N.Moneke - Additional Flashcards
Thunen (1826)
The Isolated State: Monocentric cities
Single market surrounded by farmland, transport costs related only to distance and volume shipped
Farmers produce highest market value crops, with transport costs being determining factor. Location rent decreases with distance –> Different crops possess different rent gradients, perishables are steeper
Asher et al (2020)
New dataset study on rural economies in India, 1990-2013.
Urban shocks led to…
- Increased aggregate activity and estlabishment size in villages 20km and further away
- Distant villages saw net population loss as people relocated closer
- Service sector gained employment share in nearby villages, manufacturing gained far away
Baum-Snow et al. (2018)
Impact of Chinese highway system on hinterland city growth. Averaging impact of such programs hides clear winners and losers.
- Output and population winners close by, those in periphery lose manufacturing and gain agriculture
- Average effects probably larger in hinterland than close in
- Investing in local transport infrastructure has opposite effect, emphasising divergence between hinterland and city –> Specialisation in agriculture
Michaels (2008)
US interstate highway system as a policy experiment used to identify effects of reduced trade barriers with less confounds.
Rurual counties also fell into better connectivity as an outcome not a goal. These regions saw a boost of 7-10% points per capita of trade related activities.
- Higher demand for skilled manufacturing workings
- Consistent with Heckscher-Ohlin model
- Trucking and retail sales driving growth
Hornbeck and Rotemberg (2019)
19th Century railroad expansion in US. County market access increased –> Manufacturing productivity, largely driven by marginally productive counties
Abebe et al (2018)
FDI into plants in Ethiopia manufacturing sector. Spillovers from FDI identified by TFP comparisons, 11% higher in treated districts - more plants open and also more employment.
Ellison et. al (2010) (AER)
Pairwise coagglomeration indices for US manufacturing industries in goods, labour and ideas.
Support for Marshallian theories of agglomeration. Proximity to reduce the costs of moving
- Goods
- People
- Ideas
Faggio et al (2017)
Not all sectors agglomerate in the same way, here UK establishment level data on coagglomeration shows heterogeneity across industries.
Nagy (2020) [Second Paper]
Assembles a novel dataset combining population, trade routes and agricultural productivity at a high spatial resolution.
West expansion: had lots of land, farmers came, it grew into a city, expanded to hinterland attaracting more farmers
Railroads: responsible for 8.2% of urban population and 27% of real GDP in 19th century.
Fried and Lagakos (2017)
Ethiopian panel during electricity expansion, raised irrigation rates, agricultural yields and non-agricultural business
- Electrification led to increases in agricultural and non-agricultural production in rural Ethiopia.
- Electrified villages also saw decreases in out-migration and increases in-migration.
- These findings are consistent with a multi-region model of electrification and regional migration.
Moneke (2020)
Bundled infrastructure investments: roads+ electricity
Spatial GE model to understand combined infrastructure investment impact.
Ethiopian expansion over last two decades:
road alone –> services employment increases, only 2%
road + elec. –> reversals in the manufacturing employment shares but 11% increase.
Bryan et al. (2014)
Randomly assign $8.50 incentive in rural Bangladesh to temporarily out migrate during lean harvest season where hunger is usually an issue.
Incentive induces 22% of households to send a seasonal migrant. Treated households..
- 8-10% point more likely to re-migrate 1 and 3 years after removed incentive
Might be explained by a model where migration is risky, but hard to match in a model where agents can save up to migrate.
Bryan and Morten (2019)
Indonesian reduction in internal migration barriers.
22% increase in labour productivity from removing all barriers, 7.1% if reduced to match US benchmark. Significant heterogeneity of the extent of winners, upto 104% increase in average earnings for complete removal.
- Personal productivity improvements - “sorting”
- More productive locations accessible - “agglomeration”
Desmet and Rossi-Hanberg (2014) (AER)
Theory of spatial development:
- Firms choose each period how much to innovate. - - —- Firms trade subject to transport costs and technology diffuses spatially.
Model can explain the reduction in the manufacturing employment share, the increased spatial concentration of services, the growth in service productivity starting in the mid-1990s, the rise in the dispersion of land rents in the same period, as well as several other spatial and temporal patterns.
Eckert and Peters (2018)
US data 1880 - 2000: spatial reallocation across labour markets does not explain aggregate decline in agricultural employment
Population flows were not strongly correlated with agricultural specialisation.
Migration is good for per capita income, 15% lower in counterfactual of no migration.
Migration evens out spatial welfare inequality.
Michaels et al. (2012) (QJE)
Rural and urban US areas 1880 - 2000.
Positive correlation between initial pop. density and subsequent pop growth for intermediate densitites –> increases dispersion of density distribution
Pop. growth pattern due to agr. initial share and structural transformation that shifts employment away from agriculture.
Krugman (1991) (JPE)
Model giving endogenous outcome of industrialized core and agricultural periphery. (Agriculture)
Manufacturing firms locate where demand is to minimuse transport cost, core-periphery depends on transport costs, economies of scale and manufacturing intensity.
Model:
- 2 regions, cobb douglas utility, 2 factors of production in each region
- CRS agriculture and IRS manufacturing
- Monopolistic competition from Dixit Stiglitz (1977)
- Workers move between regions
- Iceberg transport costs for manufacturing, zero cost for agr.
Low transport costs –> Extreme agglomeration
Helpman (1998)
Model: (Housing –> Traded in a region not between regions)
- 2 regions
- Live and buy in one region: local produce or import brands
- Pop drives up housing costs, but also get more choice in wider variety of products
- Housing / land availability is main driving force behind dispersion
- Industrial sector supplies differentiated goods to both regions, traded between regions at cost
- Industrial sector main source for agglomeration: from brand specific economies of scale
- Occupation in eq* proportional to land availability…or unequal pop when same land
- Not freely traded across regions
Eq* equalisies standard of living but depends upon: intensity of preferences for differentiated goods, degree of substitution across brands and transport costs
Low transport costs –> Little agglomeration
Allen and Arkolakis (2014) (QJE)
Gravity structure of trade + labour mobility –> Geographic location accounts for at least 20% of spatial variation in US income.
Interstate highway increased welfare by 1.1-1.4% pts >> Costs!
Model:
- Continuum of locations, each producing differentiated product
- Iceberg trade costs
- Local amenities and variety consumption –> Utility
- Mobile workers, sole factor of production which is perfectly competitive
- CES preferences
Nests Helpman (1998) and Redding (2012) with certain parameter combinations.
