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on Economic Growth |
| By: | Miguel D. Ramirez (Department of Economics, Trinity College) |
| Keywords: | Convergence; Economic Growth;Foreign Direct Investment (FDI);Net Reverse Flows; and Remittances Capital. |
| JEL: | O40 O50 |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:tri:wpaper:2503 |
| By: | Chilosi, David; Lecce, Giampaolo; Wallis, Patrick |
| Abstract: | Adam Smith’s claim that the division of labour is one of the major engines of economic growth is a foundational concept in economics. Despite this, we lack measures of the scale and growth of Smithian specialisation over the long run. This paper introduces a novel method based on job titles to measure specialisation. We apply this method to document patterns of Smithian specialisation in early modern Britain. National trends in specialisation were closely associated with economic growth. By 1800, the division of labour was over two and a half times as advanced as in the early sixteenth century, with particularly marked changes within English manufacturing, especially in the mechanical subsector, and, to a lesser extent, services. Specialisation was far less advanced in Wales and Scotland. We study several possible explanations for this change with an IV panel analysis. We find that this significant increase in the division of labour was mostly driven by the growth of the domestic market, in line with Adam Smith’s predictions. Intensive specialisation was concentrated in Middlesex and was helped by a supply factor, Marshallian externalities. Finally, we explore the connection between Smithian Growth and the Industrial Revolution. We find that early specialisation did not lead to later industrial success. Like Adam Smith himself, Smithian specialisation did not predict the Industrial Revolution. |
| Keywords: | economic growth; division of labour; specialisation; tasks; Adam Smith; Britain; productivity; industrial revolution; market potential |
| JEL: | N13 O47 J21 |
| Date: | 2025–07–04 |
| URL: | https://d.repec.org/n?u=RePEc:ehl:lserod:128849 |
| By: | Enriqueta Camps |
| Abstract: | In this paper we account for the early demographic transition of Catalonia and impact on population ageing and productivity levels. Female vocational training and female real wage increase during the first third of the 20th century, high female participation levels in the workforce and the influence of libertarian practices of fertility control seem to be the main reasons of low fertility levels (below replacement) during the first third of the 20th century. The sustained low fertility trend has resulted in a high dependency ratio during the first decades of the 21st century caused in turn by longevity The high proportion of dependents on population has as a result the small adequacy of GDP per capita to measure the evolution of productivity levels during the 21st century. |
| Keywords: | economic growth, fertility, Gender Gap, Longevity |
| JEL: | A11 A12 I15 J11 N3 |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:bge:wpaper:1498 |
| By: | Paker, Meredith; Stephenson, Judy; Wallis, Patrick |
| Abstract: | Understanding long-run economic growth requires reliable historical data, yet the vast majority of long-run economic time series are drawn from incomplete records with significant temporal and geographic gaps. Conventional solutions to these gaps rely on linear regressions that risk bias or overfitting when data are scarce. We introduce “past predictive modeling, ” a framework that leverages machine learning and out-of-sample predictive modeling techniques to reconstruct representative historical time series from scarce data. Validating our approach using nominal wage data from England, 1300-1900, we show that this new method leads to more accurate and generalizable estimates, with bootstrapped standard errors 72% lower than benchmark linear regressions. Beyond just bettering accuracy, these improved wage estimates for England yield new insights into the impact of the Black Death on inequality, the economic geography of pre-industrial growth, and productivity over the long-run. |
| Keywords: | machine learning; predictive modeling; wages; black death; industrial revolution |
| JEL: | J31 C53 N33 N13 N63 |
| Date: | 2025–06–13 |
| URL: | https://d.repec.org/n?u=RePEc:ehl:lserod:128852 |
| By: | Sarun Kamolthip |
| Abstract: | This paper examines weather fluctuations’ effects on subnational economic growth in Thailand (1982-2022). The identification strategy employs fixed-effects panel regressions on plausibly exogenous yearto- year weather variations within provinces, isolating local temperature’s causal effects on economic outcomes. Results reveal a statistically significant inverted-U relationship between temperature and per capita GPP growth. These adverse effects operate as persistent growth impacts, which appear more prominent in lower-income provinces, though formal statistical differences in response functions across income levels were not observed. Agriculture is highly vulnerable, while industrial and service sectors show no significant direct temperature impacts in this analysis. Integrating these estimates with RCP4.5 and RCP8.5 climate projections, future damages are widespread and severe. Without bias-correction, climate change is projected to reduce per capita output for 63-86% of Thai population, with median GDP per capita impacts from -4% to +56% (RCP4.5) and -52% to -15% (RCP8.5). However, accounting for climate model biases, even without lagged dynamics, median losses increase to 57-63% (RCP4.5) and 80-86% (RCP8.5). With lagged temperature effects, projections show substantially higher losses, leading to near-total output loss by 2090 with negligible positive likelihood. These findings highlight critical masked within-country disparities, as initial benefits in colder regions are reduced. This projection sensitivity underscores significant caveats in quantifying future economic burdens. Policy implications stress the imperative for decentralized, tailored responses leveraging granular data for highly vulnerable provinces. The persistent growth effects necessitate urgent proactive adaptation strategies, including investments in climate-resilient infrastructure, particularly for vulnerable agricultural regions. |
| Keywords: | Weather fluctuations; Economic growth; Thailand |
| JEL: | O44 Q51 Q53 R11 |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:pui:dpaper:235 |
| By: | Martin Neil Baily; David M. Byrne; Aidan T. Kane; Paul E. Soto |
| Abstract: | With the advent of generative AI (genAI), the potential scope of artificial intelligence has increased dramatically, but the future effect of genAI on productivity remains uncertain. The effect of the technology on the innovation process is a crucial open question. Some inventions, such as the light bulb, temporarily raise productivity growth as adoption spreads, but the effect fades when the market is saturated; that is, the level of output per hour is permanently higher but the growth rate is not. In contrast, two types of technologies stand out as having longer-lived effects on productivity growth. First, there are technologies known as general-purpose technologies (GPTs). GPTs (1) are widely adopted, (2) spur abundant knock-on innovations (new goods and services, process efficiencies, and business reorganization), and (3) show continual improvement, refreshing this innovation cycle; the electric dynamo is an example. Second, there are inventions of methods of invention (IMIs). IMIs increase the efficiency of the research and development process via improvements to observation, analysis, communication, or organization; the compound microscope is an example. We show that GenAI has the characteristics of both a GPT and an IMI—an encouraging sign that genAI will raise the level of productivity. Even so, genAI’s contribution to productivity growth will depend on the speed with which that level is attained and, historically, the process for integrating revolutionary technologies into the economy is a protracted one. |
| Keywords: | Artificial Intelligence; Machine Learning; Productivity; Technological Growth |
| JEL: | C45 O31 O33 O40 |
| Date: | 2025–07–17 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedgfe:2025-53 |
| By: | Xu, Tao Louie |
| Abstract: | This research examines the limits of neoliberal economic growth in poverty mitigation and spotlights the role of social policy in mediating social and economic objectives. Based on double movement, fictitious commodities, and social embeddedness of plural markets, with a Polanyian perspective on Bolivia’s Water Wars case, it disenchants the marketism legacy in our contemporary neoliberalism materialised in growth-poverty dynamics. The findings indicate that economic growth is accessible but insufficient to mitigate multi-layered poverty around societal demands and structures. The research argues for a holistic, contextualised anti-poverty framework with protective social policy and mediated socio-economic development embedded in social relations during the neoliberal era. |
| Keywords: | neoliberalism; economic growth; poverty; fictitious commodities; double movement; social policy |
| JEL: | B5 B52 O1 O2 Z1 Z13 Z18 |
| Date: | 2024–04–13 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:122658 |
| By: | José Pedro Pontes |
| Abstract: | We model the expansion of (higher) education in an economy composed by regions that only di??er in population density. The schooling process takes place sequentially across regions in descending order of demographic density and it implies a substitution of modern industrial technologies for traditional land-based ones. Under the crucial assumption that young people may travel to school within the region where they live, but not across regions, the model explains why both the literacy rate and per capita income increase, albeit at a decreasing rate. Furthermore, is allows us to understand why the average students’ commuting distance tends to rise despite the geographical decentralization of the educational system. |
| Keywords: | Education Spread, Population Density, Spatial Monopolistic Competition, Wage Premium of Education, Modern versus Traditional Technology. |
| JEL: | O18 R11 I20 |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:ise:remwps:wp03832025 |