APPENDIX A - Complementary Tables and Additional Results

Transcription

1 APPENDIX A - Complementary Tables and Additional Results Table A1. Summary statistics cities 10,000 inhabitants variable overall (2,597 obs.) Christian (1,940 obs.) Muslim (657 obs.) mean st.dev mean st.dev mean st.dev city size ('000) sea river roman road hub roman road caravan caravan hub bishop archbishop holy city - Christian holy city - Muslim capital university madrasa plundered ln UP - Muslim ln UP - Christian ln UP Muslim D Mecca (km) D Rome (km) D Byzantium (km) elevation (m) ruggedness (m) P(cultivation) ecozone D near Muslim (km) D near Christian (km) D near Muslim city (km) D near Christian city (km) commune parliament large state Free Prince Middle East & North Africa

2 Table A2. The largest urban centers over the centuries rank \ year Baghdad 350 Baghdad 450 Baghdad 300 Baghdad 250 Baghdad 200 Paris Byzantium 250 Byzantium 300 Byzantium 300 Byzantium 200 Cairo 200 Cairo Basra 100 Cairo 150 Cairo 135 Cairo 150 Paris 110 Granada Wasit 100 Alexandria 100 Cordoba 100 Tinnis 110 Byzantium 100 Venezia Kufa 100 Cordoba 95 Seville 90 Damietta 100 Damietta 100 Damietta 108 rank \ year Cairo 250 Istanbul 280 Istanbul 700 Istanbul 700 London Paris 200 Paris 200 Paris 300 London 575 Paris Granada 100 Cairo 180 Napoli 275 Paris 500 Istanbul Tunis 100 Adrianople 127 Cairo 250 Cairo 330 Napoli Venezia 100 Napoli 125 London 200 Napoli 300 Cairo 263 Notes: population in thousands behind city name. Table A3 results on additional control variables: Table 1 extended 1 Muslim Christian all cities Muslim Christian ME NA Europe ME NA Europe Muslim 0.25*** ** - - [0.00] - - [0.17] [0.05] - - ln D Mecca -0.1*** -0.12** *** 2.02*** -0.14** - [0.01] [0.01] - [0.00] [0.00] [0.01] - ln D Rome [0.32] - [0.1] [0.51] [0.45] - [0.26] ln D Byzantium -0.16*** ** -0.83*** [0.00] - [0.16] [0.04] [0.00] - [0.67] ln elevation [0.47] [0.56] [0.36] [0.12] [0.43] [0.36] [0.35] ln ruggedness [0.77] [0.66] [0.59] [0.65] [0.98] [0.78] [0.61] P(cultivation) [0.49] [0.53] [0.75] [0.15] [0.56] [0.8] [0.72] ecozone ** 0.71** 0.23* 0.82*** 0.24** 0.66* 0.22* [0.01] [0.01] [0.05] [0.01] [0.05] [0.06] [0.07] ecozone * * [0.55] [0.26] [0.09] [0.18] [0.44] [0.09] [0.15] ecozone * * ** * [0.06] [0.27] [0.07] [0.49] [0.03] [0.45] [0.10] ecozone * 0.33* 0.19* 0.47** 0.23* [0.06] [0.09] [0.09] [0.02] [0.06] [0.15] [0.23] Notes: The dependent variable in all columns is ln(city population). p-values, based on standard errors that are clustered at the individual city level, in brackets. *, **, *** denotes significance at the 10%, 5%, 1% respectively. Results obtained allowing for city-specific random effects. See Notes to Table 1 in the main text for more detail. 1 Some remarks to the results in Table A3: The results may appear to suggest that Muslim cities are, all else equal, larger than non-muslim cities. However, this finding is not very robust to e.g. allowing for city-specific fixed effects (see Table A4 below) or excluding Andalusia from the European sample (focusing on the Muslim cities in southern Italy and Portugal only). We also note that, by distinguishing Christian cities into Protestant and non-protestant cities following the Reformation, we are unable similar to Acemoglu et al. (2005) or Cantoni (2010) to confirm a significantly positive Protestant effect. Results available upon request. 2

3 Table A4. Controlling for city-specific FE in our baseline specification Mediterranean Mediterranean all cities Muslim Christian Muslim Christian Geography bishop -0.15* [0.09] [0.57] [0.49] [0.64] [0.99] archbishop * * [0.22] [0.8] [0.06] [0.79] [0.08] capital 0.57*** 0.55*** 0.48*** 0.57*** 0.45*** [0.00] [0.00] [0.00] [0.00] [0.00] university 0.19** ** [0.03] [0.54] [0.02] [0.56] [0.29] madrasa * [0.67] [0.54] [0.08] [0.41] [0.22] Muslim [0.17] plundered [0.99] [0.88] [0.69] [0.92] [0.83] ln UP - Muslim 0.29*** 0.42** ** 0.10 [0.00] [0.04] [0.11] [0.05] [0.49] ln UP - Christian 0.46*** *** ** [0.00] [0.13] [0.00] [0.13] [0.01] R nr observations Notes: The dependent variable in all columns is ln(city population). p-values, based on standard errors that are clustered at the individual city level, in brackets. *, **, *** denotes significance at the 10%, 5%, 1% respectively. Our variables for geography, but also for Muslim and Christian Holy cities drop out given their time-invariant nature. All regressions include a full set of century dummies. In columns 4 and 5 we exclude all non-mediterranean European countries and countries on the Arab Peninsula from the sample (we also exclude Northern France, i.e. all French cities at latitudes higher than 47 degrees). We also do not report results on our sea variable (the point estimate is always positive and significant) that does not drop out since three cities in the sample (Seville, Bruges and Damietta) lost direct access to the sea due to the silting up of their sea-access (other cities like Leeuwarden or Ephesus also lost their sea-access, but when they did so they do not pass our 10,000 inhabitants criterion). Our point estimates are thus only based on two, or even one, city in case of our Christian and Muslim sample respectively (see also the additional remarks below Table 1 for a detailed account of Damietta s particular history; and footnote 39 that also explicitly mentions the time-varying sea access of Seville and Bruges). 3

4 Table A5. Urban Potential robustness checks sample: Muslim Christian geography/ institutions / religion similar to baseline Check 1: distance to, and ln size of, nearest Muslim / Christian city ln dist near Muslim -0.11* 0.04 [0.06] [0.39] ln dist near Christian *** [0.32] [0.00] ln size near Muslim [0.85] [0.66] ln size near Christian 0.09** [0.04] [0.31] Check 2: weighted urban potential De Vries (1980) ln WUP Muslim 0.21* [0.098] [0.23] ln WUP Christian ** [0.32] [0.04] Check 3: urban potential no split Muslim / Christian ln UP 0.46** 0.28*** [0.02] [0.00] Check 4: UP + Muslim / Christian city within distance bands? ln UP 0.45** 0.20** [0.04] [0.02] Muslim city within 0-20 km *** [0.33] [0.00] km [0.78] [0.17] km [0.67] [0.33] Christian city within 0-20 km -0.56** 0.02 [0.04] [0.79] km * [0.32] [0.08] km [0.67] [0.11] Check 5: UP + Muslim / Christian urban population within distance bands ln UP 0.48** 0.24*** [0.03] [0.01] Muslim urban pop km *** [0.84] [0.00] km * [0.97] [0.06] km [0.71] [0.37] Christian urban pop km -0.25*** [0.00] [0.69] km [0.19] [0.41] km [0.43] [0.41] 4

5 TABLE A5 CONTINUED Check 6: number of Muslim / Christian within distance bands ln UP 0.46** 0.20** [0.03] [0.02] # Muslim cities 0-20 km *** [0.29] [0.00] km [0.66] [0.16] km [0.98] [0.39] # Christian cities 0-20 km -0.88*** [0.01] [0.93] km [0.29] [0.62] km * [0.43] [0.09] Notes: Results per robustness check are from separate regressions including all variables used in the baseline specification, but with the two urban potential variables replaced by the variable(s) above. The dependent variable in all regressions is ln(city population). p-values, based on standard errors that are clustered at the individual city level, in brackets. *, **, *** denotes significance at the 10%, 5%, 1% respectively. Results obtained using panel data estimator allowing for random city-specific effects. When including city-specific effects instead, the results generally only become stronger; they are available upon request. In check 2 we replace our urban potential variables constructed using (1), by two different variables that, following De Vries (1980), not only weight a city s contribution to the urban potential of another city by its distance to that city, but also make it dependent on whether or not one or both of the two cities has a preferential location for longdistance trade based (located at sea or at a river). In particular, instead of weighting by D ij only in (1), we now weigh by (w ij D ij ). w ij equals 1 if neither city i nor city j is a located a sea or at a navigable river, it is if one of the two cities is located on a navigable river, 0.75 if one of the two is located at sea, if one is located at sea and the other at a navigable river, 0.25 if both are located on a river and if both are located at sea. We do not use this weighted variable in our baseline regressions as it is, by construction, correlated with our seaand navigable river-dummies. 5

6 Table A6. Including cities as soon as more than 5,000 inhabitants sample: all cities Muslim Christian sea 0.22*** 0.25* 0.20*** [0.00] [0.093] [0.00] river 0.09** [0.04] [0.28] [0.19] roman road [0.44] [0.42] [0.26] hub roman road 0.10* [0.06] [0.67] [0.12] caravan * -0.35** [0.42] [0.05] [0.01] caravan hub 0.53*** 0.78*** [0.00] [0.00] [0.67] bishop 0.20*** 0.22** 0.24*** [0.00] [0.03] [0.00] archbishop 0.42*** *** [0.00] [0.36] [0.00] holy city - Christian [0.61] - [0.66] holy city - Muslim [0.23] [0.87] - capital 0.91*** 0.69*** 0.99*** [0.00] [0.00] [0.00] university 0.34*** *** [0.00] [0.65] [0.00] madrasa 0.24** [0.01] [0.13] - plundered [0.57] [0.73] [0.60] ln UP - Muslim 0.15** 0.28* 0.13 [0.04] [0.051] [0.12] ln UP - Christian 0.30*** *** [0.00] [0.14] [0.00] R nr observations Notes: the dependent variable in all columns is ln(city population). p-values, based on standard errors that are clustered at the individual city level, in brackets. *, **, *** denotes significance at the 10%, 5%, 1% respectively. Results obtained allowing for city-specific random effects. Similar to Table 1 in the main text, all regressions also include as additional control variables: a Muslim dummy (in case of the total sample), ln distance to Mecca, ln distance to Byzantium, ln distance to Rome (in the Muslim and Christian samples we only include ln distance to Mecca and to Byzantium and Rome respectively), ln elevation above sea level, ln ruggedness of the surrounding area (10km), and cultivation potential; they also include a full set of ecozone-, country- and century- fixed effects. Results for these variables are available upon request. Additional remarks: In column 2 we also estimate a large significantly positive effect of Christian holy cities in the Muslim sample, and in column 3 a large significantly negative effect of Muslim holy cities in the Christian sample. These results are not very informative however. They are, given our definition of Muslim and Christian holy cities basically a Constantinople-effect in the Muslim sample and a Jerusalem-effect in the Christian sample. Similarly, in column 1 and 3 the madrasa-variable shows a large significantly positive effect. This is solely a Granada-1500 effect, the only city in the Christian sample, besides Jerusalem, that we classify as having a madrasa (due to the fact that we only have madrasa information for Muslim cities) Granada was conquered by Christian forces in

7 Table A7a: Transport, capital status and UP over time Muslim (no FE) MUSLIM year roman road caravan hub capital UP - Muslim UP - Christian *** 0.62* 0.81*** -0.86* *** 0.86** 0.73*** c 0.76*** 0.76** 0.34* a b *** 0.48*** 0.23 a *** 0.60*** 0.12 a 0.08 b * c 0.78*** 0.54*** 0.16 a 0.27 b * c 0.78*** 0.56*** 0.28* b 0.39 b * c 0.77*** 0.66*** 0.09 a 0.25 b *** 0.67*** 0.09 a 0.42* b *** 0.86*** a 0.07 b *** 0.80*** 0.08 a p-value F-test: same effect over the centuries? [0.20] [0.55] [0.54] [0.01] [0.01] other variables: see baseline observations: 656 Notes: the dependent variable in all columns is ln(city population). *, **, *** denotes significance at the 10%, 5%, 1% respectively, and c, b, a denotes significantly different from its effect in 800 at the 10%, 5%, 1% respectively (both based on standard errors that are clustered at the individual city level). Results obtained allowing for city-specific random effects. The regression also includes a full set of ecozone-, country- and century-dummies. Table A7b: Transport, capital status and UP over time Christian (no FE) Christian year sea river capital UP - Muslim UP - Christian *** b ** ** a 0.59** b *** a ** c ** a *** c 0.25* 0.39*** b * *** b *** c *** b *** ** 0.13* 1.16*** ** *** *** *** *** *** b p-value F-test: same effect over the centuries? [0.86] [0.49] [0.00] [0.05] [0.02] other variables: see baseline observations: 1940 Notes: the dependent variable in all columns is ln(city population). *, **, *** denotes significance at the 10%, 5%, 1% respectively, and c, b, a denotes significantly different from its effect in 800 at the 10%, 5%, 1% respectively (both based on standard errors that are clustered at the individual city level). Results obtained allowing for city-specific random effects. The regression also includes a full set of ecozone-, country- and century-dummies. 7

8 Figure A2a. Dependency of marginal effect local participative government and parliamentary representation on Christian urban potential Christian sample ln christian urban potential ln christian urban potential marg.eff commune density of ln Christian urban potential 95% confidence interval marg.eff active parliament density of ln Christian urban potential 95% confidence interval Notes: figures correspond to the results presented in column 4 of Table 3 in the main text. When using 90% confidence intervals the marginal effect of an active parliament remains significant for larger values of Christian urban potential. Figure A2b. Dependency of marginal effect large state on Muslim urban potential ln muslim urban potential marg.eff large state density of ln Muslim urban potential 95% confidence interval Notes: figures correspond to the results presented in column 3 of Table 3 in the main text. 8

9 A.2 Geographical conditions in Europe and the Islamic World in some more detail As mentioned in section 2.1 in the main text, we decided to not give a prominent role to the differences in agricultural potential between Europe and the Islamic World. For one, the data we have available concerns, although spatially very detailed, agricultural potential based on twentieth century conditions. Second, when taking this data seriously, we do not think that the results warrant a big role for the difference in agricultural conditions between the two regions in explaining their divergent urban development over our sample period. We explain why in a bit more detail in this section of the Appendix. First, we give a brief discussion of the differences in agricultural potential between cities in the two regions. Table A8 shows descriptives on the four geographic variables that can be related to agricultural potential. To abstract from the changes in religious boundaries over our sample period, they are based on a split of the sample along geographic boundaries: i.e. we compare Europe to the Middle East and North Africa (see the map in Figure 1 in the main text). See Table A1 for the descriptives based on splitting the sample along the (timevarying) religious boundaries (i.e. the Muslim and Christian samples used in our regressions). Table A8. Geography in Europe and the Middle East & North Africa variable Europe Middle East & North Africa elevation (m)* 177 (214) 366 (495) ruggedness (m)* 72 (77) 106 (109) P(cultivation)* 0.73 (0.23) 0.53 (0.39) ecozone 3.98 (0.78) 4.04 (1.76) nr. observations Notes: * denotes a significantly different mean in Europe and in the Middle East & North Africa at the 5% level. The European cities are generally located at lower altitudes, and in less rugged environments than their counterparts in the Middle East & North Africa (but these differences become insignificant when excluding cities in Anatolia). The results on our two agricultural potential variables are arguably somewhat more interesting. The average probability of cultivation of the hinterland surrounding the cities in the Middle East and North Africa is about 20 percentage points lower than in Europe. However, when using Buringh et al. (1975) s spatially less disaggregated classification, we find that cities in both regions are on average located in ecozones with very similar agricultural potential. These two different findings can be explained by looking in some more detail at the distribution of agricultural potential over the cities in Europe and in the Middle East and North Africa respectively. Figure A3a plots the share of cities surrounded by a hinterland with an agricultural potential corresponding to each of the six productivity classes 9

10 defined in Buringh et al. (1975). Similarly, Figure A3b plots the density of cities cultivation potential in each of the two regions. Figure A3a. Distribution of agricultural potential in Europe and the Middle East & North Africa agricultural potential Fraction Middle East & North Africa ecozones Europe ecozones Notes: the histograms depict the share of cities in each of the six ecozones (for their geographical distribution see Figure A1 in the Data Appendix). Figure A3b. Probability of cultivation in Europe and the Middle East & North Africa density Middle East & North Africa Europe P(cultivation) Notes: kernel density estimate of the probability of cultivation of each city s hinterland. Figure A3a shows that whereas in Europe most cities are surrounded by lands of average agricultural potential (ecozone 4), this is the case for only 4% of cities in the Middle East and North Africa. There, we find 40% of cities, mainly in the Nile and Euphrates and Tigris river 10

11 valleys, in an above-average (ecozone 3 or lower), and 55% in a below-average (ecozone 5 or higher) agricultural potential class. In Europe these percentages are much lower (12% and 10% respectively); even none of the European cities is surrounded by lands of excellent agricultural potential (class 1). Figure A3b reveals a slightly different picture based on our more disaggregated cultivation potential variable. In the Middle East and North Africa the distribution of cultivation potential is clearly twin-peaked : most cities are surrounded by lands of very high or very low agricultural potential. In Europe this is not the case. There we find hardly any cities whose hinterland has a low probability of cultivation: most mass of the European distribution is found closer to the mean probability of cultivation (see also the lower standard deviation in Table A8). In sum, whereas agricultural potential in Europe is more homogenous and of average to good quality, in the Middle East and North Africa it is very heterogeneous: either very good or very bad. A.2.1 Agricultural development and city development Following this brief description of the agricultural conditions in Europe and the Middle East and North Africa, we now turn to our findings regarding the relevance of these factors in explaining the two regions different long-run urban development. We do this based on Table A3 that shows the estimated effects of our agricultural potential proxies in the baseline estimates discussed in section 4.2 in the main text. The results show that we never find a significant effect of elevation or ruggedness on city development. Also, we never find a significant effect of the cultivation potential of a city s immediate hinterland on its urban prospects. The results on our second, geographically less disaggregated measure of agricultural potential, also do not give us much indication that agricultural potential matters 2. Basically the only robust result 3 is that cities with the best agricultural potential are significantly larger than those with the worst agricultural potential (the effect of location in ecozone 2 is not significantly different from that in other ecozones than ecozone 6). Looking at the map in Figure A1 in the Data Appendix, this basically says that the highly fertile Euphrates and Tigris river-valleys, the Po-river valley and the French Provence are significantly more conducive to urban development than the Pyrenees, Alps, the 2 The coefficients on the ecozone dummies show the effect of having a particular agricultural potential relative to the effect of having the worst possible agricultural potential (ecozone 6). Also, given that only cities in Egypt are located in ecozone 1, this dummy drops out as it is perfectly captured by Egypt s country dummy. 3 Other significant effects disappear when using slightly different samples. The only other effect that is always significant, be it only at the 10% level, is that in Europe cities are larger in ecozone 4 than those in ecozone 6. 11

12 Anatolian Highlands, Northern Scandinavia or the Sahara desert. This is not that surprising, moreover this effect is present in both Europe and the Islamic World. Overall, and in combination with the earlier-mentioned fact that our agricultural potential information is based on twentieth-century conditions, these results do in our view not warrant too strong conclusions regarding the role for agricultural potential in explaining the shift of urban gravity from the Islamic World to Europe. In effect, the distribution of high quality land (see Figure A3a and A3b) appears to be better able to explain the urban situation at the beginning of our sample period, when we do find the largest cities in those regions with the best agricultural potential (e.g. Baghdad and Cairo, but also Alexandria, Basra, Damietta and Tinnis). While not excluding the possibility that differences in agricultural conditions between the two regions may be driving their different overall (urban) population trends, we think that these differences have a harder time in explaining why the largest cities also moved from the Islamic World to Europe 4. 4 Campopiano (2011) points out that the cities in the Islamic World relied on complex water management systems (see also Watson, 1983). He argues that these systems were heavily dependent on the state: when the state faltered so did its water-management system with possibly disastrous results for urban and rural people alike. This suggests other (more institutional) reasons than agricultural conditions per se diminishing actual agricultural production (with possible negative effects on urban population) in the Islamic World. 12