultural Productivity Gaps with Longitudinal Microdata. Working Paper 23253. National Bureau of Economic Research. doi: 10.3386/w23253.
    • Abstract

      Recent research has pointed to large gaps in labor productivity between the agricultural and non-agricultural sectors in low-income countries, as well as between workers in rural and urban areas. Most estimates are based on national accounts or repeated cross-sections of micro-survey data, and as a result typically struggle to account for individual selection between sectors. This paper contributes to this literature using long-run individual-level panel data from two low-income countries (Indonesia and Kenya). Accounting for individual fixed effects leads to much smaller estimated productivity gains from moving into the non-agricultural sector (or urban areas), reducing estimated gaps by over 80 percent. Per capita consumption gaps between non-agricultural and agricultural sectors, as well as between urban and rural areas, are also close to zero once individual fixed effects are included. Estimated productivity gaps do not emerge up to five years after a move between sectors, nor are they larger in big cities. We evaluate whether these findings imply a re-assessment of the current conventional wisdom regarding sectoral gaps, discuss how to reconcile them with existing cross-sectional estimates, and consider implications for the desirability of sectoral reallocation of labor.

  33. Johnson, T. R. and Vollrath, D. (2017) “How Tight are Malthusian Constraints?”   Paper
    • Abstract

      We provide a methodology to estimate the elasticity of agricultural output with respect to land - the Malthusian constraint - using variation in rural densities across different locations. We use district-level data from around the globe on rural densities and inherent agricultural productivity to estimate the elasticity for various sub-samples. We find the elasticity is highest in areas that are suitable for temperate crops such as wheat or rye, and loosest in areas suitable for (sub)-tropical crops such as cassava or rice. We show theoretically that a higher elasticity results in greater sensitivity of non-agricultural employment and real income per capita to shocks in population size and productivity, and confirm this with evidence from the post-war mortality transition.

  34. Johnston, B. F. and Kilby, P. (1975) Agriculture and Structural Transformation: Economic Strategies in Late-Developing Countries. New York, NY: Oxford University Press.
  35. Johnston, B. F. and Mellor, J. W. (1961) “The Role of Agriculture in Economic Development,” American Economic Review, 51(4), pp. 566–93.
  36. Jorgenson, D. and Gollop, F. (1992) “Productivity Growth in U.S. Agriculture: A Postwar Perspective,” American Journal of Agricultural Economics, 74(3), pp. 745–50.
  37. Kogel, T. and Prskawetz, A. (2001) “Agricultural Productivity Growth and Escape from the Malthusian Trap,” Journal of Economic Growth, 6(4), pp. 337–57.
    • Abstract

      Industrialization allowed the industrialized world of today to escape from the Malthusian regime characterized by low economic and population growth and to enter the post-Malthusian regime of high economic and population growth. To explain the transition between these regimes, we construct a growth model with two consumption goods (an agricultural and a manufacturing good), endogenous fertility, and endogenous technological progress in the manufacturing sector. We show that with an exogenous increase in the growth of agricultural productivity our model is able to replicate stylized facts of the British industrial revolution. The paper concludes by illustrating that our proposed model framework can be extended to include the demographic transition, i.e., a regime in which economic growth is associated with falling fertility. Copyright 2001 by Kluwer Academic Publishers

  38. Martin, W. and Mitra, D. (2001) “Productivity Growth and Convergence in Agriculture versus Manufacturing,” Economic Development and Cultural Change, 49(2), pp. 403–22. Available at: Link.
    • Abstract

      No abstract is available for this item.

  39. Matsuyama, K. (1992) “Agricultural Productivity, Comparative Advantage, and Economic Growth,” Journal of Economic Theory, 58(2), pp. 317–334.
    • Abstract

      The role of agricultural productivity in economic development is addressed in a two-sector model of endogenous growth in which a) preferences are non-homothetic and the income elasticity of demand for the agricultural good is less than unitary, and b) the engine of growth is learning-by-doing in the manufacturing sector. For the closed economy case, the model predicts a positive link between agricultural productivity and economic growth and thus provides a formalization of the conventional wisdom, which asserts that agricultural revolution is a precondition for industrial revolution. For the open economy case, however, the model predicts a negative link; that is, an economy with a relatively unproductive agricultural sector experiences faster and accelerating growth. The result suggests that the openness of an economy should be an important factor when planning development strategy and predicting growth performance.

  40. Mellor, J. W. (1995) “Introduction,” in Mellor, J. W. (ed.) Agriculture on the Road to Industrialization. Baltimore: Johns Hopkins University Press.
  41. Mundlak, Y. (2000) Agriculture and Economic Growth: Theory and Measurement. Cambridge, MA: Harvard University Press.
  42. Ramankutty, N. et al. (2002) “The global distribution of cultivable lands: current patterns and sensitivity to possible climate change,” Global Ecology and Biogeography. Blackwell Science Ltd, 11(5), pp. 377–392. doi: 10.1046/j.1466-822x.2002.00294.x.
  43. Rao, P. D. S. (1993) Intercountry comparisons of agricultural output and productivity. Rome: FAO Economic and Social Development Paper.
  44. Restuccia, D. and Santaeulalia-Llopis, R. (2015) Land Misallocation and Productivity. Working Papers tecipa-533. University of Toronto, Department of Economics. Available at: Link.
    • Abstract

      Using detailed household-farm level data from Malawi, we measure real farm total factor productivity (TFP) controlling for a wide array of factor inputs, land quality, and transitory shocks. The distribution of farm TFP has substantial dispersion but factor inputs are roughly evenly spread among farmers. The striking fact is that operated land size and capital are essentially unrelated to farm TFP implying a strong negative effect on agricultural productivity. A reallocation of factors to their efficient use among existing farmers would increase agricultural productivity by a factor of 3.6-fold. We relate factor misallocation to severely restricted land markets as the vast majority of land is without a title and a very small portion of operated land is rented in. The gains from reallocation are 2.6 times larger for farms with no marketed land than for farms that operate marketed land.

  45. Restuccia, D., Yang, D. and Zhu, X. (2008) “Agriculture and Aggregate Productivity,” Journal of Monetary Economics, 55(2), pp. 234–250.
    • Abstract

      A decomposition of aggregate labor productivity based on internationally comparable data reveals that a high share of employment and low labor productivity in agriculture are mainly responsible for low aggregate productivity in poor countries. Using a two-sector general-equilibrium model, we show that differences in economy-wide productivity, barriers to modern intermediate inputs in agriculture, and barriers in the labor market generate large cross-country differences in the share of employment and labor productivity in agriculture. The model implies a factor difference of 10.8 in aggregate labor productivity between the richest and the poorest 5% of the countries in the world, leaving the unexplained factor at 3.2. Overall, this two-sector framework performs much better than a single-sector growth model in explaining observed differences in international productivity.

  46. Ruthenberg, H. (1976) Farming Systems in the Tropics. Oxford, UK: Clarendon Press.
  47. Ruttan, V. W. (2002) “Productivity Growth in World Agriculture: Sources and Constraints,” Journal of Economic Perspectives, 16(4), pp. 161–184. Available at: Link.
    • Abstract

      During the last half-century, advances in crop production came from expansion in areas irrigated from more intensive application of fertilizers and crop protection chemicals, and from crop varieties that were more responsive to technical inputs and management. Advances in animal production came from genetic improvements and advances in animal nutrition. Differences among developed and developing countries in output per hectare and per worker have widened. If these gaps are to be narrowed agricultural research capacity in developing countries will have to be substantially strengthened.

  48. Schultz, T. W. (1953) The Economic Organization of Agriculture. New York, NY: McGraw-Hill.
  49. Sposi, M. J. (2015) Evolving comparative advantage, sectoral linkages, and structural change. Globalization and Monetary Policy Institute Working Paper 231. Federal Reserve Bank of Dallas. Available at: Link.
    • Abstract

      I quantitatively examine the effects of location-and sector-specific productivity growth on structural change across countries from 1970-2011. The results shed new light on the “hump shape" in industry’s share in GDP across levels of development. There are two key features. First, otherwise identical changes in the composition of final demand translate differently into changes in the composition of value added because of systematic differences in sectoral linkages. Second, the mapping between sector-specific productivity and the composition of final demand systematically differs because of the relative importance of two components within final demand: final domestic expenditures and net exports.

  50. Temple, J. (2005) “Dual Economy Models: A Primer for Growth Economists,” The Manchester School, 73(4), pp. 435–478.
    • Abstract

      This paper argues that dual economy models deserve a central place in the analysis of growth in developing countries. The paper shows how these models can be used to analyse the output losses associated with factor misallocation, aggregate growth in the presence of factor market distortions, international differences in sectoral productivity and the potential role of increasing returns to scale. Above all, small-scale general equilibrium models can be used to investigate the interactions between growth and labour markets, to shed new light on the origins of pro-poor and labour-intensive growth, and to explore the role of the informal sector.

  51. Temple, J. and Woessmann, L. (11ADAD) “Dualism and Cross-Country Growth Regressions,” Journal of Economic Growth, 3, pp. 187–228.
    • Abstract

      This paper develops empirical growth models suitable for dual economies, and studies the relationship between structural change and economic growth. Changes in the structure of employment will raise aggregate productivity when the marginal product of labor varies across sectors. The models in the paper incorporate this effect in a more flexible way than previous work. Estimates of the models imply sizeable marginal product differentials, and indicate that the reallocation of labor makes a significant contribution to the international variation in productivity growth.

  52. Timmer, P. (2002) “Agriculture and economic development,” in Gardner, B. L. and Rausser, G. C. (eds.) Handbook of Agricultural Economics. Elsevier, pp. 1487–1546.
  53. Tombe, T. (2015) “The Missing Food Problem: Trade, Agriculture, and International Productivity Differences,” American Economic Journal: Macroeconomics, 7(3), pp. 226–58. Available at: Link.
    • Abstract

      Agriculture in poor countries has low productivity, high employment, and negligible trade flows relative to other sectors. These facts motivate a multisector, open-economy view of international productivity differences. With a quantitative multicountry model featuring nonhomothetic preferences, multiple interrelated sectors, distorted labor markets, and costly trade, I find: trade amplifies the negative effect of labor market distortions; trade costs—large for poor countries, especially in agriculture—significantly contribute to international productivity differences; and explicitly modeling agriculture reveals additional channels through which poor countries may gain from trade. (JEL F41, J24, J43, O13, O19, Q11, Q17)

  54. Uy, T., Yi, K.-M. and Zhang, J. (2013) “Structural change in an open economy,” Journal of Monetary Economics, 60(6), pp. 667–682. Available at: Link.
    • Abstract

      We study the importance of international trade in structural change. Our framework has both productivity and trade cost shocks, and allows for non-unitary income and substitution elasticities. We calibrate our model to investigate South Korea’s structural change between 1971 and 2005. We find that the shock processes, propagated through the model’s two main transmission mechanisms, non-homothetic preferences and the open economy, explain virtually all of the evolution of agriculture and services labor shares, and the rising part of the hump-shape in manufacturing. Counterfactual exercises show that the role of the open economy is quantitatively important for explaining South Korea’s structural change.

  55. Vollrath, D. (2013) “Measuring Aggregate Agricultural Labor Effort in Dual Economies,” Eurasian Economic Review, 3(1), pp. 39–58. Available at: Link.   Paper
    • Abstract

      Wide differences in labor productivity are observed between agriculture and industry in most developing countries. Research suggests that these differences - often denoted a “dual economy” effect — can explain a significant portion of low output per capita levels in these countries. A central input to the labor productivity calculation is the aggregate labor effort in the agricultural sector. Using findings from the Rural Income Generating Activity (RIGA) database, I reconsider the measure of labor productivity in agriculture and industry. I use several methods to extract information on labor effort and human capital from the household data in RIGA, and this is used to estimate the aggregate labor effort in the agricultural sector. With these new estimates, dual economy effects are found to be less severe for most of the RIGA countries. Using these estimates to adjust a wider sample of country-level data shows that the share of variation in output per capita explained by dual economy effects is around half of previous estimates. Copyright Eurasia Business and Economics Society 2013

  56. Vollrath, D. (2012) “Land tenure, population, and long-run growth,” Journal of Population Economics, 25(3), pp. 833–852. Available at: Link.   Paper
    • Abstract

      This paper brings together the development literature on land tenure with current research on population and long-run growth. Land-owners make a decision between fixed-rent, fixed-wage, and share-cropping contracts to hire tenants to operate their land. The choice of tenure contract affects the share of output going to tenants, and within a simple unified growth model this affects the relative price of food and therefore fertility. Fixed-wage contracts elicit the lowest fertility rate and fixed-rent contracts the highest, with share-cropping as an intermediate case. The implications of this for long-run growth depend on the assumptions one makes about scale effects in the aggregate economy. With increasing returns to scale, as in several models of innovation, fixed-rent contracts imply higher growth through a market size effect. Without such increasing returns, though, fixed-rent contracts lower output per capita through a depressing effect on accumulation.

  57. Vollrath, D. (2009) “How important are dual economy effects for aggregate productivity?,” Journal of Development Economics, 88(2), pp. 325–334.   Paper
    • Abstract

      This paper brings together development accounting techniques and the dual economy model to address the role that factor markets have in creating variation in aggregate total factor productivity (TFP). Development accounting research has shown that much of the variation in income across countries can be attributed to differences in TFP. The dual economy model suggests that aggregate productivity is depressed by having too many factors allocated to low productivity work in agriculture. Data show large differences in marginal products of similar factors within many developing countries, offering prima facie evidence of this misallocation. Using a simple two-sector decomposition of the economy, this article estimates the role of these misallocations in accounting for the cross-country income distribution. A key contribution is the ability to bring sector specific data on human and physical capital stocks to the analysis. Variation across countries in the degree of misallocation is shown to account for 30 — 40% of the variation in income per capita, and up to 80% of the variation in aggregate TFP.

  58. Vollrath, D. (2009) “The dual economy in long-run development,” Journal of Economic Growth, 14(4), pp. 287–312.   Paper
    • Abstract

      This paper provides a dynamic model of the dual economy in which differences in productivity across sectors arise endogenously. Rather than relying on exogenous price distortions, duality arises because of differences between sectors in the separability of their fertility and labor decisions. The model demonstrates how a dual economy will originate, persist, and eventually disappear within a unified growth framework. It is also shown that agricultural productivity growth will exacerbate the inefficiencies of a dual economy and slow down long-run growth.

  59. Vollrath, D. (2007) “Land Distribution and International Agricultural Productivity,” American Journal of Agricultural Economics, 89(1), pp. 202–216. Available at: Link.   Paper   Data
    • Abstract

      The unequal distribution of agricultural land is often cited as a source of inefficiency in agriculture. Previous cross-country studies of agricultural productivity differences, though, have not considered land inequality. This article addresses this issue by using cross-country data on inequality in operational holdings of agricultural land from Deininger and Squire (1998) . In an estimation of an agricultural production function, the Gini coefficient for land holdings is found to have a significant negative relationship with productivity. This is consistent with the existence of heterogeneity in productivity by farm size within countries. A one standard deviation drop in the Gini coefficient implies an increase in productivity of 8.5%. Copyright 2007, Oxford University Press.

  60. Weil, D. N. and Wilde, J. (2009) “How Relevant Is Malthus for Economic Development Today?,” American Economic Review Papers and Proceedings, 99(2), pp. 255–60. Available at: Link.
    • Abstract

      No abstract is available for this item.

  61. Wiebe, K. et al. (2003) “Resource Quality and Agricultural Productivity: A Multi-Country Comparison,” in Wiebe, K. (ed.) Land Quality, Agricultural Productivity, and Food Security. Northhampton, MA: Edward Elgar Publishing.
  62. Wilde, J. (2012) How substitutable are fixed factors in production? evidence from pre-industrial England. MPRA Paper 39278. University Library of Munich, Germany. Available at: Link.
    • Abstract

      Whether fixed factors such as land constrain per-capita income growth depends crucially on two variables: the substitutability of fixed factors in production, and the extent to which innovation is biased towards land-saving technologies. This paper attempts to quantify both. Using the timing of plague epidemics as an instrument for labor supply, I estimate the elasticity of substitution between fixed and non-fixed factors in pre-industrial England to be significantly less than one. In addition, I find evidence that denser populations – and hence higher land scarcity – induced innovation towards land-saving technologies.

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