History, Geography, and Economies of Agglomeration: Evidence from Italian Cities

Transcription

1 IEFE Bocconi Seminar, May 22, 2009 History, Geography, and Economies of Agglomeration: Evidence from Italian Cities Marco Percoco DAIMAP and CERTeT Università Bocconi

2 1 Introduction a. Large di erences in local development Di erences in GDP per capita: Lombardia 132.5, Calabria 59.2 (EU=100) Di erences in unemployment rates: Trentino 3.8%, Campania 25% Di erences in taxable income: Milano 22,735 e vs Naples 13,915 e (per capita, in 2001)

3 b. Persistence of (under-) development Past population is a good proxy of contemporary development There is certainly mean reversion for some cities (e.g. Venice), but cities that are relatively rich at a point in time tend to remain relatively rich. Persistence in city hierarchy

4 The persistence of development: income and population in 1861 Per capita income (in log; 2001) Population in 1861 (in log)

5 Firm density and income (2001) Firm density (in 2001) Per capita income (in 2001)

6 Firm density in 2001 and population in 1861 Firm density in 2001 (in log) Population in 1861 (in log)

7 Firm density in 2001 and Population density in 1861 Firm density in 2001 (in log) Population density in 1861 (in log)

8 Table 2: The ten largest cities in Italy, Milano 150 Napoli 150 Napoli 220 Napoli 419 Napoli 564 Roma 1702 Roma 2460 Venezia 110 Venezia 102 Roma 140 Milano 196 Milano 492 Milano 1277 Milano 1183 Firenze 110 Milano 100 Venezia 138 Roma 188 Roma 463 Napoli 1025 Napoli 993 Genova 60 Genova 70 Palermo 110 Torino 181 Torino 336 Torino 722 Torino 857 Bologna 50 Bologna 55 Milano 109 Palermo 168 Palermo 310 Genova 688 Palermo 653 Siena 50 Roma 55 Firenze 72 Genova 128 Genova 235 Palermo 503 Genova 604 Palermo 50 Firenze 50 Genova 64 Venezia 114 Firenze 206 Firenze 391 Bologna 370 Brescia 45 Palermo 50 Bologna 63 Firenze 114 Bologna 152 Bologna 351 Firenze 352 Cremona 45 Brescia 48 Messina 50 Bologna 90 Venezia 152 Venezia 323 Bari 313 Padova 40 Cremona 40 Torino 44 Livorno 84 Messina 150 Catania 302 Catania 307

9 c. Determinants of long run urban development Geography vs institutions Geography view: importance of climate, disease environment, morphology. Institutions view: importance of organization of society that provides incentives to individuals and rms. Temporal pattern of causation: Time-varying e ects of geography; Longlasting e ects of past institutions

10 2 The aims of the research 1. To identify the driving forces of urban development (and path dependence) of Italian cities over the very long run ( ) =) Geography and Institutions? 2. To estimate the impact of geography and past institutions on rm density (rather than culture) and hence on per capita income in 2001 in Italian cities =) Instruments for the identi cation of economies of agglomeration? 3. How (i.e. through which channel) does geography a ect local development? What is not clear a priori is whether the e ect of geography on current economic development works through its impact on historic events or through a direct e ect on current economic outcomes.

11 3 Review of the literature 3.1 Institutions view North (1981, p. 3): "Institutions are the rules of the game in a society or, more formally, are the humanly devised constraints that shape human interaction. Acemoglu et al. (2001) In colonies in which the mortality rate was high, European colonizers established extractive rent-seeking institutions, while in countries in which the mortality rate was low, colonizers established growth promoting institutions that protected property rights.

12 DeLong and Shleifer (1993) nd that, in the case of Europe in the period between 1000 and 1800, cities under absolutist regimes grew less than cities under non-absolutist governments, possibly because of higher taxation under revenue-maximizing monarchs. Putnam (1993), Tabellini (2007), Guiso et al. (2007): Past institutions =) Current culture =) Current development However, Tabellini (2007) cannot reject the hypothesis of correlation between past institutions and an omitted variable in the second stage regression.

13 3.2 Geography view In backward economies, specialized in agriculture, climate may have a significant impact on productivity and technology adoption (Comin et al., 2006; Diamond, 1997; Sachs, 2001). The disease burden, (Bleakley, 2005; Diamond, 1997; Gallup and Sachs, 2001; Percoco, 2009; Voth, 2009). See the Black Death in and the plague in The "tyranny of distance": transport costs and urban competitive advantage (Bairoch, 1988; Braudel, 1984; Duranton, 1998; Pirenne, 1925).

14 Nunn and Puga (2008) look at the "blessings of geography" (in the case of slavery in Africa) i.e.: Geography =) Past Events =) Current development Remark 1 Most of the institutions literature propose the estimation of long run regressions in the form: Institutions t x =) Culture/Institutions t =) Development t Only correlation? Remark 2 How to measure geography at national level? Is it irrelevant?

15 3.3 Economies of agglomeration Ciccone and Hall (1996) estimate that the elasticity of productivity to rm density is 0.1. Problem of endogeneity =) Quest for good instruments In the case of agglomeration economies, Combes et al. (2009), in the case of French municipalities, test: o Population density in 1831 Geographical features Current rm density =) Productivity and wage Remark 3 Is history all in Population density in 1831? Long run determinants of economies of agglomeration? The case of Italian district.

16 4 The research hypotheses a) Past institutions matter in explaining rm density. Institutions are regional institutions, Repubbliche comunali and universities. b) Geography matters only in explaining rm density. o Geography Past institutions Firm density=)development Both the quality of Past institutions as instruments for rm density and whether geography matters only for rm density will be tested.

17 4.1 Why institutions matter in explaining rm density? Regional institutions Putnam (1993) and later literature postulates that past institutions have an impact on current development only through culture. DeLong and Shleifer (1993) and Tabellini (2007) coded regional institutions in Europe over the period The idea is that good (i.e. proto-market oriented institutions) institutions promote growth/ good culture

18 4.1.2 The role of Repubbliche Comunali - Why do we observe di erences within regions? Toward the end of the rst millennium Carolingian Holy Roman Empire is disintegrating. The vacuum created by the weakening of the imperial authority lead to the emergence of local power in the Center-North of Italy, i.e. the Repubbliche Comunali (comuni) In X-XII centuries comuni were governed by consolati (mainly judicial bureaucracy). Starting from XIII up to XIV century, they were under the power of a foreign born podestà (a sort of city mayor with ample powers). In the South there was little autonomy and the rule of law was very weak.

19 The geography of Comuni

20 The legal revolution of Comuni In the XIII century, Repubbliche Comunali introduced three types of documents and practices in order to impose the rule of law and enforce property rights (Milani, 2005): the census of private and public goods; libri iurium, i.e. a collection of norms and agreements on property rights and scal policy; statuti comunali, i.e. a sort of local constitution, in which political and commercial laws were particularly relevant.

21 Previously, such norms were not written or not collected sistematically. The rationale for the implementation of such documents was the prevention of economic disputes between privates or between privates and the Comune. Among the issues: price levels, property rights, market surveilance, commercial law dispute resolution, taxes, weights, transport infrastructure management, etc. The application Statuti Comunali and libri iurium has been very strong also under absolutist regimes (see the case of Central Italy under the Papal State) and generated a congenial environment for rm birth. They were the bulk of norms on which local Chambers of commerce were designed (Cattini and Romani, 2001).

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23 4.1.3 The role of universities According to the analysis of Ascheri (1994), medieval universities should be thought to be as liberal institutions as teaching was relatively free, as well as the internal organizations of both students and professors cultural activities. The presence of a university in pre-modern cities should not be considered as a proxy for human capital, rather as an institutions improving urban governance. Cantoni and Yuchtman (2008) provide evidence on the relevance of universities for the establishment of legal systems and for the "commercial revolution".

24 4.1.4 The argumentation strategy In order to overcome (at least in part!) problems of spurious correlation between variables far in the time, I follow a two stage approach: 1) I rst "track" the relevance of geography and institutions in the economic history of 563 Italian cities over the centuries ; 2) After having veri ed their "continuous relevance", I use past institutions as instruments for rm density.

25 5 The data 5.1 Urban population Urban population is often used as a proxied of development in historical perspective. The data I made use of are from Malanima (1998) and cover population of more than 500 cities over the period (centennial estimates); The sources are various. Basically Bairoch s works and local history books. Only population size >5,000 inhabitants are reported.

26 Whole Sample Population> 10,000 Population< 10,000 Average in 1300 Average in 1861 Average centennial growth rate Table 3: Average city size population Italy North Center South ,564 13, ,710 34,152 25,100 17,611 6,449 6,437 6,303 6,527 13,197 21,416 13,096 8,984 13,436 21,340 13,537 10, % -0.07% 0.66% 3.01%

27 5.2 The explanatory variables - Geography Port: dummy variable Road accessibility: 1 if the city was on the Roman road network 0 otherwise. Waterways accessibility: dummy variable taking the value of 1 if the city was on a navigable river. Altitude: dummy variable >500 metres N.B. All those variables are time-invarying

28 Urban potential of city i (Bosker et al., 2008; de Vries, 1984): Urban Potential i = nx j=1 Population j w ij distance ij where w ij = 8 >< >: if i and j are both seaports if i and j are both on inland waterways if i and j are both on the main road network and Distance 50 km otherwise or i = j

29 5.3 The explanatory variables - Institutions Regional institutions (time-varying): This is a seven point scale. 1 Absolutism; 2 Non-bureaucratic absolutism; 3 Strong-prince proto-absolutism; 4 Feudal anarchy; 5 Princes checked extra-constitutionally by powerful magnates; 6 Independent city-republics; 7 Weak prince standestaats. The sources for this variable is De Long and Shleifer (1993) and Tabellini (2007). Comune (time invarying): It is a dummy variable. The sources for this variable are: Ascheri (2006), Guiso et al. (2007), Milani (2005) and from the atlas Atlante Storico De Agostini and Nuovissimo Atlante Storico Mondiale. University (time varying): The sources for the university variable are Atlante Storico De Agostini and Nuovissimo Atlante Storico Mondiale.

30 6 Evidence on the role of geography and institutions on past development I consider two types of panel estimation. The random e ect estimates provide direct evidence on the role of geography for city i in region j at time t: population it = d t + j + X i + institutions jt + $comune i + university it it In X all geography-related variables. logs. Population and urban potential are in In the xed e ect speci cation, all the time-invarying variables are interacted with a full set of time dummies.

31 Table 5: Geography, Institutions and Urban development (Dependent variable is log city population, in 000) Panel A: Random effect estimates Panel B: Fixed effect estimates 1 Whole sample 2 Whole sample 3 Whole sample 4 Population > 10,000 5 Population < 10,000 6 Only Center- North 7 Whole sample 8 Whole sample 9 Population >10, Population <10, Only Center- North Altitude [0.02] [0.02] [0.03] [0.04] [0.04] Road Accessibility Waterways accesibility (-1.95)** (2.27)** (1.98)** (-0.84) (2.16)** (1.99)** (-0.75) (2.21)** (2.01)** (-2.05)** (2.23)** (2.07)** (0.17) (2.19)** (2.09)** (1.25) (2.07)** (1.99)** Port [0.03] [0.03] [0.05] [0.08] [0.12] (6.61)*** (6.76)*** (6.62)*** (6.68)*** (-0.40) (5.27)*** Urban potential 0.59 (4.60)*** 0.30 (12.73)*** 0.17 (1.79)* 0.40 (1.54) (-0.17) 0.12 (1.03) 0.09 (0.45) 0.24 (1.03) 0.31 (0.60) 0.16 (0.75) 0.48 (1.09) University 1.10 (12.73)*** 0.94 (9.95)*** 0.63 (7.38)*** 0.09 (2.30)** 0.91 (7.66)*** 0.66 (2.78)*** 0.53 (1.74)* 0.09 (0.21) 0.02 (0.29) 0.53 (0.80) Institutions 0.02 (2.13)** 0.02 (1.00) 0.01 (0.06) 0.02 (1.74)* 0.01 (1.44) 0.07 (2.07)** 0.00 (0.08) 0.03 (2.03)** Comune [0.01] [0.01] [0.01] (5.92)*** (0.21) (4.27)*** (6.10)*** R Note: GLS random effect estimation with robust standard errors. z-statistics are in parentheses. A constant and a full set of time and regional dummies (North, Centre, South, Sardinia and Sicily) is always included though not reported. *** indicates significance at 99% confidence level; ** indicates significance at 95% confidence level; * indicates significance at 90% confidence level. In models 7-11, altitude, road accessibility, waterways accessibility, port and comune are interacted with a full set of time dummy variables. The corresponding values in brackets report p-values for a test of joint significance of the interacted variables. [0.09] [0.04] [0.08] [0.04] [0.08] [0.03] [0.08] [0.07] [0.06] [0.02]

32 Population t- 1 1 Whole sample (-6.06)*** 2 Whole sample (-7.79)*** Table 6: Geography, Institutions and Urban growth Panel A: Random effect estmates Whole Population Population < Only Whole sample > 10,000 10,000 Center- sample (-7.62)*** Altitude (-3.08)*** (-2.57)*** (-2.37)** Road accessibility (2.19)** (2.31)** (2.13)** Waterways accessibility (1.99)** (2.01)** (2.17)** Port (3.33)*** (3.71)*** (3.73)*** Urban potential t-1 (-1.50) (-2.03)** (-2.04)** University (5.99)*** (5.64)*** Institutions t (0.93) Comune 0.03 (1.22) (-8.33)*** (-1.67)* (2.22)** (2.19)** 0.34 (3.07)*** (-1.02) 0.18 (3.54)*** 0.01 (0.87) 0.04 (0.84) (-25.87)*** (-0.74) (2.21)** (2.05)** 0.08 (0.97) 0.04 (1.05) 0.04 (1.06) 0.03 (0.35) 0.07 (2.67)*** North (-5.54)*** (-1.20) 0.45 (3.33)*** (-2.06)** 0.22 (4.61)*** 0.02 (1.77)* 0.07 (1.95)* (-16.86)*** Panel B: Fixed effect estimates 9 Population >10,000 8 Whole sample ( )*** (-12.24)*** 11 Population <10, (-22.93)*** 11 Only Center- North (-12.84)*** [0.11] [0.15] [0.22] [0.17] [0.18] [0.02] [0.03] [0.02] [0.03] [0.02] [0.03] [0.03] [0.04] [0.01] [0.02] [0.01] [0.01] [0.01] [0.02] [0.01] 0.39 (1.45) 0.36 (2.15)** 0.19 (1.34) 0.09 (0.20) 0.15 (0.53) (-0.22) (2.08)** (0.24) (0.67) (1.87)* (2.62)*** (-0.79) (2.32)** [0.34] [0.22] [0.03] [0.08] R Note: GLS random effect estimation with robust standard errors. z-statistics are in parentheses. A constant and a full set of time and regional dummies (North, Centre, South, Sardinia and Sicily) is always included though not reported. *** indicates significance at 99% confidence level; ** indicates significance at 95% confidence level; * indicates significance at 90% confidence level. In models 7-11, altitude, road accessibility, waterways accessibility, port and comune are interacted with a full set of time dummy variables. The corresponding values in brackets report p-values for a test of joint significance of the interacted variables.

33 Table 7: Geography, institutions, urban development and growth Balanced panel Panel A: Random effect estimates Panel B: Fixed effect estimates Panel C: Random effect estimates - Growth Panel D: Fixed effect estimates - Growth 1 Whole sample 2 Only Center- North 3 Whole sample 4 Only Center- North 5 Whole sample 6 Only Center- North 7 Whole sample 8 Only Center- North Population t (-7.62)*** (-5.54)*** (-16.86) (-11.93)*** Altitude [0.09] [0.08] [0.09] [0.11] Road Accessibility Waterways accessibility (-0.75) (2.11)** (2.33)** (1.25) (2.14)** (2.17)** [0.01] [0.02] [0.01] [0.02] (-2.47)*** (2.11)** (1.97)** (-1.20) (2.13)** (2.02)** Port [0.01] [0.01] [0.01] [0.01] (6.62)*** (5.27)*** (3.73)*** (3.33)*** Urban potential t (1.69)* 0.12 (1.03) 0.09 (0.45) 0.34 (0.85) (-2.04)** (-2.06)** 0.39 (1.45) (-0.49) University 0.94 (9.95)*** 0.91 (7.66)*** 0.66 (2.78)*** 0.63 (1.22) 0.23 (5.64)*** 0.22 (4.61)*** 0.36 (1.99)** (-1.11) Institutions t (2.13)** 0.02 (1.74)* 0.02 (1.73)* 0.02 (1.48) 0.01 (0.93) 0.02 (1.77)* 0.02 (1.72)* 0.02 (1.73)* Comune [0.01] [0.01] [0.01] [0.02] (5.92)*** (6.10)*** (1.22) (1.98)** R Note: GLS random effect estimation with robust standard errors. z-statistics are in parentheses. A constant and a full set of time and regional dummies (North, Centre, South, Sardinia and Sicily) is always included though not reported. *** indicates significance at 99% confidence level; ** indicates significance at 95% confidence level; * indicates significance at 90% confidence level. In models 3, 4, 7, 8 altitude, road accessibility, waterways accessibility, port and comune are interacted with a full set of time dummy variables. The corresponding values in brackets report p-values for a test of joint significance of the interacted variables. [0.02] [0.03] [0.01] [0.03]

34 To sum up Evidence of the relevance of institutions (especially in terms of comune and university) Evidence of the relevance of rst nature geography No conclusive evidence on urban potential.

35 7 The historical roots of agglomeration economies The model: rm density i = +X i +institutions j +$comune i +university i + i income i = a + bx i + c rm density i + i This model is estimated via IV. As a robustness check, another variable will be used in place of comune, that is statuto which is a dummy variable for the presence of a Statuto Comunale in the city. It is likely to be measured with error.

36 Discussion of the variables Firm density is computed as in 2001 (number of rms/surface; Source: Censimento ISTAT). In X all rst nature variables are considered. Institutions is the simple average across time of institutions. University denotes the presence of a university in Income is per capita taxable income.

37 Discussion of the underlying assumptions The exclusionary restriction is that institutional variables enters the second stage regression only through rm density. This is di erent from Guiso et al. (2007) and Tabellini (2007). Taxable income is not wage or productivity, however, it is likely to be very correlated with those variables. Denser places produce higher productivity and wages which is reasonably associated with higher income per capita Combes et al. (2009) make use of population density in 1831 as an instrument. As from panel analysis, there is evidence that urban population of Italian cities is clearly related to past institutions and some of the geographical variables. Hence, in rst stage regression institutional (and geographical) variables appear in place of past population.

38 Table 8: Long run regressions Panel A: First stage regressions. Dependent variable is Firm Density in 2001 (in logs) 1 Whole sample 2 Whole sample 3 Whole sample 4 Only cities in the Center- North 5 Only cities in the Center- North Altitude (-0.62) (-0.94) (-1.18) (-0.06) (-0.39) Road accessibility 0.54 (2.03)** 0.52 (2.01)** 0.49 (2.07)** 0.52 (2.13)** 0.52 (2.12)** Waterways accessibility 0.23 (1.78)* 0.23 (1.79)* 0.19 (1.78)* 0.23 (2.01)** 0.23 (1.99)** Port 1.11 (3.93)*** 1.16 (4.01)*** 1.02 (3.18)*** 0.62 (1.38) 0.60 (1.20) University 1.27 (5.12)*** 1.28 (5.03)*** 1.23 (4.78)*** 0.90 (3.16)*** 1.08 (3.79)*** Comune 0.41 (2.58)*** 0.37 (2.28)** 0.53 (3.11)*** Institutions 0.53 (2.38)** 0.52 (2.32)** 0.26 (0.57) 0.19 (0.40) Statuto 0.57 (2.84)*** 0.32 (1.83)* Panel B: Second stage regressions. Dependent variable is Income per capita in 2001 (in logs) Firm density 0.11 (6.18)*** 0.09 (4.96)*** 0.09 (5.57)*** 0.09 (4.74)*** 0.10 (4.82)*** Road accessibility 0.12 (1.76)* 0.13 (1.42) 0.12 (1.55) 0.09 (0.98) 0.09 (0.99) Waterways accessibility 0.03 (0.89) 0.02 (0.91) 0.03 (0.99) 0.02 (1.01) 0.02 (1.07) Altitude (-1.81)* (-1.82)* (-1.84)* (1.62) (1.52) Port (-0.61) (-0.04) (-0.06) (-0.39) (0.42) R Number of observations p- value of Hansen J statistic Kleibergen-Paap rk Wald F statistic Note: t statistics are in parentheses. A constant and a full set of regional dummies are always included though not reported. *** indicates significance at 99% confidence level; ** indicates significance at 95% confidence level; * indicates significance at 90% confidence level.

39 Table 9: Robustness checks on the validity of the instruments: Second stage regressions (Dependent variable is log income per capita) 1 Whole sample 2 Whole sample 3 Whole sample 4 Whole sample 5 Only cities in the Center- North 6 Only cities in the Center- North 7 Only cities in the Center- North Firm density 0.08 (2.89)*** 0.08 (3.73)*** 0.11 (6.04)*** 0.08 (3.66)*** 0.08 (3.18)*** 0.13 (3.70)*** 0.09 (4.81)*** 0.11 (4.10)*** Altitude (-1.80)* (-1.81)* (-2.01)** (-1.71)* (-2.22)** (-1.28) (-1.55) (-1.52) Road accessibility 0.11 (1.45) 0.12 (1.41) 0.11 (1.23) 0.06 (1.08) 0.09 (0.99) 0.10 (1.09) 0.11 (1.13) 0.10 (0.99) Waterways accessibility 0.03 (0.89) 0.02 (0.91) 0.03 (0.99) 0.02 (1.01) 0.02 (1.07) 0.04 (0.97) 0.05 (1.08) 0.04 (0.84) Port 0.05 (0.87) (0.10) (-0.72) (-0.03) (0.13) (-0.56) (-0.38) (-0.49) University 0.10 (1.27) 0.08 (1.50) Comune 0.01 (0.65) (-1.51) Institutions (-1.45) (0.10) Statuto 0.01 (0.03) (-0.62) R Number of observations p- value of Hansen J statistic Only cities in the Center- North Kleibergen- Paap rk Wald F statistic Note: z- and t statistics are in parentheses. A constant and a full set of time and regional dummies is always included though not reported. *** indicates significance at 99% confidence level; ** indicates significance at 95% confidence level; * indicates significance at 90% confidence level.

40 8 Conclusions (First nature) Geography and institutions view to explain the development of (pre-)modern city. Note that road and waterways accessibility, port are measured in "historical terms". Repubbliche comunali are at the heart of Italian industrialisation because of long lasting laws. Past institutions seem to be good instruments for rm density. This is an explanation on why some cities grew more than others, however it is not an explanation on the decline of some cities (e.g. Venice) and the persistence of others (e.g. Genoa). Any role for guilds and élites?

41 Figure 8: Population dynamics in Genoa and Venice 2.50% 2.00% 1.50% % 0.50% 0.00% Venice Genoa Note: The graph draws the dynamics of city population with respect to total Center-North Italian population. Source: Malanima (1998; 2002).

42 Table 10: Elites and urban growth in three Italian cities Parma Piacenza Modena Panel A: Turnover rate in city council Panel B: Percentage of city council seats to top 10 historical families Panel C: Centennial growth rate of city population Source: Cattini (1984) and Cattini and Romani (2004).

43 Thank you for your attention! Marco Percoco