1# RECORD 28afe169f4e2851e590b8152147dbd5bb4ee90f02# TITLE: Quality of Life in Chinese Cities3# YEAR: 2021.0 VENUE: Social Science Research Network DOI: 10.2139/ssrn.37643344# AUTHORS: Tiechou Shi; Wenzhang Zhu; Shi-He Fu5# PAGES: 39 CANDIDATE TABLE PAGES (1-based): [27, 30, 32]6# ABSTRACT: Abstract The Rosen-Roback spatial equilibrium theory states that cross-city variations in wages and housing prices reflect urban residents' willingness to pay for urban amenities or quality of life. This paper is the first to quantify and rank the quality of life in Chinese cities based on the Rosen-Roback model. Using the 2005 1% Population Intercensus Survey data, we estimate the wage and housing hedonic models. The coefficients of urban amenity variables in both hedonic models are considered the implicit prices of amenities and are used as the weights to compute the quality of life for each prefecture-level city in China. In general, provincial capital cities and cities with nice weather, good air quality, and accessible public transit have high quality of life. We also find that urban quality of life is positively associated with the subjective well-being of urban residents.789===== PAGE 1 (score 1, numeric-paren 0, table-words 0, var-words 1) =====1011Munich Personal RePEc Archive12Quality of Life in Chinese Cities13Shi, Tie and Zhu, Wenzhang and Fu, Shihe14Xiamen University, Xiamen University, Xiamen University1512 January 202116Online at https://mpra.ub.uni-muenchen.de/105266/17MPRA Paper No. 105266, posted 19 Feb 2021 06:32 UTC181920===== PAGE 27 (score 94, numeric-paren 67, table-words 8, var-words 11) =====212226 23 24 25Table 2 Results from hedonic models 26 27(1) 28(2) 29(3) 30Variables 31Housing price 32Housing rent 33Monthly income 34Temperature index 35-0.001 36-0.005 370.007*** 38 39(0.00) 40(0.00) 41(0.00) 42Mean monthly precipitation 430.001 440.002*** 450.002*** 46 47(0.00) 48(0.00) 49(0.00) 50Mean daily sunshine duration 510.056*** 520.078*** 530.025** 54 55(0.02) 56(0.02) 57(0.01) 58Annual mean concentration of PM2.5 59-0.000 60-0.002 610.001** 62 63(0.00) 64(0.00) 65(0.00) 66Green coverage ratio 670.002 680.002 690.003** 70 71(0.00) 72(0.00) 73(0.00) 74Coastal city 750.178*** 760.019 770.138*** 78 79(0.04) 80(0.05) 81(0.03) 82Population density 83-0.002 84-0.003 85-0.003*** 86 87(0.00) 88(0.00) 89(0.00) 90Population size 91-0.001*** 92-0.000 93-0.000 94 95(0.00) 96(0.00) 97(0.00) 98Number of theatres 990.055*** 1000.108*** 1010.032* 102 103(0.02) 104(0.03) 105(0.02) 106Number of hospital bed 1070.001 1080.003*** 1090.000 110 111(0.00) 112(0.00) 113(0.00) 114Teacher-student ratio in high schools 1150.001 116-0.003 1170.001 118 119(0.00) 120(0.00) 121(0.00) 122Number of higher education institutions 1230.001 1240.001 1250.002** 126 127(0.00) 128(0.00) 129(0.00) 130Road area ratio 131-0.003 1320.006 1330.003 134 135(0.00) 136(0.00) 137(0.00) 138Number of airline routes 1390.003*** 1400.002* 1410.001** 142 143(0.00) 144(0.00) 145(0.00) 146Capital city 1470.163*** 1480.107* 1490.029 150 151(0.06) 152(0.06) 153(0.03) 154R2 (full model) 1550.734 1560.542 1570.616 158R2 (excluding amenities) 1590.702 1600.511 1610.589 162Obs. 163421660 164227594 1651157385 166Note. The dependent variables in Columns (1)-(3) are ln(housing price), ln(housing rents), and 167ln(wage), respectively. Standard errors are in the parentheses and clustered at the city level. “*”, 168“**”, “***” denote significance at the 10%, 5%, and 1% levels, respectively. The housing hedonic 169models in Columns (1) and (2) also include housing attributes and group fixed effects as listed 170in Table A1; the wage hedonic model in Column (3) also include worker attributes and 171demographic group fixed effects as listed in Table A2. 172 173 174175176===== PAGE 28 (score 12, numeric-paren 0, table-words 1, var-words 10) =====17717827 179 180 181Table 3: Compensating differentials and implicit prices for amenities (CNY) 182 183(1) 184(2) 185(3) 186Amenity 187Compensating 188differential: housing 189Compensating 190differential: Wage 191Implicit price 192Temperature index 193-4.38 194-52.512 195-56.90 196Mean monthly precipitation 1974.38 198-15.01 199-10.63 200Mean daily sunshine duration 201245.28 202-187.56 20357.72 204Annual mean concentration of PM2.5 2050 206-7.50 207-7.50 208Green coverage ratio 2098.76 210-22.51 211-13.75 212Coastal city 213779.64 214-1035.34 215-255.70 216Population density 217-8.76 21822.51 21913.75 220Number of theatres 221240.9 222-240.08 2230.82 224Number of hospital bed 2254.38 2260 2274.38 228Teacher-student ratio in high schools 2294.38 230-7.50 231-3.12 232Number of higher education institutions 2334.38 234-15.01 235-10.63 236Road area ratio 237-13.14 238-22.51 239-35.65 240Number of airline routes 24113.14 242-7.50 2435.64 244Capital city 245713.94 246-217.57 247496.37 248Note. Column (1) = Monthly housing expenditure (CNY365)×𝛽2×12; Column (2) = (-1)×249Average monthly income (CNY625.20)×𝛾2×1.83×12; Column (3) = Column (1) + Column 250(2). 𝛽2 and 𝛾2 are coefficients in Models (8) and (9). 251 252 253254255===== PAGE 30 (score 35, numeric-paren 21, table-words 6, var-words 2) =====25625729 258 259 260 261Table 5 Correlation matrix for QOL index and its components 262QOL indexes 263Climate 264Environmental quality 265Urban condition 266Environmental quality 2670.154** 268 269 270 271(0.009) 272 273 274Urban condition 2750.009 276-0.162*** 277 278 279(0.875) 280(0.006) 281 282Overall 2830.244*** 2840.027 2850.960*** 286 287(0.000) 288(0.573) 289(0.000) 290Note. “*”, “**”, and “***” denote statistical significance at the 10%, 5%, and 1% levels, 291respectively. p values are in the parentheses. 292 293 294Table 6 Subjective well-being and quality of life in cities 295 296(1) 297(2) 298(3) 299(4) 300(5) 301QOL 3020.052** 303 304 305 306 307 308(0.02) 309 310 311 312 313QOL: Climate 314 3150.580*** 316 317 3180.625*** 319 320 321(0.20) 322 323 324(0.20) 325QOL: Environmental quality 326 327 328-0.513 329 330-0.593 331 332 333 334(0.36) 335 336(0.36) 337QOL: Urban condition 338 339 340 3410.043** 3420.028 343 344 345 346 347(0.02) 348(0.02) 349R2 3500.016 3510.054 3520.018 3530.011 3540.085 355adj. R2 3560.008 3570.045 3580.009 3590.002 3600.061 361N 362117 363117 364117 365117 366117 367Note. The dependent variable is subjective well-being of cities, which are the coefficients of city 368dummy variables from regressing individual self-reported life satisfaction on city dummy 369variables and a set of individual demographic traits using the 2010 CFPS data. The quality of 370life index has been divided by 10,000. Robust standard errors are in the parentheses. “*”, “**”, 371and “***” denote statistical significance at the 10%, 5%, and 1% levels, respectively. 372 373 374 375376377===== PAGE 31 (score 2, numeric-paren 0, table-words 1, var-words 0) =====37837930 380 381Table 7: Comparison between our ranking and the ranking by The Research Center 382for Urban Quality of Life 383 384City 385 386 387Ranking by authors 388 389Ranking by RCUQL 390Average rank between 2011 and 2018 391Ranking based on subjective 392satisfaction index 393Ranking based on 394objective index 395Shanghai 3961 39719 3986 399Beijing 4002 40124 4022 403Guangzhou 4043 40522 4063 407Kunming 4084 40924 41011 411Shenzhen 4125 41331 4144 415Chengdu 4166 41710 41822 419Xi’an 4207 42123 4228 423Haikou 4248 42512 42627 427Xiamen 4289 42914 43013 431Urumqi 43210 43319 43423 435Chongqing 43611 43710 43832 439Shenyang 44012 44116 44211 443Dalian 44413 44520 44619 447Qingdao 44814 44910 45020 451Changsha 45215 45322 45416 455Hangzhou 45616 4576 4588 459Nanjing 46017 46118 4623 463Harbin 46418 46521 46628 467Wuhan 46819 46921 47011 471Guiyang 47220 47325 47418 475Zhengzhou 47621 47719 47832 479Nanning 48022 48123 48228 483Yinchuan 48423 48515 48614 487Xining 48824 48911 49033 491Fuzhou 49225 49316 49426 495Shijiazhuang 49626 49716 49820 499Taiyuan 50027 50124 50224 503Lanzhou 50428 50525 50628 507Huhehaote 50829 50923 5109 511Ningbo 51230 5137 51413 515Jinan 51631 5179 51821 519Hefei 52032 5219 52222 523Changchun 52433 52510 52615 527Nanchang 52834 52923 53027 531Tianjin 53235 53316 53423 535 Note. The ranking by the subjective satisfaction index in 2018 is not provided by the RCUQL. 536 537 538539540===== PAGE 32 (score 24, numeric-paren 21, table-words 1, var-words 1) =====54154231 543 544 545Table 8 Spearman’s rank correlation coefficients between our and the RCUQL’s 546rankings for 35 cities 547 548(1) 549(2) 550 551Ranking based on 552subjective satisfaction index 553Ranking based on 554objective index 5552011年 556-0.075 5570.047 558 559(0.694) 560(0.807) 5612012年 562-0.230 5630.382** 564 565(0.221) 566(0.037) 5672013年 568-0.303 5690.292 570 571(0.104) 572(0.117) 5732014年 574-0.202 5750.401** 576 577(0.285) 578(0.028) 5792015年 5800.192 5810.471*** 582 583(0.308) 584(0.009) 5852016年 5860.037 5870.445** 588 589(0.846) 590(0.014) 5912017年 5920.023 5930.455** 594 595(0.903) 596(0.012) 5972018年 598 5990.523*** 600 601 602(0.003) 603Note. The ranking by the subjective satisfaction index in 2018 is not provided by RCUQL. P values are in 604the parentheses. “*”, “**”, and “***” denote statistical significance at the 10%, 5%, and 1% levels. 605 606 607608609===== PAGE 33 (score 13, numeric-paren 0, table-words 1, var-words 11) =====61061132 612 613Online Appendix: 614 615Table A1 Housing attributes of owner-occupied units (housing price equation) 616Variable 617Description 618Mean 619Standard 620deviation 621Number of rooms 622 6232.473 6240.917 625Floor area (sq. 626meter) 627 62879.763 62936.431 630Building age 631year 2005 minus completed year 63213.382 63310.554 634Residential 635Dummy variable, 1= houses for both residential 636and business 6370.015 6380.121 639Building type 640Dummy variable, 1= storied building 6410.877 6420.328 643Structure 644Dummy variable, 1= reinforced-concrete 6450.448 6460.497 647Tap water 648Dummy variable, 1= with tap water 6490.947 6500.224 651Kitchen 652Dummy variable, 1= with kitchen 6530.983 6540.128 655Cooking fuel 656Dummy variable, 1= with gas/electricity 6570.862 6580.345 659Toilet 660Dummy variable, 1=with toilet 6610.940 6620.237 663Bathroom 664Dummy variable, 1= with bathroom 6650.745 6660.436 667Commodity 668housing 669Dummy variable, 1= commodity housing 6700.401 6710.490 672Public housing 673Dummy variable, 1= new public housing 6740.150 6750.357 676Old public 677housing 678Dummy variable, 1= old public housing 6790.449 6800.497 681Sample size: 421,660. We create the following categorical variables from the data: urban status 682(city, county, town); building structure (reinforced-concrete, mixed, other); number of floors (one, 683two to six, seven or more floors); building age (less than 5, 5~10, 10~20, more than 20 years); 684floor area (less than 60, 60~75, 75~90, more than 90 square meters). Combining these categories, 685we create 3×3×3×4×4=432 groups for all housing units and include these group fixed effects in 686the housing hedonic model. 687 688 689