Week 4: geographical clustering of firms a nd concentration of industries Core periphery model

- Activity departs from even distribution and gets concentrated in a small number of places- Firms in industry don’t distribute evenly – instead they are concentrated Silicon Valley is like a ‘core’ for IT

Industrial Clustering Model- With high trade costs it is too costly to serve foreign markets no clustering - NEG highlights the incentive for firms to agglomerate following trade liberalization. - NEG and NTT: With trade cost and IRS, firm settle in location minimising transport cost related to inputs/ outputs. - Shows regional integration favours the larger members and could generate a core-periphery industrial pattern.- Krugman (1991): Agg. of activity due to labour mobility, input–output linkages between sectors or capital mobility - Agglomeration forces reduce transportation costs and hence cause firms to cluster

Agglomeration mechanism 1 - Thick labour market Firms locate near “thick” labor markets- large pool of suitably skilled workers means locating where there are already firms and workers in the industry- On supply side: workers locate in clusters as higher productivity leads to higher wages

Agglomeration mechanism 2 – Knowledge spillovers- Intellectual breakthrough cross hallway and street more easily than ocean and continent (Glaeser et al. 1992).- The importance of externalities for localisation is firstly discussed by Marshall (1890)

Ellison, G. and Glaeser, E., (1997) “Geographic concentration in U.S. manufacturing industries: A dartboard approach”- Create index measures amount of clustering beyond that which expect to find based on randomness alone.- Characterize extent of co-agglomeration among 2 digit industries, showing that there many instances of industries

apparently affecting each other. - Forces governing co-agglomeration: When there’s upstream-downstream linkage, co-agglomeration is greater- This leads them to conclude that much of the effects are localized, but that there are also spillovers. - Most industries are more concentrated than they would be if firms decided to place their plants randomly.- Follow-up paper: Duranton and Overman, 2002: localization take place at relatively small scale < 50 Km.

Ellison et al. (2010) “What Causes industry Agglomeration? Evidence from Coagglomeration Patterns- They employ approach to disentangle the effects of Marshallian agglomeration economies. - Find evidence that input-output dependencies, labor pooling and knowledge spillovers are all significant

determinants of agglomeration, but input-output dependencies appear to be the most important channel. Ellison and Glaeser (1999)

- Marshall (1920) : Firms group around limited natural resource, are chief cause for localization. - Unique geographical features do not explain the presence of all agglomerations. - Ellison and Glaeser (1999)  natural advantages present in only 50% of the clusters they studied.

Week 5: Cities, skills and growth - Wages differ over space + Wages are positively correlated with urban status (Urban wage premium) - Weber (1899) urban wage premium in Germany was over 50%- Wages and population are positively correlated Wages are 33% higher in big cities than outside.- If wage in cities high, why don’t people flock urban areas Disincentives: cost of living, pollution, crime- If wage in cities high, why so many firm locate thereProductivity compensate wages (reduced transport cost)

Research questions on urban wage premium - Does urban wage premium just reflect the fact that more able workers choose to live in cities?

o Ability bias: Question should be: why cities attract more able workers? - Is wage premium determined by a level effect or by a growth effect?

o Wage level effect: worker going to city immediately receive wage gain; those who leave get wage losso Wage growth effect: urban wage premium is determined by the fact that wages grow faster in cities

Why more able worker go to cities?- Flow of information more valuable to individuals with high human K AND More consumption possibilities

Why higher wages in cities? (Urban wage premium?Wage level Hypothesis: Workers who move have immediate gains or losses

- Larger demand (market access), Cheaper inputs (Vertical linkages) Info externalities = MPN > in CitiesWage growth Hypothesis: gains accrue with time and there might be no losses for workers who leave city

- Human K Accumulation, labour market matching = higher wages How to disentangle empirically the two hypotheses? Examine migrants

- Looking at moves: test whether wage of migrants increases, falls and/or accrue over time:o If levels effect dominates: migrants should get a bump when arrive to city/ leavers should get a losso If growth effect dominates: Premium should rise with experience/ When leave no loss to wages

Glaeser, E. and Mare, D. (2001) (Wage premium large taking into account skill bias AND evidence of growth effect) - Consistent with existence of agg. economies find significant wage premium associated with urban areas- Workers earn higher wages in large cities urban wage premium = 33%.

It is possible that this result could be explained by selection instead of by agglomeration economies.

- Addressed by looking at effects of urbanization on recent migrants. - If selection at work: migrants would receive higher wages since they would be, by hypothesis, the most able. - The conclusion of this analysis is There remains substantial urban wage premium = 20%.- Significant part of urban wage premium grows to workers over time and stays with them when they leave city.

o Therefore: Portion of urban wage premium is wage growth, not a wage level, effect. o This evidence suggests that cities speed the accumulation of human capital.o Consistent with model where people acquire skill by interacting: urban increase prob of interaction

Combes et al. (2009 ): The most able move to cities Human capital as a key driver of earnings differences - Using French data find evidence of significant sorting of high ability workers into urban areas. - Find 40%-50% of wage differences across space accounted for by difference in skill composition of their

employed workforces.- High skill workers attracted to certain locations due to differences in industrial structure: some location favoured

by industry demanding higher skills.Moretti, E. (2004 ): Human capital externalities a fundamental cause of the urban wage premium

- HCE exist firm/ worker more productive in area with high levels of human k should attract firms/ worker- Given a fixed supply of land, this process increases land prices and rents in equilibrium firm/ worker must be

indifferent between locations. - Higher productivity must be offset=higher wage/ rent wage premium grows amid urban and non-urban area

Dumais et al. (1997): E vidence in favour of labor pooling Using LRD manufacturing data base for the US - Examined relative importance of Marshall's 3 reasons for agglomeration for location of manufacturing plants;

(Proximity to suppliers and customers, labor pooling and information spillovers)- Labor pooling was by far the most important force for aggfound labor pooling especially strong as an agg force

in high tech industriesMatch differentiation likely more important in job requiring more advanced skills.Quigley, J. (1998 ) how do diversity and size affect the level of output and the level of well-being achievable in a city?

- Large city: more chance to create critical mass of particular industry: which will then act as strong attractant to more participants in that industry

- Quigley underlines “labor market matching”o From the production side: The return on human K to a worker in city rises as stock of human K in city

rises, and return on physical K investment to an investor also increases with the stock of K in the city”. o From the demand side, consumers benefit from lower transaction costs in large cities.

Week 6: Geographical Distribution of Innovative ActivitiesTwo major features of social and economic systems have characterised last decade (features are strongly influenced by (and show complex relationships with) geography and space)

- Knowledge, innovation and technology more critical in economic activities: Increased speed/ variety of info and knowledge sources and flows

- Scope of all economic/ firm activity become more global: Increased economic interdependence/ integrationEconomic growth (a recent phenomenon)

- Economic theories suggest that growth will stagnate eventually - Solow neoclassical model suggests that technological progress is an exogenous driver of economic growth- Growth is uneven growth driven by technology Knowledge must be uneven (BUT aren’t knowledge an ideas

easily able to traverse space?) Distinction: fully vs partly non-excludableWhy does geography matter?

- Relations with information sources external to the firm strongly influenced by spatial proximity- Informal channel for knowledge diffusion (f2f/ tacit knowledge) spur tendency innovation to geographically split- Distribution of innovation across space dependent on: economic factor, social and institutional characteristics- Innovative endogenous capabilities highly cumulative, distinct and geographically-specific- Firms and workers chose to locate in dense agg to take advantage of productivity enhancements- Degree of clustering differs across industries/ technologies (Cluster of innovation > clustering of production)

Innovation is created in a few certain areas of the world (BUT could still get disseminated fully if we live in a world of flat idea-sharing in which case shouldn’t idea-creation be unrelated to idea-exploitation and growth? NO…

- You have to make the critical distinction between information and knowledge- MC of transmitting information (Codified) across space doesn’t change across space (ICT revolution)- MC of transmitting knowledge (tacit) increases with distance (Audretsch, 1998)

o New idea hard to codify: rely on shared interpretation of ideas that don’t yet have standardized meanings And, it has a strong spatial dimension

o Tacit don’t inevitably need proximity but cost of transmission low with frequent social interaction- Knowledge spillovers/ externality = sources of innovative output and productivity growth for all firms

Direct external effects (We focus on Knowledge externalities)

- Localised knowledge spillovers : unintended positive externalities of scientific/technical discoveries on the productivity of firms which neither made the discovery themselves, nor licensed its use from the holder of IPR

- Bounded in space – firm located nearby source of knowledge benefit more than firm located elsewhereintroducing innovations at faster rate

Theoretical foundations of knowledge externalities ( Audretsch, 1998) - Knowledge produced by firms and universities (partly) spills over- Knowledge spillovers mainly ‘tacit’: highly contextual, difficult to codify, easily transferred through f2f contact- Tacit knowledge: pure public good but local one (Breschi) Most available to firm located nearby source - Spatial proximity raises the likelihood of establishing a contact among agents

Institutional/ relation/ cognitive prox: new idea (when dependent on local context/institutions) tastiness need colocation - Direct learning from linkages + interactions with people in other organisations and Provision local public goods- Small (particularly) firm embedded in a thick web of relations (formal and informal) within region (Saxenian)

o Silicon Valley: Network form of organization flourish in regional agg. Proximity facilitates repeated, f2f interaction that fosters mix of competition and collaboration required in fast-paced tech industry

- Network of relations: Trust/shared norms = “socialisation” of knowledge + processes of “collective learning “relational capital” reduces uncertainty (Trust issue) associated to innovation, reduces coordination problems

- ‘Cognitive proximity’: shared knowledge base (related/ complementary bodies) facilitate exchange/ learning - Cultural and institutional differences shape spatial distribution of innovation as well as microeconomic linkages

Audretsch, D.B. (1998) “Agglomeration and the location of innovative activity”- The seeming paradox of the rise in “importance of local proximity and geographic clusters when globalization

seems to dominate economic activity” attributed to fact that more innovative activity is associated with high-tech SME clusters than with “footloose multinational corporations”

- Fundamental observation of innovative activity is that it is remarkably concentrated in space - Knowledge generated/ transmitted more efficiently via local proximity, economic activity based on new knowledge

has a high propensity to cluster within a geographic region” o S tatistical relationship between R&D input + innovation output at level of country and industrial sectoro Less robust relationship at level of individual firmo Suggests small firms, in particular, derive their knowledge inputs not from own R&D but from knowledge

spillovers from other firms and from universities within the country or industrial sector- Regional differences are due to the fact that proximity facilitates transmission of tacit knowledge and learning

Beaudry, C., Breschi, S. (2003): Is clustering beneficial for innovative activities?(Patent data from UK and Italy)- Only firm located in clusters populated by other innovative firm in same industry are more innovative than firm

located in cluster with more non-innovative firms (neg effect assoc. with solid presence of non-innovative firm)- Clusters can be linked to diseconomies resulting from increase of competition for qualified personnel and key

inputs. Diseconomies may result in lower profit margins = a decrease of resources available for R&D.- Show that the benefits of spatial proximity may not automatically arise from simple co-location with relevant actors

but rather they may be conditional on the quality of those actorsBaptista and Swann (1998) : link between firm clustering and prob to innovate (UK data on innovation counts/ patents)

- Found positive effect of the strength (the size of total employment) of clusters on the prob of innovation or the propensity to perform innovation by clustered firms.

- Strong cluster employment in a firm's own sector significantly improves innovative capabilities- Show that a firm is considerably more likely to innovate if own-sector employment in its home region is strong.- Industrial centre generates externalities related to transmission of knowledge among firms with proximity. - BUT : Effect of strong employment in other industries not significant congestion effects outweigh benefits that

come from diversification within cluster higher levels of employment in other industries entail negative effects on firm's innovative performance thus evidence of mild congestion effects.

Paci, R., Usai, S. (2000) Explores spatial distribution of innovative and productive activity across 109 regions of EU - Technological activity in EU appears to be highly concentrated (concentration tended to decline over 80s)- Positive association between the regional distribution of innovative activity and labour productivity. - Spatial and sectorial specialization of innovative and productive activities is significantly+ positively correlated.

Lecture 7: Externalities I - R&D spillovers and agglomeration Productivity of firm/ industry related to their R&D spending ALSO related to R&D spending of other firm or industryMarshallian agglomeration externalities: Pooling of specialised workers, Specialised intermediate goods industries, Knowledge spilloversJacobs’s spillovers: Knowledge spillovers are related to the diversity of industries in an area Evidence tends to support the Jacobs story over MAR

- Feldmann and Audretsch (1999): less industry-specific innovation in MSAs that specialised in a given industry + more local competition between firms is more conducive to innovative activities

- Glaeser et al. (1992): indirect evidence the industrially diversified areas grow more rapidly.Evidence suggests Knowledge (patents) is a localised phenomenon in urban areas

- Jaffe et al (1993): new patent is 5-10 X more likely to cite patent from same metropolitan area - However, innovation and patenting might be faster in denser areas because of other reasons than spillovers

o Difficulty keeping (industrial) secrets in urban areas Carnegie Mellon Survey: manufacturing firms typically protect their innovations including patents, but also secrecy.

o Other confounding factors More productive firms locate in citiesLocation matter: Transmission cost high for Innovative concentrate in those industries with more knowledge input Thoughts on common innovation indicators used in studies on spillovers (See below for studies) Innovation counts: Approach focused on the object of study: counting significant technological innovations

- Advantages : Focus on journals, experts, allows an external assessment of the importance of the innovation - Limitations : It biases sample towards innovations significant enough to be published or identified by expert, lost

large set of innovations that are routine, incremental, etc., where the innovation process might be very different, expensive and time consuming collection

Patent citations: Use citations to other patents or to scientific literature in the patent documentation for the measurement of the linkages between inventors and between the science base and the productive system

- Advantage “Knowledge flows leave a paper trail in the form of citations in patents ….to the extent that regional localization of spillovers is important, citations should come disproportionately from the same state/area as the originating patent” (Jaffe 1993)

- Limitations: To what extent patent citations effectively reflect inter-personal or inter-organisational linkages? (, Breschi, 2005) Often biased towards the behaviour of particular fields of science and/or industrial activities (i.e. scientific fields susceptible to patent generation or high-tech manufacturing industries)

R&D: comprises of creative work undertaken on a systematic basis in order to increase the stock of knowledge, including knowledge of man, culture and society, and use of this stock of knowledge to devise new applications(Covers 3 kinds of activities: Basic Research, Applied Research and Experimental Development)

- Advantages : Systematic collection at firm, industry and country level, Long time series (since 1970), Good harmonisation across countries, Detailed sub-classifications according to performance and funding

- Limitations : Its innovation input measure, measures only 1 innovation input (much innovation occur outside R&D labs), underestimates innovation by SME firms (they lack R&D labs), underestimates innovation by low-tech industries where a lot of innovation is acquired, It underestimate innovation by developing countries, It is mainly biased towards manufacturing activities. Poor coverage of services sector ICT related innovation

Jaffe et al. (1993),- Challenge measuring knowledge spillover: Krugman (1991): abandon attempts at measuring knowledge spillover

as knowledge flows are invisible: leave no paper trail by which they may be measured/ tracked’.- Jaffe et al: knowledge flow sometime leave trail’ in form of patented invention and new product introduction- They identify a "paper trail" of knowledge spillovers in location of patent citations Results: patent citations

highly spatially concentrated: citations 5-10 X as likely to come from same SMSA as control patent- They provide evidence for idea that knowledge spillovers are important and spillovers lessen with distance.

Audretsch and Feldman(1996): What extent industrial activities spatially concentrated and whether such a concentration is due to knowledge spillovers

- Assumption: innovative activity spatially concentrated in industries where knowledge input/ spillover greatest- Main Results: The propensity of innovative activity to cluster geographically tends to be greater in industries

where new economic knowledge plays a more important role.- Then calculate Gini coefficients for geographic concentration of innovative activity to test relationship.

o Results indicate that key determinant of extent to which the location of production is geographically concentrated is relative importance of new economic knowledge in the industry.

- Suggest greater propensity for innovative activity to cluster spatially in industries in which industry R&D, university research and skilled labor are important inputs.

- They found that knowledge created in university laboratories spills over to contribute to the generation of commercial innovations by private enterprises. Even after controlling for the location of industrial R&D, knowledge created at universities results in greater innovation.

Breschi, S., Lissoni, F. (2001), BUT: what and how exactly knowledge spills over?- Spillovers mostly assumed, rather than proved: LKS still a black box whose content remain ambiguous - Need to understand the specific mechanism through which geography and innovation are linked

Krugman (1991 ) dismissed role of LKS: claiming that although they may exist in some high-tech industries, they are not an important force for agglomerationThree Main criticisms of LKS

- What conceived as pure knowledge spillover might be mediated by market mechanisms (Financial externality) o Breschi and Lissoni  Results of patent citation analysis constitute weak evidence of presence of LKS.o Fact incidence of patents and citations from these patents correlate geographically just indicates that

knowledge flow more intensively amid local firm than among firm situated long distance from eachothero Doesn’t imply that all knowledge actually circulates freely without compensation payments being made.

o Zucker : Show empirically knowledge traded between firm and university involve market transaction- ‘Tacitness’ not necessarily a property of technical knowledge and can also be communicated over long distances

(e.g. epistemic communities)o Breschi and Lissoni : Biotechnology firms compared with software firms more likely to attribute greater

importance to search for knowledge resources and to academic sources, and to establish more frequently formal relationships with them, given need to protect this key asset. But, informal exchange can also be critical, since new scientific knowledge often circulates in informal “epistemic communities”

- Localised labour mobility generates spillovers only if moving workers create a pool of knowledge from which all firms are able to access.

Zucker et al. (1998) explore the technology by which apparent geographically localised knowledge spillovers operate.- Attempt to open ‘black box’ of LKS by looking at working relationships between university star scientists making

discoveries and firms utilising them commercially.- Focus on biotechnology sector in California and found scientific ‘stars’ collaborating with firms had greatly higher

citation rates than pure academic stars- Notion of LKS would imply that scientists are pursuing disinterested research- All parties involved (scientists, firms, governments, funders) are very likely to be linked by contractual ties- Market mechanisms underlie knowledge diffusion in the biotech industry in California- Location of academic experts at the leading edge of basic bioscience strongly influenced the location of new

biotechnology enterprises in the US firms and star scientists were not merely located in same area, but that scientists were deeply involved in operations of firms.

o What might have been interpreted as localized knowledge spillovers using standard methodologies and data sets (Jaffe, 1989), was to a large extent a matter of market exchange.

Lecture 8 Externalities II: knowledge, linkages and interactions The black box of LKS ( Breschi & Lissoni, 2001 )

- Studies show how different knowledge flows link together different actors (individuals, firms, universities, public labs), each serving different purposes and being affected in different ways by geographical distance

o Intermediate cases between knowledge as a pure ‘(local) public good’ and knowledge as pure ‘private good’: e.g. knowledge sharing between academic and public research institutions and business sector

Trend in U-I linkages: 80s onwards Change is driven by... - Increased globalisation/ competition/ emphasis on innovationFirms need to get closer to knowledge sources- Budgetary constraints faced by governments and universities search for new funding sources- Falling profits and/or increasing cost of research encourage many firms to outsource more basic research - Gov policies encourage tech transfer, collaborative research, U-I links, commercialisation of research - Examples highly localised e.g. MIT & Route 128, Stanford & Silicon Valley, ‘the Cambridge Phenomenon’- Industry more interest in university research and specialised personnel specific opportunities for corps.- Knowledge flow directly from U to I no longer limited to endowments, as in the past

Main motivations for U-I research collaborationUniversity: Obtain financial support for its missions; Broaden experience of student and faculty; Identify significant/ interesting research problems; Increase employment opps; Enhance regional economic developmentIndustry: Access research infrastructure and expertise; Aid renewal of company’s tech; Gain access to potential employees; Expand contacts for corporate lab; Increase pre-competitive research; internal research capabilitiesD’Este et al (2012): Clustering and proximity are substitutes in terms of shaping formation of U-I research partnerships

- Geographic proximity has very strong positive impact on likelihood of U-I research partnership formation, - BUT: Clustering of technologically complementary firms makes the proximity of industry and university partners

far less important in case of the most densely clustered firms, entirely unimportant- Organizational proximity = extent to which firms share relations in an organizational arrangement; autonomy and

control (Greater control and possibilities to regulate interactions means greater organizational proximity -Conversely, firms with links that prompt independence have less organizational proximity)

o Organizational proximity usually appears through prior collaboration experiences o Prior joint experience in partnerships (measure of org proximity) makes partnerships more likely

Conclusion - Knowledge flows are a very important source of spatial agglomeration- ‘Pure’ knowledge spillovers many actually be other kinds of externalities mediated by economic mechanisms

(labour and technology markets, networks)- ‘Pure’ knowledge spillovers may actually be knowledge flows between different actors (Unis and firms)

Arundel and Geuna (2004) explore geographic dimension U-I knowledge flows through the PACE survey- Public science is among most important source of tech knowledge for innovative activities of EU largest firms- EUs largest firms assess public research output by hiring trained scientists and engineers, through informal

personal contacts, by contracting research out to public research organizations, and joint research projects.

- MNCs in search of technological and scientific knowledge likely to try to gain access to knowledge held and produced by host country universities and public research centres.

o 3 most important channels of transfer: Hiring uni grads, informal contacts and contracted out research - Results: firms interested in codified knowledge (publications and patents) less likely to find distance a barrier.

o Contrary to expectations/ theory find that firms that access public science through informal contacts with individual researchers rely less upon domestic scientific institutions goes against the intuition that informal contacts convey tacit knowledge, requires frequent personal exchanges and can’t be transmitted over long distances pharmaceutical firms value foreign public science more than domestic science, and do not have a preference for academic knowledge generated in their vicinity.

o BUT Regressions for location of most important source of public science suggest that usefulness of informal contacts for acquiring public science doesn’t go beyond other EU countries to North America.

Weak evidence for link between proximity effects and informal contacts Possible explanation: development of ‘network proximity’ supra-national EU policies have

subsidised cross-country collaboration between firms and public researchers.- Local university not major source of innovationinnovative firms can collaborate with distant academic partners 

D’Este P. and Iammarino S. (2010), Features of the university itself influence manner of local cooperation. - Show differences exist in spatial patterns of cooperation depending on sector and scientific fields concerned. - Quality of academic research (measured by uni ranks) also impacts spatial dimension of cooperation large and

top-ranking universities display a higher propensity to collaborate locally.- Firms trade off faculty quality for geographical proximity- Higher the quality of department more likely it will attract distant business partners BUT also provide evidence that

beyond certain threshold of research quality, collaborations with industry become geographically closer curvilinear relationship between research quality and geographic distance Suggests when research quality reach certain threshold, effect of geographic proximity becomes significant again

Laursen, et al. (2011): Paper concerns the geographical distance between a firm and the universities in its local area. - Theory: firms’ decisions to collaborate influenced by: proximity to uni and quality of these universities. - Results: Being close to low-tier uni reduces propensity for firms to collaborate locally. Being co-located with top-

tier uni promotes collaboration. However, if faced with choice, firm appear to give preference to research quality of university partner over geographical closeness particularly true for high-R&D intensive firm.

Week 10: Trade, technology and competitiveness Trade theory and technology

- Ricardo: countries gain from trade due to technological differences when producing the same goods.- NTT: Technological differences major determinants of intra-industry trade Advantage of advanced countries

linked to the market power generated by a continuous flow of innovation Innovation (& Schumpeterian) theories of trade

- Literature on innovation and technical change emphasises intra-firm learning as process of technological acc- Countries continue to hold patterns of technological advantage over quite long periods of time- Country differ in their institution and social organisation, not only in abundance/scarcity of factor of production - Property of technological change imply possible irreversible processes: vicious circle (contradict convergence) - Critical importance of industry specificities (e.g. structure, composition) - Nonprice competitivenes-Kaldor paradox: more market share positive correlate with growing relative unit cost

o Kaldor paradox, Kaldor (1978) fastest growing countries (in terms of export share) also experienced highest rise in unit labour cost

- Longer term technological competitiveness as opposed to shorter term price competitiveness The technology-trade causal relationship

- Two way relationship: Tech competence = positive impact on exports and competitiveness; international trade boosts the generation and the transfer of innovations, giving rise to cumulative causation

- Empirical evidence shows that:o Link between technological intensity and internationalisation has considerably strengthened over timeo Trade specialisation cumulative nature: Place do what did in pasttacit info acc in production + tech o Comparative advantage structures also evolve progressively and incrementally over time

Fagerberg (1996), “Technology and Competitiveness”- Technological factors are important for countries’ international competitiveness and trade specialization. - Empirical evidence that the international competitiveness of sectors and technologies is greatly influenced by the

competitiveness of interlinked sectors - In lots of high-tech industries, competitiveness is probably affected by the size of the domestic market

o Small countries should not try to follow unsustainable specialisation patterns - large country advantage of greater domestic profitability of innovation and of decidedly fuller spillover effects - On other hand, smaller countries can exploit greater concentration of industries in few strong sectors, and thus be

in position to act as global player’s smaller fragmentation of their economic and political interests.

Laursen and Meliciani (2002) : Tech flows/sources of learning come from local firms in other industries (national linkages/ spillovers ) OR from firms in the same industry located abroad (international linkages/ spillovers )?

- National channels: Tacit knowledge, Common institutional framework, Country specific technological trajectories, Domestic market as an inducement mechanism for technological change

- International channels: Users and suppliers cross-border interactions, Exchange of products and competences with foreign actors through trade linkages, Role of MNEs

Results: National technological upstream linkages positively related to Trade Balance; foreign technological upstream linkages have generally no impact mixed resultsArchibugi and Iammarino (2002) : Explain fundamental analysis of concept of globalization

- Dealing with innovation that is conceived as zip between 2 fundamental phenomena of modern economies: o Increased international integration of economic activities o Rising importance of knowledge in economic processes.

- Globalization = expansion of global forces has remained restricted to most developed part of the world but with increasing division of economic and innovative activities in emerging economies.

- Show high correlation between R&D and export intensity for industrialized countries venture overseas to exploit their current R&D capabilities across a larger market.

Week 1 - Should I buy or should I rent?- Homeownership rates vary significantly across countries - Homeownership rates vary strongly within countries and region (Lower in cities than in suburb)

Who determines whether property is owner occupied or renter occupied the existing owner of the property decide- Self-occupy represents an implicit demand OR rent-out out which affects supply on rental markets - Which choice (self-occupy or rent-out) maximise profit given relative prices (PV of income streams) of 2 choices

A stylised model – Supply Side should investor Self-occupy OR rent out property - Outcome Without Taxes and transaction costs = PV of income stream equal Indifferent - Outcome with Tax on rental income = PVOwner > PVLandlord increases Home Ownership propensity- Outcome with deductibility of Mortgage interest = PVOwner > PVLandlord Increase Ownership propensity- Outcome with Transaction cost = PVOwner < PVLandlord Reduce Home Ownership propensity

o less relevant if Household immobileo If Duration increase ownership more likely As transaction cost in the future will be very low

- Outcome with property transaction cost=cost for owner=PVOwner < PVLandlordReduce Home Ownership propensityDemand side : How determine if individual own or rent: Compute return from decision to own and occupy investment Obstacles to home ownership down payment constraint (Wealth) OR Liquidity constraint (income)The impacts of borrowing constraints on homeownership (Linneman & Wachter (1989))

- Down payment constraints have substantial effect on HO and credit constraints matter more than income in determining HO Wealth poor households are less likely to own

What actually matters for housing tenure choices? General empirical evidence - Income/wealth positively associated with Home Ownership- Households with kids and married couples more likely to own- Minorities (and foreigners) negatively associated with Home ownership- Consistent with prediction from theory

o High income and wealthy have no issues paying down payment/ no liquidity constraintso Households with kids Longer expected duration in property – lower transaction costs

More on transaction costs and expected duration ( Haurin and Gill, 2002 ) - Studies find that homeowners less likely to move than renters transaction costs are greater for homeowners

than renters HENCE this reduces homeowner mobility - BUT : higher costs cause households to sort:

o People expecting to be stable in their demand for housing will more likely select to become HO o Sorting complicates empirical analysis of causal relationship between ownership and mobility

- In their sample, length of stay of members of military set exogenously tenure choice directly affected by difference in household anticipated length of stay and resulting differentials in transaction cost of owning v renting

Findings: transaction costs are a substantial amount compared with the transaction costs faced by renters- Increase in expected stay by 1 year increases probability of Home ownership by 3% points- Implied Transaction Costs of selling a house: 3% of House Value + 4% of Household Hold earnings- Substantial amount compared with the transaction costs faced by renters

Implications: Knowing that transaction costs are greater for ownership, households with short expected lengths of stay will tend to rent Owners likely to be less mobile than renter’s transaction costs induce geographic stability

Do tax policies matter? ( Hilber 2007 ): Concludes that tax policy reforms have only had relatively minor effects on homeownership attainment and counter to widespread perception and theory Are minorities really discriminated against in the housing market? (Hilber & Liu, 2007 - Use PSID with binary logit model)

- African Americans in US are considerably less likely to own their homes compared to Whites. - Difference in household income and socio-economic and demographic characteristic only partially explain gap

and previous studies suggest that ‘unexplained’ gap has increased over time. - Show that black-white HO gap mostly disappear once household wealth and locational preference accounted for.- If compare black and white family of similar wealth levels and who both live in a comparable urban environment,

they will have similar probability of owning their homes- Wealth inequality and suburbanization (more evident for whites) driving rising black-white HO gap that can’t be

explained by other variables. - Data indicates that blacks are more likely to live in counties with a large city and that this gap in location

preference has been increasing

How can we explain spatial differences in homeownership? Interaction of Property types/ characteristics and space Example 1: detached, mainly in rural areas, open land

- Typically owned by family with children who demand space - Hence home ownership will be mainly outside urban areas

Example 2: High rise building, very little open land, high density - Typically owned by real estate developer/. Large institutional investors due to efficiency of landlords and risk - Hence low ownership in urban centres

Does risk matter for homeownership matter and its spatial distribution?- From theoretical point of view: Owner-occupier ‘over invest’ in housing due to credit constraints, Distortions due to

inadequate ‘diversification’, Different for institutional/corporate investors- HO should avoid risky neighbourhoods (e.g. urban areas) Support from empirical evidence by Hilber (2005)

Neighborhood externality risk and the homeownership status of properties (Hilber 2005)- Argues that neighborhood externalities are associated with increased property value risk reduce HO rates.- Locations differ not only in their housing stock but also in their housing investment risk. - House prices in more urbanized places tend to be less stable than in suburban/ rural properties in

neighborhoods with high levels of externality risks (high levels of variation in junk and litter in the street, in street noise, in neighborhood noise and in neighborhood crime) should be less likely to be owner-occupied.

- HO has disadvantage v renting = Single owner–occupiers can’t adequately diversify their housing investment risk- If Neighborhood externality risk partly explains low HO rates moral hazard problems of renter occupiers,

neighborhood uncertainty may also partially explain the decay of buildings in many inner city neighborhoods.Brueggeman & Fisher (2007)

- Homeownership seen as important goal for individuals in US – Wealth accumulation - Many institutions developed to support growth of housing industry: Housing and related services = 15% GDP - Members of congress believe that HO encourage a stronger social network, better educational achievement and

lower crime rates – why there are schemes such as deductibility of mortgage interest

Factors affecting DemandAppreciation in house prices when considering housing investment need to consider rate of appreciation

- Factors positively impacting housing demand: pop growth, household formation, employment, income, cost rent- Factors negatively impacting housing demand: Increasing interest rates, federal income tax

Income and employment important driver for housing affordability - Demand increasing with more single parent households

Interest rates- Loans made as large % of property value monthly payments may fall or rise considerably from month to month - Interest rate changes slipovers onto related industries: construction, land development, appraisals etc.- Increase interest rates leads to fall in demand for housing – as interest rates rise cost of borrowing rises- Magnitude of impact of interest rates complex due to variety of mortgage loan options: fixed rate mortgages,

Factors affecting Supply- Supply of housing is determined by the relative cost of land labour and capital - Influences on supply costs on local scale: restrictions (Zoning, building codes, land terrain)

The influences of neighbourhoods/ municipalities- Attributes include accessibility to goods and services that must be acquired once investment is made (public

goods) = appreciation of prices – explain differences in prices among neighborhoods and jurisdictions- BUT how much do these influences actually impact – in cash terms – the price of a home (Capitalisation effect)

o Capitalisation effect relates to quality of public services that individuals receive relative to taxes that are paid for these services when they choose to purchase house in a particular neighbourhood

o If benefits > taxes there = net benefit of owning house and these benefits are capitalised ion house prices Renting v ownership: Cost of renting impacts demand for HO If cheaper to rent less demand for HO Historical trends

- In US: 67% of all housing is owned and 33% rented this suggests that HO is more financially viable and has been seen as a better investment than renting

- BUT Renting could be favoured despite better to own: Flexibility, down payment constraint, No desire to take riskOverall conclusion for week 1

- HH characteristic matter as they determine expected duration and relative utility derived from 2 tenure choice- Location matters because of: Types of available properties, Landlord production efficiency, Locational risks- Together these explain spatial patterns of HO

Lent term - Week 2   - Estimating the Housing Supply and Demand Curve Estimating supply price elasticity’s Topel and Rosen (1988)

- Estimates: Long-run supply elasticity: 3.0 AND Short-run supply elasticity (1 quarter): 1.0 Housing supply is much more elastic in LR than in SR

Estimating supply price elasticity’s Dipasquale and Wheaton (1994)- Research recognized market for housing is often inefficient and adjusts slowly to change in market condition- They incorporate a stock adjustment process in their model of housing supply and estimate

o Elasticity of LR housing (stock): 1.2-1.4 AND elasticity of new housing (SR) supply (flow): 1.0-1.2- Conclusion: prices in owner-occupied market adjust slowly, in contrast to the general assumptions of

instantaneous market clearing takes many years for market changes to fully incorporated into housing priceCritique by Mayer and Somerville (2000) : Changes in housing stock should be explained by changes in prices

- They argue that residential construction responds not to level of real house prices, but to rate of appreciation reflects builder incentives to earn rents on land component of house prices

- Present results from alternative stock adjustment model:o Stock (LR) elasticity of 0.08 (i.e. a 10% increase in price yields a 0.8% increase in total housing stock)o But flow elasticity of about 6 (starts increase 60% from a 10% price increase).

New housing is only 2.2% of total stock, that’s why small total elasticity- Overall, new buildings respond quite a lot However overall long-run response is very small

Summary of main findings Housing supply is more price elastic in LR than in SR- Heterogeneous findings depending on level or changes specification - Speed of adjustment still debated, probably quite short lived effects…- If use changes-in-changes, quick response mainly due to new buildings

Critique – heterogeneous effects - Is elasticity of supply constant across different residential markets?- Supply price elasticity may vary across space (Rural locations with plenty of open land likely have elastic supply

AND Urban locations with little open land likely have inelastic supply)Locations with less undeveloped land have more inelastic housing supply Hilber and Mayer (2004)Look at 208 communities in Massachusetts and Split into 2 equal groups based on % of land undeveloped in 1984

- Estimate structural model with change-in change specification to estimate supply elasticity’s for locations with more and less developable land for future construction

- Locations with less undeveloped land have more inelastic supply Supply price elasticity in urban locations: 0.014 - 0.13 Supply price elasticity in rural locations: 0.16-0.1

- Their findings suggest that more developed communities have more inelastic supply of new housing and greater extent of house price capitalization of local public school spending and local amenities.

Demand Price elasticity: Findings of various studies are more consistent than supply side studies estimated demand price elasticity typically between -0.1 and -1.0 (UK around –0.4/-0.5)Demand side studies – Ermisch et al. (1996) using data from 1988 to 1989

- Analyse housing demand in Britain, using a switching regression model for recent movers vs. nonmovers. - Find income elasticity’s of around 0.5 (low compared to range earlier estimates for US (0.7–1.5) or UK (0.5–1.1). - The demand price elasticity’s they find are about −0.4, which is again somewhat low compared to US findings (a

range of −0.8 through −0.5), but in line with earlier UK findings. - Interesting evidence on quantity/quality: Most of response along quality dimension Price and income elasticity

of n. of rooms are: -0.054 and 0.128 Must be buying better quality, not just sq. metresDemand side studies Rosenthal et al. (1991) Look at 1981 American Housing Survey (75,000 households)

- Veterans loans: no down-payments but cannot be excessively burdensome relative to income- This imposes a constraint on quantity (non-price)

o Estimate demand price elasticity based on all households: -0.92o Based on credit constrained HH only: between -0.38 and -0.54o Credit constrained households have more inelastic reaction to changes in price

Summary of main findings - Price (and income) demand elasticity estimates more stableMore consistent evidence than supply elasticity- The demand price elasticity is lower for credit constrained households

- There seem to be important ‘peer’ effects in housing consumption

GY305 LT Week 3 - Hedonic Analysis and Housing Demand: What Do People Value?Mono-centric city models suggest distance to business district; other factors include: Structural and spatial factors Consumer choice: Theory assumes choice rational: Consumer seek maximise utility and Utility functions stable over timeAnalysing demand for housing

- Household choose residential location that maximises utility Housing is composite good (has price, but price of component are not explicit) Only observe household total expenditure on the goods combinedHouse sale price

Hedonic prices and implicit markets: the intuition Pricing elements of composite goods: housing markets- If we estimate regression model like this: (House price) = α + β (No. of Rooms) + ∆ (Other factors) + ε

o Coefficient=estimated impact of additional room on housing ‘expenditure’ price associated to this ‘demandHedonic prices and implicit markets: theoretical foundations

- Provides economic model of ‘hedonic’ (utility maximising) equilibrium in market where goods are not explicitly traded Such as different items in housing bundles

- Valuations of various components are determined implicitly through regression analysis.Assumptions

- Perfect information: consumers observe all the characteristics and hedonic price function correctly- Can purchase any bundle of characteristics: find a house with any combination of bedrooms, age etc. - Implicit price allows us to value marginal changes in the characteristics only Unless all consumers are identical- Estimation of non-marginal changes requires estimation of individual’s demand parameters very challenging

Estimation of Marginal WTP: econometric challenges- Supply of amenity is partly determined by socio-economic composition of community

o E.g., wealthy people want to live close to good schools. Can I separately identify?- Supply of other, potentially unobservable, amenities can be correlated with amenity of interest

o E.g.: good faith school near wonderful gothic church. What if people pay for the view? How to control?- Exact supply of amenity accessible from a location is hard to measure

o E.g. school with implicit/hard-to-measure catchment areasThe Determinants of Residential Property Values with Special Reference to Air Pollution : Ridker & Henning (1967)

- First application of hedonic methods to estimate effect of air pollution on property values in St. Louis.- Findings: Coefficient -245.0 = A negative and significant relationship between property values and sulfate

measures if Sulfation levels exposure were to drop by 100cm2 the value of that property would rise by $245 - Limitations of the study : Ridker and Henning implicitly assume that value placed on a marginal improvement in air

pollution concentration is independent of level of air pollution and independent of household income and tastes. This is equivalent to assuming a linear damage function for air pollution that is identical for all households.

Do Better Schools Matter? Parental Valuation of Elementary Education: Black (1999)- Evidence of parents’ willingness to pay for higher public school quality by comparing house prices for comparable

homes in same neighborhoods on different sides of borders between adjacent elementary school attendance districts for suburban areas in Massachusetts estimate implicit valuation of diferent school

o School quality differs across school boundaries, but nature of the neighborhood may not: Considers houses close to school district boundary: contamination of unmeasured neighborhood quality likely small Controls for neighborhood differences.

- Finds that test scores of local schools have a strong effect on house priceso For sample within 0.15 miles of bounds Effect is about 2.1% for a 5% change in the test score

- Gibbons and Machin (2008): Report consensus estimate: 3–4% house price premium: 1 S.D rise in av. test scorePaying for primary schools: admissions constraints, school popularity or congestion ( Gibbons and Machin, 2006)  

- Suggest popularity in itself raises price: Over-capacity schools command additional premium relative to under-capacity schools with equal performance

Valuing school quality using boundary discontinuities: Gibbons et al. (2009)- Look at valuation of primary school quality in England and consider relative importance of two school variables:

o Value added in test scores from age 7 to 11 AND the initial peer intake at age 7- Both test score variables attract significant positive coefficient parent’s value both: value added improvements

and initial test scores. o Value added: 1 point change in value added variable leads to a 3.7% increase in log house priceso Mean age-7 test score for schools that can be accessed from location: 1 point change in this

variable leads to a 2.8 increase in log house prices - Thus it is evident that school choice is driven by the demand both for expected academic gain and for aspects

of expected peer group quality that are uncorrelated with current academic gains.- Size of these house price responses consistent with previous results which show a consensus estimate of around

3–4% house price premium for one standard deviation increase in school average test scores. 

Week 4: Do Places Matter? Neighbourhood Effects and Area-Based Policies

Policy relevance of social interactions and externalities- If there are externalities – Positive - you have sub optimal investment as spillovers are not taken into account - State Education: results in higher wages for me AND educated workforce generate innovation-externalities, state pay

for education, individuals don’t make investment as they don’t consider positive externalities- Social interactions involve externalities: actions result in outcomes that benefit/cost other people

o Example: if having higher-educated neighbours raises children’s educational attainments…o Then educational attainments of neighbours generate benefits over and above their personal gains

Multiple equilibria: different community outcomes can arise even with the same incentives- Social interaction have potential to lead to multiple equilibrium - In presence of strong interactions same neighbourhood might coordinate in such a way that everybody goes to uni OR

in another instance everybody might decide not to go to uni as they expect everybody will not go - Potential of generation of poverty trapsYou should intervene: pushing people to coordinate positive equilibrium - INTUITION : interaction must be strong relative to individual payoff so you have multiple equilibrium: otherwise if your in

case were interaction had relatively minor effect, won’t deviate from own unique optimal outcome The social multiplier/ area based policies: By treating some individuals can achieve > just improving their outcomes

- If individual outcome rises by X percent of his/her neighbours’ average outcome improvements Then social multiplier is 1/(1-X) for large communities

Social interactions: can neighbourhood effects lead to segregation? Community stratification Local public goods provided in different quality/amounts in different areas might lead to stratification…Tiebout (1956)

- Individuals sort across local jurisdictions according to preferences for local public goods, e.g. schools - Residents reveal preferences for local public goods- “Spatial mobility provides local public counterpart to the private market’s shopping trip”- Implication: ‘Tiebout’ demand for local public good (‘schools’) leads to stratified communities

‘Endogenous’ stratification equilibrium: Household think that some characteristic determined by composition of neighbourhoods is valuable for children education might lead to stratification endogenously of neighbourhood even if they start of similar in composition - find in few years that we'll find that these two neighbourhoods are completely different

- 2 alike neighbourhoods: A (little less educated adult) and B/ fix stock of house: each person moves in in 1 leaves- The children will learn faster in B than A - so everybody wants live in B but housing is inelastic…

o Some people want to move from A to B (good for children) prices will go up in Bo Those who can’t afford to live there OR have lower valuation of utility of educated children are pushed out

- People with lot money/ high value on education move in AND people with little money/ low value on education move out This continues until we find that the two communities are completely stratified

- Start situation with little difference: In presence of neighbourhood effects and through price pressure there could be multiple equilibrium lead to segregation (Need intervention= small differences can grow to be large)

How do neighbourhood interaction exert outcome on individual behaviour?1. Endogenous interaction: my behaviour is affected by another person’s behaviour

a. Propensity to behave in some way varies with the behaviour of the groupb. Peer group effect: Peer group effects: effect of class attainments on own attainments

2. Contextual/ exogenous interaction :my behaviour is affected by characteristics of people a. Propensity to behave in some way varies with exogenous characteristics of the groupb. Correlation between outcome of individual and characteristic of others in reference group (neighbour)

3. Correlated effects: We behave same as we’re similarly affected by factor of area we live that push our behaviora. Propensity to behave similarly because have similar characteristic/institutional constraintsb. Correlation between outcome of all individuals in group (neighbourhood) induced by (un)observed factorsc. Measures the common influence of (un)observed factors on group behaviour

Policy implications Policies different depending on exo vs. endo. - Endogenous effects: changing behaviour of some knock-on effects to all members

o If score in a test improves with the average test scores of other students in classroomo Then raising some students’ achievements impacts on all students: social multiplier!

- Exogenous effects: policy must change group characteristicso Increasing attainments of some class members has no impact on others if…o It is the gender mix of the class that impacts on behaviour!

Conclusion Whenever external effect policy, you can intervene - Neighbourhood effects : Characteristics of people living proximity might have impact on your behaviour - Reason for study : if neighbourhood effects/ externalities then possibility to intervene produce better outcome in term

of education or labour market opportunities for people targeted by policy - Endogenous stratification : If look at neighbourhood effect from static point of view = underestimate dynamic effect

May become progressively more stratified, overtime disadvantage/ advantage across different types may swell- Social multiplier and multiple equilibrium : May result in poverty trap similar neighbourhoods may coordinate to more

positive outcome - need policy to trigger exchange in expectations- Social multiplier (= potential multiple equilibria) Target all neighbourhood OR specific part of neighbourhood?

o Hinges on having strong (Give incentive for policy makers to intervene to correct suboptimal equilibrium in face of externalities) and non-linear (know where neighbourhood effects are strong) neighbourhood effect

- Endo (behaviour affected by your behaviour) class performance affect my performance; we influence each other- Exo (behaviour is affected by your characteristic) class gender balance share of female in group on test score

- Important to distinguish as it tells you what aspect of the neighbourhood you want to manipulate with policy o change aspiration by doing so change behaviour of people - social multiplier effect o OR do you want to change composition as you think it’s actually who lives there with their characteristics

Neighbourhood externality effects: main findingsAaronson (1998): Effect of neighbourhood poverty/ drop-out rates on prob that child grads high school: exogenous effect

- Regression compares outcomes children from the same family effect of family background is controlled for - Sibling fixed‐effects model: Compare effects of child who moved relative to 1 stayed as function of how they change in

neighbourhood affect individual outcome - 10% increase in % of young adult in neighborhood are high school dropout increase likelihood of dropout by 3.6%- However unclear whether observed between-sibling differences in outcomes really caused by neighbourhood

differences OR by other factor associated with changing neighbourhood (divorce of parents or a parent's job loss)- Technique requires that parents of household do not change with respect to neighbourhood location.- Weakness of method = data availability Method requires panel study data on many households +these households

must have multiple siblings with enough outcome variation to obtain testable results. Case and Katz (1991) Understand how your propensity to be a criminal, single parents do drugs, gang, church, alcohol is related to exact same behaviour of your peers in area you live - Attempt of pick up endogenous effects

- Using survey of disadvantaged youths in Boston, find that individual's propensity to commit a crime rises when his peers are also engaged in criminal activities. Similar effects found for drug/ alcohol use

- Strong social interaction effects for violence, drug and alcohol use, and gang membership.- Suggest that concentration of poor in dense area generates harmful local spillover that exacerbate social problem- In related paper, Glaeser et al. (1996) emphasize the role of social interactions in explaining the continuous prevalence

of high crime rates in certain places and the significant variance of crime rates across space.Crane (1991) Hypothesis: There should be strong evidence for neighborhood effects in the very worst neighborhoods.

- Crane finds strong evidence of neighborhood effects in the very worst neighborhoods.o For whites and blacks there’s: threshold at 5% affluent neighbors: below which dropout rates skyrocket;

- Crane interpreted these findings as consistent with intra-neighborhood social interactions, BUT unable to distinguish whether high-status neighbors created an endogenous effect (such as serving as positive role models)

- As neighborhood decline in quality the prob an individual in that neighborhood develops a social problem increasesneighborhood with few role model lack support structure necessary to prevent bad social outcome

- Evidence that neighbourhood effect are nonlinear They’re more important for deprived area, if move from less deprived area to really deprived area your probability of dropping out goes up substantially

- BUT if your in an already affluent neighbourhood there seems to be less evidence that neighbourhood are thereArea-based policies: the UK policy debate: Possibility of targeting policy if multiplier is strong and non linear

- UK government put a lot of emphasis on creation of mixed communities Some areas are clearly ‘nicer’ than others- In theory: Better to have mix communities (if social multiplier is strongly non-linear) so if you get rich people who live

with poor people: poor benefit lot (as social multiplier non-linear) and rich don’t get affected too much adversely and by mixing you raise outcome across whole community because poor benefit: BUT…

Very little evidence that neighbourhood effects are particularly strong ( Gibbons et al .) - Quality of people around you no affect you achievement no matter how good/ bad you are -primary school achievement- Neighbourhood effect either zero or negative when it comes to individuals wellbeing and happiness - if you live with rich

you find individuals who are different to these people are less happy and more involved with crime - All in all UK/ US: evidence not support that mixing communities is effective in raising individuals outcomes

Yet policy makers still do it: V alue idea of active create less unequal society (MIX COMMUNITIES UNSUSTAINABLE) - Individuals sort in different communities depending on amenities they provide – - These amenities generate change in prices when trying to reduce inequality by making deprived neighbourhood

better OR by targeting and making people in these neighbourhoods better off by giving them more amenities starting dynamics that imply in LR policy effect become undone as communities aren’t fixed change in a way that is determined by prices and rentsCommunity improve =Poor pushed out/ Rich attracted in

- When you try improve quality of some community: initially inequality across communities going down BUT in LR inequality will open up again because of sorting mediated by house prices

Example Paul Cheshire evaluated policy City challenge in London - Policy try’s to train people in community to acquire skills and come out of unemployment supposed to target people in

community by improving these individuals you raise quality of whole neighbourhood - This programme was successful as all found jobs BUT they decide not to live there anymore they left and that

triggered prices going down people who moved in had even worse characteristics as they can’t afford rents- Programme did improve the lives of the certain individuals but made the area worse than before

Policy conclusion: Shouldn’t worry about communities - Don’t focus on idea that intervention at macro level that there will be externalities as evidence here is thin- Focus on individuals and provide these guy with training and oppsif community consequently worsen don’t worry - Help the people who moved in - target individuals as differences in composition and social fabric of different areas is not

cause of inequality they are outcome- the cause is sorting, driven by different prices in London- Don’t worry about outcome that is spatial inequality worry more about underlying reason for inequality - which is

individuals lack of skillsbest policies is providing training + improve schools (not changing composition as peer and neighbourhood effect)

Week 5: The Value of Homeownership: Positive and Negative Externalities

- Local HO rate affects investment in local public services E.g. publicly funded local school- Homeownership status affects social capital investments E.g. participation in local clubs and activities- Oswald (1996) higher unemployment in countries where HO high

What distinguishes homeowners from renters? Two crucial differences 1. Homeowners less mobile: high property transaction costs + expected duration

a. If married (Looking for two jobs harder than 1 job) / have kids likely to stay put (school) b. Lower mobility of certain groups imply there more likely to be HO as individual who know won’t be moving

around know they won’t be facing transaction cost so become HO 2. Homeowners have house price induced incentives to improve community / neighbourhood / own home

a. if you improve community and amenities are preferred by people reflected in higher house prices

HOW DO THESE DIFFERENCES AFFECT ECONOMIC AND SOCIAL OUTCOME…?Case 1: what is effect of HO on provision of Local public goods? (Example of publicly funded local schools)School investment decision: individuals want to understand how much to invest in school expenditure (Higher taxes)

- HO with kids will directly benefit schools are better endowed with expenditure of stock - HO with no children have no direct benefits but indirect benefits better schools = high house price- HO decision depend on direct benefit ALSO indirect benefit (Capitalisation of improvement in school quality)

is benefit in terms of value of my asset if I sell tomorrow > than cost of investment - higher taxes? - Increase quality of school= high demand= house prices up: By how much depends on elasticity of supply…

o Elastic: Able to host lot of people without putting pressure on supply price don’t respond very mucho Inelastic: Not many houses: people move in bid up prices and value of asset goes up

- If short horizon in property = beneficial BUT If long horizon benefit of improved school not felt for long time - If median voter renter - unlikely he will decide for improvement of school quality through higher taxes

o Renters unlikely to have kids ≠ direct benefit ALSO won’t sell asset ≠ indirect benefit Hilber and Mayer (2004): HO without children more willing to support investment if capitalization is high & owners have relatively short horizon in property (Using data from 46 states)

- HH without children support investments to improve school qualityimprovements capitalized in house value- HO without children support investment, as long as expected duration in property is short enough- Correlation between public school expenditure and fraction of population who are elderly only negative in areas

with low residential density and area located outside Metropolitan Area citizen recognize capitalization effect relate to school quality only important in densely-populated areas (inelastic supply)

- Also important that elderly in favour of investment (Elderly have short horizon in property ∴ beneficial)Case 2: what is the effect of homeownership on social capital & club good?

- Club good: Partially non excludable/ partially non rivalry (neighbourhood watch, childcare group of parents)- Who decides on invested amount individuals, but need sufficient incentive to invest in good - social capital - Club good produced on basis on social capital, people invest for reciprocating activitiesthere are 2 phases:

o 1st when you start club good - begin child care 2nd maintenance and consumption Club good only provided if sufficient number of initiators

o Free rider problem = Issue: partial excludability: People move between 1st and 2nd period and enjoy benefit of social capital despite not investing in 1st period- can’t exclude: enjoy good/ won’t contribute

Hilber (2007) : E xplore how social capital is acc based on individual decision making, - HO more likely (Than renters) to invest in social capital because of the lower mobility rates of homeowners - Positive link between HO and individual social capital investment restricted to built-up neighborhood (inelastic) - In these localities house price capitalization provides additional incentives for HO to invest in social capital. - Built-up neighborhoods provide protection from inflow of newcomer that could upset mutually beneficial

equilibrium involving reciprocal cooperation- No link between homeownership and non-neighborhood specific social capital

Di Pasquale/ Glaeser (1999) : People who become HO or renter after being HO: Do they stop investing social capital? - Local social capital investment increases in response to becoming HOMore likely talk to local politician etc. - Breakdown social capital investment driven by fact HO less mobile so in end will eventually talk to neighbour- 50% association between HO and social capital investment driven by fact HO been around for longer and

eventually talk to neighbourother 50% explain by incentive driven by house price up if neighbourhood good Case 3: What is the effect of home-ownership on employment outcomes?

- HO reduces labour mobility increase propensity of being unemployed - in case of geographically asymmetric shock that only affect north - if you are HO less likely to relocate = higher unemployment for HO

- High HO rate partly explains unemployment in OECD countries Empirical evidence Oswald (1996) 10% increase in HO leads to 2% increase in unemployment

- Reduced mobility associated with HO creates labor market inefficiency and higher unemployment rate- 1 of most significant barriers to mobility is HO as it’s easier to move when living in rented accommodation.- % of HH who are HO significantly correlated with unemployment, both across countries and across US States.- BUT contradicted by empirical fact that renters have lower employment rates (Böheim and Taylor, 2002) 

- CA very macro analysis neglects to appreciate other casual factor that might affect both unemploymentCritique of Oswald: Mayer, Nicoll they find the effects substantially smaller but still there

- They agree with Oswald that if you HO less likely to move to different part of a country to find employment - But if you’re HO and if you have mortgage you can’t lower reservation wage (if offered a job more likely to accept

than if you didn’t have mortgage)- Overall : negative effect on employment probability of HO due to reduced mobility, contrasted by positive effect

that come around due to fact your willing to take pay cut to pay mortgage - Find that overall actually lower reservation wage wins out - HO do relocate less but in need they tend to accept

job offers at higher rate as they reduce the minimum amount willingness to acceptMunch et al. (2007): Home ownership, job duration, and wages – Data from Danish labour market

- Find that unemployment spells for owners shorter than those for renters HO accept jobs more readily- Suggest that HO hampers propensity to move for job reasons but improves the chances of finding local jobs. - Due to high costs of moving HO not only will have a higher reservation wage for distant jobs ALSO will have a

lower reservation wage for local jobs possible rising HO goes with higher employment (but at lower wages)BATTU et al. (2008): Housing tenure, job mobility, and unemployment in the UK

- Conclude that HO have stronger job commitments than renters and thus HO limits not only job mobility (the rate at which workers change jobs), but also decreases the probability of becoming unemployed

Negative equity: Want sell house move place with job if sell can’t pay loan does this impact mobility, thus employment? - NO : feature of US loan is if you’re in negative equity and go to bank and can’t pay loan can’t seize assets and

chase you for next job in order to recoup money - they just foreclose and make whatever it makes - negative equity impacting mobility impacting employment in US not strong

- UK loans recourse: banks seize next home = likely to have negative effect on employment outcomeHome ownership and entrepreneurship: HO less likely become entrepreneur: Negative association between entrepreneurship and mortgage leverage

- You know you have to pay certain amount mortgage back less likely to take risk to become entrepreneur and maybe make a lot of money or maybe not enough to pay back mortgage payments

Homeownership and Entrepreneurship: Bracke et al (2012) – Individual panel dat on UK HH - Examine influence of decision to become a HO on self-employment. - Propensity for selfemployment reduced by 25% in 1st few year after being HO (But effect dissipate over time)- They call portfolio thought to explain HO and selfemployment risky and HO may seek secure employment

(Dietz and Haurin, 2003) Found overwhelming evidence of positive externalities (Not just economic) of HO. - HO increase incentives for investment in turn lead to improved living environment and improved health.- Higher rate of HO can have positive effects on social capital and may stall urban decay- They find that there’s evidence of HO impacting HH, labor force participation, home maintenance, political and

social activities, physical and mental health, demographics, and outcomes for children. - HOs generally have better health status compared to renters

Conclusion - Positive externalities of HO relate to invest in local social capital, club good/ public good driven by fact HO interest

in price staying up: externalities will be internalised in future ∴ positive outcome argument not strong- Negative argument of HO: HO and negatively impacting employment (been demolished through new studies)

ALSO mortgages recourse in UK = reduced mobility of individuals AND negative effects on entrepreneurship - On balance negative externalities seem to be as large if not even larger than HO so the way you think about

policy not always wise to promote HO as only positive externalities have to do with house price capitalisation Polices to simulate Homeownership Tax deductibility of mortgage interest payments Evidence for this policy that work is small - very expensive in US

- If mortgage deductibility in places with elastic supply don’t see effect - people don’t become more or less HO- When you go place with inelastic supply, promote demand but space limited only thing achieve = high price

Promote HO for low-income people - give them transfer for them to afford to buy house - Same problem: People try live area inelastic supply: transfer mean demand up, price up and effect sterilised - 5 people benefit but another 5 lose zero sum game

Subsidise supply policies – subsidise private housing companies- If you don’t direct policy well you will crowd out un subsidised private provision of housing - Overall supply not changed: Increased demand, crowd out private provision so again house prices go up

UK - help to buy where shall we build? House prices in London high in the North they are low - so problem is not only one of promoting HO but also trying to understand that there is some geographic disparity

between where people want to live and where houses are available - people want to live in south - jobs - but we still give people help to buy subsidise in areas where no jobs- what we should do is think carefully how to trigger more supply housing in places with jobs - London greenbelt -

1% of green belt would solve housing crisis in London

Week 6: Crime and the City: Theories of Crime and its Spatial Distribution- Crime is associated with urbanisation Main city average crime rate > While country average

- Crime not evenly distributed within cities E.g. Crime in Hackney > Crime in Sutton (Central v periphery)- Bigger cities have more crime (More people to steal from AND easier to get away with crime)

Economics of crime - Relates likelihood individual engage crime to cost and benefit of these activities, compared to legal jobs. - At aggregate level, the more prevalent the conditions which make crime attractive, the higher the crime rates.- Criminals not deviant people - respond to incentives of making money and calculating risk of being caught - If you recognise principal that individuals are engaging in criminal activities trying to earn living wage through elicit

activities then your model of crime simply becomes model of labour supply Ehrlich (1973) P ropensity to commit crime was function of payoffs and punishments associated with criminal activity

- Constructed model of participation on illegitimate activities: individual allocates time legal and illegal activities. - Positive effect of inequality: Finds positive relationship between relative poverty (percent of population under half

the median income in the state) and crimeo Crime against property positively related to percentage of families below median income (inequality)

- Payoffs to activity (like robbery) depend on level of transferable assets (proxied by median income in community.o Crime positively related to median income (returns to crime)

- Rate of crime inversely related to estimates of probability of apprehension and length of prison stay- Criminal activity will positively relate to returns on crime - Criminal activity depends on how much you as a criminal are going to earn if you engage in legal activities - BUT: When you look at relationship between city size and crime - when you increase city size and you don’t

correct from underreporting number of crimes don’t increase enough (relationship flat) - When you take into account underreporting crime relationship is observed that bigger cites = more crime

Glaeser/ Sacerdote 1999: USE DATA ADJUSTED FOR UNDERREPORTING: relationship between crime/ city size- Higher benefit of urban crime and lower prob of arrest partial explanation for higher crime rate in city

o City characteristics: Returns to crime explain around 25% AND Probability of detection around 20% - Much larger fraction of effect of city size on crime (30%) can be accounted for by household characteristics. 

o Most important predictor: presence of female-headed household is positively related to the probability of being a crime target. lack of strong social punishment - less risk = more crime

- Factor accounting for remaining difference is degree of social interaction in cityCan this help explain crime variation within cities? Cities agglomeration forces give criminal advantages Like legal trade crime tend to agg in larger market: true for drug gang concentration in city (Glaeser/ Sacerdote, 1999)

- Agglomeration = less likely to be caught/ Don’t have to travel long to find potential victim/ need info about who is around, where to steal - info flow like knowledge spillovers

- We do have areas in cities more agglomerated like stations and this may make crime more likely to occur - BUT all in all return to crime homogenous within city: can’t explain variation we saw before - only part of it

Statistical methods for detecting crime ‘hot-spots’: Sorting of people across space gives rise to spatial autocorrelation- What is happening when we just observes some spatial autocorrelation is that individuals sort – they choose

location to live considering local amenities they want to consume or they can’t afford to live there - Sorting generate positive association between characteristics/behaviour of certain area and characteristics and

behaviour of individuals in the area Estimate correlation of incidence of crime with incidence of crime in neighbouring area and we get stat Moran I

- Measure correlation between crime in Clapham and criminal activity in all neighbourhood in other parts of London - inversely weighted when you take average with distance from Clapham itself

- This measures how crime in certain area are correlated with all average criminal actives in rest of London - you may want to use weights that take Battersea and other areas surrounding Clapham giving it more weight

- Moran I: essentially regression coefficient: you try regress extent to which neighbouring areas have high and low incidence of crime on incidence of crime in neighbourhood excluding in which you’re interested in

Example: Moran’s I for crime in US Southern States: Mencken and Barnett (1999) : - Does murder per capita in certain neighbourhood positively associated with murder in neighbourhood in all other

area in city and try get measure that sum up spatial factor of autocorrelation across all southern state- Found inconclusive evidence that high rates of violent crime diffuse from urban to nearby locations.

LISA: Local indicators of spatial association LISA gives local descriptive analysis + captures more localised tendency - Positive significant or insignificant Moran’s I at global level can hide interesting local pattern

o Locally could have positive, negative or no relation- Local version of Moran’s I allow exploring clustering of socio-geographical features (crime)- LISA - come up with some density analysis that tries to pin down at neighbourhood level and at each street point

on map how in the incidence of crime is for that specific areaAnselin (1995): Although Moran's I could demonstrate that there is spatial amalgamation on global level, it can’t show where spatial amalgamation is strong or weak, as Global Moran's I index is simply overall statistic.

- In order to figure out where local spatial amalgamation is high or low which regions have made more contribution to global spatial autocorrelation, Moran's scatter plot and Moran's local index should be used

- Specifically, global Moran's I index measures the overall spatial association among geographical areas,- Local Moran's I index examines local similarities and variations.- Both global and local Moran's I indices typically range from −1 to 1 with positive scores indicating similar values

are spatially correlated and negative values indicating that unlike values are clustered.

Week 7: Local Labour Markets and the Spatial Mismatch Hypothesis- Suburbanisation of jobs - London in 80s lost jobs relative to the peripheral regions within same region - After period of losing jobs, cities experienced a decade of renaissance the 2000’s

o Mechanisation: job displaced with tech: sectoral specialisation of advanced nation tech orientated o Specialised in sectors where staying close and benefiting from labour pooling etc. become important

Why unemployment is unevenly distributed within cities?Jobs created in places where people don’t live and distance is a mediating factor Person-related factors

- Single labour market in city: unemployment exist as some individual less likely to find job, have wrong skill- Housing market drives unemployed to live in some place: housing market/preference sort less/more employable

people into different locations = ‘clustering’ of unemployment- Person related factors summarised: Unemployment in city is everywhere to start with some people more likely

to have jobs as they went to uni people with good jobs sort in neighbourhoods with amenities and in this neighbourhood fewer people will be unemployed as house prices go up unemployed move to cheaper parts of city = stratification that we observe is result of presence of unemployment and subsequent sorting

Concentration effects: The opposite of person related factors theory- Areas that start with high levels of unemployment or low levels of unemployment has casual effect on new

generation when they are looking for jobs - If you’re in area with high unemployment you find it difficult to find job as unemployment causes unemployment

through neighbourhood effects/ role models/ peer influences - THIS IS NOT CREDIBLE : Peer effects weak when you factor in all variables that affect unemployment

Uneven distribution of unemployment: spatial mismatch hypothesisSMH: Because of firms’ relocation towards city periphery, (black) workers, who generally reside in inner cities, face strong geographic barrier to finding and keeping well-paid jobs There is a ‘spatial mismatch’ between worker home and workplace yielding low incomes and urban unemployment that persists because of housing discriminationSMH and the facts

- Job decentralisation : low skill jobs in US moved from centre to suburbs in 70s and 80s- Residential segregation : unskilled Blacks lived in CBD; did not follow jobs to suburbs Mismatch- Result of mismatch between where people live and where jobs created: individuals stuck in city centre have lower

labour force participation as opposed to same group of people if they move to suburb Why were jobs moving to the suburbs?

- Fall in transport costs - allowed people to relocate to suburbs whilst keeping central jobs - Outer area becoming more rich: Demand space in suburbs so more income and wealth moves to suburbs

company’s follow started wanted to reach workforce/ generate agg in suburbs/exploit labour pool relocated - Period during globalisation: Important be part of intl. chain of production and value added creation and to tap in to

improved input output linkage chain need be close to good transport linkages/ highways (some intermediate goods not produced in city) so you want to be in outskirt to be close to transport routes

Important: degree of suburbanization depends on skill content of jobsJobs that can be suburbanised:

- Entry level jobs (nurses, home-carers ): Rich work in centre but live outskirt need for nanny’s and gardeners etc. so from consumption point of view low skilled jobs being suburbanized

- Routine roles: big manufacturing plants can have input output linkages come from abroad Some jobs can’t be suburbanized High skilled jobs agglomerated in cities to learn from each other Minorities (Proxy low skilled workers who perform low skill job well) didn’t move due to residential/ racial discrimination

- Consequence: Poor job access increase unemployment: have to commute longer from where you are if your poor skilled to where jobs are individual more distanced from labour markets explains unemployment

Min wage and SMH : Different people are affected differentially by minimum wages depending on skills they have - Low skilled affected: Kind of jobs they do more affected skill imply that they do menial jobs for which minimum

wage is binding - so unemployment among unskilled or black minorities - SBD and CBD have same minimum wage (can’t reduce it in certain areas of city) can’t change demand for

unskilled workers will be same in SBD and CBD But skewed sorting of blacks in CBD implies higher black unemployment there (eventually excess demand in suburbs)Discrimination =no black move suburb

- Min wage + housing mkt discrimination= Unemployment Discrimination in housing mkt =people can’t move aroundunemployment due to fact min wage is same but more supply unskilled in CBDlabour mkt no clear

- Demand not high enough given unskilled / wages can’t go down (m. wage): unemployment only outcome- Assumption: Poor can only accept job in suburbs areas if relocated there (discrimination means can’t do that)

Efficiency wage: Too high relative to what clear mkt, endogenously determined by firms: keep high to reduce shirking

- Blacks = low skilled and live in CBD/ Low skilled jobs are harder to monitor - Low skill job hard to monitorthey pay higher efficiency wageunemployment higher in low-skill job (Black)- Higher unemployment in CBD (Where poor skilled work) can only look there, can’t relocate ALSO they’re

discriminated against have low skills job that affected substantially by efficiency wages that try exert effort - Double disadvantage =house mkt. discrimination+need to set higher eff. wage (view these job hard to monitor_- Theory is not convincing : assumption that no matter what you will only look at market where you live

Brueckner & Zenou (2003): Suburban housing discrimination skew black toward CBD+keep black remote from suburb- Develop efficiency wage model which workers not mobile and are therefore stuck in their residential location 2

areas in city: centre and suburbs (Assumption relocation cost are so high workers never change location)- Blacks residentially concentrated in CBDjobs exogenously decentralisehousing discrimination = blacks can’t

follow jobs = MISMATCH Black who work SBD=costly commutefew accept SBD job black CBD labor pool big relative to SBD pool

- Under min-wage/ efficiency wage model: increase CBD pool leads to high unemployment among CBD worker- Policy: Subsidize commuting costs of black workers, so as to improve job access

Search + matching models individual looking for job will be matched when a company he finds is willing to hire him - Distance from job (fact you that live in CBD/job create SBD) doesn’t prevent you from looking there- When commute to look job elsewhere disconnection and commuting make you look with less efficiency- Also Workers who live further away from jobs have less information about vacancies- Makes more likely you won’t find job and hence more likely you will stay unemployed in the place you reside

o Not because you don’t look in the other area but because the distance affects capacity to be efficient - Model has trade off at heart : Look far away more likely to find job as that’s where jobs created BUT less likely to

look efficiently and less likely to enjoy your wage utility if you have to commute long distance - Firms like suburban areas as cheaper/ agg/ transport hub there is an asymmetry here as firms only opening

jobs in SBD not CBD but symmetry in sense that you can look in both SBD and CBD for jobs nowCoulson et al. (2001): S earch equilibrium model in a duocentric city with central/ suburb labor mkts:

- Assume: Fixed entry cost of firm in CBD > SBD/ Worker heterogeneous in disutility of transport/ search cost- 2 assumptions sufficient to generate equilibrium in which central city resident experience higher unemployment

than suburban and suburban firm create more job than central firms (higher vacancy rate)- Interaction between land/ labor market: Workers/firms mobile and look for “partner” to form match in both area- SMH implies: More vacancy for low-skilled in suburb; wages for low-skilled be higher in suburbs than in centre- Although commuting is allowed, unemployment rate will be higher in the sector with the highest vacancy costs- P resence of search and entry friction crucial for explaining SMH phenomenon beyond racial discrimination.- Model policy implications : improvements in efficiency of matching function and/or in transportation infrastructure

yield lower level of unemployment ALSO gov should reduce differential in fixed entry cost in order to partially alleviate spatial mismatch; for example, by subsidizing the entry of firms in the CBD

Search with friction and SMH: Differential entry cost of firm + search frictions crucial: sufficient to generate predictions:- Higher unemployment and lower vacancy rates in CBD than suburbs- Distance and ‘detachment’ from jobs induces equilibrium unemployment- An increase in matching efficiency improves unemployment and vacancy rates in each location- An improvement in transport technology lowers unemployment rate for CBD residents

SMH evidence (1): Compare commuting behaviour: implication of SMH: Black have longer commute than White- BUT : Comparing distance of individuals travel considering only those that have found employment you miss

majority of point here - black and unskilled do not travel to jobs as they don’t find them Your only comparing a select few black individuals who find jobs (when most don’t ) to whole group of white

- ALSO : Difference in skill related to difference in income: higher income people consume more housing white people live in outskirts have to commute for job=Problem: Compare rich to poor and your bound to find white commute more (contrary to SMH) as not controlling for more income=more home=relocate suburb

SMH evidence (2): measures of job accessibility: Explore relation between labour mkt outcome and job availability- If job accessibility affects unemployment and Blacks live in areas with poor access might affect racial

differences in labour mkt outcomes attributed to housing discrimination- Relate chance of finding job by looking at area characteristics in terms of job availability + test for SMH- Problem of simultaneity: job access affects employment, but employment can affect access Rich want to live

away from jobs to consume more house (So rich in this test will be predicted to find less jobs) SMH evidence (3): city/suburban resident: Based on argument minorities in suburbs adv relative to minority in cities

- Observe 2 individuals with same background (skills) one is living in SBD and one love in CBD - Look if person living in SBD (low skill) has higher prob to find job V Person with same skill but live in CBD- BUT : Very difficult to test: endogenous residential choice, need ‘instruments

o Income for accessing these facilities higher SBD: Livelily that black in suburbs intrinsically different o Difficult to find instrument to get two people with one living in SBD and one living in CBD and considering

them as fully comparable to study which one find jobs easier SMH evidence (4): labour market tightness: SMH states that there are fewer jobs and job vacancies in central cities

- SMH implies that affected areas have lower vacancy rates and/or lower wages

Ihlanfeldt (1998) SMH: Majority of SMH studies have concluded that their results support the SMH - Studies suggest: SMH partly explain employment gap between certain area in city + SBD where job relocate- Studies that reject/ find little support for SMH fail to adequately account for endogeneity of residential location

o Problem with (3): Low employment of central black may also reflect unmeasured difference between black residing in central areas and blacks residing suburb arise from endogenous location decisions those with jobs (higher income) choose to live in suburban area= Bias toward finding of SM

- Conclusions from other SMH studies o In areas with high levels of housing segregation and poor transport SM plays a more important role in

explaining labour mkt problems of inner-cities o Therefore spatial mismatch might be a big-city problems in developing countries only (which don’t have

strong infrastructure) Week 8: International and Interregional Migration: Theory and EvidenceMigration as human capital investment: Workers calculate value of employment opps available in each location

- Consider cost of moving and choose option that maximizes present value of lifetime earnings- Net benefit = PV of earnings from migrating – PV of earnings by staying home – Moving costs

Implication - Improved economic condition abroad (Increase wage difference between home/ away) increase migration - Improved economic condition at home (increased rate employment growth home) reduce prob of migrating- Higher mobility costs (Distance/ cost of moving/info gathering for new jobs market) reduce prob of leaving

Migration facts Evidence from US - 10% increase in wage differential between H-state and A-state increases prob. of going by 7%- 10% increase in rate of employment growth in H-state reduces prob. of moving by 2%- Double distance migration rate down 50% (Not just physical but social distance)

US migration and individual characteristics- Younger/ Educated people move more: 7% college graduates migrate in their 20/30’s; down to 1% in 60/70s- Expected future benefits depend on how many years of working life you have ahead of you (young have more)

ALSO educated people better getting info about ‘foreign’ markets = reduced mobility cost of MigrationFlows and return migration Probability that a migrant goes back is 13% AND 15% prob will move to different place

- Could be due to: optimisation, made a mistake, (aspirations) migration as stepping stone to better careerHow big are migration costs?

- UK observe wage and employment differentials Economic theory: rational would migrate to high wage area- But migration is not closing this gap Explanation = mobility costs must be too high

Migration costs: US/Puerto Rico ( Borjas ) 75% of Puerto Ricans don’t move US despite 120% wage gap (Puerto Rico are US citizens so legal barriers are small)

- Borjas figures out that implied migration cost = $500k Not just physical cost of migration: Psychological costs + Cost of gathering info in a distant country for which you may not have right skillset or institutional framework + Coordinate with family considerations

- If want to understand whether workers move or not you have to expand simple calculation you’ve made There is evidence that there are tied stayer and tied moversMigration as a family choice

- In labour market women more discriminated often follow men and when they move they experience substantial wage gap –1000 dollars per year –fact that 1 person moving drag other along potentially at a loss explains why when you consider calculation a joint couple mobility costs are substantially larger

- Individual in labor power couple – both college grads Cluster in cities: highly agg market that tend offer jobs that are agg in cities as they benefit from being in city (knowledge of jobs) = If 2 college grads have to look for a job together you’re more likely to look in big city as chances are you will find a job

International Migrations: Immigrants in the US The labour market performance of immigrants to the USMigrant upwardly mobile: Immigrant earn 15% less than native on arrival; same after 14 year; 10% more after 30 year

- Earn less when they arrive- Converge: Attain specific human K for country migrating toMoving US need to learn language/ adjust acent- Earn more 30 years later: self-selection Survival of the fittest only best migrants stay

The performance of immigrants: cohort effect - When estimating how much more earning individual earn if they stay in country for longer period of time we’re not

taking 1 person and following his wage profile we take single cross section- In single cross-section, migrants with different labour market experiences in country of destination belonged to

different entry cohorts If there was decrease in quality of migrant entering country over time (occurred in the US (Borjas, 1999) wage growth estimated would be an upward biased measure of the actual growth.

The Decision to Migrate: The Roy Model with different skill sets : D escribes how workers sort into different occupations based on their skills and return to those skills in different sectors/countriesMigrating from Sweden/ Mexico to US

Sweden (Positive selection): Wage inequality higher in US low rate of returns to skill in Sweden compared to US US contributed to brain drain from developed country as wage inequality mean if you want to rewards skill go to USMexico (Negative selection) Wage inequality higher in Mexico High skill scarce in Mexico Inequality is higher in Mexico in relation to the skill spread than it is in US Poor in Mexico more tempted to move US as know that further down skills distribution in Mexico you’ll be struggling if move to US (relative to Mexico) skill inequality is less substantial, have chance to get job as cleaner and you earn higher wages in US vis-a-vis Mexico given your skill set Push/ pull factors in migration: Change in condition at home/ other country make migration more attractive /convenient

- Can broadly split into pull and push forces; simple definition:o Internal factors that affect one country’s demand for workers + affect migration are pullo All external or internal factors that affect supply of migrants are push

Push/pull factors and the history of European migration- War adjustment/de-colonization:1945- 1960s return to/from colonies to original countryPush factors - 1950-1973: active recruitment migrant expanding economies, pro-cyclical immigration Pull factors - Oil crisis: 1973-1988: oversupply migrant, social tensions due recession: opposite direction Push factors- Closure of communist bloc 1988: increase asylum seekers, negative outlook sending countries Push factor

Zimmerman (1996): Studied pull factor by proxying them by real GDP growth of Germany as attractor for migrants for other countries – higher GDP growth = higher demand for worker

- No. countries within EU that respond alot to pull migration (Italy strongest) 60s/70s Italian moving to Germany- In Yugoslavia: After fall of berlin migration of this country explained by push factors: Collapse of soviet regime

workers find it more profitable – low skilled – to migrate abroad to earn higher wages abroad - If look at Italy, Greece, Spain and Portugal – you see shift: After 70s pull strength of GDP from Germany is

reduced migrant from 70s onwards due to push as opposed to pull – sending countries own conditions :economies driving to halt – and after that only significant migrations flows were explained by fall of soviet bloc

Internal Migration in the UK: The Flows across Regions- Young and more educated people more likely to move - No kids = move more: couple/ children into equation means transaction costs higher - Full time workers move more: part time = less effective years of earnings to benefit from after you move more

likely to move if you work full time as you'll earn full wages- Single people less likely to do ‘sponsored moves’

Distinction between sponsored and unsponsored moves explain the north-south divide- Sponsored mobility – Relocate as job offer is made

o Sponsored job account for South-North migration- Unsponsored mobility - Individuals who don’t have a job and look for something in the place they live

o Account for nearly all of North-South flow: young people come south look-in for better job opportunityo Riskier for individual: Likely be discouraged from moving in time of economic recessiono Likely to be influenced by general local labour market conditions, including unemployment rates

- Overall migration flows between north and south are balanced What determines regional flows? Multi-level stream analysis ( Gordon and Molho, 1998 ) A nalyse interregional movement in Britain between 1960 and 1991 AND argue that migration is not homogenous phenomena

- 2 stream of migrants: 1 primarily associated with employment (National stream) and other which is housing/ environment relate (Regional stream)

- Employment related migration in UK respond more to employment opportunities in destination (measured by rates employment growth) than to wage effects

- At no time, has direction of labour migration simply been determined by employment factors, with substantial evidence of influence from environmental preferences also (regional account for 10-15%)

Why little migration across region ( Hughes/ McCormick ) Social housing acting as drag of potentially mobile individual- Mobility of council tenants lower than mobility of private renters Council tenants wishing to migrate have to

obtain council housing in their destination region, and that process does not seem to work well. - 70/ 80s many people council tenants (council estates up market) Poor live in semi-formal private rental sector- In 70s North in turmoil due to sectoral change council tenants in north want to move south but less than

70% of these people had mobility They wanted to move but found it hard to relocate- Coordination failure across local councils: Individuals try switch estate difficulthad to enrol on waiting list in

London, this meant they had to 1st relocate to show they local resident can’t do that without jobs = Drag - Negative equity: Right to buy scheme people begin purchase houses Areas experience poor economic

performance prices go down not able move as bought property and value is less than what you bought Conclusion

- If you want to think about unemployment gap between north v south: Do 2 things - Think about cost of living in these different regions

- Think about skill that person have and how these skills may not make them suitable to relate south - Policy: change planning restriction to make south more affordable ALSO provision of skills, if you make house

more affordable in South but didn’t solve lack of skill compatibility between unemployed in North and work in southperson move south as cheaper (you relax supply of space) but this individual will still not be employed

Boheim and Taylor (2002) : Residential mobility across regions is low in Britain – Data from BHPS- ‘‘[A] desire to move motivated by employment reasons has single largest effect on the probability of moving

between regions’’ and that it is the unemployed who are most likely to move, particularly between regions- Over 50% of the unemployed live in rented accommodation while less than 20% of those in work are renter- They find that income and education raise interregional mobility and that private renters are most likely to move

between regions when income and education are held constant.Bonin et al. (2008) Cultural, social, linguistic and other barriers have kept internal mobility in EU low compared to US

- Older people less mobile than younger ones, women less than men, families less than singles (unmarried without children), low skilled less than high skilled, and employed less than unemployed, past migration experience increases the propensity to move.

- Study highlights role of “national tastes and preferences” for migration, arguing mobility rates vary across EU member states also due to factors beyond observed differences in such areas as economic development, sizes of immigrant communities and proximity (Factors include cultural/ historical variables)

Migration in a Segmented Labour Market (Gordon, 1995)- Labour migrants move to regions of economic growth because they are forced to do so due to structural

conditions of peripheral regions, such as the effects of a segmented labour market or the spatial mismatch between job opportunities and educational achievements.

Week 9: Immigration and Local Labour Markets: Any Effect on Employment and Wages?Immigration to US facts

- Great migration period, 1880-1924; Restrictions in 1924 + Depression: reduced migration to low levels in 30s- By late 1990s nearly 1 million per year and 400,000+ illegal- How skilled are they ? Roy model different waves of migration were characterised by different types of skills

and hence potentially have different types of effects on local economy and this is justified by fact that different migration periods were attracting migrants from different types and different kinds of countries around world

Education of immigrants to US in 2000 ( Card ) Bi modal distribution of individual education when it comes to migrant - More likely to be college dropouts than natives but there is trend that most recent migrants that have arrived only

5 years ago even more disadvantaged (education) than guys who have moved more than 5 years ago- Migrants are highly educated when it comes to having doctorate degree, migrants twice as likely to have a

doctorate compared to natives who have one –- Downwards pressure for locals competing for more advanced jobs and people with doctorate degrees

o Ratio skilled: unskilled migrants < 1 in US but is >1.3 in UK)The simple ‘partial equilibrium’ model In Equilibrium wages in north = wages in south Demand falls for northern workers (Push migration into the south)

o North excess supply o South excess demand

- WAGE GAP: Wage adjust to lower level in the south and higher level in north because some of the additional supply has migrated south and you have new equilibrium

- This equilibrium is characterised by more individuals working in the south but wages in south being lower than before the labour demand shock in the north and lower than if there was no migration

- What Borjas had in mind when he talked about negative impacts of migrants on native labour marketsDemand in south goes up (Pull migration into the South)

o Higher demand in south = higher wages in south o Individuals would like to migrate from the north to the south = excess demand in south

- WAGE GAP: Migrate until wages in north and south are equalized people don't have incentive to migrate - When migrants are being pulled in they go on to satisfy excess demand which means wages slowly move down

relative to the higher levels they would have experienced in the absence of migration Extension of simple model (1): Long run effects with mobile capital

- In LR capital is mobile (And returns to capital fixed on world market)- Migrants in SR reduce wages of similarly skilled natives- Reduced wages is an incentive to provide more capital to host (It temporarily increases returns to capital)- Increases demand for individuals with these lower wages and push wages up again - Hence no LR effects on wage

Two ways to interpret of this model with capital flows- Capital is coming from other parts of the world and hence in UK more capitalists have moved in as more polish

are here has happened to the disadvantage of capital coming to another country

o E.g. as everybody moving to UK capitalists in Italy decide also go to UK additional demand has come from capital coming from Italy similarly skilled people in Italy will have will have lost their jobs

- Bank always can make capital available: supply capital when see entrepreneur has good way to make money Extension (2): Selective migration and skill composition Roy Model

- Different workers have different skills/qualifications- Migrants likely to move to where their skills are scarce and returns highest (selective migration/Roy model)- Migrants change the skill composition of the local labour force- Migration = downward pressure on wages for workers who are substitutes for natives, relative to workers with

complementary skillsExtension (3): Mixed goods trade and factor price equalization

- Countries/region produce and trade a mix of goods according to CA at world goods prices- Migration changes labour skill composition and lowers costs in migrant-skill intensive sectors in SR (Migrants

have potential to affect CA of a country)- Production shifts towards migrant-skill intensive goods and restores migrant-skill group wages in LR- Migration has no long-run effects on relative wages because relative factor prices and within-industry factor

shares determined by relative goods prices on world marketsImportant thing of this model is that a couple of things have to happen:

1. Economy is open as when wages are compressed in certain sectors you’re going to find it more profitable to move production of skilled to unskilled as you will then sell to international market

2. in order for migrants to change CA of a country they need to come in a different ratio of the local workers Extension (4): Labour supply and mobility of natives

- Immigration temp. lower wage of migrant-skill group workers (Complementary group) in host labour market A- Native worker in migrant-skill group drop out of labour force or move to labour market B where wages are higher,

restoring wages in A- Immigration lower wage in other labour market (Causes excess supply in B) OR lower participation rate in (A)- No spatial correlation between immigrant inflows and local labour market (A) wages

This is main criticism for Borjas: If you try understand labour market effect of migrants by looking at local spatial correlation - looking just at local labour market you find no effect (or find paradoxical effect - immigrant increase wage)Who’s right: Empirical evidence on effects of immigration: Evidence in 90s find small labour market effect from migrant

- Pischke and Velling (1997): German data small and not significant effects on employment - higher levels of migration are not making individuals more unemployed

- Card (1990): Mariel boatlift 1980 = 7% increase in labour force of Miami (large exogenous random shock to Miami labour economy/ in specific space) - Finds No effect on wage/ employment outcome of other worker relative to comparison city Due to capacity of Miami's labor market to adjust to this increase in labor supply

- Angrist and Kugler (2003): Yugoslavian conflict=large increases in non-EU immigrant share during 90s Find unemployment up in Italy and Spain, where labour markets highly regulated where wages cannot adjust SO this paper finds evidence of negative effect. Although this is very different depending on whether you consider countries which have very flexible or inflexible labour markets - the more inflexible they are the worse it is

- Borjas: Need study spatial aspect of migration: native/ capital supplier reply to shock of entry of migrant City level comparison not show a thing as every city affected through mobility of capital or of individuals…

o Mobile capital: Capital leave non-immigrant cities for immigrant cities Capital is affecting workforce in non-immigrant areas Local labour mkt analysis unable to capture this effect

o Mobile natives: natives relocate somewhere else - depresses wages in another cityDo natives ‘vote with their feet’? - If native not happy with local economy they will move somewhere else

- If Borjas was right - if lots of migrants come in then you should see a negative change in native population- But Immigrants don’t reduce population of natives migrants go places where native population is expanding

o Economy is expanding Observe more migrants coming into economy at same time as more natives They all flock there as economy is expanding this doesn’t tell you that migrant displace locals

- Borjas suggest you need to look at trends not just two points at time o After boat lift does native population increase on trend OR population of Miami native experience

downwards trend in terms of growth rates after migrants coming in o 60-70 trends that natives population was increasing after there is a large influx of migrant (boat lift)

Observe population in 70-90 trend increasing at a lower rate evidence that relative to trend in absence of migration - migrants pushed out some native workers

- But How plausible is assumption that (linear) trend would have continued? could just be a natural slowdown the population wool have experienced anyway in the absence of migrants

Borjas (2003) empirically, it is case that labour demand curves slope downwards and that immigration lowers wages whose wages are lowered, however, depends on composition of immigrationMain idea: one should consider skills and experience in labour markets to classify workers

- Workers with different education and levels of experience are not perfect substitutes...

- Recent immigration has disproportionately affected certain experience/skills groups- Find negative effect of immigration harmed employment opportunities of competing native worker

Results Migration = lower wages for natives with similar skills to migrants- 10% supply shock of immigrant (similar education/experience) reduce weekly earning in category by 4%- 10% supply shock of immigrant (“……………..”) reduce annual earnings by 6.4%/time worked by 3.7% (Demand

is elastic - if wages are depressed you work less - compounding effects over the course of the year) - US immigration between 1980 and 2000 had lowered average native wages by about 3% and the wages of the

least-educated natives by 9%Takes this approach and tries to replicate it in panel of cities takes local labour market approach (That he critiqued and said would lead you to underestimate the effects of migration)

- He finds that if you take local approach these effects are 2/3 smaller only finds 1/3 effect if he looks at national levels Paper shows that if you don't consider spatial spillovers you may miss part of the action

But regression under lots of assumptions (No push factors so can’t always take correlations as causal effects) and this Is the point card makes Card (2005)

- Card reanalyses data taking the extension (3) approach (look at inflow of migrants with certain skills and education changes relative supply of skilled v unskilled workers change CA of economy)

- Finds flat relationship between immigrant dropouts and wages of native workers (effect of immigration near 0)- Suggests that this is in line with other evidence based on relative supply

o Relative supply matters if migrant composition identical to natives there should be effects- Card concludes no crowd out effect by migrants

o Migrants change CA but Theres no negative effects entrepreneurs respond (not by changing what they produce) By adapting technology with which they produce their current things

o Directive technical change (not from lab) change that’s done by local innovators to exploit local labour market condition in which inputs are cheaper destroys negative effects (it expands the production)

Borjas V Card- Borjas : influx poor skilled=competition at low-end of labor market=displace some worker+lower wage for other

o Native-born minorities who possess few job skills are most adversely affected - Card influx low-skilled=absorbed by receiving labor market without moving workers and undercutting wages

o Impact of in-migrating workers depends on the context of the receiving labor market. o E.g.: if intergenerational educational advancement = labor market losing low-skilled labor at faster pace

than losing low-skilled jobs arriving low-skilled workers fill what labor shortage. o Inflow of new labor may also increase local demand for goods = more employment and production

Light and Rosenstein’s (1995) specific demand hypothesis: Not only are new workers increasing demand, they are helping to supply the labor that meets that demandDustman et al. (2005): The Impact of Immigration on the British Labour Market D ata from the 1983-2000 LFS

- Immigration to UK and no overall significant effect on the natives and effects of high-skilled immigration on wages are, if anything, positive.

- Some significant negative effects for intermediate education group (not large) It’s people in middle affected – sector of economy where migrants and natives are more comparable

Manacorda et al. (2007) since immigrant and native complement in production NO negative wage effect on natives - Research on labour market effects of immigration to UK finds little evidence of overall adverse effects of

immigration on wages and employment for people born in the UK. 10% rise in share of immigrants in local population increases native-migrant male wage differential by just 2% (As a result immigration tends raise wages of natives relative to immigrants) Complementarity between low-skilled migrant and national worker

- BUT: find evidence newly-arrived immigrants substitutes in production with immigrants already residing in UK – old and new migrants closer substitutes Migrant flows affect wages of existing recent migrants, not native

- OVERALLo Immigrants and native-born workers not close substitutes: On av. (existing migrants closer substitutes for

new migrants) Native-born worker cushioned from rise in supply caused by immigration o The less skilled are closer substitutes for immigrants than the more highly skilled, so any pressures from

increased competition from jobs is more likely to be found among less skilled workers - Possible LR effects on natives Migrants tend to be more skilled in LR they accumulate necessary country

specific human K in LR possibility that they accumulate skills and become closer substitutes for native worker.

Week 10 - The effects of immigration and entrepreneurship? U.S. evidence. What role can/should policy play?- Casual evidence suggests that immigrants bring valuable skills: innovation and entrepreneurship- Positive effects of immigration to natives/ receiving country Innovation crucial determinant of LR growth

How can migrants increase innovation and entrepreneurship?- Migrants are highly educated and focus on areas of engineering and science

- Immigrants positively affect natives: externalities/ complement skill natives innovate/migrants provide bring entrepreneurial spirit (or vice versa)

- Effects amplified if only most innovative immigrants self-select and migrate: Roy model of self-selectiono US attract immigrants that positively self-select and are more motivated: inequality in US large so reward

for innovation high Kerr (2008) Shows knowledge diffusion is importantly affected by interpersonal links within same ethnic community.Uses data on ethnic inventor names in US patent /..on foreign citations to patent/ ..on migration/ production pattern

- Migration of skilled human K from poor countries not just “brain drain;” could also have more a positive effect: “brain bank,” acc knowledge abroad and facilitating transfer back to domestic inventor

- Evidence of knowledge diffusion through ethnic networks is apparent from fact that foreign researchers cite U.S. researchers of their own ethnicity 30–50% more frequently than researchers of other ethnicities.

- Knowledge flows are shown by Kerr to be associated with higher manufacturing output in the foreign countries- Ethnic research communities facilitate knowledge diffusion/ increase output in innovative sector in China/ Indi

Immigrants and Silicon Valley AnnaLee Saxenian: focussed on Asian immigrants and was interested how and if they created positive spillovers in terms of innovation to the local economy

- Overall evidence suggest immigrants are highly skilled and entrepreneurialo 25% of Silicon Valley workforce is foreign-born, but 30%+ of high-tech workers are immigrantso In 1999, 25% of the high-tech companies founded by Chinese and Indian immigrants o Accounted for $16.8 billion sales + 58,282 jobs

- Immigrant allowed region to grow more global Set-up 2-way bridge with original communities US business links to low-cost software expertise in India and Local know-how: language, culture and contacts to build relationship with Asian business

- Reconfigured international functioning of firm No longer large foreign marketing offices and branches (Crucial resource = identifying local partner: 1st generation immigrants key players to establish links)

BUT: Do immigrants cause innovation OR are they crowding out native entrepreneurship Hunt & Gauthier-Loiselle Using the 2003 National survey of college graduates, show that the large number of immigrants with science and engineering degrees in the US assisted significantly to number of patents generated

- Hence no crowding out; more crowding in 1% increase in immigrant college graduates population leads to an increase in patents per capita by 9-18%

Problems? Endogeneity of migrations - Choice of where migrants go could be endogenous: Could be a shock that makes an area better than another

which hasn’t been considered for example state invests and makes area more productive causality going the other way round- state makes more productive so migrants come in Solution = shift share approach

Shift share approach - Macroeconomic shift: 90s china makes easier citizens to migrate = massive increase of migrants shift to US- Shares at which you Apportion that macro shift: west coast vis a vis the east coast

Kerr and Lincoln (2010) Are migrants crowding in or crowding out local innovators: - Estimate reduced form regressions of effect of predicted flows of H-1B visa holders on patent intensity. - They find that increased predicted H-1B immigrant inflows significantly increases local patenting. - Also match patent to ethnic surname: Find that much of increase attributable to Indian/ Chinese surnames. - Positive correlation between increase H-1B visas by Indian/ Chinese migrant and higher US patenting activity- Find evidence of increased patenting for Anglo-Saxon surnames due to H-1B inflows suggest positive

innovation spillover from foreigners to natives: natives may be crowded into innovation instead of crowded out- More migrants working in science won’t have negative effect on natives finding jobs in these sectors – they are

becoming compliments or they learn from each otherDo all immigrants contribute in the same way? Different immigrants, different contribution (Hunt, 2011)

- Compare range of different immigrants (based on the entry visa category) - Finds that gross contribution by immigrants can be ranked from highest to lowest in following order: post-doctoral

fellows and medical residents, graduate students, temporary work visa holders, college students- Immigrants are more likely than natives to start a successful company, suggesting that immigrants have a niche in

start-ups based on technical knowledge from master’s and doctoral degrees.- Success of skilled immigrants determined by combination of immigrant self-selection in wanting to come to US

o Entry visa framework provided by government, o Behavior of U.S.-based agents who select immigrants applying for particular visaso Immigrant self-selection in wanting to remain in the US, o Visa framework for remaining.