Report No. 37759- CN China Governance, Investment Climate, and Harmonious Society Competitiveness Enhancements for 120 Cities in China October 9, 2006 Poverty Reduction and Economic Management Financial and Private Sector Development Unit East Asia and Pacific Region Document of the World Bank ACKNOWLEDGEMENTS The task manager and principal author for this report i s William Mako. L.Colin Xu was responsible for development of the survey instrument, oversight of survey administration, statistical analysis, and initial presentation of results. Anqing Shi provided supplemental data analysis and assistance in development of various exhibits. Xiuzhen .Zhang assisted with coordination. The survey was administered by the Survey Service Center of the National Statistics Bureau, under the direction of Hui Li. This work was made possible by financing from the United Kingdom's Department for International Development (DFID) for survey administration. Work proceeded under the supervision of David Dollar and Homi Kharas. Peer review was provided by Joseph Battat (Foreign Investment Advisory Service), Gary Fine (Europe and Central Asia Region), Holger Grundel (DFID), and Junkuo Zhang (Development Research Center of the State Council). Additional written comments came from Louis Kuijs, Yufei Pu (at the State Information Center of the National Development and Reform Commission), Hiroaki Suzuki, Xiaofang Shen, and Lihong Wang. In addition, Mats Anderson, Sudarshan Gooptu, Bert Hofman, Chris Athayde (DFID), Weizhong Meng, and K e Jin provided additional useful comments. ... 111 TABLE OF CONTENTS Acknowledgements.,.......................................................................................................... ... Executive Summary............................................................................................................ 111 v 1 I1Regional Comparisons................................................................................................... I.Introduction..................................................................................................................... . A City Characteristics.................................................................................................... . 7 B.Government Effectiveness ....................................................................................... 7 10 C .ProgressToward a Harmonious Society .................................................................. 21 I11.City Rankings............................................................................................................... . 24 24 B. GovernmentEffectiveness ....................................................................................... A Overall Investment Climate ..................................................................................... 30 C.ProgressToward aHarmonious Society .................................................................. . 40 46 IV.Recommendations....................................................................................................... D China's "Golden Cities"........................................................................................... 48 A. Short-Term Improvements inGovernment Effectiveness ....................................... 48 B.ProgressToward a Harmonious Society.................................................................. .Enhancing 56 D.MonitoringProgress................................................................................................. C City Characteristics................................................................................ 59 60 Annex A: City Performance. By Region .......................................................................... 61 61 B Bohai........................................................................................................................ A. Southeast.................................................................................................................. . C.Northeast .................................................................................................................. 69 74 D.Central...................................................................................................................... 78 E.Southwest ................................................................................................................. . 85 Annex B: Methodology andData..................................................................................... F Northwest.................................................................................................................. 90 A.PerformanceMeasures............................................................................................. 95 95 B Investment Climate Factors...................................................................................... . C.Effect of Investment Climate on FirmPerformance................................................ 96 98 Bibliography ................................................................................................................... 125 D.ProjectedPerformanceGainsfrom Investment Climate Improvements ...............100 iv EXECUTIVESUMMARY A survey of 120 cities (and 12,400 firms) in China shows that city-level investment climate varies widely. Business law and regulation are basically the same throughout China. Hence, differences mainly reflect local government efforts (or lack of efforts) to operate efficiently. For instance, Taxes and fees average 3.1 percent of sales revenue at the top lothpercentile of cities, versus 6.9 percent at the bottom lothpercentile. Firm interactions with major bureaucracies average 36 daydyear at the top loth percentile o f cities and 87 daydyear at the bottom lothpercentile. Firm expenditures on entertainment and travel, which can be a vehicle for corruption, average 0.7 percent of revenues at the top lothpercentile of cities and 1.9 percent of revenues at the bottom lothpercentile. Combined time for customs clearance o f exports and imports averages 5.4 days at the top lothpercentile and 20.4 days at the bottom lothpercentile. University-educated workers account for 28.5 percent or more of workforces in the top lothpercentile and 10.8 percent or less inthe bottom lothpercentile of cities. While comparability with earlier surveys i s extremely limited, it does appear that losses due to power/transport problems have increased since 2001/2002, especially in southern China; that informal payments by firms, relative to sales, may have declined; and that taxedfees relative to sales have increased inmany cities. The survey also finds significant (or noteworthy) differences in the importance of state- owned enterprises (SOEs) in local industry; labor over-staffing; firm access to bank loans; confidence in protection of property and contract rights; and adequacy of local power and transport. The prevalence of different types of industry or ownership in a particular locale seems to have some effect on tax/fee burdens, bureaucratic behavior, overstaffing, and other measures of government effectiveness. Ingeneral, the investment climate of China's regions can be ranked from best to worst as follows: 1. Southeast (Jiangsu, Shanghai, Zhejiang, Fujian, and Guangdong; 2. Bohai (Shandong, Beijing, Tianjin, and Hebei); 3. Central (Anhui,Henan, Hubei, Hunan, and Jiangxi); 4. Northeast (Heilongjiang, Jilin, Liaoning); 5. Southwest (Yunnan, Guizhou, Guangxi, Sichuan, Chongqing, and Hainan); and V 6. Northwest (Shanxi, Shaanxi, Neimenggu, Ningxia, Qinghai, Gansu, and Xinjiang) , Six cities offer outstanding performance in overall investment climate (both for domestic firms and for foreign firms), in government effectiveness (toward both domestic firms and foreign firms), and in progress toward achieving a "harmonious society.'' These "golden cities" are Hangzhou, Qingdao, Shaoxing, Suzhou, Xiamen, and Yantai. Cities in the bottom quintile of "government effectiveness" could expect near-term gains off 25-35 percentage points in firm productivity and 15-25 percentage points in foreign ownership by improving local government efficiency, labor flexibility, and financing to those of the leading cities in Shandong, Guangdong, Zhejiang, and Jiangsu. Significant gains in firm productivity and/or foreign investment could result from the following reforms: a Further simplification of licensing and other procedures to start a business; a Greater transparency and simplification of approvals for urban land use; a Elimination of tax preferences, for instance, for foreign investors; a Elimination of many city-specific administrative fees and adoption of objective measures for remaining fees; a Adoption of best-practice customs clearance procedures at inland customs posts; a More consistent labor practices, by tightening enforcement where necessary to improve worker protections and loosening labor rules where possible to enhance labor flexibility; a Completing the ownership transformation of small/medium state-owned enterprises (SOEs) that are viable; liquidating non-viable SOEs; and improving governance at large SOEs; a Encouraging foreign investment in local banks, e.g., city commercial banks; a Providing additional legal/regulatory protection for lenders; encouraging more widespread credit reporting; and making it easier for small and medium enterprises (SMEs) to use assets other than real estate as collateral; and a Encouraging wider use, by local banks, of international best-practices in S M E lending. In addition, the survey finds a correlation between local government efforts to achieve a good investment climate and local progress toward achieving a harmonious society. For cities in the bottom quintile of investment climate, improvements in educatiodtechnical training, healthcare, and environmental quality to the levels of the leading Southeast and Bohai cities could lead to a 25 percentage point gain in firm productivity and a 10 percentage point gain in foreign ownership. This would require sustained spending on education, health, and environmental protectionhemediation. The survey finds that about one-third of observed differences in firm productivity, and one-fifth of differences in the extent of foreign ownership, are associated with differences in "city characteristics" - such as population size, GDP and GDP growth rate, and vi transport costs. Government reforms and/or medium-term investments can bring about improvements in important city characteristics: Firmsinmore-populatedcities tend to be more productive, due perhaps to greater competition and agglomeration benefits. This offers support for continued migration, especially to Southeast cities where water scarcity i s less an issue.' To absorb migrants without worsening urban poverty and unemployment, destination cities will have to invest in infrastructure, housing, and public services, while creating an investment climate that stimulates private sector investment and business development. Through both physical improvements and local income gains, continued investment in urban infrastructure and services could make lagging cities more appealing to investors, especially foreign investors. Investment in urban infrastructure has been shown to support growth and to counteract urban unemployment and poverty. State-of-the-art information technology i s necessary for cities seeking to cultivate high-tech industry. Greater reliance on private providers of public services offers opportunities for increasing competition, counteracting urban poverty, and enhancing local quality of life. Transport costs for moving goods to/from seaports i s a key consideration, especially for foreign investors. Survey data indicate that a 50 percent reduction inoverlandtransport costs could raise foreign ownership by perhaps 5 percentage points in such deep-interior cities as Lanzhou and Wulumuqi. Key measures to reduce transport costs include major improvements in China Rail's governance and management, including full or partial privatization; development of real nationwide trucking companies; more regular air cargo service for interior cities; and regulatory reforms to encourage domestic and international integrated logistical service providers to expand into interior regions. Lastly, to encourage local governments to pursue sustained improvements in government effectiveness, urban development, and progress toward a harmonious society, it would make sense to repeat this survey regularly (e.g., bi-annually) in order to track progress or deterioration over time. 1Dismantlement of the hukou system could also reduce urban-rural income differences. vii I.INTRODUCTION Market potential and investment activity vary widely throughout China (Table 1-1). For instance, per capita GDP in Southeast China averages more than 50 percent above the Northeast's and 150 percent above the averages for Central and Southwest China. Attracted by richer markets and cheaper access to ocean transport, per capita foreign direct investment (FDI) in the Southeast provinces averaged $128 in 2004. This i s 130 percent above per capita FDIfor the Northeast, more than 7 times the average for Central China, and more than 25 times the average for western China. Consistent with this pattern, foreign-invested enterprises (FIE) account for 43 percent of industrial assets in the Southeast, versus 15 percent in the Northeast, 9-10 percent in Central and Southwest China, and 5 percent inthe Northwest. Table 1-1. Market and InvestmentIndicators, By Province and Region, 2004 Per capita Per capita FIE Population GDP GDP FDI industrial (millions) (RMB millions) (RMB) (US $1 assets Liaoning 42.2 687,265 16,286 128 19% Jilin 27.1 295,821 10,916 7 14% Heilongjiang 38.2 530,500 13,887 9 6% Northeast 107.5 1,513,586 14,080 55 15% Beijing . - 14.9 428,331 28,747 171 28% Tianjin 10.2 293,188 28,744 168 34% Hebei 68.1 876,879 12,876 10 13% Shandong 91.8 1,549,073 16,874 94 15% Bohai 185.0 3,147,471 17,013 74 20% Shanghai 17.4 745,027 42,818 362 53% Jiangsu 74.3 1,540,3 16 20,73 1 120 35% Zhejiang 47.2 1,124,300 23,820 121 21% Fujian 35.1 605,314 17,245 55 55% Guangdong 83.0 1,603,946 19,325 121 56% Southeast 257.0 5,618,903 21,863 128 43% Anhui 64.6 481,268 7,450 7 12% Jiangxi 42.8 349,594 8,168 48 11% Henan 97.2 881,509 9,069 4 8% Hubei 60.2 630,992 10,482 29 13% Hunan 67.0 561,226 8,377 21 8% Central 331.8 2,904,589 8,754 18 10% Shanxi 33.4 304,241 9,109 3 5% Shaanxi 37.1 288,351 7,772 4 5% 1 Per capita Per capita FIE Population GDP GDP FDI industrial (millions) (RMBmillions) (FWB) (US $> assets Gansu 26.2 155,893 5,950 1 2% Qinghai 5.4 46,573 8,625 5 1% Ningxia 5.9 46,035 7,803 11 11% Xinjiang 19.6 220,O15 11,225 2 2% Neimengu 23.8 271,208 11,395 14 8% INorthwest 151.4 1.332.316 8.800 5 5% Guangxi 48.9 332,010 6,790 6 15% Chongqing 31.2 266,539 8,543 8 15% Sichuan 87.3 655,601 7,5 10 4 7% Guizhou 39.0 159,190 4,082 2 3% Yunnan 44.2 295,948 6,696 3 5% Hainan 8.2 76,936 9,382 15 16% Southwest 258.8 1,786,224 6,902 5 9% Sources: CEIC DataCompany Ltd.; China Statistical Yearbook,2005; and staff calculations. This diversity highlightsthe importance of investmentclimate for economic development throughout China. In lagging regions, FDI could provide a powerful boost to growth. For a foreign investor interested in access to a lagging region's markets or resources, city-level differences in investment climate could have a decisive effect on city selection. Domestic private investment i s likely to play a much greater role, however, in the development of China's lagging regions. Thus, the investment climate for domestic business start-up and development i s vitally important for China's lagging regions. As for China's vibrant coastal areas, city-level differences in investment climate may also exert a powerful influence on the choice of investment destination. Given the importance of both domestic and foreign investment for growth, previous World Bank investment climate surveys of Chinese cities attracted substantial interest. Ease of entry or exit for domestic businesses, skill and technology endowments, labor flexibility, access to finance, private sector participation, and courts efficiency emerged as significant factors in business investment decisions.2 These earlier surveys suffered from some limitations, however, including the exclusion of some important cities and provinces; insufficient consideration of important "city characteristics" (e.g., market size, transport costs); and a lack of attention to linkages among investment climate, urban infrastructure and quality of life, and social benefits consistent with progress toward a harmonious s o ~ i e t y . ~ Hence, the current survey collected city-specific data on 120 cities. Included are data on each city's characteristics and quality of life, local economy, health, education, and David Dollar, Shuilin Wang, Lixin Colin Xu, and Anqing Shi, "Improving City Competitiveness Through the Investment Climate: Ranking 23 Chinese Cities," January 2005, www.worldbank.ordcn/ , summarizes results from a 2001 survey of 1500 firms in five cities (Beijing, Tianjin, Shanghai, Guangzhou, and Chengdu) and a 2002 survey of another 2400 firms in an additional eighteen cities. Because of changes in survey method and analysis, ratings from the current survey and the previous survey cannot and should not be compared. 2 en~ironment.~Cities are included from all provinces in mainland China except Tibet.' For each province, the capital city i s included. The inclusion of additional cities for a particular province depends on provincial GDP. The 120 cities included in this survey account for 70-80 percent of China's GDP. The current survey controls for each city's GDP, factor costs, and cost of access to ocean-shipping. The current survey also includes various measures of urban infrastructure and quality of life and evaluates progress toward a harmonious society `I ble1-2. CitiesSurveyed,2( 5 Province City Province City Province City Anhui Anqing Henan Luoyang Neimenggu Baotou Chuzhou Nanyang Huhehaote Hefei Shangqiu Ningxia Wuzhong Wuhu Xinxiang Yinchuan Beijing Beijing Xuchang Qinghai Xining Chongqing Chongqing Zhengzhou Shaanxi Baoji Fujian Fuzhou Zhoukou Xian Quanzhou Hubei HuwXang Xianyang Sanming Jingmen Shandong Jinan Xiamen Jingzhou Jining Zhangzhou Wuhan Linyi Gansu Lanzhou Xiangfan Qingdao Tianshui Xiaogan Taian Guangdong Dongguan Yichang Weifang Foshan Hunan Changde Weihai Guangzhou Changsha Yantai Huizhou Chenzhou Zibo Jiangmen Hengyang Shanghai Shanghai Maoming Yueyang Shanxi Datong Shantou Zhuzhou Taipan Shenzhen Jiangsu Changzhou Yuncheng Zhuhai Lianyungang Sichuan Chengdu Guangxi Guilin Nanjing Deyang Liuzhou Nantong Leshan Nanning Suzhou Mianyang Guizhou Guiyang Wuxi Yibin Zunyi Xuzhou Tianjin Tianjin Hainan Haikou Yancheng Xinjiang Wulumuqi Hebei Baoding Yangzhou Yunnan Kunming Cangzhou Jiangxi Ganzhou Qujing Handan Jiujiang Yuxi Langfang Nanchang Zhejiang Hangzhou Qinhuangdao Shangrao Huzhou Shijiazhuang Yichun Jiaxing These data are drawn from the National Bureau of Statistics and have not been independently verified for this study. According to the Survey Service Center, insufficient time, resources, and enterprises meeting survey criteria made it impractical to include Tibet in this survey. For similar reasons, Tibet i s not usually included in monthly, quarterly, and special surveys by the National Bureauo f Statistics. 3 Province City Province City Province City Tangshan Jilin Changchun Jinhua Zhangjiakou Jilin Ningbo Heilongjiang Daqing Liaoning Anshan Shaoxing Haerbing Benxi Taizhou Qiqihaer Dalian Wenzhou Fushun Jinzhou Shenvang Survey data are also aggregated and organized around six regions: 1. Southeast includes Jiangsu, Shanghai, Zhejiang, Fujian, and Guangdong. While this i s a wide swathe of territory, the analysis shows consistently good investment climate all across the Southeast. 2. Bohai includes Beijing, Tianjin, Hebei, and Shandong. Following the Pearl River delta and Yangtze River delta, Bohai Bay has emerged as a third center for growth. 3. Northeast includes Liaoning, Jilin, and Heilongjiang. Geographically distinct, the Northeast remains dominatedb y state-owned industry. 4. Central includes Anhui, Henan, Hubei, Hunan, and Jiangxi. While also dominated by state-owned industry, many cities in these interior provinces could benefit from cheap river-borne access to ocean shipping. 5. Southwest includes Yunnan, Guizhou, Guangxi, Sichuan, Chonqing, and Hainan. Exports from these western cities are more typically oriented toward overseas markets in Southeast Asia, often through such ports as Guangzhou and Shanghai. 6. Northwest includes Shanxi, Shaanxi, Neimenngu, Ningxi, Qinghai, Gansu, and Xinjiang. Exports from these cities would more naturally move through ports in Bohai Bay (e.g., Tianjin) or overland to markets inCentral Asia. In addition to collecting city-level data, the current survey has collected firm-level data. For all but the four directly-governed cities, the survey samples 100 firms. For each of the four mega-cities (Beijing, Tianjin, Shanghai, Chongqing), the survey samples 200 firms. Thus, the sample includes 12,400 firms. O f these, 8 percent are registered as majority state-owned; 28 percent as foreign-invested; and 64 percent as domestic non- state. All firms are from industry (Table 1-3). This is intended to promote consistency since some services (e.g., financial services) are prone to greater regulation and the inclusion of higher numbers of such service businesses in some cities (e.g., Beijing, Shenzhen, Shanghai) could distort survey results. For each city, the top 10 industries in terms of sales revenue are drawn. For each industry, all firms in the sample universe are divided into large, middle and small firms, each accounting for 1/3 of total industr revenue. Then from each of three types of firms, an equal number of firms are drawn. Firms are 2 required to have a minimumof 10employees. Incase the segment of large firms do not have sufficient number of firms, local Survey Centers re-divide the remaining sample into large, medium and small firms, and draw from the new segment of large firms, continuing to do so until the required number of large firms are drawn. 4 Table1-3. Distributionof Industrial FirmsSurveved inChina. 2005 Code Industry # %. 13 agricultural and side-line food processing 969 7.81 14 food production 243 1.96 15 beveragesproduction 178 1.44 16 tobacco production 46 0.37 17 textiles manufacturing 952 7.68 18 garment, shoes, and caps manufacturing 206 1.66 19 leather, furs, down, and relatedproducts 139 1.12 20 timber processing, bamboo, cane, palm fiber and straw products 141 1.14 21 furniture manufacturing 55 0.44 22 papermakingand paper products 235 1.90 23 printing and record medium reproduction 62 0.50 24 cultural, educational and sports goods 41 0.33 25 petroleum processing and coking 182 1.47 26 raw chemical materials and chemical products 1441 11.62 27 medical and pharmaceutical products 426 3.44 28 chemical fiber products 47 0.38 29 rubber products 21 0.17 30 plastic products 329 2.65 31 nonmetal mineral products 1299 10.48 32 smelting and pressing of ferrous metals 491 3.96 33 smelting and pressing of non-ferrous metals 345 2.78 34 metal products 366 2.95 35 general machinery 1077 8.69 36 equipment for special purposes 486 3.92 37 transportation equipment 989 7.98 39 electrical equipment and machinery 864 6.97 40 electronic and telecommunications equipments 598 4.82 41 instruments, meters, cultural and office machinery 60 0.48 42 handicraft products and other machinery 109 0.88 43 renewable materials processing 3 0.02 Total 12400 100 Analysis of survey data focuses on the relationship between the total factor productivity (TFP)or foreign ownership of firms and various investmentclimate factor^.^ Section 11 compares the investment climate of the six regions. City characteristics, government effectiveness, and progress toward a harmonious society all contribute to investment climate. In addition to cross-regional comparisons of these contributors, industry-specific investment climate issues are discussed. City-level performance data on government effectiveness and progress toward a harmonious society are presented - by region -inAppendix A. Section 111ranks the 120 cities in terms of investment climate, government effectiveness, and progress toward a harmonious society. In order to provide benchmarks, this section 7TFP should be understood as the productivity level of a firm after netting out the effects of capital, labor, and industry-specific technology. For a discussion of the estimation of TFP from the data collected in this survey, see the Annex B. TFP i s subsequently referred to simply as "firm productivity." 5 also presents objective performance data for the Top 20 Cities in government effectiveness and inprogress toward a harmonious society. Section IV offers policy and program recommendations, both national and local, to improve investment climate at the city level. These include a mix of short-term measures to enhance government effectiveness and medium- or longer-term measures to address health, education, and environmental issues; upgrade urban infrastructure; and address transportation issues. 6 11.REGIONALCOMPARISONS Investment climate i s the result of a process. A city starts with certain characteristics and endowments - e.g., market size and relative prosperity; location; mix of businesses (domestic and foreign, private and state-owned); a local government; human resources; and some level of urban infrastructure and quality of life. Some factors, such as market size and transport costs, may change only slowly. Other factors, especially the efficiency of local government, are more easily remedied in the short term. Improvement of other factors - such as education and technical skills, environmental quality, and urban infrastructure - may require sustained government spending. Short-term government reforms and medium-term government spending on public goods will ideally result in additional business investment and economic growth over the long term. This section presents survey findings on (a) city characteristics; (b) local government effectiveness; and (c) progress toward achieving a harmonious society. A. CITY CHARACTERISTICS Differences in "city characteristics" - per capita GDP, economic growth, and transport costs - explain about one-third of observed differences in firm productivity.* This i s not surprising, since market opportunity and costs are key considerations inbusiness. GDP. China's richest regions tend also to be premier destinations for domestic and foreign business investment. Among provinces, the correlation between per capita GDP and FDI, for instance, i s quite high (Figure 11-1). This reflects the greater ability o f residents in richer regions to afford the products supplied by foreign firms. Similarly, richer local governments are better able to afford the investments in infrastructure, environmentalquality, and education needed to attract foreign investors. * Surveydata indicate that city characteristics are much less important in explaining differences in foreign ownership. For more detail, see Table B-7,Annex B. 7 Figure 11-1. Per Capita GDP 8z FDI I 400 350 300 250 -P 8t! 200 + Series1 P 150 100 ce 50 0 10,000 20,000 30,000 40,000 50,000 RMB GDP per capita Costs. The survey finds that factor costs vary widely, but tend to be lower in interior cities. For instance, compared with some of China's major seaports, real estate costs in interior cities may be 25-50 percent lower.' Labor costs appear to be consistently lower ininterior cities (Table 11-2). Table 11-2. Representative Factor and Transport Costs For Typical City-Pairs, 2005 (inRMB) Interior city Land Labor Transport Designatedseaport Land Labor Changchun 5,240 10,491 3,948 Dalian 10,556 14,061 Ha'erbin 12,341 9,080 5,244 Dalian 10,556 14,061 Taipan 16,539 8,666 3,342 Tianjin 19,274 14,429 Huhehaote 8,014 7,983 4,176 Tianjin 19,274 14,429 Xi'an Ll 10,188 10,786 6,684 Shanghai 24,603 21,095 LanzhouI/ 5,899 8,695 11,016 Shanghai 24,603 21,095 Wulumuqi11 13,930 9,937 22,710 Shanghai 24,603 21,095 Chengdu 19,049 10,618 15,048 Shanghai 24,603 21,095 Changsha 8,911 9,917 4,770 Guangzhou 6,760 20,772 Guiyang21 8,824 8,987 5,058 Guangzhou 6,760 20,772 Kunming21 11,850 10,967 6,432 IGuangzhou 6,760 20,772 31 Sources: survey data; World Bank (2003); ADB (2005); Institute of ComprehensiveTransportationof the NDRC; and staffcalculations. Note: Land cost is average monthly rent for 1000 square meters. Labor i s monthly wages for ten workers, assumed to include 6 full-time and4 part-time. Transport costs are assumedto be 6 RMB per kilometer to truck a 20-footcontainer to the seaport designatedby the Instituteof ComprehensiveTransportation, except as follows: 11Transport costs are to Lianyungang. 21Transport costs are to Fancheng. 31Negligible; analysis assumes RMB 400 for handlingcosts within each seaport. Some land prices insome coastal cities, however, appear to have been distorted by artificially low rents in development zones. 8 The survey finds that transport costs more clearly affect foreign investment and - to a lesser extent - firm productivity." Numerous factors contribute to high transport costs in China (Box 11-1). Box 11-1.Transport Sector Issues While rail shipment can be 40-60 percent cheaper than truck shipment over longer distances (e.g., >700 kilometers), China Rail's near monopoly on railtransport has discouraged development of a service orientation. For instance, freight wagons are in short supply. Loading times are subject to extensive delays and uncertainty. Loading can be subject to approval of the consigner's loadingplan, one or more inspections of containers, and loading fees that represent 20-30 percent of actual transport fees. Access to service may depend upon relationships with local rail authorities. Transit times are variable and beyond the consignor's control. Additional fees may be incurred in-transit and upon off-loading. China Rail has been slow to introduce new services (e.g., inter-modaltransport) that would enhancethe competitiveness of customers. Trucking has remained largely a cottage industry. Historically, manufacturers have provided their own trucking. Hence, utilization has tended to be inefficient. For-hire trucking has largely remained limited to informal leasing from unofficial local operators. Development of modem truckingnetworks sufferedfrom local protectionism. Cities may prohibit entry by outside trucks without tedious city-specific licensing and registration. Hence, many trucks traversing provincial borders haul goods only one way. There are still no clear procedures and qualifications for obtaining a "national" license for cargo trucking. Toll road tolls can increasetrucking costs by as much as 20 percent. Domestic air cargo still relies mainly on space in the belly of passenger aircraft and remains under-developed. Many warehouses are poorly designed and equipped (e.g., for climate control, segregation, automation). This increases actual inventory losses and impedes efficient inventory management. Ingeneral, ChinaRailand other "legacy" carriers do not providereal-timetracking and tracing of cargoes, estimates of travel time, or reports of en-route service failures. Legacy carriers are not prepared to provide "proof of delivery" to facilitate payment. The enforcement of cargo liability can be difficult. Few carriers are prepared to act as reliable custodians for cargoes or advise customers on such issues as damage prevention, pallet or container exchange, or insurance. Carriers are generally unwilling to "deconstruct" services and allow customers to reassemble service components inways that support customers' planning and distribution requirements. While WTO has facilitated entry by internationalproviders of logistical services, their expansion into less-lucrative interior markets i s discouraged by multiple licensing requirements (e.g., for different business segments, for new branch offices), possible minimumcapital requirements, and requirements for actual ownership of assets (e.g., trucks, warehouses) that may discourage the development of genuine 3`d party and 4' party logistics services that rely on in-house systems to manageexternally-owned assets. Sources: Woetzel(2003);World Bank (2004): AmCham(2005): and industrvinterviews. loSee TableB-1,Annex B. 9 The impact of transport on the competitiveness of manufacturers in interior cities will tend to depend on their products. At least for now, interior cities are more likely to be able to compete in the production of bulk goods (e.g., coal) suitable for leisurely shipment by rail or high-value goods (e.g., computer chips) suitable for air cargo. Transport will tend to have the most deleterious impact on medium value/high volume goods that are too valuable to ship by rail but not worth shipping b y air. Transport burdens are magnified for firms in interior cities seeking to import low or medium-value inputsfrom the coast. Incases where globalization leads to more-or-less uniformworldwide prices for products and material inputs, transport costs will also tend to depress labor prices in interior cities." Hence, to enhance competitiveness and raise incomes in interior cities, nationwide transport sector reforms and initiatives to make local government more effective are both important. B.GOVERNMENTEFFECTIVENESS Not surprisingly, the survey reveals China's Southeast cities as making the best efforts in investment climate; followed by the Bohai cities; with Northeast and Central cities typically somewhere inthe middle; and with Southwest and Northwest cities lagging. Government eficiency. The survey measured taxes and fees, firm expenditures on entertainment and travel, time spent on bureaucratic interactions, and customs clearance performance. Taxes and fees relative to sales appear to be lowest in the Southeast and Bohai and highest in the Southwest and Northwest (Table 11-3). The generally lower VAT burden for Southeast and Bohai firms may reflect several factors: e.g., greater reliance on component purchases (versus in-house production); a higher number of VAT-advantaged high-technology firms; and higher export sales (with VAT refunds for exports). Corporate income tax burdens appear to be higher in Southeast and Bohai, however, which i s somewhat surprising given the larger number of foreign-invested enterprises (FIEs) in these regions and lower income tax rates for many FIEs. l2 Hence, the apparently lower income tax burden among firms in North, Central, and Southwest China may reflect lower profits (and more unprofitable enterprises, including state-owned). Since the actual tax burden of individual firms may mainly reflect firm characteristics (e.g., industry, ownership, exports, or "make vs. buy" preferences), other taxes and administrative fees - while small - could be significant.l3 City-specific administrative 11 ''World Bank, China: Promoting Growth with Equity, Country Economic Memorandum, 2003, pp. 20-1. Inmany locations (e.g., special economic zones, coastal cities, river delta zones, high-technology zones), corporate tax rates for FIEs are 15-24 percent, versus the standard 33 percent for other enterprises. PricewaterhouseCoopers, Doing Business and Investing in the People Is Republic of China, 2002. l3 Survey data indicate that combined land taxes, real estate taxes, and administrative fees average 0.27 percent of sales for Southeast and Bohai firms, versus 0.45 percent for Northeast firms and 0.60 percent for Northwest firms. 10 fees (e.g., for construction, land use, enterprise registration, water, road transport) may be less numerous and lower in coastal cities than in lagging regions. A "virtuous circle" may be at work whereby more-prosperous cities receive sufficient financial resources (e.g., corporate income taxes) so that they are better able to eliminate or reduce city- specific administrative fees. Table 11-3. Average Taxes and FeesRelative to Sales, 2005 (inDercent) Southeast Bohai Northeast Central Southwest Northwest Value-added tax (VAT) 2.8 3.1 3.9 3.5 4.5 4.1 Income tax 0.8 0.8 0.6 0.6 0.7 0.5 Other taxes and fees I/ 0.5 0.6 0.9 0.9 1.1 1.2 Total 4.1 4.5 5.4 5.0 6.3 5.8 Taxedfees sometimes differ with type of industry or type o f ownership. The burden from taxedfees appears pretty similar across regions for manufacturers of low-value products (e.g., processed food, textiles, garments) (Table 11-4). Taxedfees are consistently higher for producers of bulk goods (e.g., chemicals, minerals, metals), which may reflect additional resource depletion or environmental charges. Regional differences in taxedfees are greatest for manufacturers of high-value goods (e.g., pharmaceuticals, electronics, telecoms equipment). For manufacturers of such high-value goods, the taxedfees burden i s lowest in the Southeast (3.0 percent) and highest in the Southwest, Northwest, and Central China (6.6-8.7 percent). This may reflect a combination of tax concessions for foreign firms in coastal cities (especially inthe Southeast) and a tendency among coastal producers of high-value products to buy inputs - versus a presumed tendency among such producers in interior cities to produce inputs/components in-house and thereby create more value-added subject to VAT. Low average taxedfees for the Southeast may partly reflect the presence of a disproportionately large number of high- value manufacturers and small number of bulk goods producers. Table 11-4. Average Taxes and FeesRelativeto Sales, By Type of Industry Note: Low-value includes firms in agricultural and side-line food processing; food production; textile manufacturing; and garments, shoes, and caps. Bulk goods producers include raw chemical materials andchemical products; nonmetal mineral products; and smelting and processing of ferrous metals and/or nonferrous metals. High value includes pharmaceuticals, medical equipment, and electronics and telecoms equipment. These sectors include 6,970 of the 12,400 firms surveyed. For foreign-invested enterprises (FIEs), the average taxes/fees burden varies modestly (4.2-5.6 percent of sales) across most of China. For the Southeast, however, the taxedfees burden for FIEs i s much lower (2.8 percent). Again, this likely reflects a combination of tax concessions and preferences for outsourcing. Compared with FIEs, the taxedfees burden tends to be higher for SOEs, especially in the Southwest (7 11 percent). Small domestically-invested enterprises generally enjoy a lower taxedfees burden, except in the Southeast where the average for such enterprises (5 percent) i s higher than the burden for SOEs (4.6 percent) or for FIEs (2.8 percent). The lower overall average for the Southeast reflects inclusion of a large number of FIEs in the survey sample. Southeast Bohai Northeast Central Southwest Northwest State-owned 4.6 4.4 5.2 5.4 7.0 6.2 Foreign-invested 2.8 4.7 5.1 4.2 5.6 4.9 Small domestic 5.0 4.7 4.4 3.9 4.5 4.1 While laws and regulations tend to be consistent nationwide, the amount of time enterprise staff must spend interacting with government bureaucracies i s not. Reported interactions with four major government bureaucracies (tax administration, public security, environmental protection, and labor and social security) vary widely. For the top 90thpercentile of surveyed cities, surveyed firms reportedly average 36 days/year or less of bureaucratic interaction. For the bottom lothpercentile, the reported average i s 87 days or more. On a regional basis, the low "time tax" for Southeast cities i s consistent with their pro-business reputation. The similarly low figure for Central cities i s somewhat surprising. It i s also noteworthy that the average for Northeast cities falls below that for Bohai cities,14 despite the Northeast's legacy of greater government intervention,inthe economy (Table 11-6). From an industry perspective, producers of high-value goods have the most exposure to major bureaucracies) - followed by bulk goods producers - in the Southeast, Bohai, Northeast, and Central. This may reflect greater interaction with tax administration which administers various tax preferences. In Southwest and Northwest, bulk goods producers report the most interactions, followed by producers of high-value goods. This may reflect greater interactions on environmental protection. Manufacturers of low-value goods face the least demand from local bureaucracies in all six regions. Thus, the nature of local industrylikely has some effect on local bureaucratic behavior and demands. 14 Because the survey included only industrial firms and excluded service firms, including financial service firms, the higher figure for Bohai cities does not reflect greater regulatory oversight of financial service firms. 12 Table 11-6. Average Annual Bureaucratic Interaction, By Type of Industry In all regions, SOEs face the highest demands for bureaucratic interaction (Table 11-7). Demands on FIEs are somewhat lower. Compared with SOEs and FIEs, small domestically-invested firms report the lowest levels of bureaucratic interaction in all six regions. Again, the nature of the local enterprise sector will tend to affect the overall average for this investment climate factor. Southeast Bohai Northeast Central Southwest Northwest State-owned 58 84 74 62 76 89 Foreign-invested 56 71 69 50 60 87 Small domestic 30 45 34 31 43 45 A third measure of government efficiency is firm expenditures on travel and entertainment. Such expenditures can serve as a conduit for informal payments to officials. Averages reportedly range from 0.7 percent or less of sales revenue in the to 90th percentile of cities to 1.9 percent or more of sales revenue in the bottom 10!I percentile of cities. Regionally, expenditures on entertainment and travel are reported to be lowest inBohai and Southeast cities and highest inNortheast (Table 11-8). Table 11-8. Average Travel & Entertainment Costs Relative to Sales, By Type of Industry Compared with similar producers in other regions, Northeast manufacturers of low-value goods and bulk goods spend the most on entertainment andtravel. Among manufacturers of high value goods, spending for entertainment and travel in the Southwest and Northeast i s 2-5 times higher than among similar firms in the Southeast and Bohai. In general, manufacturers of high-value products appear to spend more on entertainmenvtravel than do other types of manufacturers. This may reflect valid marketing costs to some extent. The Southeast, where high-value manufacturers spend less than do low-value or bulk goods producers, i s an exception. This likely reflects several factors - including a good local investment climate and effective local 13 government, as well as greater prevalence of well-developed brands and integration into global supply chains, the latter reducing requirements for entertainment and travel. Looking at ownership, small domestically-invested firms typically have the highest rates of expenditure on entertainment and travel (Table 11-9). FIEs spend less, and in Southeast and Bohai have the lowest rates of expenditure. Compared with Southeast and Bohai, entertainmenvtravel costs are about 3x higher for FIEs inthe Northwest. Southeast Bohai Northeast Central Southwest Northwest State-owned 1.1 1.o 1.5 1.2 1.2 1.3 Foreign-invested 0.7 0.9 1.5 0.9 1.3 2.3 Small domestic 1.6 1.1 1.5 1.5 1.6 1.8 Another study has found that entertainment and travel expenditures tend to be higher where local government provides poorer service or where the tax burden i s higher.I5 This pattern also appears in current survey, especially for Southeast, Central, Southwest, and Northwest cities (Table 11-10). Table 11-10.Informalpayments,bureaucracy,taxes & fees 1I Travel & 1 Days of I entertainment/ bureaucratic sales 1 interactions Taxes & feeshales Southeast 1.0% 51.6 days 4.1% Bohai 0.9 71.9 4.5 Northeast 1.4 63.1 5.4 Central 1.2 51.7 5.0 Southwest 1.2 65.9 6.3 Looking at customs clearance, a vital business service provided by government, the days or less in 90th percentile cities and 20.4 days or more in loth percentile cities. survey finds that combined times for export clearance and import clearance average 5.4 Regionally, survey data indicate that customs clearance tends to be 33-50 percent faster inSoutheast or Bohai cities than inthe rest of China (Table 11-11). ''Cai,Fang, and Xu (2005) also find that higher entertainmenutravel expenditures tend to be associated with poorer firm performance and that the "quality" of entertainmenutravel expenditures is correlated with corporate governance. 14 Table 11-11.Average Days for Customs Clearance, Exports Imports CombinedI/ Southeast 3.5 4.2 7.3 Bohai 4.4 5.O 8.6 Northeast Central Southwest 7.4 14.0 Northwest 9.0 7.8 16.8 The superior performance of Southeast and Bohai cities in customs clearance appears to extend to all types of industry (Table 11-12). Customs clearance times can be surprisingly long for bulk goods and low-value goods in the Northeast, Central, Southwest, and Northwest regions. High-value goods seem to receive more expedited customs clearance inthe Southeast, Bohai, andNortheast. Type of industry Southeast Bohai Northeast Central Southwest Northwest All firms 7.3 8.6 12.6 13.8 14.0 16.8 Low-value 7.9 7.7 15.1 13.1 17.2 16.5 Bulkgoods 9.6 10.7 13.8 17.6 15.9 18.1 Hieh-value 4.7 6.6 8.5 13.7 12.6 16.4 SOEs and FIEs in the Southeast and Bohai similarly seem to enjoy faster customs clearance than do similar firms elsewhere in China (Table 11-13). Customs clearance often takes the longest for small domestically-invested firms, including in the Southeast where reported average customs clearance time (9.7 days) i s slower than times for similar firms in Bohai cities (6.9 days) and Central cities (8.5 days). Southeast Bohai Northeast Central Southwest Northwest State-owned 8.3 10.1 13.8 14.0 13.4 16.3 Foreign-invested 5.4 6.8 8.4 10.2 8.2 10.1 Small domestic 9.7 6.9 14.4 8.5 22.7 17.1 Balance betweenprivate sectorfirms and state-owned enterprises. Ineach region, three- quarters or more of survey respondents are private-sector firms, either foreign-invested or domestic-invested. Not surprisingly, the proportion of private sector respondents i s highest in the Southeast and lowest in the Northeast: Southeast 92% Bohai 80 Northeast 71 15 Central 82 Southwest 81 Northwest 76 Private firms are more numerous, but typically smaller than state-owned enterprises (SOEs). SOEs account for 50-75 percent of industrial sales revenue in Northeast, Central, Southwest, and Northwest provinces (Table II-14).16 SOEs account for less than half of industrial sales revenue in only two regions: the Southeast (23 percent) and Bohai (43 percent). Liaoning 61% Jilin 76 Heilongjiang 81 Northeast average 73 Beijing 54 Tianiin 39 Shandong 34 Hebei 44 Bohai average 43 Jiangsu 19 Shanghai 40 Zhejiang 12 Fujian 22 Henan 47 Hunan 54 Hubei Yunnan Guizhou Guangxi 56 Sichuan 48 Hainan Shanxi Shaanxi l6 While value-addedwould be a preferable measure, provincial-level data on the value-added from SOEs i s not available. 16 Ningxia 60 Neimengu 58 Qinghai 86 Gansu 81 Xinjiang 83 Northwest average 72 Unable to rely on a soft budget constraint or guaranteed sales, private firms have greater incentives to innovate and respond to market signals. Private firms have significantly higher rates of investment and returns on investment. Thus, the current survey finds substantially higher returns on capital for private firms: Majority state-owned enterprises 7% Domestic non-state enterprises 19 0 Foreign-invested enterprises 22 Competition among privately owned firms i s also more likely to occur on a level playing- field, with resources flowing to the most productive users. SOEs can continue to operate despite their lower efficiency because of preferential access to finance and the possibility of bailout - thereby taking up resources and distorting competition among firms. Cities free from state domination of local industry are likely to be more dynamic and more likely to bolster the investment climate. Laborflexibility. Surveyed firms were asked what share of their workforce they would lay off if no penalties (e.g., severance payments or outright restrictions) were associated with worker redundancy. Reported over-staffing averages were lowest in the Southeast and Bohai and highest inthe Northwest and Northeast (Table 11-15). Table 11-15. Average Labor Over-staffing, By Type of Industry In all regions, over-staffing tends to be highest among bulk goods producers (Table II- 15). These are often SOEs, which also tend to have higher over-staffing (Table 11-16). Manufacturers of high-value goods in the Southeast report virtually no over-staffing (0.1 percent). By contrast, high-value manufacturers in the Northeast have the highest reported over-staffing (6.2 percent). In all regions, over-staffing tends to be lowest among FIEs and small domestically-invested enterprises. 17 Southeast Bohai Northeast Central Southwest Northwest State-owned 1.9 4.0 3.9 5.5 4.2 5.5 Foreign-invested 0.3 0.4 0.5 0.5 1.5 1.o Small domestic 0.6 0.6 0.9 1.3 1.5 1.o These patterns may reflect differences inregional demand and state ownership, more than legal or regulatory issues. While China's laws and regulations on the treatment of labor are stringent, a survey of actual practices in one region indicates that labor laws and regulations are not consistently enforced.l7Thus, variations in overstaffing may mainly reflect higher demand for labor in the faster-growing Southeast and Bohai regions; the prevalence of SOEs in the other regions; and a disinclination among SOEs to lay off redundant workers. Access to bank loans. In thinking about access to finance, it i s important to distinguish between foreign-invested enterprises (FIEs) or state-owned enterprises (SOEs), which often enjoy preferential access to finance, and private domestically-owned small and medium enterprises (SMEs). Such SMEs may find it difficult to access finance, despite the important role that SMEs may play in providing critical products or services for industrial clusters. The survey generates some interestingfindings: 0 Lookingjust at private domestically-owned businesses with 100or fewer workers that have bank loans, the average for Southeast cities (43 percent) somewhat lags behind the averages for the slower-growing Southwest (49 percent) and Central region (46 percent) (Table 11-17). Given the Southeast's growth rates, S M E access to finance i s apparently not a problem. Southeast SMEs likely find it easier to meet financing needs from cash flow and other sources (e.g., trade credits, leasing, equity investment) But this pattern provides additional evidence of some "stickiness" in bank lending, whereby slower-growth regions (e.g., Central, Southwest) have greater access to finance than what would be expected based on their growth and growth prospects relative to faster-growing regions (Le., Southeast, Bohai)." The particularly low level of access to bank loans by Northeast SMEs reflects a broader problem - the de-capitalization of Northeast bank branches by loss- making SOEs.19 This suggests that unresolved non-performing loans to SOEs make it difficult for Northeast bank branches to lend to SMEs World Bank, China: Facilitating Investment and Innovation: A Market-Oriented Approach to Northeast Revitalization, Report 34943-CN, 15 February 2006, p. 63. World Bank, China: Integration of National Product and Factor Markets - Economic Costs and Policy Recommendations, 13 June 2005, pp. 50-3. l9Bad assets (e.g., un-saleable inventory, uncollectible receivables) accounted for 31-37 percent of the assets of locally-administered industrial SOE assets in the three Northeast provinces at end-2003, versus just 4 percent for Zhejiang and 14percent for Guangdong. Bad assets of locally-administered SOEs totaled 18 Among all surveyed firms, 5-10 percent responded affirmatively when asked whether there i s a need for informal payments to bank staff in order to obtain loans. The lower average for Southeast firms provides further indication that access to finance i s less of an issue in Southeast cities. The high rate for the Northwest (10.4 percent) i s consistent with the Northwest's pattern of high taxes and fees, high firm expenditures on travel and entertainment, high time demands by government bureaucracy, and slow customs clearance -a key service. Small domestic-private Expectation of need for firms with bank loans informal payment to obtain bank loans Southeast 43.3% 4.9% Bohai 51.2 6.6 Northeast 24.8 5.6 Central 45.7 9.9 I Southwest 48.5 5.8 Northwest 39.7 10.4 Much of the previous discussion suggests that SOEs tend to crowd out private sector development. Many less-competitive SOEs in lagging regions (e.g., the Northeast) with high excess capacity may be kept on financial "life support." Distressed SOEs may be overly willing to sell at low prices in order to cover variable costs (e.g., suppliers, utilities, labor). Investing little or nothing in R&D, modern facilities, product devel~pment,or "soft" supply chain assets, SOEs are less able to compete in terms of product quality, brand recognition, flexible production, responsive delivery, or after-sales service. Hence, SOEs may tend to rely too much on price competition. Especially if customers are less-discriminating, excessive price competition will tend to depress profit RMB 307 billionat end-2003, while their liabilities totaled RMB 765 billion, which - since bank debts may account for 60-70 percent of SOE liabilities - could include RMB 500 billion or so of bank debt. The implication is that perhaps 60 percent of loans to locally-administered SOEs in the Northeast were non- performing as of end-2003. World Bank, China: Facilitating Investment and Innovation: A Market- OrientedApproach to Northeast Revitalization, Report 34943-CN, 15 February 2006, pp. 14-17. 19 opportunities for other more-competitive firms. Lastly, especially in the Northeast, SOEs likely crowd out private sector access to finance. Skills and technology. Evidence of the impact of human resource endowments on investment i s mixed. On the one hand, some observers note that the Southeast developed rapidly without much of a starting HR endowment,20 while the Northeast and Northwest have lagged despite higher education and technology levels. The current survey shows, for instance, that percentages of workers with a university education are highest in the North and lowest inthe Southeast (Table 11-18), Bohai 20.3 Northeast 22.5 Central 17.2 As a result of the relative slowness of SOE reform in the Northeast, the educational advantage of Northeast workers has likely been under-utilized. B y contrast, given appropriate policies, the Southeast has grown more rapidly since the early 1990s with a lesser (albeit adequate) supply of trained and technical personnel. Protection of property and contract rights. The survey asks about the likelihood that the responding firms' property and contract rights would be protected and enforced. Firms' confidence seems highest in Bohai and the Southeast and lowest in the Northwest (Table 11-19). Southeast 66 Bohai 68 Northeast 56 Central 64 I Southwest I I 61 I Northwest 46 The survey tried to determine whether local parties to a commercial dispute enjoy an advantage over non-local firms, but results from the current survey are inconclusive. An earlier study found evidence of local protectionism, arising from administrative decrees 2oFor instance, Shenzhen has grown from a small fishing village of 20,000 in the late 1970s to a megalopolis whose total population (migrants included) may now exceed 10million. 20 ostensibly designed for other purposes.21 A more recent 2003 survey of 3,500 firms nationwide highlighted the following "legal protectionism" concerns as relatively common: Reluctance o f local courts to respond to commercial lawsuits brought by firms from outside the province; Tendency of courts to favor local firms injudicial proceedings; Passivity of local courts in enforcing judgments against local firms in favor of firms from outside the province; Preferential treatment toward local construction companies in government contract awards; and Unwillingness of local authorities to protect the intellectual property of firms from outside the province.22 Thus, concerns about local protectionism or fair treatment by the legal system in another city or province may still discourage firms from investing or doing business elsewhere in China. Adequacy ofpower and transport. Respondents were asked about output losses due to inadequate power or transport infrastructure. Consistent with news reports and popular expectations, the survey found that reported output losses are lowest in the Northeast and Bohai and highest inthe South (Table 11-20), Southeast 3.1 Bohai 1.4 Northeast 0.9 Central 2.4 Southwest 5.6 Northwest 3.0 c.PROGRESSTOWARDHARMONIOUS A SOCIETY Progress toward a harmonious society tends to be correlated with government efforts to achieve a good investment climate, as indicated by regional differences in the environment, health, and education. 21C. Bai, Y. Du, Z. Tao, S. Tong, "Local Protectionism and Regional Specialization: Evidence from China's Industries," Journal of International Economics,2003, pp. 3-6, 19. This study, however, analyzed data from 1985-1997. 22World Bank, China: Integration of National Product and Factor Markets, 2005, pp. 15-20. 21 Environment. Air quality tends to be best in the South and worst inthe Northwest (Table 11-21), The latter likely reflects a combination of climate and industrial emissions, including from extractive industries. The proportion of industrial waste disposals meeting environmental standards tends to be lower outside China's coastal cities. Average per capita green space, another measure of urban quality of life, i s relatively similar among coastal, central, and southern cities, but somewhat low in the Northeast andNorthwest. Region Days with good or Industrial waste Green space per excellent air quality disposals meeting capita environmental (square meters) standard Southeast 86% 96% 9.5 Bohai 83 98 10.7 Northeast 81 92 6.8 Central 80 88 10.4 Southwest 89 87 9.6 Northwest 69 88 6.7 Health. Health insurance coverage for permanent workers appears relatively high in the Southeast and Southwest (Table 11-22). The former may reflect faster economic growth and greater competition for labor. Regional averages for infant mortality closely correspond with bothbroadandnarrow investment climate indicators. Permanent workers with Infant mortality per 1000 healthinsurance Southeast 78% 7.4 I Bohai I 70 I 7.9 I Northeast 69 8.2 Central 64 11.7 Southwest 76 12.6 I Northwest I 66 I 14.4 I Education. Regional averages for female share in total school enrollment do not vary much (Table 11-23). Per capita expenditures on education are highest in Southeast and about 40 percent lower in the Northeast and Southwest, which are the lowest-ranking regions. 22 Region Per capita expenditures on Female enrollment education (RMB) Southeast 715 46% Bohai 593 47 Northeast 425 49 Central 690 46 Southwest 457 47 Northwest 538 48 23 111.CITY RANKINGS The potential investor selecting a site from among alternative cities would typically consider a broad set of factors - including city characteristics (e.g., per capita GDP, coastal access and transport costs), local government effectiveness and efficiency, and local quality of life (e.g., environment, health care). This section provides city rankings for overall investment climate, government effectiveness, and progress toward a harmonious society. A. OVERALL INVESTMENT CLIMATE Both common experience and survey data suggest that domestic firms and foreign firms are sensitive to somewhat different sets of investment climate issues. For instance, in China we would expect domestic firms to be more sensitive to taxes, local bureaucracy, corruption, and access to finance; and foreign firms to be more sensitive to transport costs, customs clearance, quality of local management, and urban quality of life. These expectations are confirmed by survey data (see Table B-1, Annex B). Hence, the discussion here uses a ranking based on total factor productivity (TFP) to indicate the investment climate for domestic firms (Table 111-1) and a ranking based on foreign ownership to indicate the investment climate for foreign firms (Table 111-2). The quintile of cities ranked highest for investment climate for domestic firms includes, not surprisingly, twenty coastal cities (14 Southeast, 5 Bohai Bay, and the Northeast city of Dalian) (Table 111-1). But the top quintile also includes four inland cities (1Northeast, two Central, and 1 Southwest), which indicates that location and transport costs are not necessarily criticalfor domesticfirms. N o Southeast cities are in the quintile of cities ranked lowest for investment climate for domestic firms. Included are 12 cities from Central, 6 from Northwest, and 2 each from around Bohai Bay, Northeast, and Southwest. All cities in the lowest domestic investment climate quintile are locatedinland. 24 Table111-1.InvestmentClimatefor DomesticFirms, City Rankings Expected Lower bound at Upper bound at Rank City TFP Gain 95% confidence 95% confidence Standard error 1 beijing 0.038 -0.021 0.097 0.03 2 hangzhou 0.096 0.038 0.154 0.03 3 suzhou 0.105 0.061 0.149 0.023 4 guangzhou 0.150 0.091 0.209 0.03 5 dalian 0.213 0.168 0.257 0.023 6 shanghai 0.233 0.178 0.289 0.028 7 shenzhen 0.266 0.180 0.353 0.044 8 dongguan 0.292 0.223 0.362 0.035 9 tianjin 0.327 0.279 0.376 0.025 10 yantai 0.335 0.298 0.373 0.019 11 jiangmen 0.351 0.306 0.396 0.023 12 qingdao 0.358 0.318 0.398 0.020 13 nanjing 0.359 0.275 0.444 0.043 14 chengdu 0.369 0.305 0.433 0.033 15 xiamen 0.370 0.303 0.436 0.034 16 ningbo 0.370 0.323 0.417 0.024 17 zibo 0.392 0.341 0.442 0.026 18 wuxi 0.397 0.342 0.452 0.028 19 wuhan 0.397 0.328 0.467 0.035 20 foshan 0.400 0.349 0.451 0.026 21 hefei 0.417 0.359 0.474 0.029 22 shenyang 0.444 0.392 0.496 0.026 23 shaoxing 0.450 0.396 0.505 0.028 24 fuzhou 0.454 0.404 0.504 0.026 25 jinan 0.455 0.394 0.516 0.031 26 chongqing 0.458 0.371 0.545 0.044 27 nanchang 0.459 0.394 0.524 0.033 28 nantong 0.459 0.407 0.512 0.027 29 jinhua 0.462 0.400 0.523 0.031 30 weihai 0.465 0.404 0.525 0.031 31 huizhou 0.469 0.42 0.518 0.025 32 haerbing 0.475 0.415 0.534 0.03 33 xian 0.48 0.412 0.547 0.034 34 changchun 0.481 0.428 0.534 0.027 35 taian 0.497 0.432 0.562 0.033 36 linyi 0.498 0.425 0.57 0.037 37 changzhou 0.504 0.453 0.556 0.026 38 huzhou 0.505 0.446 0.565 0.03 39 zhengzhou 0.506 0.458 0.554 0.025 40 shijiazhuang 0.515 0.463 0.568 0.027 41 taizhou 0.525 0.458 0.593 0.034 42 weifang 0.529 0.477 0.581 0.026 43 tangshan 0.532 0.478 0.586 0.027 44 zhuhai 0.547 0.464 0.63 0.042 45 changsha 0.548 0.471 0.624 0.039 25 Expected Lower bound at Upper bound at Rank City TFP Gain 95% confidence 95% confidence Standard error 46 quanzhou 0.548 0.497 0.6 0.026 47 jiaxing 0.551 0.484 0.618 0.034 48 qinhuangdao 0.551 0.49 0.612 0.031 49 zhangzhou 0.56 0.506 0.615 0.028 50 langfang 0.562 0.505 0.619 0.029 51 jining 0.57 0.517 0.624 0.027 52 guiyang 0.577 0.517 0.637 0.03 1 53 haikou 0.578 0.468 0.688 0.056 54 lianyungang 0.587 0.499 0.676 0.045 55 kunming 0.596 0.516 0.675 0.04 56 wenzhou 0.604 0.5 14 0.694 0.046 57 huhehaote 0.605 0.539 0.672 0.034 58 liuzhou 0.616 0.552 0.681 0.033 59 maoming 0.626 0.56 0.691 0.034 60 xuzhou 0.626 0.559 0.693 0.034 61 xianyang 0.626 0.561 0.691 0.033 62 baoding 0.631 0.566 0.697 . 0.033 63 guilin 0.632 0.578 0.685 0.027 64 shantou 0.634 0.561 0.707 0.037 65 yichang 0.638 0.582 0.694 0.028 66 jinzhou 0.64 0.58 0.699 0.03 67 nanning 0.642 0.578 0.707 0.033 68 taiyuan 0.643 0.569 0.717 0.038 69 daqing 0.647 0.579 0.714 0.034 70 deyang 0.652 0.583 0.722 0.035 71 yangzhou 0.655 0.595 0.715 0.031 72 anshan 0.66 0.59 0.73 0.036 73 wuhu 0.662 0.603 0.722 0.03 74 mianyang 0.672 0.602 0.742 0.036 75 zhoukou 0.677 0.605 0.75 0.037 76 baotou 0.679 0.592 0.766 0.044 77 yinchuan 0.684 0.615 0.752 0.035 78 yancheng 0.694 0.63 0.757 0.033 79 yueyang 0.704 0.608 0.8 0.049 80 sanming 0.708 0.645 0.772 0.032 8 1 jingzhou 0.71 0.648 0.771 0.031 82 jilin 0.712 0.65 0.774 0.032 83 xiangfan 0.715 0.656 0.775 0.03 84 yuxi 0.728 0.656 0.801 0.037 85 jingmen 0.732 0.673 0.791 0.03 86 cangzhou 0.733 0.665 0.801 0.035 87 shangqiu 0.735 0.668 0.802 0.034 88 nanyang 0.735 0.645 0.825 0.046 . 89 shangrao 0.742 0.663 0.821 0.04 90 leshan 0.749 0.675 0.823 0.038 91 fushun 0.75 0.683 0.817 0.034 92 changde 0.752 0.694 0.811 0.03 93 wulumuqi 0.757 0.647 0.866 0.056 26 Expected Lower bound at Upper bound at Rank City TFP Gain 95%confidence 95% confidence Standard error 94 qujing 0.757 0.687 0.827 0.036 95 baoji 0.758 0.693 0.824 0.033 96 luoyang 0.762 0.681 0.842 0.041 97 yuncheng 0.762 0.688 0.836 0.038 98 qiqihaer 0.765 0.703 0.826 0.031 99 handan 0.769 0.697 0.842 0.037 100 ganzhou 0.779 0.707 0.851 0.037 101 lanzhou 0.789 0.704 0.875 0.044 102 xuchang 0.799 0.735 0.862 0.032 103 zhuzhou 0.799 0.716 0.882 0.042 104 chuzhou 0.801 0.738 0.864 0.032 105 chenzhou 0.802 0.741 0.863 0.03 1 106 zhangjiakou 0.808 0.74 0.876 0.035 107 anqing 0.809 0.744 0.874 0.033 108 zunyi 0.813 0.727 0.899 0.044 109 xiaogan 0.824 0.757 0.892 0.034 110 xining 0.826 0.746 0.907 0.041 111 xinxiang 0.831 0.728 0.933 0.052 112 jiujiang 0.85 1 0.781 0.921 0.036 113 datong 0.853 0.768 0.938 0.043 114 wuzhong 0.863 0.787 0.94 0.039 115 yibin 0.873 0.779 0.966 0.048 116 yichun 0.876 0.815 0.937 0.031 117 hengyang 0.878 0.798 0.959 0.041 118 benxi 0.922 0.844 1 0.04 119 tianshui 0.998 0.888 1.109 0.056 120 huanggang 1.088 1.005 1.171 0.042 Note: Resultsexpressedinpercentagepointsof TFP gain. The quintile of cities ranked highest for investment climate for foreign firms includes only coastal cities - 17 in Southeast, 5 around Bohai Bay, 1 Northeast, and Haikou in Southwest (Table 111-2). This is consistent with the apparent importance of transport costs and coastal access to foreign firms. The lowest-ranked quintile includes only inland cities - 10 in Central, 8 in Northwest, 5 inSouthwest, and 1inNortheast. No Southeast or Bohai cities are inthe lowest quintile. Table 111-2.InvestmentClimatefor ForeignFirms, City Rankings Expected Foreign Ownership Lower bound at Upper bound at Standard Rank City Gain 95% confidence 95% confidence error 1 dongguan -0.095 -0.120 -0.071 0.012 2 shenzhen -0.094 -0.125 -0.064 0.016 3 suzhou -0.060 -0.075 -0.044 0.008 27 Expected Foreign Ownership Lower bound at Upper bound at Standard Rank City Gain 95% confidence 95% confidence error 4 zhuhai -0.059 -0.091 -0.028 0.016 5 huizhou -0.057 -0.080 -0.035 0.011 6 foshan -0,022 -0.042 -0.003 0.010 7 qingdao -0.005 -0.022 0.011 0.009 8jiangmen -0.001 -0.022 0.020 0.011 9 xiamen 0.005 -0.021 0.030 0.013 10 guangzhou 0.005 -0.015 0.026 0.010 11 dalian 0.005 -0.010 0.021 0.008 12 weihai 0.007 -0.018 0.031 0.012 13 hangzhou 0.009 -0.012 0.030 0.011 14 shantou 0.046 0.016 0.076 0.016 15 yantai 0.057 0.044 0.071 0.007 16 shaoxing 0.059 0.038 0.080 0.011 17 shanghai 0.060 0.042 0.079 0.010 18 ningbo 0.087 0.070 0.104 0.009 19 tianjin 0.094 0.080 0.109 0.007 20 fuzhou 0.106 0.088 0.124 0.009 21 nanjing 0.125 0.089 0.161 0.018 22 haikou 0.127 0.080 0.174 0.024 23 beijing 0.129 0.109 0.150 0.010 24 wuxi 0.133 0.111 0.154 0.011 25 zibo 0.135 0.117 0.153 0.009 26 weifang 0.136 0.117 0.156 0.010 27 nantong 0.145 0.126 0.163 0.010 28 tangshan 0.145 0.126 0.164 0.010 29 zhangzhou 0.145 0.124 0.167 0.011 30 daqing 0.150 0.131 0.170 0.010 31 langfang 0.153 0.133 0.174 0.010 32 maoming- 0.157 0.133 0.180 0.012 33 huzhou 0.159 0.136 0.181 0.011 34 nanchang 0.160 0.137 0.184 0.012 35 quanzhou 0.161 0.142 0.180 0.010 36 ganzhou 0.165 0.137 0.192 0.014 37 qinhuangdao 0.167 0.145 0.189 0.011 38 lianyungang 0.169 0.130 0.207 0.020 39 changzhou 0.169 0.150 0.189 0.010 40 jiaxing 0.177 0.150 0.205 0.014 41 shijiazhuang 0.178 0.160 0.195 0.009 42 xuchang 0.178 0.152 0.204 0.013 43 shenyang 0.179 0.162 0.197 0.009 44 taian 0.181 0.156 0.205 0.012 45 jinan 0.186 0.166 0.205 0.010 46 kunming 0.187 0.158 0.217 0.015 47 jining 0.192 0.173 0.212 0.010 48 taizhou 0.193 0.168 0.218 0.013 49 hefei 0.195 0.173 0.216 0.011 28 Expected Foreign Ownership Lower bound at Upper bound at Standard Rank City Gain 95%confidence 95% confidence error 50 baotou 0.201 0.171 0.232 0.016 51 linyi 0.203 0.176 0.229 0.013 52 jinhua 0.204 0.181 0.226 0.011 53 changchun 0.205 0.188 0.222 0.009 54 jingmen 0.209 0.185 0.232 0.012 55 anshan 0.212 0.186 0.238 0.013 56 huhehaote 0.213 0.190 0.236 0.012 57 shangrao 0.214 0.184 0.244 0.015 58 yangzhou 0.214 0.192 0.236 0.011 59 wuhan 0.214 0.191 0.237 0.012 60 zhengzhou 0.217 0.200 0.234 0.009 61 wuhu 0.222 0.197 0.246 0.012 62 jinzhou 0.222 0.198 0.246 0.012 63 fushun 0.227 0.203 0.250 0.012 64 guilin 0.236 0.216 0.255 0.010 65 shangqiu 0.236 0.212 0.260 0.012 66 sanming 0.239 0.215 0.262 0.012 67 nanning 0.240 0.218 0.262 0.011 68 chengdu 0.240 0.218 0.262 0.011 69 jiujiang 0.243 0.217 0.268 0.013 70 xian 0.244 0.221 0.267 0.012 71 deyang 0.245 0.221 0.270 0.013 72 zhoukou 0.247 0.221 0.274 0.014 73 yuxi 0.248 0.222 0.273 0.013 74 guiyang 0.250 0.227 0.273 0.012 75 baoding 0.250 0.228 0.272 0.011 76 xuzhou 0.252 0.230 0.275 0.011 77 xianyang 0.255 0.231 0.278 0.012 78 xiangfan 0.255 0.232 0.279 0.012 79 . anqing 0.265 0.240 0.291 0.013 80 cangzhou 0.266 0.242 0.290 0.012 81 haerbin 0.267 0.247 0.288 0.010 82 wulumuqi 0.268 0.232 0.303 0.018 83 chuzhou 0.274 0.249 0.299 0.013 84 liuzhou 0.277 0.252 0.302 0.013 85 jilin 0.279 0.258 0.299 0.010 86 yichun 0.280 0.256 0.305 0.012 87 yichang 0.284 0.262 0.306 0.011 88 qiqihaer 0.287 0.263 0.309 0.012 89 jingzhou 0.288 0.263 0.312 0.012 90 wenzhou 0.289 0.258 0.319 0.016 91 hengyang 0.295 0.267 0.323 0.014 92 taiyuan 0.295 0.268 0.322 0.014 93 handan 0.300 0.275 0.326 0.013 94 xiaogan 0.301 0.214 0.328 0.014 95 yancheng 0.301 0.277 0.326 0.012 29 Expected Foreign Ownership Lower bound at Upper bound at Standard Rank City Gain 95%confidence 95% confidence error 96 zhangjiakou 0.302 0.277 0.327 0.013 97 changsha 0.306 0.279 0.333 0.014 98 chongqing 0.306 0.274 0.339 0.016 99 wuzhong 0.307 0.277 0.338 0.016 100 yueyang 0.313 0.279 0.347 0.017 101 baoji 0.314 0.289 0.340 0.013 102 qujing 0.315 0.289 0.342 0.013 103 lanzhou 0.322 0.292 0.352 0.016 104 xinxiang 0.324 0.286 0.361 0.019 105 changde 0.326 0.303 0.350 0.012 106 luoyang 0.334 0.307 0.361 0.014 107 mianyang 0.336 0.310 0.362 0.013 108 zhuzhou 0.339 0.308 0.369 0.016 109 yinchuan 0.354 0.325 0.384 0.015 110 yuncheng 0.355 0.327 0.383 0.014 111 leshan 0.360 0.331 0.389 0.015 112 xining 0.361 0.333 0.389 0.015 113 datong 0.366 0.336 0.396 0.015 114 chenzhou 0.372 0.345 0.399 0.014 115 benxi 0.380 0.346 0.414 0.017 116 tianshui 0.402 0.363 0.442 0.020 117 nanyang 0.403 0.372 0.433 0.016 118 zunyi 0.414 0.381 0.446 0.016 119 yibin 0.427 0.392 0.462 0.018 120 huanggang 0.440 0.408 0.472 0.016 Note: Presented as percentage point change in foreign ownership. Reflects application of a 0.317 standarddeviationinforeignownership to standardizedresults inTable B-4. B.GOVERNMENTEFFECTIVENESS Analysis of survey data shows a statistically significant relationship between several measures of government effectiveness - extent of state vs. private ownership, burden from taxedfees, labor redundancy, travel/entertainment expenditures, access to bank loans, bureaucratic time demands, and customs clearance - and firm productivity or foreign ownership.23 The quintile of cities ranked highest for government effectiveness vis-a-vis domestic firms includes mostly coastal cities - 13 Southeast, 7 Bohai Bay, and 1 Northeast. But this highest quintile also includes three inland cities - 2 Southwest and 1 Central (Table 111-3). Thus, an inland location does not necessarily preclude efSective government outreach to domestic businesses. 23See Table B-1in Annex B. 30 The quintile of cities ranked lowest for government effectiveness vis-a-vis domestic firms includes cities from all regions - 7 Central, 7 Northwest, 6 Northeast, 2 Southwest (including Haikou), 1Bohai, and 1Southeast. Table 111-3. Government Effectivenessvis-a-vis DomesticFirms, City Rankings Expected Lower bound at Upper bound at Rank City TFPgain 95% confidence 95% confidence Standard error 1 linyi -0.018 -0.036 0 0.009 2 jiangmen -0.013 -0.032 0.007 0.01 3 hangzhou -0.005 -0.02 0.01 0.008 4 weihai -0.004 -0.014 0.006 0.005 5 suzhou 0 -0.017 0.017 0.009 6 jiaxing 0.034 0.022 0.047 0.006 7 yantai 0.037 0.024 0.049 0.006 8 huzhou 0.041 0.03 0.053 0.006 9 zibo ,0.042 0.022 0.061 0.01 10 zhangzhou 0.053 0.04 0.065 0.006 11 jinhua 0.053 0.032 0.073 0.01 12 qingdao 0.055 0.043 0.067 0.006 13 xiamen 0.057 0.035 0.079 0.011 14 leshan 0.06 0.047 0.074 0.007 15 nantong 0.062 0.04 0.085 0.011 16 weifang 0.063 0.042 0.085 0.011 17 taian 0.063 0.049 0.078 0.007 18 dalian 0.065 0.038 0.092 0.014 19 shaoxing 0.066 0.053 0.08 0.007 20 ningbo 0.07 0.05 0.089 0.01 21 fuzhou 0.074 0.06 0.088 0.007 22 deyang 0.077 0.062 0.092 0.007 23 wuxi 0.077 0.061 0.093 0.008 24 shangqiu 0.083 0.061 0.105 0.011 25 zhoukou 0.085 0.069 0.102 0.008 26 tangshan 0.087 0.063 0.112 0.012 27 langfang 0.088 0.069 0.107 0.01 28 jining 0.089 0.074 0.104 0.007 29 dongguan 0.089 0.058 0.12 0.016 30 guangzhou 0.094 0.066 0.121 0.014 31 foshan 0.094 0.075 0.113 0.01 32 shantou 0.095 0.08 0.111 0.008 33 nanchang 0.097 0.081 0.113 0.008 34 huizhou 0.101 0.074 0.128 0.014 35 shangrao 0.101 0.081 0.12 0.01 36 shenzhen 0.102 0.062 0.142 0.02 37 changzhou 0.102 0.083 0.122 0.01 38 hefei 0.106 0.081 0.131 0.013 39 sanming 0.11 0.094 0.126 0.008 40 quanzhou 0.114 0.096 0.132 0.009 41 yinchuan 0.114 0.09 0.138 0.012 31 Expected Lower bound at Upper bound at Rank City TFPgain 95% confidence 95% confidence Standard error 42 jingzhou 0.118 0.097 0.139 0.011 43 taizhou 0.12 0.094 0.146 0.013 44 maoming 0.122 0.103 0.142 0.01 45 zhengzhou 0.123 0.105 0.14 0.009 46 mianyang 0.125 0.105 0.145 0.01 47 chongqing 0.125 0.102 0.148 0.012 48 yangzhou 0.125 0.104 0.146 0.011 49 wuzhong 0.129 0.111 0.147 0.009 50 xuchang 0.129 0.108 0.151 0.011 51 wenzhou 0.13 0.104 0.155 0.013 52 jinan 0.132 0.112 0.151 0.01 53 wuhu 0.132 0.112 0.153 0.01 54 qinhuangdao 0.134 0.103 0.164 0.015 55 yibin 0.134 0.11 0.159 0.013 56 shijiazhuang 0.135 0.108 0.163 0.014 57 jingmen 0.135 0.113 0.157 0.011 58 anqing 0.135 0.113 0.157 0.011 59 yichang 0.136 0.113 0.159 0.012 60 kunming 0.137 0.103 0.171 0.017 61 lianyungang 0.14 0.119 0.161 0.011 62 yuncheng 0.141 0.113 0.169 0.014 63 yueyang 0.141 0.114 0.169 0.014 64 chengdu 0.143 0.114 0.172 0.015 65 yancheng 0.145 0.114 0.175 0.016 66 baoji 0.146 0.12 0.172 0.013 67 cangzhou 0.15 1 0.122 0.18 0.015 68 liuzhou 0.151 0.129 0.173 0.011 69 xiangfan 0.152 0.126 0.177 0.013 70 nanyang 0.158 0.131 0.185 0.014 71 chuzhou 0.158 0.127 0.189 0.016 72 beijing 0.159 0.128 0.19 0.016 73 jinzhou 0.162 0.138 0.186 0.012 74 baoding 0.167 0.137 0.197 0.015 75 anshan 0.168 0.136 0.2 0.016 76 ganzhou 0.17 0.143 0.197 0.014 77 shanghai 0.172 0.144 0.2 0.014 78 wuhan 0.172 0.134 0.21 0.019 79 yuxi 0.182 0.152 0.213 0.016 80 qujing 0.184 0.155 0.213 0.015 81 zhuhai 0.189 0.148 0.23 0.021 82 nanjing 0.191 0.158 0.225 0.017 83 xiaogan 0.194 0.163 0.225 0.016 84 xianyang 0.197 0.16 0.233 0.019 85 guilin 0.199 0.171 0.227 0.014 86 xinxiang 0.199 0.168 0.231 0.016 87 guiyang 0.203 0.164 0.242 0.02 88 chenzhou 0.204 0.175 0.233 0.015 89 jiujiang 0.204 0.175 0.233 0.015 32 Expected Lower bound at Upper bound at Rank City TFPgain 95% confidence 95% confidence Standard error 90 huhehaote 0.207 0.164 0.25 0.022 91 lanzhou 0.209 0.17 0.247 0.02 92 tianjin 0.211 0.174 0.249 0.019 93 zhangjiakou 0.217 0.177 0.256 0.02 94 changchun 0.218 0.184 0.253 0.018 95 qiqihaer 0.219 0.178 0.26 0.021 96 zunyi 0.222 0.184 0.261 0.02 97 taiyuan 0.228 0.186 0.271 0.022 98 yichun 0.232 0.199 0.264 0.017 99 shenyang 0.232 0.195 0.269 0.019 100 handan 0.235 0.191 0.278 0.022 101 xining 0.237 0.2 0.274 0.019 102 changde 0.239 0.201 0.277 0.019 103 xuzhou 0.239 0.204 0.275 0.018 104 changsha 0.241 0.196 0.286 0.023 105 jilin 0.246 0.208 0.284 0.019 106 fushun 0.248 0.213 0.283 0.018 107 luoyang 0.249 0.208 0.289 0.021 108 xian 0.251 0.201 0.3 0.025 109 nanning 0.257 0.22 0.294 0.019 110 datong 0.26 0.217 0.302 0.021 111 baotou 0.265 0.21 0.32 0.028 112 tianshui 0.266 0.226 0.307 0.021 113 wulumuqi 0.267 0.226 0.307 0.021 114 hengyang 0.291 0.24 0.342 0.026 115 haerbing 0.3 0.247 0.353 0.027 116 benxi 0.303 0.258 0.348 0.023 117 zhuzhou 0.316 0.269 0.362 0.024 118 huanggang 0.329 0.285 0.373 0.023 119 haikou 0.336 0.282 0.389 0.027 120 daqing 0.341 0.292 0.391 0.025 Note: Resultsexpressedinpercentagepointsof TFP gain. The quintile of cities ranked highest for government effectiveness vis-aLvis foreign firms includes mostly coastal cities - 17 Southeast, 3 Bohai Bay, and 1 Northeast. But this highest quintile also includes three inland cities in Central Region (Ganzhou, Yuchang, Nanchang) (Table 111-4). Thus an inland location does not necessarily preclude efective government outreach toforeign businesses. The quintile of cities ranked lowest for government effectiveness,vis-a-vis foreign firms includes only interior cities - 9 in Central, 7 in Northwest, 5 in Southwest, and 3 in Northeast. No Southeast or Bohai cities are included inthis lowest quintile. 33 Table 111-4. Government Effectivenessvis-a-vis Foreign Firms, City Rankings Expected Foreign Ownership Lower bound at Upper bound at Standard Rank City Gain 95% confidence 95% confidence error 1 huizhou -0.078 -0.088 -0.068 0.005 2 dongguan -0.072 -0.081 -0.063 0.004 3 shenzhen -0.070 -0.083 -0.057 0.006 4 jiangmen -0.065 -0.073 -0.056 0.004 5 qingdao -0.049 -0.057 -0.041 0.004 6 shantou -0.047 -0.055 -0.040 0.004 7 zhuhai -0.036 -0.046 -0.026 0.005 8 suzhou -0.024 -0.031 -0.018 0.003 9 shaoxing -0.013 -0.018 -0.007 0.003 10 foshan -0.008 -0.015 -0.002 0.003 11 weihai -0.004 -0.007 -0.001 0.002 12 hangzhou 0.008 0.003 0.013 0.003 13 ganzhou 0.009 0.005 0.013 0.002 14 guangzhou 0.011 0.003 0.020 0.004 15 maoming 0.012 0.009 0.016 0.002 16 xiamen 0.013 0.007 0.018 0.003 17 dalian 0.016 0.007 0.026 0.005 18 fuzhou 0.028 0.024 0.032 0.002 19 yantai 0.031 0.025 0.036 0.003 20 zhangzhou 0.036 0.030 0.041 0.003 21 xuchang 0.042 0.035 0.049 0.003 22 lianyungang 0.044 0.038 0.050 0.003 23 nantong 0.046 0.037 0.055 0.004 24 nanchang 0.048 0.042 0.054 0.003 25 linyi 0.055 0.046 0.065 0.005 26 shanghai 0.055 0.048 0.063 0.004 27 weifang 0.056 0.046 0.065 0.005 28 langfang 0.063 04055 0.070 0.004 29 tianjin 0.064 0.052 0.077 0.006 30 taian 0.067 0.058 0.075 0.004 31 jining 0.067 0.059 0.075 0.004 32 beijing 0.068 0.058 0.079 0.005 33 ningbo 0.070 0.061 0.079 0.005 34 tangshan 0.071 0.060 0.082 0.006 35 quanzhou 0.072 0.063 0.081 0.004 36 shangqiu 0.075 0.065 0.085 0.005 37 huzhou 0.075 0.066 0.084 0.004 38 zhoukou 0.078 0.069 0.088 0.005 39 jinan 0.079 0.070 0.088 0.005 40 nanjing 0.082 0.069 0.094 0.006 41 wulumuqi 0.082 0.071 0.093 0.006 42 shijiazhuang 0.082 0.070 0.094 0.006 43 sanming 0.082 0.073 0.092 0.005 34 Expected Foreign Ownership Lower bound at Upper bound at Standard Rank City Gain 95%con$dence 95% confidence error 44 zibo 0.084 0.074 0.095 0.005 45 yangzhou 0.085 0.074 0.095 0.005 46 anqing 0.085 0.074 0.095 0.005 47 qinhuangdao 0.085 0.074 0.097 0.006 48 jingmen 0.089 0.078 0.100 0.005 49 baoding 0.091 0.079 0.103 0.006 50 jinhua 0.092 0.080 0.103 0.006 51 jinzhou 0.094 0.082 0.106 0.006 52 taizhou 0.094 0.082 0.106 0.006 53 wuzhong 0.094 0.084 0.106 0.006 54 haikou 0.096 0.080 0.112 0.008 55 changzhou 0.096 0.084 0.108 0.006 56 shangrao 0.096 0.084 0.108 0.006 57 xi'an 0.097 0.079 0.114 0.009 58 hefei 0.099 0.086 0.112 0.007 59 deyang 0.099 0.087 0.111 0.006 60 wuxi 0.100 0.088 0.112 0.006 61 fushun 0.101 0.089 0.113 0.006 62 xuzhou 0.103 0.089 0.116 0.007 63 guilin 0.103 0.091 0.115 0.006 64 chengdu 0.103 0.090 0.116 0.007 65 yichun 0.104 0.091 0.118 0.007 66 zhengzhou 0.105 0.093 0.117 0.006 67 chongqing 0.107 0.093 0.120 0.007 68 wenzhou 0.108 0.094 0.121 0.007 69 yuxi 0.109 0.095 0.123 0.007 70 cangzhou 0.109 0.095 0.123 0.007 71 baotou 0.109 0.089 0.129 0.010 72 xiangfan 0.110 0.096 0.123 0.007 73 wuhan 0.110 0.094 0.125 0.008 74 kunming 0.112 0.096 0.127 0.008 75 jiujiang 0.112 0.099 0.125 0.007 76 jingzhou 0.113 0.099 0.126 0.007 77 jiaxing 0.113 0.099 0.126 0.007 78 huhehaote 0.113 0.098 0.128 0.008 79 changchun 0.117 0.103 0.131 0.007 80 shenyang 0.119 0.104 0.134 0.008 81 yueyang 0.124 0.109 0.139 0.008 82 xiaogan 0.125 0.109 0.140 0.008 83 daqing 0.126 0.110 0.141 0.008 84 wuhu 0.128 0.113 0.143 0.008 85 nanning 0.131 0.115 0.146 0.008 86 lanzhou 0.131 0.113 0.148 0.009 87 qiqihaer 0.131 0.114 0.148 0.009 88 handan 0.135 0.116 0.154 0.010 89 hengyang 0.135 0.116 0.153 0.010 35 Expected Foreign Ownership Lower bound at Upper bound at Standard Rank City Gain 95% confidence 95% confidence error 90 guiyang 0.139 0.122 0.157 0.009 91 anshan 0.141 0.123 0.159 0.009 92 baoji 0.141 0.124 0.158 0.009 93 zhangjiakou 0.141 0.123 0.159 0.009 94 xinxiang 0.144 0.127 0.161 0.009 95 qujing 0.146 0.128 0.163 0.009 96 chuzhou 0.147 0.129 0.165 0.009 97 luoyang 0.148 0.130 0.166 0.009 98 jilin 0.150 0.133 0.167 0.009 99 xianyang 0.150 0.132 0.169 0.010 100 yuncheng 0.153 0.134 0.172 0.010 101 yancheng 0.153 0.135 0.172 0.010 102 changsha 0.156 0.136 0.176 0.010 103 xining 0.157 0.138 0.175 0.009 104 haerbin 0.157 0.136 0.177 0.010 105 liuzhou 0.159 0.140 0.177 0.010 106 mianyang 0.164 0.145 0.184 0.010 107 leshan 0.165 0.145 0.184 0.010 108 yichang 0.165 0.146 0.185 0.010 109 zhuzhou 0.166 0.146 0.187 0.010 110 taiyuan 0.171 0.149 0.193 0.011 111 changde 0.172 0.152 0.192 0.010 112 datong 0.182 0.160 0.203 0.011 113 yibin 0.183 0.161 0.205 0.011 114 nanyang 0.185 0.163 0.207 0.011 115 tianshui 0.189 0.166 0.210 0.011 116 yinchuan 0.197 0.174 0.222 0.012 117 zunyi 0.209 0.184 0.234 0.013 118 huanggang 0.222 0.197 0.248 0.013 119 chenzhou 0.239 0.210 0.267 0.014 120 benxi 0.276 0.244 0.308 0.016 Note: Presented as percentage point change in foreign ownership. Reflects application of a 0.317 standarddeviationinforeign ownership to standardizedresultsinTable B-5. An overall ranking of government effectiveness for both domestic investors and foreign investors may be developed simply by averaging the TFP and FDIrankings (see Table B- 6, inAnnex B). A comparison of rankings for overall investment climate and for government effectiveness suggests that some cities are resting on their inherent advantages, while other cities are actually trying harder. Several cities in the lSt quintile for overall investment climate end up in the 3rdquintile for government effectiveness: e.g., Beijing, Nanjing, Shanghai, and Tianjin. More admirably, several coastal cities in the 2ndquintile for overall investment climate appear in the lSt quintile for government effectiveness: e.g., Linyi, Taian, and Weifang. 36 At the other end of the spectrum, the twenty-four cities ranked lowest for government effectiveness include 9 from the Central region, 6 from the Northwest, 5 from the Northeast, 2 from the Southwest, and 2 from the Bohai Bay region. Of the 13 cities ranked in the lowest quintile for both domestic firm and foreign firm investment climate, 9 are also ranked in the lowest quintile for government effectiveness: Zhuzhou, Chenzhou, Zunyi, Xining, Xinxiang, Datong, Benxi, Tianshui, and Huanggang. Even in this investment climate "cellar," however, the other four cities are more highly-ranked (in the 3rdor 4thquintile) for government effectiveness: Yuncheng, Lanzhou, Wuzhong, and Yibin. These cities appear to be trying to make the best of a difficult situation. Other cities are ranked lower for government effectiveness than for investment climate, and thus appear to be making a difficult situation worse. Cities ranked lower for government effectiveness than for overall investment climate (both domestic- and foreign-firm) include the Northeast cities of Daqing, Haerbin, and Jilin; the Northwest cities of Baotou, Xianyang, and Taiyuan; the Central city of Changsha; and the Southwest city of Nanning. To encourage local government reforms, it is useful to have objective benchmarksfor performance improvements. Even among the Top 20 Cities in terms of government effectiveness, not every city i s outstanding in every measure (Table 111-5). However, a useful and realistic set of benchmarks for government effectiveness could include the following: a Total taxes andfees represent 2-4 percent of sales revenue. a Firm expenditures on entertainment and travel represent 0.5-1.O percent of sales revenue. a Firms interact less than 60 days per year with major bureaucracies (e.g., tax administration, public security, environmental protection, labor and social security). a At least half of small domestically-owned enterprises have access to bank loans. a Less than 5 percent of businessesperceive a needfor informal payments to obtain loans. a An export shipment or an import shipment can be cleared through customs in 3 days or less. a Problems withpower or transport costfirms less than I percent of output. a Firms consider less than I percent of their workforce to be redundant. a More than 75 percent of firms expect that the courts will protect legitimate property and contract rights. a 15-20percent of workers are university-educated. a Local industry and commerce is dominated by private (i.e., non-state) enterprises. 37 38 Comparability with previous surveys is extremely limited. Due to changes in survey method, it i s impossible to compare rankings in this 120-city survey with relative rankings in the previous 23-city survey. Because of changes in the wording of survey questions or in ways o f evaluating survey data, even objective performance indicators cannot be compared in most cases. In the case of output losses due to problems with power/transport infrastructure, firm expenditures on entertainment and travel, and taxedfees burden, however, it i s reasonable to make comparisons between findings in the World Bank's 2001/2002 survey of 23 cities and findings for these cities inthis 2005 survey (Table 111-6): It appears that output losses due to power/transport infrastructure shortcomings have increased in about two-thirds of the 23 cities. The increase in losses i s particularly large in such Southeast, Central, and Southwest cities as Guangzhou, Chengdu, Changsha, Chongqing, Guiyang, Jiangmen, Kunming, Nanning, Shenzhen, and Wenzhou. This presumably reflects the inability of local power and transport infrastructure to keep up with these regions' surge in economic activity and growth. In almost all of the 23 cities, firms appear to be spending less on entertainment and travel. This may indicate greater transparency in government (i.e., fewer informal payments by business) as well as a more mature business environment. In almost half the previously-surveyedcities, firms report that taxedfees relative to sales have increased. The rise in taxedfees burden appears especially sharp in Beijing, Chengdu, Guangzhou, Shanghai, and Tianjin - although the absolute burden i s not extreme in comparison with the suggested 4 percent maximum benchmark. Firms report lower taxedfees in several Northeast cities - Benxi, Dalian, and Haerbin. Already high in 2002, taxedfees in Guiyang are reported to have increased further to 8.1 percent of sales. 39 Table111-6.Comparisonof 2001/2 vs. 2004 ObjectivePerformance Wuhan 1.4 2.7 1.3 3.1 1.6 -1.5 6.7 6.9 0.2 Xi'an 2.3 2.9 0.6 3.7 1.7 -2.0 6.0 6.1 0.1 Zhengzhou 0.5 0.8 0.3 3.0 0.9 -2.1 5.9 5.1 -0.8 c.PROGRESS TOWARD HARMONIOUS A SOCIETY Analysis of survey data shows a statistically significant relationship between several measures of progress toward a harmonious society. One measure, the percentage o f days that a city has good or excellent air quality, reflects environmental quality. A second measure, the percentage of females in total student enrollment, addresses education. A third measure, the share of permanent workers with health insurance, is one indicator of quality of health care. To provide a more complete picture, this index and rankings for progress toward a harmonious society include additional performance measures. The percentage of industrial waste disposals that meet environmental standards and per capita green space are two additional measures of environmental quality. Per capita spending on education i s an additional measure for education, while infant mortality i s an additional health care 40 indicator. Finally, to reflect each city's ability to provide a decent livelihood for residents, the index includes two indicators of prosperity: the rate of unemployment and the annual average wage. O f the twenty-four cities ranked as having made the greatest progress toward a harmonious society, 15 are in the Southeast, 7 are in the Bohai region, 1 i s in the Southwest, and 1 i s in the Central region (Table 111-7). Nineteen of the cities ranked highest for progress toward a harmonious society are also ranked in the highest quintile for overall investment climate. Table 111-7. City Rankings for ProgressTowards a Harmonious Society Rank City Index 1 shanghai 1.061939 2 shenzhen 0.984165 3 beijing 0.931717 4 dongguan 0.909169 5 weihai 0.850343 6 hangzhou 0.830047 7 nanchang 0.819729 8 guangzhou 0.748055 9 ningbo 0.690786 10 suzhou 0.670736 11 zhuhai 0.645995 12 mianyang 0.633359 13 jinan 0.630521 14 zibo 0.540083 15 yantai 0.529748 16 huizhou 0.527538 17 qingdao 0.511149 18 jinhua 0.490865 19 foshan 0.490246 20 shaoxing 0.479552 21 xiamen 0.478936 22 jiaxing 0.471405 23 nantong 0.421684 24 tianjin 0.42019 25 langfang 0.414212 26 haikou 0.377484 27 qinhuangdao 0.365267 28 weifang 0.363009 29 kunming 0.348942 30 shangrao 0.341884 31 dalian 0.340027 32 shantou 0.327857 33 jiangmen 0.313584 34 quanzhou 0.31334 35 wuxi 0.308075 36 daqing 0.302631 41 Rank City Index 37 taian 0.284385 38 changzhou 0.273488 39 huzhou 0.267457 40 changde 0.231451 41 guilin 0.172883 42 chengdu 0.170967 43 hengyang 0.150785 44 xiangfan 0.145738 45 fuzhou 0.129403 46 taizhou 0.12709 47 nanjing 0.125553 48 linyi 0.086413 49 jingmen 0.078772 50 nanning 0.045978 51 haerbing 0.027019 52 wuhu 0.011115 53 cangzhou 0.00352 54 taiyuan -0.0233 55 jining -0.02847 56 zhengzhou -0.04275 57 tangshan -0.04405 58 hefei -0.04729 59 changchun -0.04906 60 qiqihaer -0.05762 61 xuzhou -0.06361 62 xianyang -0.0656 63 yuxi -0.07119 64 yuncheng -0.07273 65 yangzhou -0.07767 66 changsha -0.07876 67 jiujiang -0.08131 68 wuhan -0.0901 69 zhangzhou -0.09231 70 lianyungang -0.10685 71 shijiazhuang -0.10714 72 yueyang -0.11711 73 baoding -0.12014 74 zhoukou -0.15907 75 wulumuqi -0.19638 76 leshan -0.19798 77 jilin -0.21655 78 maoming -0.21976 79 shenyang -0.22676 80 wenzhou -0.23101 81 chuzhou -0.25001 82 deyang -0.25222 83 yinchuan -0.25477 84 yichun -0.2555 85 xiaogan -0.27171 42 Rank city Index 86 xian -0.27742 87 yancheng , -0.28004 88 xuchang -0.29035 89 chenzhou -0.2918 90 guiyang -0.29471 91 xining -0.30373 92 baotou -0.30574 93 yichang -0.30815 94 liuzhou -0.32921 95 anqing -0.3326 96 handan -0.34947 97 jingzhou -0.36328 98 xinxiang -0.36612 99 shangqiu -0.3681 100 benxi -0.4001 101 baoji -0.41251 102 datong -0.41268 103 fushun -0.4284 104 huhehaote -0.42947 105 yibin -0.43611 106 chongqing -0.44517 107 qujing -0.44954 108 lanzhou -0.45051 109 sanming -0.45417 110 zhuzhou -0.61236 111 anshan -0.63826 112 jinzhou -0.71543 113 zhangjiakou -0.73838 114 tianshui -0.78822 115 luoyang -0.83651 116 nanyang -0.8576 117 wuzhong -0.90965 118 ganzhou -1-01069 119 zunyi -1.02721 120 huanggang -1.08377 Note: This index is an averaae of the normalizedvalues of nine harmonioussociety indicatorspresentedin Table 111-7. Among the twenty-four cities in the lowest quintile, 8 are in the Central region, 6 are in the Northwest, 4 are in the Southwest and 4 in the Northeast, while 1 each are in the Southeast and Bohai Bay regions. Fifteen of these cities are also in the lowest quintile for overall investment climate. Several cities are ranked higher in terms of investment climate (Ganzhou, Sanming, Chongqing, Anshan, Fushun, and Huhehaote), but appear not to have translated this into commensurate progress toward a harmonious society. Tofocus attention on development of an all-around well-off society, it is useful to have objective benchmarks for progress. Not every top-rated city is outstanding in every measure of progress toward a harmonious society (Table 111-8). However, a useful and 43 realistic set of benchmarks for progress toward.a harmonious society could include the following: Per capita government spending on education exceeds RMB 1100 More than 95 percent of disposals of industrial waste meet environmental standards. Green space exceeds 10square metersper capita. Air quality is good or excellent at least 300 days each year. Unemploymentis 3 percent or less. Annual wages average RMB 20,000or more. Infant mortality is 6 or less per 1,000. At least 85percent of permanent workers have health insurance. Total student enrollment is at least 47percent female. 44 I Y E TT T 3 I 45 D.CHINA'S"GOLDENCITIES" Among the 120 cities in this survey, there i s a reasonably close correlation among overall investment climate, government effectiveness, and progress toward a harmonious society. For instance, cities ranked higher in terms of government effectiveness also tend to be rankedhigher interms of progress toward a harmonious society (Figure 111-1). Figure 111-1.Government Effectiveness and ProgressToward a Harmonious Society I 1.5 1 0.5 0 -0.5 -1 -1.5 Investmentclimate (governmenteffectiveness) Only 6 cities are ranked in the highest quintile for all five measures - overall investment climate, for both domestic and foreign firms; government effectiveness, toward both domestic and foreign firms; and progress toward a harmonious society. All are in the coastal Southeast or Bohai Bay regions, with two in Zhejiang, two in Shandong, one in Jiangsu, and one inFujian. These 6 ``golden cities" cities, in alphabetical order, are: Hangzhou Qingdao Shaoxing 0 Suzhou Xiamen 0 Yantai Another 12-13 cities could be characterized as "silver medal finalists": Beijing 0 Dalian 46 Dongguan Foshan Fuzhou Guangzhou 0 Jiangmen Ningbo Shanghai Shenzhen Tianjin Weihai Zhuhai Almost all these cities are ranked inthe highest quintile for both measures (TFP and FDI) of overall investment climate, but fall short elsewhere. Dongguan, Foshan, Guangzhou, Shenzhen, and Zhuhai could hope to raise their ranking by improving government effectiveness vis-a-vis domestic investors; Ningbo, by making government more effective for foreign investors; Beijing, Shanghai, and Tianjin, by improving government effectiveness for both domestic and foreign investors; and Dalian, Fuzhou, and Jiangmen by achieving greater progress toward a harmonious society. Weihai receives top scores for government effectiveness, but narrowly missedthe top quintile for investment climate for domestic firms.24 Of course, there i s no single "right" approach to such measurement. Hence, this survey compensates by employing multiple criteria (firm productivity and foreign ownership) to measure overall investment climate and government effectiveness; by emphasizing rating quintiles rather than absolute rankings; and by basing "golden city" status on top-quintile rankings in five measures: overall investment climate, for both domestic and foreign firms; government effectiveness, toward both domestic and foreign firms; and progress toward a harmonious society. The assessment of investment climate, like the valuation of companies, ultimately depends on the investor. An investor may have a particular goal - such as access to a market, raw materials, or human resources - that inclines the investor toward a particular region or city. Thus, rather than focusing on rankings, it will likely be more productive for cities to communicate with potential domestic and foreign investors and to focus on improving objective performance for indicators that potential investors identify as most significant. 24 Survey data point to worker education and training as an issue for Weihai. For instance, university- educated workers averagejust 12%of the labor force at surveyed firms. 47 IV. RECOMMENDATIONS This survey's finding of a correlation among overall investment climate, government effectiveness, and progress toward a harmonious society i s consistent with the earlier characterization of investment climate as a process. Among China's coastal cities, favorable geography and market mass have contributed to prosperity. International tradehnvestment and local prosperity has, in turn, facilitated local government reforms and investment in urban quality of life that encourage further investment. While this might suggest a perpetual "virtuous cycle," in fact, the Southeast's rapid post-1979 growth was spurred by policy reforms and government programs.25 Time horizons for enhancing the components of investment climate will vary. Through concentrated efforts at reform, the municipal government of a lagging city may become more efective in a short time, perhaps 1-2 years. Gains typically associated with progress toward a harmonious society - such as improvements in education and health, environmental protection and remediation, and urban infrastructure and quality of life - may require sustained government spending over the medium term, for example, 3-5 years. Other city characteristics - such as market size and prosperity, or location-related transport costs - are usually assumed to change only over the long-term (if at all). Through appropriate transport/logistics sector reforms and sustained investment in urban infrastructure, however, it may be possible to improve such city characteristics sooner rather than later. Gains in firm productivity and foreign ownership from short-term improvements in administrative and mediudlong-term gains in harmonious society attributes and city characteristics could be substantial, as indicated below. A. SHORT-TERM IMPROVEMENTS INGOVERNMENTEFFECTIVENESS Cities inthe bottom quintile of government effectiveness could expect near-term gains of 25-35 percentage points in firm productivity and 15-25 percentage points in foreign 25I t is worth recalling that over one-quarter of China's entire industrial capital stock was concentrated in Liaoning province by 1957 and that China's Northeast provinces were ahead of Guangdong, Zhejiang, and Jiangsu in per capita GDP until the early 1990s. The Southeast's rise is attributed to a laissez faire approach that allowed comparative advantage to determine the composition of industry, supported by the post-1979 development of five special economic zones (SEZs) and complementary policies on trade reform, fiscal decentralization, and enterprise - especially township and village enterprise (TVE) - ownership. Shahid Yusuf, "Two Tales of Regional Development in China: the Pearl River Delta vs. the Northeast," September 2005, cited inWorld Bank, 2006, pp. 5-7. 48 ownership by improving government efficiency, labor flexibility, and financial access to those of the leading cities of Shandong, Guangdong, Zhejiang, and Jiangsu.26 Government eficiency. Some combination of regulatory simplification (e.g.. ,inbusiness licensing, land use, taxedfees, customs), rule of law, and increased service orientation by local government i s needed to improve the investment climate of lagging cities. While an October 2005 amendment of China's company law has made new business registration more flexible and less expensive, additional licensing requirements are relatively complex. In addition to company registration, twelve additional procedures are standard for starting a business in China. Half of these procedures are not required in most other countries around the world (Table IV-1). In addition, many other special- purpose approvals may be required.27 Procedure Percent of countries requiring the procedure Tax registration 93 Labor registration . 87 II Administrative registration 76 Bank deDosit II 68 II Notarization 63 Healthbenefits 63 Publication of notice innewspaper 38 Company seal 36 Court registration 32 Chamber of commerce registration 27 I Environmental Statistical office 17 registration 12 While China's system of land use rights generally provides sufficient security of tenure, massive urban development has aroused fears about the accelerated loss of agricultural land. This has led to tighter enforcement of central government policies on land use. Municipal officials may be uneasy about the potential conflict between faster economic 26These estimates and estimates of the impact of particular investment climate factors should be viewed with some caution. Such estimates should be seen as indicative of the relative importance of various investment climate factors, rather than as exact predictors of how performance will change after the enactment of reforms. *'Before registration, approvals may also be required from the Party Discipline Committee; Foreign Trade Committee; Public Security; Fire Control; Sanitation; Quality Examination; Cultural; Commerce; Property; Capital Examination. After registration, approvals may be required from Public Security; Quality Examination; the company's bank; State Administration of Foreign Exchange; and Customs in order to complete such procedures as making seals, code registration, activation of bank accounts, and registration. Foreign Investment Advisory Service (FIAS), China: Policy, Legal and Administrative Framework for Investment in Liaoning Province: Volume II:Assessment of Administrative Procedures for Doing Business in Liaoning Province, January 2005. 49 development and rural safeguards, while entrepreneurs may worry about the risks (e.g., confiscation, extortion) of illegally granted land.28 Hence, municipal governments should ensure that businesses are protected from entering "development zones" or receiving usage rights for land whose transfer to construction land has not been duly approved. Municipal governments should verify whether current land use practices conform to central government and whether the rights of legitimate land users are sufficiently protected. Land approval remains one of the least transparent processes that businesses must engage into implement andinvestment project. InChina, the project approval process is notjust a technical review to ensure compliance with building and zoning laws, but also a process for allocating a public asset. The latter aspect may provide an excuse for local interference in business decisions that would be left solely to private investors elsewhere. Land use and project approval processes may involve factors other than the merits of the proposed project and may lack sufficient clarity, transparency, and predictability. Moreover, after approval of a land use application or investment project, any significant change by the investor may require re-approval by the municipal government. Ingeneral, review and approval of investment projects should be limited in focus and scope. The focus should be on broad requested land-use categories, such as "commercial" or "industrial." The scope of municipal government involvement should be limited to those agencies specifically concerned with land use - and, in specifically defined cases, protection of the environment, roads and traffic, andperhaps fire protection access.3o Taxes and fees involve two main issues: tax concessions to foreign investors and local administrative fees that may be excessive andor non-transparent. Since 1979, China has introduced various tax preferences to encourage investment. As of 2002, 110 regions or zones offered tax preferences, most frequently involving lower corporate income tax rates for foreign-invested enterprises (FIES).~~ Tax preferences raise many issues. For instance, tax preferences for FIEs would seem to disadvantage domestically-invested enterprises. In reality, tax preferences may not make much of a difference to foreign investors covered by treaties to prevent double-taxation. Indeed, data from this survey suggest that foreign investment i s not so sensitive to tax rates.32 It may be that many localities in China have been engaged in a "race to the bottom," giving away tax revenues that could have be better spent on infrastructure, education, international-standard health facilities, or other public goods.33 Lastly, by creating 28For instance, according to press reports, between March and June 2004, 3,763 development zones were abolished and about 1100 square kilometers of land were returned to farmers. 29FIAS, 2005. 30Ibid. 31 Typically, tax preferences involve corporate income tax rates o f 15-24 percent for FIEs, versus the standard rate of 33 percent. PricewaterhouseCoopers, 2002. 32Among the 120 surveyed cities, each one standard deviation increase (decrease) in tax rates is associated with a 3 percentage point decrease (increase) in foreign ownership, versus a 6 percentage point decrease (increase) for overall firm productivity. 33 World Bank, World Development Report 2005: A Better Investment Climatefor Everyone, pp. 107-8, 168-70. 50 additional complexity and opportunities for discretion, tax and other preferences for foreign investors or encouraged industries may actually discourage investment and undermine good governance. While perhaps too low in some localities, taxes and fees are clearly too high elsewhere. Firms report that taxes and fees average 5.8-8.7 percent of sales in the most-burdened quartile of surveyed cities, versus 3.1 percent or less in the top lothpercentile. Locally- imposed administrative fees can be an issue. The problem of local administrative fees in China i s ~ e l l - k n o w n .For~less-prosperous lagging cities, administrative fees may be an ~ important source of revenue. Methods of assessing fees vary widely (Box IV-1). In "good" examples, the fee structure i s simple and the fee base i s objectively measurable, with no ambiguity and virtually no room for abuse by either side. Among "bad" examples, the fee structure i s more complex and the fee base i s changeable and often dependent on another value (e.g., land value, business revenue, project investment) that may not be easy to define. The latter situation encourages abuse, of both payers and collectors. GoodExamples BadExamples Construction permit cost based on square For enterprise registration: 0.8% of total meters of construction, e.g., RMB 1.38 per registered capital, with 0.4% for the excess square meter for a "brick and concrete over RMB 10million and no charge for the structure outside the city planning area" excess over RMB 100. Minimum charge Fixed cost for a certificate, such as a land of RMB 50. registration certificate at RMB 20 per Quality supervision fee for water certificate conservancy project: 1.0-2.5% of project investment. Road transportation management fee: 0.8% of businessrevenue Per cubic meter of surface water: RMB 0.1 for residents; RMB 0.25 for industry and administrative institutions; RMB 0.4 for catering businesses; RMB 3.0 for special industries One-time takeover of farmland: fee based on land "value" Source:FIAS, 2005. Regulatory complexity may encourage intrusive inspections by the local authorities. A 2002 survey found that businesses in one western province were subject to an onerous volume and variety of official inspections - including police, technical supervision, tax, sanitation, business registration, environmental, neighborhood committee, fire safety, and 34See, for example, "Would You LikeThat With Fees?' inWorld Bank, China: National Development and Sub-National Finance: A Review of Provincial Expenditures, CHA-22951, 9 April 2002, p. 12. One city provided a list of 94 different types of fees, inaddition to local and national taxes. FIAS, 2005. 51 labor.35 Useful fixes would include following the new Administrative Law's requirements for at least two functionaries to participate in any inspection, allowing inspected firms to access inspection records, and using some sort of risk assessment methodology to eliminate needless inspection^.^^ Customs efficiency i s of critical importance, especially for foreign investors. Customs procedures are generally more demanding in China, compared with nearby economies (Table IV-2). Source:World Bank,Doing Business, 2006 But customs clearance times vary widely throughout China. Firms report combined times of 3-4 days for export and import clearance in Huizhou, Dongguan, Shantou, Jiangmen, Shenzhen, Qingdao, Shaoxing, and Zhuhai. Combined clearance times are about 5 days for 90thpercentile cities (Suzhou, Weihai, Foshan, Ganzhou). Combined clearance times reportedly average 17-35 days for the slowest cities. At the bottom loth percentile, combined clearance times are about 20 days. Such cities (e.g., Changsha, Yuncheng, Taiyuan, Benxi, Anshan, Tianshui, Xining) could anticipate a 17 percentage point increase in foreign ownership b y reducing combined customs clearance times to 5 days. Although General Administration of Customs has rationalized procedures, automated more systems, and increased professionalismin recent years, the lack of uniform systems and procedures throughout China creates disparities in efficiency. Some customs posts use ED1 links and streamlined procedures, while others still rely on transfer of documents.37 Key recommendations for resolving issues identified in some cities include the following: Further updating customs law, regulations, and procedures to provide further clarity, transparency, and simplification, especially for import clearance documentation, guarantees, and inspectiodquarantine clearance; 35China Project Development Facility, December 2002, processed. Fines were relatively common, especially from technical inspections and tax inspections. Inspections, especially tax inspections, sometimes gave rise to "gifts and banqueting." 36FIAS, 2005. 37AmericanChamber of Commerce, White Paper 2005: American Business in China, pp. 162-66. 52 Increasing accessibility to customs laws, regulations, procedures, and g~idelines;~*Officia1written notifications and refusals for all issues that cannot be immediately resolved; Relying more on computerizedandremote clearances; Methodically tracking elapsed time between goods anival and release, to enable customs authorities to identify and correct clearance problems; Facilitating inland clearance by training inland customs officials inmethods used by highly efficient posts - such as Jiangmen, Suzhou, and Qingdao; Introducing an automated risk management system to guide physical in~pections;~~and Considering merger of Customs Administration and CCIQ processing in order to provide one-stop service. Excessive licensing and compliance requirements can distract local businesses and create opportunities for graft. In the lagging cities in central and western China, firms report interactions with major local bureaucracies average anywhere from 45-130 days a year. In addition, compared with leading cities along the coast, the firm outlays on entertainment and travel i s 3-4 times higher in central/west China's lagging cities. Foreign investors appear better-able to avoid such burdens.40 While business law and regulations are broadly similar across China, implementation by progressive municipal governments in coastal cities i s more "business-friendly.'' Examples include the following: Establishment of "one stop halls" to facilitate contact between entrepreneurs and the various agencies that may need to provide licenses and permit^;^' Local efforts at streamlining business start-up procedures; elimination of regulatory ambiguities and opportunities for bureaucratic discretion; and public dissemination of standard procedures for business start-up; Public dissemination of information on the status of the land quota for development and how land development quotas are applied to decisions on specific investment projects; Public education on key tax rules and tax rule changes; Promotion of electronic filing for tax submissions; Elimination of some administrative fees, or simplification through adoption of unambiguous fee structures; 38 International best-practice is to provide a comprehensive website from which all relevant laws, regulations, process manuals, guidelines, and forms can be downloaded. For one of the best examples, see Sweden Customs' at www.tullverket.se 39FIAS, 2005, pp. 115-29. 40 Interaction time with major bureaucracies and entertainmendtravel expenditures are significant in explaining variations inTFP, but not variations inFDI. 41One stop halls apparently work well as initial points of contact and information providers, but not at solving complex inter-agency issues. FIAS, 2005, pp. 18-24. Hence, simplification of business entry requirements remains the preferable solution. 53 Providing newly-registered companies with a definitive list of administrative fees that can later be charged; and 0 Encouraging new-style business associations, genuinely representative of member interests, in place of old-style industrial associations, in order to foster more government-business dialogue on localhational-level measures to improve investment climate. To encourage city officials to serve as "helping hands" rather than "grabbing hands," it i s important to create appropriate incentives whereby officials are rewarded for positive steps (e.g., reduced waiting time) rather than just for punitive measures (e.g., issuance of fines). Since inter-provincial trade and investment may be affected, some attention should also be paid to concerns about potential unfair treatment in commercial disputes outside a business' home locality. For starters, this could involve some empirical analysis as to whether place of business origin has an impact on court rulings, their enforcement, and contract awards by local governments. Given findings in the previous survey that courts in some cities are particularly sl~w-moving,assessments of the adequacy of resources ~ ~ and training for commercial courts may also be appropriate. Laborjlexibility. China's rules on employment are generally more flexible than those in Taiwan (China) and South Korea, but less flexible than those in Hong Kong (China), Japan, and Singapore. By law, it i s more difficult to fire workers in China than in any of those other East Asia economies and the cost of dismissal in China can amount to 30 weeks of salary.43 This may well contribute to overstaffing in some cities in China, and hurtboth foreign investment andfirmproductivity. It appears, however, that labor laws and regulations are not consistently enforced in China. In actual practice, firms may enjoy considerable autonomy in dismissing employees as well as in setting wages and arranging overtime. Inconsistent enforcement of employment rules i s undesirable for several reasons. First, the legitimate interests of workers may not receive sufficient protection. Second, those firms that rigorously adhere to employment rules may incur a competitive disadvantage. Multinational companies - who often must also answer to shareholders, international media, and non-governmental organizations - may feel compelled to honor labor lawsh-egulations and be disadvantaged by competing firms who do not. Third, "differential compliance" will expose firms to some risk of selective harassment and rent- seeking. State-owned enterprises (SOEs). Since SOEs appear to crowd out private sector development - for instance, through excessive reliance on price competition and preferred access to finance - it makes sense to reduce SOE dominance of industry, 42Dollar, et al, 2005, p. 31. 43World Bank, Doing Business 2006. Severance costs average 90 weeks of wages in Taiwan, China and South Korea, butjust 4 weeks in Singapore, 13 weeks inHong Kong, China, and 21 weeks inJapan. 54 especially in China's northeast, central, and western regions. Small and medium-sized SOEs that are viable as going concerns shouldbe sold, usingcommercial best practices to minimize the loss of state assets. While distressed but probably-viable large SOEs may be restructured, non-viable SOEs should be liquidated. Large or strategic SOEs that are healthy and destined to remain in the State portfolio should follow international best practices in corporate g~vernance.~~ This segmented approach is consistent with the 4* Plenum Decision of the 15th Central Committee of the CPC in 1999 to "grasp" large SOEs and "let go" the small. Both labor flexibility and ownership transformation of SOEs - as well as broader goals for achieving a harmonious society - would benefit from additional development of worker safety nets. In most parts of the country, pension schemes are still at city level and pension accounts are not portable. It i s usually difficult for an employee to leave a firm and move to work inanother city. Inadequate social protectionhinders further labor market reform. Inclusion of all types of firms in a universal and nationally-portable system of pensions, medical insurance, unemployment insurance, and other necessary support would facilitate labor mobility and market flexibility. SOEs could more readily reduce over-staffing, since laid-off workers would have more flexibility to pursue opportunities inthe private sector or elsewhere inthe public sector. Access tofinance. While access to finance does not appear to affect foreign investment, since foreign firms can more readily tap offshore sources of funds, there i s a strong correlation between access to bank loans and firm productivity. Bank lending i s especially important for small and medium enterprises (SMEs), which increasingly provide critical material inputs and services to other firms inindustrial clusters. The October 2004 liberalization of interest rates should help, but a number of factors still discourage lending to small domestically-invested enterprises: Banks perceive that risks are lower for large SOEs or public infrastructure projects. Relative to loan size, loan processing and administration costs are lower for large SOEs and infrastructure projects. Legal protections for creditors are still weak. S M E borrowers sometimes disappear. Enforcement of court orders inuncertain. S M E financial statements and audits are unreliable. The profitability of formerly state-owned SMEs may be constrained by hold-over contractual obligations from ownership transformation, for example, to retain redundant workers or excessive debt. While banks prefer real estate as collateral for secured loans, S M E assets tend to be mostly inventory, receivables, and intellectual property. Banks lack access to reliable credit information on enterprises that are not already clients. 44For further description, see World Bank, China: Facilitating Investment and Innovation: A Market- OrientedApproach to Northeast Revitalization, report 34943-CN, 15 February 2006, pp. 18-29. 55 The credit assessment skills of bank loan officers remain underdeveloped and based on assessments of collateral rather than company cash flow. Lack of familiarity with good S M E lending practices makes S M E lending seem more risky. To improve S M E access to finance, the authorities should focus on measures to encourage additional competition in lending; create a more supportive legal and regulatory environment; disseminate best practices in S M E lending; and promote non- bank sources of S M E finance.45 To attract qualified foreign investment -e.g., into city commercial banks - it will be necessary for bank financial statements to be presented according to international accounting standards and be audited. Key legal/regulatory enhancements would include a new law on enterprise bankruptcy that reflects international best practices, including priority for secured creditors; more widespread development and use of credit reporting systems; and revision of the secured transactions part of the Property Law to provide better protection to both creditors and borrowers as well as greater opportunities for using movable assets (e.g., receivables, inventory, equipment, vehicles) as collateral. In other countries, successful SME lending typically involves specialized practices. Loans typically are based not on S M E assets, but on judgments regarding the SME's cash flow, debt service capacity, and character of its owners and management. Since confidence regarding thesejudgments naturally increases as borrowers repay loans, default risk i s often controlled in part by the practice of "graduating" SMEi borrowers to larger, longer-term, and sometimes less- expensive loans as their repayment performance i s established. Frequent (e.g., monthly) repayments make it easier to monitor cash flow and repayment performance and to minimize risk. Lastly, while banks will likely continue to provide the biggest share of financing for SMEs, additional efforts to expand S M E access to non-bank financing could help. This would require legal, regulatory, and institutional developments to support more use of factoring and leasing (which a new leasing law would help), venture capital, private equity, and small public share offerings. B.PROGRESSTOWARDHARMONIOUS A SOCIETY Over the medium-term, by improving educatiodtechnical training, healthcare, and environmental quality to the levels of leading Southeast and Bohai cities, cities now in the bottom quintile of investment climate could anticipate gains of about 25 percentage points in firm productivity and 10percentage points inforeign ownership. 45Many localities have established credit guarantee schemes to support SME access to finance. Worldwide experience indicates, however, that such credit guarantee schemes are not a panacea and may simply create moral hazard and distortions, especially in an underdeveloped financial system. 56 Education endowments and programs appear to have various effects on firm productivity and foreign investment. TFP i s somewhat sensitive to mean years of CEO schooling and more sensitive to the education level of employees. Foreign ownership i s sensitive to information technology skillshsage and to the level of female enrollment, the latter of which serves as a measure of progress toward a harmonious society. As seen earlier, per capita education expenditures vary widely between cities. A significant part of the public school budget i s self-financed b y local governments. This increases the disparity in educational attainment between rich and poor regions, which i s undesirable for both equity and efficiency reasons. Central government resource allocations to poor provinces would facilitate educational competitiveness by now- lagging cities. Since government resources for education are often constrained, private initiatives in running schools and other training centers should be further encouraged, especially for those schools that provide vocational training for skilled jobs. Recently, China passed a law governing management of schools by private entities. Through such measures, more funds can be mobilized and allocated to education and training. In addition, a voucher scheme for education also warrants consideration, particularly for poorer cities. A voucher scheme could enable participating children to attend either a public or a private school, and promote competition inthe development of educational services. Public-private partnerships to provide demand-driven vocational training could also enhance firm productivity, especially inlagging regions. Additional nationwide policies and programs to move all of China toward becoming a "knowledge economy" would include the following: Exploiting opportunities for learning based on information and communications technologies; Improving the regulatory framework, including freer access to the Internet; Diffusing new technologies throughout China by strengthening technical standards, encouraging new businesses and other agents of technology dissemination, and multiplying local support structures for information and technical assistance; Reforming government R&D programs to align incentives with the business sector, and increase funding to selected networks of public and private universities; Attracting foreign investors in strategic areas, especially service businesses; facilitating global technological alliances for Chinese enterprises; and raising incentives for Chinese experts overseas to return to China; and Training public officials to adapt public management to more knowledge-based development.46 46World Bank, China and the Knowledge Economy: Seizing the 21`` Century, 2001, p. xxv. 57 Health. Health insurance coverage for permanent workers tends to be more common, and infant mortality tends to be lower, in cities or regions with higher productivity and higher foreign ownership. Faster growth and higher demands for labor likely generate both the incentives and the financial resources for improvements in local healthcare. The availability of international-standard health care and educational services i s often a particular consideration for foreign investors. In such major destinations as Beijing, Shanghai, Suzhou, and Guangzhou, foreign employees and their families have access to medical facilities and schools oriented toward the needs o f expatriate staff. Elsewhere, a smaller market and higher costs may discourage similar for-profit schools or health care providers from setting up operation^.^^ Education and health sectors are dominated by public service units (shiye danwei). Reform of public service units; improving finances through better alignment of intergovernmental fiscal relations, resources, and priorities; devolution of some services to the private sector or SOEs; strengthened accountability; and workforce rationalization have been roposed as measures that could have far-reaching benefits for health and education.4? Environmental. Reductions in air pollution and increases in urban green space would be among the most readily-noticed improvements. Recommendations for reducing air pollution focus broadly on adoption of a "clean development mechanism" for China.49 At the city-level, this may involve improvements in traffic management and use, development of public transport, fiscal policies to encourage fuel conservation and cleaner fuels, and improvements in vehicle technology, vehicle maintenance, and fuel qua~ity.~' Measures to eliminate or clean up water pollution and solid waste could also improve perceptions about each city's quality of life. Key recommendations for addressing solid waste have included waste minimization strategies; legislation and policies to promote inter-regional and inter-agency coordination; upgrading o f local recycling industry; more development of sanitary landfills; increased planning and service provision for "special" (e.g., hazardous) waste; and remediation of an estimated 5,000 "brownfield" sites already contaminated from inadequate disposal practices or chemical spills.51 47 Itwas suggestedearlier that tax concessions for foreign investors are probably a waste. I t might be more productive to collect the additional taxes which could be used for such public purposes as additional investment in urban infrastructure and initial subsidies for international-standard schools and medical facilities. 48World Bank, China: Deepening Public Service Unit Reform to Improve Service Delivery, 2005. 4q World Bank, Clean Development Mechanismfor China: Taking a Proactive and SustainableApproach, 2004. 50World Bank, Reducing Air Pollution From Urban Transport, 2004. 51 World Bank, "Waste Management in China: Issues and Recommendations," Working Paper #9, May 2005. 58 C.ENHANCING CHARACTERISTICS CITY Firm productivity and foreign ownership are also related to city population, per capita GDP and GDPgrowth, andtransport costs. Population. Firms in larger cities tend to be more productive, probably due to greater competition and benefits from agglomeration and clustering. This offers support for continued migration, especially to Southeast cities where water scarcity i s less of an issue. Other analyses suggest that dismantlement of the hukou system would reduce urban-rural income differences. To absorb migrants without worsening urban poverty and unemployment, destination cities will have to invest in infrastructure, housing, and public services, while creating an investment climate that stimulates private sector investment and business de~elopment.'~ GDP. Firms located in cities with higher per capita GDP tend to have much higher productivity and foreign ownership. The rate of GDP growth also appears to have a small beneficial effect on firm productivity. Thus, continued investment in urban infrastructure and services would likely make many cities more appealing to investors, especially foreign investors. More generally, investment in urban infrastructure may counteract urban unemployment and poverty. Investment in urban transport and infrastructure has been shown to support growth, while state-of-the-art information technology infrastructure i s now viewed as a practical necessity for cities seeking to cultivate high-tech industry. Lastly, greater reliance on private providers of public services offers opportunities for increasing competition, counteracting urban poverty, and enhancing quality of life - especially in lagging citie~.'~ Transport costs. Foreign investment, inparticular, i s sensitive to transport costs to access seaports. Survey data indicate, for instance, that a 50 percent reduction in overland transport costs could raise foreign ownership in such deep-interior cities as Lanzhou and Wulumuqibyperhaps 5 percentage points. Since shipment by rail can be 40-60 percent cheaper than shipment by truck over longer distances (> 700 kilometers), reform of China Rail into a customer-oriented flexible service organization would do a lot to improve the competitiveness of interior cities. This could involve, for instance, partial or full privatization and major improvements in China Rail's governance, management, procedures and information systems, performance monitoring and incentive compensation. 52World Bank, Policiesfor the Il`hFive-Year Plan, 15 December 2004, processed, pp. 37-9. 53Shahid Yusuf, "Two Tales of Regional Development inChina: The Pearl River Delta vs. the Northeast," August 2005, processed. For support, Yusuf cites Sylvie Demurger, "Infrastructure Development and Economic Growth: An Explanation for Regional Disparities in China?' Journal of Comparative Economics (29) 1:95-117; and Diane Coyle, Wendy Alexander, and Brian Ashcroft, eds., New Wealthfor Old Nations: Scotland's Economic Prospects,(Princeton University Press, 2005). 59 Other reforms by the national authorities that might serve to reduce transportllogistical costs and improve service include the following: Establishing clear procedures and qualifications for obtaining a "nationwide" road transport license for the cargo business; Passing unified legislation on warehousing, to reduce regulatory overlap; Preparing to meet future demand in interior cities for air cargo services, by promoting faster development of hub-and-spoke systems for China's regional airlines; Reducing or eliminating requirements for integrated logistics providers to obtain regulatory approvals or meet minimum capital requirements for distinct business activities or individual branch offices or facilities; and Abolishing requirements for logistics companies that use information technology and advanced management systems for managing external assets to actually own such assets as vehicles and warehouses. In general, a shift in emphasis from investment in fixed assets to better use of people, systems, and management would hasten development of China's logistics sector, cost reductions, and service improvements. D.MONITORING PROGRESS Lastly, to encourage local governments to pursue sustained improvements in government effectiveness, progress toward a harmonious society, and city characteristics amenable to local redress, it would make sense to repeat this survey on a bi-annual basis. To track progress or deterioration across time, it would be essential to maintain more consistency inthe survey instrument andanalysis of survey data. 60 ANNEX A: CITY PERFORMANCE,BY REGION To provide some perspective in intra-regional competitiveness, this Annex compares cities within each region. Such comparisons would be particularly relevant to an investor interested in a particular region, but unsure of which city to select. For government effectiveness, cities are ranked based on the average of their TFP and FDIrankings (see Table B-6) and objective performance measures are provided for each city. For progress toward a harmonious society, cities are ranked according to an index based on nine indicators covering economic well-being, health, education, and the environment (see Table 111-7). Transport costs (to and from the coast) are an issue for interior cities, but less so (or not at all) for Southeast and Bohai cities. Hence, the discussion for Southeast and Bohai also includes a comparison of government effectiveness and costs for land and labor. A. SOUTHEAST While transportation costs are similar, survey data indicate that factor costs and local government efforts at creating a good investment climate vary noticeably. Some cities - such as Suzhou and Jiangmen - are ranked above-average in government effectiveness, but also appear to be above-average in landlabor costs (Table A-1). The Southeast's better-known cities of Shenzhen, Guangzhou, Shanghai, and Nanjing also have high costs, but government effectiveness i s rated only average or below-average. Other cities -Zhangzhou andShantou -appear to offer both an above-average investmentclimate and below-average costs, compared with other cities surveyed in the Southeast region. In general, other apparently low-cost cities (Sanming, Huzhou, Maoming, Quanzhou, Jiaxing, Yancheng, Taizhou, and Lianyungang) might have the most to gain through more vigorous efforts by the local government to improve investment climate. 61 Table A-1. Government Effectivenessand Land/Labor Costs Among Southeast Cities Landlabor costs Average Above average Nantong Suzhou Above average Shantou Hangzhou Jiangmen Xiamen Shaoxing Dongguan Huizhou Government Fuzhou Shenzhen effectiveness Huzhou Foshan Wuxi Average Maoming Ningbo Guangzhou Quanzhou Jinhua Jiaxing Yancheng Yangzhou Shanghai Taizhou Wenzhou Xuzhou Below average Lianyungang Changzhou Nanjing Zhuhai While relatively low throughout the Southeast, average taxes and fees are reportedly higher relative to sales in Xuzhou (7.5 percent), Nanjing (5.4 percent), Wenzhou (5.5 percent), Shanghai (5.3 percent), and Taizhou (5.8 percent) than in Jiangmen (1.1 percent) and Suzhou (2.5 percent) (Table A-2). Entertainment and travel expenditures also vary, from 0.4-0.8 percent of sales revenues for the five highest-ranked cities to 1.1-1.9 percent of sales for the ten lowest-ranked Southeast cities. Average interaction with important government agencies (i.e., tax administration, public security, environmental protection, labor and social security shows no obvious patterns in the Southeast. Top-ranked cities vary widely (e.g., Suzhou at 70.6 days and Hangzhou at 8.1 days), as do bottom-ranked cities (e.g., Nanjing at 85 days and Changzhou at 28.9 days). This may at least partly reflect differences in the composition of local industry. Reported customs clearance times vary widely. Average combined clearance times for exports and imports are less than five days for Jiangmen, Suzhou, Shaoxing, Dongguan, Shantou, Huizhou, and Shenzhen. The average i s ten days or more for twelve cities - including Yancheng, Xuzhou, Wenzhou, Taizhou, Yangzhou, and Changzhou. Costs from power or transport failures reportedly average 2.2 percent of output or less in Hangzhou, Jiangmen, and Suzhou. Reported losses are more than two times higher in Jiaxing (6.1 percent), Jinhua (6.9 percent), Ningbo (7.7 percent), Donguan and Shenzhen (both 5.1 percent), Taizhou (6.9 percent), and Wenzhou (5.3 percent). Overstaffing i s generally 1.0 percent or less. Notable exceptions are Nanjing (4.4 percent) and Xuzhou (4.0 percent). 62 Access to finance seems not much of an issue for the Southeast, except for high expectations of a need for informal payments to obtain loans in Jiaxing (7.1 percent), Jinhua (9.5 percent), Maoming (15.4 percent), Yancheng (10.5 percent), Nanjing (8.2 percent), and Xuzhou (8.8 percent). Confidence in protection of propertykontract rights i s very high in the leading cities of Jiangmen, Hangzhou, and Suzhou - ranging from 93-98 percent of respondents. Confidence i s generally lower elsewhere in the Southeast, including in the largest cities - Guangzhou (61 percent) and Shenzhen (74 percent). Confidence i s just 40-50 percent in three below-average cities: Wenzhou, Shanghai, and Taizhou. The university-educated represent 15 percent or less of workers at surveyed firms in most Southeast cities, perhaps a reflection of labor migration from lagging areas. Exceptions with better-educated workforces are Jiangmen (17 percent), Hangzhou (26 percent), Suzhou (21 percent), Xiamen (17 percent), Guangzhou (26 percent), Shenzhen (17 percent), Lianyungang (16 percent), Shanghai (23 percent), Nanjing (22 percent), and Xuzhou (21 percent). Private businesses, either foreign-invested or domestic-invested, represent more than 90 percent of surveyed firms in most Southeast cities. Low-ranking exceptions are Maoming (82 percent), Nanjing (75 percent), and Xuzhou (82 percent). Turning to livability, Shanghai, Shenzhen, Dongguan, Hangzhou, Ningbo, Guangzhou, Suzhou, and Zhuhai are in the top 10 percent of surveyed cities in terms of livability (Table A-3). Disposals of industrial waste that meet environmental protection standards are around 96 percent for most Southeast cities. A few cities show 100 percent conformance: Nantong, Xuzhou, and Lianyunguang. Less than 90 percent of disposals meet environmental protection standards in several cities: Foshan (89 percent), Taizhou (88 percent), and Maoming (89 percent). For most Southeast cities, air quality i s good or excellent at least 85 percent of the time. Notable low-ranking exceptions include Jinhua (68 percent), Xuzhou (60 percent), Wenzhou (30 percent), Yancheng (76 percent), and Sanming (68 percent). Per capita green space i s 10-20 square meters for the highly-livable cities of Shanghai, Shenzhen, Dongguan, Hangzhou, Guangzhou, and Ningbo. Allocations are much lower, however, inTaizhou (4.5 m2), Maoming (2.5 m2), and Wenzhou (5.4 m2). Infant mortality i s 7.5 per 1000 or less for most cities, but noticeably less for Shanghai (3.8) and Shenzhen (4.8). The worst-performing cities are Dongguan (9.0), Xiamen (9.2), Shantou (9.2), Quanzhou (11.2), Zhangzhou (9.1), Lianyungang (12.6), Yancheng (10. l), and Sanming (9.7). 63 Some 87-99 percent of permanent workers have medical insurance in the most-livable cities of Shanghai, Shenzhen, Dongguan, Hangzhou, Guangzhou, Ningbo, Suzhou, and Zhuhai. This likely reflects demands on firms in high-growth cities to compete for skilled workers. Coverage levels are substantially lower, however, in Jiaxing (56 percent), Fuzhou (44 percent), Yangzhou (56 percent), Zhangzhou (3 1 percent), Yancheng (52 percent), and Sanming (54 percent). Female enrollment i s consistently around 46 percent for most Southeast cities. Exceptions are Ningbo (51 percent) on the high end and Nanjing (43 percent) and Maoming (40 percent) on the low end. Per capita education expenditures, which vary widely, are highest for Shanghai (RMB 3277), Hangzhou (RMB 2169), and Ningbo (1244), and lowest for Fuzhou (RMB 273), Yangzhou (RMB 220), and Maoming (RMB 227). A number of Southeast cities reflect the region's average of RMB 700-800 per capita: Shenzhen, Guangzhou, Zhuhai Jiangmen, Xuzhou, andWenzhou. 64 -r -r -7-I m a m 3 W P 8 I ** bl C 8 p- pg .I E 66 TT T 67 00 W 68 B.BOHAI Two Shandong cities - Yantai and Linyi - rank above-average in government effectiveness, but also appear above-average in landlabor costs (Table A-4). Several cities, including Beijing and Tianjin, also appear to have above-average landlabor costs, but offer only average or below-average government effectiveness. Another Shandong city, Taian, appears to offer both an above-average government effectiveness and below- average costs. Other Bohai cities that appear to have relatively low costs and which might have the most to gain through more vigorous local government efforts at investment climate improvement are Zibo, Qinhuangdao, Langfang, Jining, and Canzhou. Landlabor costs Below average Average Above average Above average Taian Weihai Yantai Qingdao Linyi Weifang Government Zibo Tangshan effectiveness Average Qinhuangdao Jinan Langfang Shjiazhuang Jining Cangzhou Handan Beijing Below average Zhanjiakou Baoding Tianiin The seven cities whose governments appear most effective in creating a good investment climate are all locatedin Shandong: Weihai, Qingdao, Linyi, Yantai, Weifang, Taian, and Zibo. O f the nineteen Bohai cities covered in the survey, Beijing i s ranked 14th and Tianjin 15th in effectiveness of government measures to create a favorable investment climate. The performance of other Hebei cities varies, with Langfang and Tangshan coming in at fIth and IOth, respectively, but with Handan and Zhanjiakou the lowest- ranked of Bohai cities. Firmsreport taxedfees at 2.4-3.9 percent of sales inBohai's top five cities, versus 5.0-6.3 percent in Beijing, Tianjin, Zhangjiakou, and Handan (Table A-5). Entertainmenutravel costs are reported to average just 0.4 percent of sales inLinyi and Weihai, versus 1.2-1.7 percent in Qinhuangdao, Beijing, Baoding, andTianjin. Days of interaction with the four major government agencies are reported to be particularly low in Langfang (32 days), Weihai (50 days), and Qingdao (36 days). Local bureaucracies appear particularly burdensome in Tangshan (83 days), Shijiazhuang (87 days), Beijing (83 days), Tianjin (93 days), Zhangjiakou (88 days), and Handan (107 days). Average customs clearance time for exports and imports (combined) i s reportedly low for Bohai's top two cities: 4.1 days for Qingdao and 4.9 days for Weihai. The combined 69 averages for most other Bohai cities i s 8-9 days. Slow-clearing exceptions are Tangshan (10 days), Qinhuangdao (11.8 days), Cangzhou (14.2 days), Baoding (10.9 days), Zhangjiakou (13.6 days), andHandan (13.7 days). For most Bohai cities, at least 50 percent of small domestically-invested enterprises report having bank loans. Notable exceptions are Qinhuangdao and Shijiazhuang (both 33 percent) and Handan (27 percent). From among all enterprises surveyed, expectations of informal payments for loans are particularly high in Taian (11.3 percent), Jining (12.9 percent), andJinan (11.2 percent). Output losses due to power or transport are reportedly below 1.4percent for most of the Bohai cities surveyed. The worst-performing exceptions are Handan (2.8 percent), Cangzhou (2.5 percent), and Baoding (2.0 percent). These losses are lower than those typically reported, however, for Southeast cities. Overstaffing averages 2.5 percent for Bohai cities, but i s reportedly higher in Tianjin (4.7 percent), Zhangjiakou (6.9 percent), and Handan (4.4. percent). This may reflect, at least in part, redundant workers at SOEs. While the survey sample for most Bohai cities included 80-90 percent private firms, private sector participation was lower for Zhangjiakou (70 percent) and Handan (74 percent). Thus, the survey sample for these cities includedmore SOEs: 30 percent for Zhangjiakou and 26 percent for Handan. Confidence in protection of property/contract rights i s 65-85 percent of firms for most of the cities surveyed in Bohai. Among surveyed firms, confidence i s notably higher in Qingdao (95 percent) and lower in Tangshan (52 percent), Jinan (56 percent), Cangzhou (47 percent), Beijing (43 percent), andHandan (41 percent). The university-educated represent 15-25 percent of the workforces at surveyed firms in most Bohai cities. Exceptions are Weihai, Tangshan, Cangzhou, Zhangjiakou (all 12 percent), Weifang (13 percent), and Handan (14 percent) at the low-end, and Beijing (42 percent) andTianjin (27 percent) at the high-end. Turning to livability, Beijing and Weihai are in the top 10 percent of surveyed cities in terms of livability (Table A-6). Other cities in Shandong - Jinan, Zibo, Yantai, and Qingdao - follow closely behind. In terms of industrial waste disposals meeting environmental protection standards, most of the Bohai cities surveyed show 98 percent or better compliance. Langfang and four Shandong cities (Weihai, Yantai, Qingdao, Linyi) show 100 percent compliance. At 93 percent, Jining shows the lowest compliance among the Bohaicities surveyed. While the air is always good or excellent in two cities along Shandong's coast, Weihai and Yantai, the frequency of goodexcellent air quality i s much lower in a number of Bohai cities: Beijing (63 percent), Jinan (58 percent), Cangzhou (78 percent), Tangshan (76 percent), Shijiazhuang (77 percent), Baoding (75 percent), and Zhangjiakou (70 percent). 70 Per capita green space is 10-20 square meters for Beijing, Weihai, Jinan, Yantai, Qingdao, Langfan, and Taian. Allocations are much smaller for Weifang (5.1 m2) and Cangzhou (2.8 m2). Infant mortality i s 7.5 or fewer per 1000 for most of the Bohai cities surveyed. The rate i s noticeably higher, however, for Jinan (9.8 per lOOO), Langfang (9.0), Linyi (9.8), Shijiazhuang (13.0), andZhangjiakou (16.3). Health insurance coverage for permanent worker varies widely. The best-performers are Beijing (97 percent), Yantai and Qingdao (both 91 percent), and Tianjin (92 percent). The worst-performers are Linyi (64 percent), Cangzhou (42 percent), Jining and Tangshan (both 52 percent), Baoding(56 percent), and Zhangjiakou (40 percent). Female enrollment i s typically 46-49 percent. Exceptions are Qinhuangdao (53 percent) on the high-end and Taian (40 percent) andLinyi (39 percent) on the low-end. Per capita expenditures on education are especially high in the leading cities of Beijing (RMB 1044) and Weihai (RMB 1631) as well as in Weifang (RMB 1363). Bohai cities spending less than RMB 400 per capita on education include Yantai, Langfang, Qinhuangdao, Liny, Cangzhou, Tangshan, Shijiazhuang, Baoding, and Zhangjiakou - which comes inlast at RMB 134. 71 1 T 1 I- 00 - 00 3 3 0 s 8 72 -r T + I.- gal 73 C.NORTHEAST While not outstanding inevery measure, Dalian i s the clear leader among Northeast cities surveyed, both in terms of local efforts at achieving a good investment climate and progress toward achieving a harmonious society. While typically offering lower cost land and labor, cities in the Northeast's interior would need to do much more to improve government effectiveness and quality of life inorder to compete with Dalian. Dalian comes out well in terms of average taxedfees reported by surveyed firms (3.1) percent of sales revenue) and firm expenditures on entertainmendtravel (0.7 percent of sales revenue) (Table A-7). The apparently lower level of fees likely reflects Dalian's prosperity and straightforward approach to administrative fees, while lower expenditure on entertainmendtravel likely reflects greater administrative transparency and a heavy presence of foreign firms in Dalian. 54 Taxedfees are reported to be notably higher in Changchun (6.8 percent of sales), Jilin (6.3 percent of sales), and Benxi (6.2 percent). Firm expenditures on entertainmedtravel are relatively high as well in Changchun (1.6 percent), Shenyang (1.8 percent), Fushun (1.5 percent), Ha'erbin (1.9 percent), and Benxi (1.6 percent). Interms of time demands by major bureaucracies, Dalian is actually the worst-performer among Northeast cities, with an average of 91 days reported by surveyed firms. Other notably poor performers are Anshan (78 days), Qiqihaer (88 days), and Ha'erbin (79.5 days). Several cities appear to minimize bureaucratic time demands: Jinzhou (47 days), Fushun(51 days), Changchun (39 days), andDaqing (49 days). Average times reported for customs clearance, export and imports combined, were fastest at the Northeast's two top-ranked cities: Dalian (6.7 days) and Jinzhou (8.9 days). Customs clearance i s reported substantially slower at Anshan (20.3 days), Shenyang (16.5 days), Jilin (16.0 days), Ha'erbin (18.7 days), and Benxi (20.1 days). Access to finance i s an issue for most Northeast cities, with a few exceptions. The survey indicates that access by small domestically-owned enterprises to bank loans i s reasonably high in Jinzhou (47 percent of such firms), Shenyang (53 percent), and Fushun (41 percent). On the other hand, access by small domestic enterprises to bank loans i s very low in Changchun (16 percent), Jilin (17 percent), Benxi (18 percent), and Ha'erbin - where only 7 percent of small domestic enterprises have bank loans.55 While access to bank loans i s reasonably good in Shenyang, almost of 10 percent of all firms perceive a need to make informal payments to bank officials in order to get a loan. Informal payment expectations are similarly high in Changchun (over 8 percent) and Benxi (over 10 percent), two cities where small domestic enterprises also suffer from low access to loans. 54World Bank, A Market-Oriented Approach to Northeast Revitalization,2006, p. 64. 55The survey defines small domestic enterprises as those with 100 or fewer employees. The zero access figure for Dalianalmost certainly reflects that lack of such small domestic firms inDalian. 74 Problems with power and transport are reported to be relatively low in most Northeast cities. Two cities where firms report somewhat high losses are Anshan (2.1 percent o f output) and Fushun(1.7 percent). Among Northeast cities surveyed, confidence among firms that property and contract rights will be protected i s highest in Dalian (65 percent), Jinzhou (79 percent), Anshan (67 percent), and Changchun (70 percent). Confidence i s lowest in Qiqihaer and Shenyang (54 percent), Fushun (40 percent), Ha'erbin (50 percent), and Benxi (55 percent). Labor overstaffing i s also lowest Dalian (1.2 percent), Jinzhou (2.1 percent), Anshan (1.5 percent), and Changchun (2.5 percent). Overstaffing i s highest in Qiqihaer (6.9 percent), Ha'erbin (6.3 percent), and Benxi (5.5 percent). The differences likely reflect the combined effects of economic vitality in the Northeast's leading cities, plus SOE and labor regulation rigidities inthe lagging cities. Education levels are high in the Northeast. Numbers of university-educated workers at surveyed firms are especially high in Dalian (26 percent), Changchun (29 percent), Shenyang (31 percent), andHa'erbin (38 percent). As for livability indicators, despite the higher levels of university education in the Northeast, per capita expenditures on education are relatively low, typically at RMB 400- 600 (Table A-8). Northeast leaders on education spending are Dalian (RMB 606), Daqing (RMB 757), and Ha'erbin (RMB 760). Laggards are Qiqihaer (RMB 234), Fushun(RMB 225), Jinzhou (RMB 216), andBenxi -at aremarkably low RMB 63. Females account for 47-49 percent of student enrollment for all surveyed cities, except for Shenyang where it i s somewhat higher (52 percent). As for health indicators, infant mortality rates vary widely. Infant mortality exceeds 10 per 1000 in Dalian, Daqing, and Anshan, and i s 6 or fewer per 1000 in Qiqihaer and Benxi. Medical insurance coverage for permanent workers i s 60-80 percent for most Northeast cities, but higher in Dalian (99 percent), Daqing (87 percent), and Ha'erbin (81 percent) and lower in Changchun, Jilin, Fushun, and Jinzhou (56-59 percent) and Anshan (49 percent). Conformity with environmental rules for the disposal of industrial waste i s highest in Dalian, Qiqihaer, and Benxi (all 97 percent), and lowest in Changchun (90 percent), Anshan (87 percent), and Jinzhou (72 percent). Good or excellent air quality i s most common for Dalian and Daqing (96-97 percent of days) and lowest for Fushun and Anshan (70 percent), Benxi (67 percent), and Jilin city (61 percent). Per capita green space, generally 6-7 square meters for Northeast cities i s highest for Dalian (8.5 m2) and lowest for Qiqihaer (5.4 m2). 75 -r T 77 D.CENTRAL The Central region's main competitor i s the Southeast - for example, cities in the Yangtze River delta for Jiangxi and Anhui cities and cities in the Pearl River delta for Hunan, Hubei, and Henan cities. The municipal governments of Central region cities need to outperform Southeast cities in creating a good investment climate in order to improve productivity and offset Central's transport cost disadvantage vis-h-vis the Southeast. The three Central cities rated tops in investment climate - Nanchangin Jiangxi province and Shangqiu and Zhoukou in Henan province - actually out-perform Southeast averages inthree out of four measuresof government efficiency. Taxes and fees represent 2.6-3.3 percent of sales revenue in Shangqiu and Zhoukou, (Table A-9), versus an average of 4.1 percent for Southeast cities. For most Central cities in the survey, firms report that taxedfees represent 4-6 percent of sales revenue. The survey indicates that the taxedfees exceed 6 percent of sales inZhuzhou, Changsha, Changde, Yichun, andWuhan. For twelve Central cities - including Shangqiu, Zhoukou, Nanchang, and Shangrao - reported entertainmendtravel expenditures are below the 1.O percent average for Southeast cities. Reported firm expenditures on entertainmenutravel were noticeably high, however, in several Central cities: Chuzhou and Hengyang (2.0 percent), Wuhan and Huanggang (1.6 percent), Changde and Luoyang (1.8 percent), and Changsha (2.3 percent). For about half the Central cities - including Shangqiu, Zhoukou, Nanchang, and Shangrao - reported average firm interactions with major bureaucracies were less than the 52-day average for Southeast cities. Bureaucratic demands were relatively high, however, for several Central cities: Hefei and Luoyang (74 days), Xiangfan and Nanyang (73 days), Wuhan (87 days), and Xiaogan and Changsha (70-7 1days). Compared with the 7.3 days average for the Southeast, customs clearance i s somewhat slower for Central's three top-rated cities: Nanchang (7.8 days) Shangqiu (11.0 days), and Zhoukou (10.1 days). The survey indicates that two Central cities are faster: Xuchang (6.9 days) and Ganzhou (5.4 days). In many Central cities, however, customs clearance takes more than twice as long as it does on average in the Southeast: Yichang and Chuzhou (22 days), Nanyang (26), Xinxiang (17), Chenzhou (19), Changsha and Changde (20-21 days), and Huanggang (29 days). Central cities compare well with the Southeast in terms of access by small domestically- invested enterprises to bank loans, but expectations of informal payments for loans are more commonplace in Central China. Cities with particular issues include Shangqiu, where access i s lower but 14 percent of firms expect to make informal payments; Xuchang, where a similar pattern in reported; and Luoyang, where only 27.5 percent of 78 the small domestically-invested firms surveyed reported having bank loans and where 15.7 percent of all firms expect to make informal payments in order to obtain loans. While one Central city beats the Southeast's 0.9 percent overstaffing average - Shangqiu (0.7 percent) - the overstaffing average among Central cities (3.3 percent) i s higher. The survey indicates that overstaffing i s notably high in Anqing (4.9 percent), Yeuyang (5.9 percent), Nanyang (4.7 percent), Wuhan (4.8 percent), Changsha (4.9 percent), Hengyang (10.2 percent), and Huanggang (6.1 percent). This likely reflects the somewhat greater role of SOEs. SOEs account for 47-62 percent of industrial sales in Central provinces, versus 12-40 percent for Southeast provinces. Central region cities may also be somewhat more stringent inenforcing China's labor laws andregulations. Confidence in protection of property and contract rights exceeds the 66 percent average for Southeast cities in about half the Central region cities, including in Shangqiu, Zhoukou, Nanchang, and Shangrao. Less than half the surveyed firms are confident, however, in several Central cities: Xiangfan (40 percent), Xinxiang (40 percent), and Hengyang (39 percent). Power and transport infrastructure seems adequate in most Central cities. Reported output losses from power outages or poor transport exceed the Southeast's 3.1 percent average in only eight cases: Yichang (4.9 percent), Yueyang and Chenzhou (5.0 percent), Changde (5.7 percent), Changsha (6.0 percent), Luoyang (3.1 percent), and Hengyang (4.9 percent). Likely reflecting a more stable workforce, reported percentages of university-educated workers exceed the 15 percent average for Southeast cities in just over half of Central cities. Rates are particularly high for Nanchang and Hefei (each 29 percent), Zhengzhou (21 percent), Wuhan (31 percent), and Changsha (29 percent). Surveyed firms report that university-educated represent just 12 percent or less of the workforce in several Central cities: Shangqiu, Shangrao, Xuchang, Anqing, Chuzhou, Jiujiang, Chenzhou, Yichun, and Huanggang. Turning to livability, the top three cities in Central China are Nanchang, Shangrao, and Changde. Indeed, Nanchang i s among the top 10 percent of surveyed cities in livability (Table A-10). Consistent with its high percentage of university-educated workers, Nanchang leads in per capita expenditures on education (RMB 4222). Other leaders in resources for education are Shangrao (RMB 2,946) and Jingmen (RMB 1513). While average spending for Central cities i s RMB 690, thirteen cities spend less than half this: Hengyang (RMB 96), Xiangfan (RMB 170), Zhengzhou (RMB 267), Hefei (RMB 294), Zhoukou (RMB 207), Chuzhou (RMB 233), Xiaogan (RMB 257), Xuchang (RMB 251), Handan (RMB232), Shangqiu (RMB 118), Luoyang (RMB 149), Ganzhou (RMB 120), and Huanggang (RMB 128). Female enrollment is typically 45-49 percent. Female enrollment i s particularly high in Chenzhou (53 percent) and low in Yueyang and Xinxiang (both 33 percent). 79 As for environmental measures, only in Anqing does 100 percent of industrial waste disposal meet environmental protection standards. The two most livable cities do relatively well, with Nanchang at 95 percent and Shangrao at 90 percent. Other leaders, at 95 percent or better, are Wuhu, Zhengzhou, Hefei, Zhoukou, Xiaogan, Xuchang, and Handan. Lagging cities are Jingzhou (68 percent), Zhuzhou (84 percent), Ganzhou (52 percent), andHuanggang (75 percent). Air quality varies widely in Central China. Twelve cities have good or excellent air quality at least 90 percent of the time: Nanchang, Shangrao, Wuhu, Jiujiang, Chuzhou, Yichun, Xiaogan, Xuchang, Chenzhou, Anqing, Shangqiu, and Ganzhou. -cities have good or excellent air quality less than 75 percent of the time: Changsha (60 percent), Wuhan (68 percent), Yichang (72 percent), Handan (69 percent), Zhuzhou (54 percent), Luoyang (43 percent), Nanyang (26 percent), andHuanggang (59 percent). In terms of per capita green space, the leading cities are somewhat cramped: Nanchang (7.2 m2) and Shangrao (7.1 m2). Several Central cities have 20-30 square meters of green space per resident: Changde, Hengyang, Xiangfan, and Yueyang. As for health indicators, several Central cities compare favorably with the Southeast's 7.4 per 1000 infant mortality rate: Changde (5.8), Xiangfan (7.2), Wuhan (4.9), and Jingzhou (4.5). But infant mortality rates are more than double in Jiujiang (15.9), Yichun (15.7), Xuchang (16.0), Chenzhou (15.9), Anqing (17.9), Luoyang (15.4), Nanyang (24.3), and Ganzhou (19.2). Consistent perhaps with higher overstaffing - suggesting less competition for labor - health insurance coverage in Central cities i s less common than in the Southeast. A few Central cities meet or surpass the Southeast's average of 78 percent of permanent workers with health insurance: Shangrao and Changsha (both 78 percent), Hefei (92 percent), Jiujiang (80 percent), Wuhan (87 percent), and Zhoukou (92 percent). But the survey indicates that fewer than half of permanent workers have health insurance inmany Central cities: Xiaogan, Xuchang, Chenzhou, Anqing, Handan, and Huanggang. 80 I 'li- 8 .5 I I 81 &"'Barn p e, .Ik 6 . W I 82 T -r 83 s-f Y e, .I- u 84 E.SOUTHWEST The Southwest's main competitor i s also the Southeast, especially such Guangdong cities as Jiangmen, Dongguan, Shantou, Foshan, and Guangzhou. These five cities should serve benchmark against which to compare the performance o f Southeast cities. Municipal governments in the Southwest will need to outperform Guangdong's "top five" in creating a good investment climate in order to begin offsetting the Southeast's market and transportation advantages. The four Southwest cities rated best in government effectiveness - the Deyang, Chongqing, Leshan, and Chengdu - lag behind Guangdong's top five in government efficiency. Performance by other Southwest cities i s reportedly worse. Taxes and fees represent 4.7-5.1 percent of sales revenue for Deyang, Chongqing, Leshan, and Chengdu (Table A-11), versus an average o f 3.1 percent for Guangdong's top five. Taxes and fees are more than double this rate in several Southwest cities: Yibin (6.6 percent), Yuxi (6.4 percent), Qujing (7.8 percent), Guilin (6.9 percent), Guiyang (8 percent), Zunyi (7.9 percent), Nanning (7.3 percent), and Haikou (8.7 percent). Leshan, Deyang, and Chonqing compare favorably with Guangdong's top five cities interms of firm expenditures on entertainment and travel (about 0.6 percent of sales revenue). Entertainment and travel expenditures are more than double this rate in several Southwest cities: Chengdu (1.6 percent), Guiyang and Zunyi (both 1.8 percent), and Haikou (2.4 percent). At 48-54 days of bureaucratic interaction, Deyang and Leshan compare favorably with the 55-day average for Guangdong's top five. Firms in Chongqing report an average of 75 days a year of interaction with major bureaucracies. The survey indicates that bureaucratic demands are especially high in Kunming (97 days), Guiyang (87 days), and Zunyi (85 days). Customs clearance for exports and imports in Deyang, Chongqing, and Chengdu (11-12 days) reportedly takes almost three times as long on average as in Guangdong's top five (4.3 days). While firms report that customs clearance in Yuxi and Haikou i s faster than in Chongqing, several Southwest cities are real laggards: Leshan (24 days), Liuzhou (23 days), andZunyi (33 days). Access to bank loans by small domestically-owned enterprises i s actually better in most Southwest cities than in Guangdong's first five. That small firms in slower-growth regions have better access than those in faster-growing regions i s somewhat surprising and, as noted earlier, suggests some "stickiness" in commercial bank operations. Access by small domestically-invested firms i s notably low in Nanning (30.8 percent) and Haikou (23.8 percent). The survey indicates that many Southwest firms perceive a need to make informal payments to get loans. The informal payments expectations rate i s lower than the average for Guangdong's five best in a few Southwest cities: Chengdu, Liuzhou, Yuxi, 85 and Guiyang. But expectations of informal payments for loans i s high in Leshan (11.3 percent), Yibin (13.7 percent), Zunyi (7.8 percent), Nanning (7.1 percent), and Haikou (9.5 percent). Among all Southwest cities, labor overstaffing i s higher than the 0.9 percent average for Guangdong's first five. The survey indicates that labor staffing i s 1.4-2.8 percent in the Southwest's nine highest-ranked cities. Overstaffing reportedly i s especially high in Liuzhou and Qujing (3.7-3.9 percent), Guiyang (4.6'percent), Haikou (4.8 percent), Zunyi (5.0 percent), and Nanning (6.0 percent). This likely reflects overstaffing at local SOEs, which account for 56-71 percent of industrial sales in Yunnan, Guizhou, and Guangxi - versus 20 percent in Guangdong. Confidence among firms in the Southwest's four leading cities in protection of their property and contract rights compares favorably with Guangdong. The survey indicates that confidence i s low, however, in Kunming and Zunyi (43 percent), Guiyang and Haikou (47 percent), and Nanning (36 percent). While infrastructure losses (mainly due to power outages) are an issue in Guangdong, this appears to be a bigger issue for firms in Southwest cities. Firms in Deyang and Chongqing report output losses of just 2.0-2.5 percent. But firms report higher output losses of 6-9 percent for Leshan, Kunming,Liuzhou, Guilin, and Nanning; 9.4 percent for Yuxi; and 10.3 percent for Qujing. For two-thirds of the Southwest cities surveyed, the survey indicates that percentages of university-educated workers exceed the average for Guangdong's first five cities. Rates are 20-29 percent for Mianyang, Chongqing, Kunming, Guilin, Guiyang, and Nanning; 30 percent for surveyed firms inHaikou; and 31percent for surveyed firms in Chengdu. Turning to livability, Mianyang is in the top 10 percent of surveyed cities in livability, which mainly reflects good performances in industrial waste disposal and air quality (Table A-12). Among all six of the Southwest's most livable cities - Mianyang, Huizhou, Haikou, Kunming, Guilin, and Chengdu - 95-100 percent of industrial waste disposals meet environmental protection standards. The rate i s also 95 percent inLeshan. Compliance with environmental standards for disposal of industrial waste i s especially low, however, in Yuxi (55 percent), Guiyang (79 percent), Yibin (78 percent), and Zunyi (53 percent). Most of the Southwest cities surveyed have good or excellent air quality at least 90 percent of the time: Mianyang, Hiuzhou, Haikou, Kunming, Guilin, Nanning, Yuxi, Leshan, Deyang, Guiyang, and Qujing. At 60-66 percent of days, only in Yibin and Chongqing does air quality approximate the standard for Beijing (63 percent). Despite Kunming's reputation as a garden city, most Southwest cities surveyed actually exceed Kunming's 7.9 square meters of per capita green space. In several cities, however, residents enjoy only 4-5 square meters of per capita green space: Leshan, Yibin, Chongqing, and Qujing. 86 Despite its overall livability, Mianyang ranks near the bottom in terms of per capita expenditures on education (RMB 143). Spending rates are similarly low in Leshan (RMB 185), Deyang (RMB 93), andQujing (RMB 198). Rates are not muchbetter inthe well-known cities of Kunming (RMB 386), Chengdu (RMB 417), Nanning (RMB 233), Guiyang (RMB 328), and Chongqing (RMB 485). Leaders among Southwest cities in education spending are Yuxi (RMB 2135 per capita) and Guilin (RMB 1306). Female enrollment is generally 45-49 percent among Southwest cities, except for Yuxi (43 percent). Infant mortality rates are less than 5 per 1000 in four Southwest cities: Huizhou, Kunming, Deyang, and Yibin. But rates are 12-18 per 1000in Chengdu, Yuxi, Guiyang, Liuzhou, andChongqing; 24 per 1000inZunyi; and 27.8 per 1000inQujing. Most of the Southwest cities surveyed do better than Guangdong's first five in terms of health insurancecoverage for permanent workers. Only inGuilin, Chongqing, and Zunyi do fewer than two-thirds of permanent workers have health insurance. 87 8 1 l- Q p a - E .3 Q 34 d 3 2 3 3 6 09 t 4 r? P il 5 jii I 09 3 L Y .I Q m It 89 F.NORTHWEST Since Bohai ports are a natural outlet to the sea for many firms in Northwest China, leading Bohai cities (e.g., Linyi, Weihai, Yantai, Zibo, and Qingdao) provide an appropriate performance benchmark for China's Northwest. The three Northwest cities rated tops in investment climate - Wuzhong, Wulumuqi, and Yinchuan - fall behind Shandong's best inmost measures of government efficiency: 0 Taxes and fees represent 3.6 percent of sales revenues for surveyed firms in Yinchuan (Table A- 13), which compares favorably with the leading Bohai cities. The rate for Wulumuqi, however, i s 5.3 percent. Taxes and fees exceed 6 percent of sales, however, in the Northwest cities of Huhehaote, Xi'an, Datong, and Tianshui. Compared with 0.7 percent of sales revenue average for Shandong's best cities, firms report relatively low entertainment and travel expenses in Yinchuan, Datong, and Taiyuan (1.3 percent), Wuzhong (1.O percent), Yuncheng (0.6 percent), Baoji (1.2 percent), and Lanzhou (1.0 percent). Firm expenditures on entertainment and travel range from 1.5-1.9 percent of sales revenues in Xianyang, Huhehaote, Xining, Xi'an, Wulumuqi, and Tianshui - more than double the rate for Shandong's top-rated cities. As for bureaucratic demands, the Northwest's three best cities compare favorably with Shandong's 58-day standard - Wuzhong at 45 days, Wulumuqi (60 days), and Yinchuan (62 days). Xining (47 days) also compares favorably. The survey indicates that local bureaucratic demands are extremely high in Lanzhou (98 days), Xi'an (121 days), Taiyuan (110 days), and Baotou (130 days) None of the Northwest cities surveyed achieve the Shandong benchmark (7 days) for customs clearance. With an average of 9.8 days, Wulumuqi i s the Northwest's fastest. Compared with the Shandong benchmark, customs clearance takes more than twice as long in most of the cities surveyed in the Northwest: Yinchuan (35 days), Yuncheng, Taiyuan, and Tianshui (20 days), Baoji (18 days), and Xining and Datong (21 days). Thus, four Northwest cities score poorly on three out of four measures of government efficiency: Xining, Xi'an, Taiyuan, and Tianshui. Looking at access to bank loans by small domestically-invested firms, Yuncheng and Lanzhou are more or less comparable to the Shandong benchmark. The survey indicates that levels of S M E borrowing are reasonable in several other Northwest cities. Access by such firms to bank loans seems especially low, however, in Xianyang, Huhehaote, and Xining (30-32 percent), Xi'an andWulumuqi (20-21 percent), andDatong (10.5 percent). More than 10 percent of all surveyed firms in Yinchuan, Yuncheng, Xianyang, Xining, Xi'an, Datong, and Taiyuan perceive a need to make informal payments for loans. Such 90 expectations are highest among surveyed firms in Taiyuan (18.4 percent) and Yuncheng (15.3 percent). Overstaffing reported by f i p s in all the Northwest cities surveyed exceeds the Shandong benchmark of 1.5 percent. Reported overstaffing i s lowest among firms in Yinchuan, Wuzhong, and Huhehaote (1.9-2.0 percent). Compared with the Shandong benchmark, overstaffing i s 3-4 times higher among firms in Baoji (4.1 percent); 4-5 times higher in Lanzhou andXi'an (6.3 percent); and more than five times higher inTianshui and Baotou (7.6-7.9 percent). It may be that China's labor laws and regulations are more rigorously enforced in the Northwest. Dominance of local economies by over-staffed SOEs, however, seems a more likely explanation. For instance, SOEs account for 81 percent of industrial sales in Shaanxi and Gansu, versus 34 percent for Shandong. Output losses due to problems with power or transport are reportedly high in Northwest cities. Firms in Yinchuan, Wuzhong, Baoji, Lanzhou, and Xi'an report the lowest losses, ranging from 0.8 percent to 1.9 percent of output. This i s more or less comparable to the Shandong benchmark (1.1 percent of losses). Losses reported b y surveyed firms are noticeably high in Yuncheng (8.0 percent), Datong and Baotou (4.1-4.3 percent), and Taiyuan (5.4 percent). Confidence that property and contract rights will be protected i s low among Northwest firms. Only Yuncheng (77 percent confident) begins to approach the confidence level (84 percent) for top-ranked Shandong cities. Confidence in property and contract rights i s 50 percent or less among surveyed firms in Yuncheng, Huhehaote, Lanzhou, Xining, Xi'an, Datong, Wulumuqi, Tianshui, and Baotou. Confidence i s particularly low in Huhehaote (27 percent) andDatong (30 percent). Most of the Northwest cities surveyed top the Shandong benchmark (17.2 percent) of workers with university education. Percentages are especially high in Xianyang, Huhehaote, andTaiyuan (23-25 percent), Wulumuqi (27 percent), and Xi'an (36 percent). The high survey findings for Xi'an and Wulumuqi may reflect these cities' traditional strengths in high-tech sectors - including nuclear energy, aviation, and information technology. Firms report reasonable percentages of university-educated workers in other Northwest cities, except for Wuzhong (9 percent). Turningto quality of life, while Taiyuan appears to be the Northwest's most livable city, nationwide it i s ranked 54thamong the 120cities inthis survey. Per capita expenditures on education are highest in Yuncheng (RMB 2058) and Taiyuan (RMB 882) (Table A-14). Despite appearing to have many university-educatedworkers, per capita education expenditures are modest in Xianyang (RMB 228), Wulumuqi (RMB 225), and Xi'an (RMB 210). Along with Tianshui (RMB 221) and Wuzhong (RMB 179), these cities devote the least financial resources (on a per capita basis) to education. Among Northwest cities, female enrollment rates are generally 45-49 percent, with a somewhat higher level (56 percent) for Tianshui. 91 Health care insurance coverage i s relatively common in a few Northwest cities: Wulumuqi (87 percent), Xi'an (83 percent), and Baotou (82 percent). Coverage i s reportedly lowest inWuzhong (42 percent) and Lanzhou (51 percent). Among the six regions, the Northeast has the highest infant mortality. Rates are 15-20 per thousand in Wulumuqi, Huhehaote, Lanzhou, and Wuzhong, and highest in Tianshui (29.2), Xianyang (22.4), and Baoji (20.8). The environment i s another sore spot for the Northwest. All or virtually all industrial waste disposal meets environmental waste standards in only two cities: Xiangyang (100 percent) and Baoji (97 percent). For half the Northwest cities surveyed, compliance i s just 90-92 percent: Taiyuan, Yuncheng, Wulumuqi, Yinchuan, Xi'an, Xining, and Lanzhou. Compliance i s even lower in Baotou and Datong (85-86 percent), Huhehaote (79 percent), Tianshui (76 percent), and Wuzhong (72 percent). Ina few cities, air quality is good or excellent at least 80 percent of the time: Huhehaote, Baoji, and Xianyang. For several cities, air quality falls below the Beijing benchmark: Taiyuan (61percent), Yuncheng (46 percent), Baotou (49 percent), Datong (44 percent), Lanzhou (57 percent). This likely reflects both desertification and pollution from extractive industries. 92 8 E i g -r -r .3 & a - 93 1 94 ANNEX B: METHODOLOGYAND DATA A. PERFORMANCE MEASURES The analysis of survey data relates two separate performance measures - total factor productivity and the percentage of firms with foreign ownership - to various investment climate factors. Foreign ownership i s self-explanatory. Total factor productivity (TFP) should be understood as the productivity level of a firm after netting out the effects of capital, labor, and industry-specific technology. In other words, the total factor productivity i s estimated using a production function as follows: where 0, i s output (as measured by deflated sales revenue) for firm i and period t. L, , Kif and Mi,are the number of employees, the capital stock, and the material inputs respectively. The capital stock i s proxied by the original value of fixed assets, the only time-varying measure we have for capital stock. Dijf i s a dummy variable that i s 1 if firm i is affiliated with sector j. There are a total of 30 industries. So equation 1 essentially allows industry-specific production functions. The total factor productivity i s then constructed as the estimate of ej + ,the partof value addedthat is not explained by capital and labor. Alternatively, the dependent variable could be value-added, and the explanatory variable could be capital and labor. However, this approach suffers from the problem that some value-added could be negative and that ignoring such observations would lead to systematic bias if the bottom performers were systematically dropped from the comparison of productivity. Moreover, when this analysis tried computing TFP based on value-added (with fixed effects), the correlation with output TFP was very high, at 0.85. Hence, it would make little difference which measure (i.e., value added TFP or output TFP) to use. Since dropping observations with negative value-added creates other problems, this analysis uses the output TFP measure. In a recent study, Haltiwanger and Schweiger (20005) also find that various measures of TFP are highly correlated, whether usingoutput or value added as the dependent ~ariable.'~ 56 John Haltiwanger and Helena Schweiger, "Allocation Efficiency and the Business Climate," 2005 working paper, University of Maryland. 95 B.INVESTMENT CLIMATEFACTORS Market potential. To generate an aggregate score of market potential, we compute an index as follows: market potential = std(log(GDPPC)) std(GDPPCgrow) std(road)-std(port -cost), + + where GDPPC i s GDP per capita, GDPPCgrow i s the growth rate of GDP per capita, road i s the total length of graded roads (in kilometers), and port cost i s the transportation cost variable. Port cost i s an estimate of the cost of trucking a 20-foot container to the seaport typically used by the manufacturers in a particular city. Each variable i s first standardized into a variable with mean zero and a standard deviation of one. Adding them up to form the index implicitly assumes that each i s of equal importance inaffecting investors' assessmentof investment potential. Laborflexibility. Firms were asked directly what share of their workforce they would consider redundant if there were no penalties associated with laying-off workers. Thus, more flexible labor markets would be characterized by lower overstaffing-ratios. Skill and technology endowments. Our index of skill and technology i s based on several indicators. The first two concern information technology (IT): the share of workers with formal IT training, and the share of employees regularly usingcomputers. Both variables are measured at the district-level, that is, we aggregate the firm-level observations to obtain a district-level average of the variable. Since these two variables are closely correlated, we add them up to form an IT index. The third variable i s the district-average of the share of employees with college or above education. This captures the human capital level of the work force in a locality. The final variable i s the district-level average of CEO education. We then form a skill and technology index as follows: std(IT) std(Univ) std(CEO-S), + + where IT i s the district-level average IT index, Univ i s the district-level average of the share of employees with college or above education, and CEO-S is the number of years of schooling for the average CEO inthe district. All three variables are standardized first before being added up to form the index. Private sector participation indicates the percentage of domestic private sector firms in the survey. Government eficiency. The survey uses four measures. The first i s the district-average of effective tax burden of firms, whereby a firm's tax burden i s measured as total taxes and fees over value-added. Hence, national-level taxes, local taxes, and local administrative fees are measured. 96 Second i s the district average of the share of entertainment and traveling expenditure over sales revenue. Since it i s pointless to ask directly about corruption, we use this as a proxy for informal payments by firms. The third variable looks at the quality of a specific government services: custom efficiency, as measured by the number of days for exporting firms to pass customs plus the number of days for importing firms to pass customs.57 Again, this variable i s measured as district-average. The fourth variable i s the time-cost of dealing with various bureaucracies. In particular, firms are asked the total number of days-per-year that the firm spends dealing with tax administration, public security, environmental protection, and labor and social protection. Dividing the total by 365 represents the firm's regulatory burden (interms of time costs). The final formula for government efficiency is: -[std(tax) std(custom) std(regu-time)+std(ETC)], + + where tax i s taxes and fees over value-added, custom is the number of days for firms to pass import and export customs, regu-time i s the share of firm's time in dealing with regulators, and ETC i s the ratio of entertainment and traveling costs over sales. All the four variables are district average of firms' reported values, then standardized into a mean-zero and variance-one variable. Contract enforcement. The survey asks firms the following question: "Amongst the commercial or other disputes that your company was involved with, what has been the likelihood (in terms of percentage) that your company's contractual and property rights (including enforcement) are protected?" The variable i s standardized to construct an index. Access to finance. The survey proxies access to finance by the district-level share o f f i r m s that have obtained bank loans. Harmonious society. To reflect the progress of each city toward achieving a harmonious society, the survey uses three variables. The first i s the percentage of days that a city has good or excellent air quality. The second i s the share of females in the total number of students in a city. The third i s the share of permanent workers with medical insurance coverage. The standardized version of these variables i s summed to create a harmonious society index, which i s included in the regression analysis predicting TFP and FDI outcomes. To provide a more complete view of each city's progress toward a harmonious society, a separate "livability index" i s constructed based on nine performance indicators: per capita 57 We add them up because they are very closely correlated, and it would be hard to distinguish their separate effects. 97 education spending, the percentage of industrial waste disposals that meet environmental protection standards, square meters of per capita green space, the percentage of days with good or excellent air quality, the unemployment rate, the annual wage rate, infant mortality per 1000, the share of permanent workers with health insurance coverage, and the percent of female enrollment. These variables are standardized and summed to create a livability index. c.EFFECT OFINVESTMENT CLIMATE ONFIRMPERFORMANCE To estimate how local investment climate affects alternative performance measures, total factor productivity and foreign ownership, the analysis estimates the following firm-level regressions: where Y could be TFP as constructed above or foreign ownership. X i s a set of control variables, including industry dummies (to allow the performance to have an industry- specific mean), the logarithm of the number of employees, log city population, and log average income of the city. City characteristics are controlled for since there might be externality from other firms in the city. IC i s a vector of indicators related to investment climate. We also control for electricity prices to allow for factor prices to affect firm performance. We have also tried controlling for land prices at various cities, along with the share of losses of sales due to transportation problems or electricity outage. However, these variables are often insignificant, and sometimes wrong-signed, due to reverse causality. Since we do not have adequate ways to address such issues, we opted to drop these variables from our list of the explanatory variables. Analysis i s at the firm level. There are several reasons to move from the city level to the firm level. The first reason i s to best utilize the firm-level variations, and allow firm performance to be directly affected by firm-level variables such as size and industry. Second, we recognize that many elements of the investment climate might differ even within the city. We thus construct all of our investment climate variables at the district- level, that is, the level below the city. We thus use multivariate regressions to relate the district-level investment climate to firm-level performance. To facilitate the interpretation of our results (but without affecting the results), each explanatory variable i s included in its standardized format, that is, first normalized into a mean-zero-variance- one variable. Table B-1summarizes the main findings. 98 Table B-1.Determinants of Local ComDetitiveness Foreign ownership TFP 11 domNstate 0.037 (3.90)*** foreign share 0.059 (6.06)*** log(numberof employees last year) 0.056 0.028 (7.25)*** (3.33)*** Standardizedvalues of (mean effective tax burden) -0.058 -0.029 (8.27)*** (4.00)*** Standardizedvalues of (Mean labor redundancy) -0.047 -0.032 (6.55)*** (4.50)*** Standardizedvalues of (Mean entertainmenttraveling costs I sales) -0.037 -0.001 (4.25)*** (0.06) Standardizedvalues of (Mean IT index) 0.000 0.048 (0.01) (3.73)*** Standardizedvalues of (Mean access to loans) 0.055 (7.80)*** Standardizedvalues of (Mean years of CEO schooling) 0.019 0.084 (1.94)* (7.26)*** Standardizedvalues of (Mean share of college-educatedemployees) 0.107 -0.075 (9.59)*** (5.33)*** Standardizedvalues of (Mean perception of protectionof property rights) -0.007 -0.014 (0.96) (1.61) log city population 0.042 -0.003 (5.13)*** (0.31) log(GDP per capita at the city) 0.053 0.139 (4.15)*** (9.21)*** City growth rate in GDP per capita 0.013 0.006 (1.69)* (0.58) City road mileage 0.023 -0.002 (3.12)*** (0.29) port costs -0.021 -0.059 (2.62)*** (7.32)*** Standardizedvalues of (Mean price of electricityfor industrial uses) -0.001 -0.013 (0.19) (1.27) City-level average employee wages 0.046 -0.029 (3.85)*** (2.13)** Standardized values of (mean share of permanent employees having medical insurance) 0.015 0.033 (1.89)* (3.56)*** Standardizedvalues of (average share of time that a firm has to deal with -0.015 -0.005 tax, public security, environment protection, labor and social protection agencies (1.97)** (0.55) Standardized values of (share of days that air quality reaches the grade of good or excellent) 0.009 0.061 99 Foreign ownership TFP 1/ (1.21) (7.69)*** Standardizedvalues of (shareof girls in enrolled students in a city) 0.012 0.028 (1.61) (3.81)*** Standardized values of (district average of the logarithm of the days for export to pass -0.191 customs plus the days for import to pass customs) (15.40)*** Industrydummies yes yes Observations 12061 12178 R-squared 0.44 0.23 -ownership ownership is presented in a standardized variable. Thus, the expected gains in foreign 1/ Foreign should be the expected gains of this standardized variable. The standard deviation for foreign ownership is 0.317. Looking for instance at "mean effective tax burden," holding other factors constant, the regression predicts that a one standard deviation in taxedfees relative to value-added would be associated with a 5.8 percentage point reduction in TFP and a 0.9 percentage point reduction inforeign ownership.58 D.PROJECTEDPERFORMANCEGAINSFROMINVESTMENT CLIMATEIMPROVEMENTS The survey data show that no single city excels at everything. Each of the 120 cities could do better at some aspect of investment climate. To estimate performance gains from improving local investment climate, simulations for all 120 cities are run in which each city assumes the characteristics of a hypothetical city that i s "nearest to investment climate excellence" - the NICE city. For NICE ,each investment climate variable i s set at the 90thpercentile level from the survey's sample of 120 cities.59(Table B-2) The 90th percentile i s chosen because use of the looth percentile, which may reflect unique endowments, would be an unrealistic target that discourages efforts at imitation. 58Le., 0.029 times the .317 standard deviation for foreign ownership. 59For variables where a higher value implies worse investment climate (e.g., taxes, informal payments), values at the 10* percentile are assigned. 100 TableB-2.90thPercentileAttributesof the NICE City Variables Measure TFP level 0.417 Domestic private ownership share 88.4% Foreign ownership share 39.3% log(number of employees) 6.225 taxhalue added 9.2% Share of overstaffing 0.4% entertainment and traveling costs / sales 0.7% ITindex 0.44 Share of firms with access to bank loans 78% CEO schooling level (inyears) 16.1 Share of employees with college education 27.9% Likelihood that propertykontract rights being protected 83.4% log(popu1ation) 6.79 log(GDP per capita) 9.908 Croad 3.06 portcost 52.8 log(Wage per capita at city level) 10.022 share of permanent employees with medical insurance 93.8% Time spent with major bureaucracies 2.5% Share of days with air of goodexcellent quality 98.7% Share of female students intotal enrollment 49.0% log(Wage per capita at city level) 9.99 City GDPper capital growth rate 21.9% Simulations make the following assumptions for each city (City X) under consideration: City X takes on the attributes of NICE, as indicated inTable B-2. In cases where City X is currently exceptional in several investment climate attributes, especially those identified as important by regression analysis, it i s possible that the existing City X could already outperform NICE without making any changes and that gains from taking on NICE attributes would be negative. In such cases, no changes are simulated or recommended. The effects of the investment climate on firm performance are captured by the multivariate regressions inTable B-3. Two counterfactuals are simulated: First, what performance gains could be expected from achieving 90thpercentile attributes across the wide spectrum of investment climate factors - including city characteristics, progress toward a harmonious society, and richer educatiodskill 101 endowments? Expected performance gains from overall investment climate improvements are presented for both TFP (Table B-3) andFDI(Table B-4).60 Second, what performance gains could be expected from achieving 90fhpercentile characteristics across a more narrow spectrum of "administrative openness" factors more typically under the shorter-run control of local governments - including government efficiency (taxedfees, bureaucratic interaction, customs clearance, informal payments), labor market flexibility, and access to finance? Other factors (e.g., city characteristics, educatiodskills endowment) are held constant at current (actual) levels. Expected performance gains from such "administrative openness" improvements are presented for both TFP and FDI (Table A-5).61 Predicted gains from both overall investment climate improvements and administrative openness reforms lead naturally to overall rankings of the 120 cities. In general, a city's investment climate (or administrative openness) i s better when the city has less to gain by achieving NICE attributes. In Tables B-3 through B-5, the "total gains" column indicates the expected percentage point gains from achieving 90th percentile attributes. Remaining columns report the percentage contribution to total gains from raising specified investment climate attributes to the 90thpercentile. A single ranking, for either overall investment climate or administrative openness, can be constructed in a variety of ways. Either TFP or foreign ownership could be used exclusively. Or TFP and foreign ownership could be given different weights or equal weights. In trying several methods, our analysis found that the overall ranking tends to be similar, albeit not identical. We have chosen to base overall rankings for government effectiveness on an average of the ranking resulting from equal weighting of TFP effects and FDIeffects, as being the most balanced and defensible approach. Any such ranking is only indicative of investment climate (or administrative openness). Actual differences between cities with similar rankings may be statistically insignificant. The variance o f expected gains are var(ckflkAX/), where flk i s the k-th ingredient of investment climate, AX/ i s the difference of j-th city with respect to NICE in the k-th ingredient of investment climate. We then use the variance matrix for the fl vector to compute the variance and standard errors of the expected gains. The 95 percent confidence interval i s then ckflkAXkj & 1.96xa( ckflkAX/ ), where i s the standard error of the expected gains. 6oFor obvious reasons, the FDIcalculation excludes foreign ownership share as an explanatory variable. The FDI calculation excludes access to finance as an explanatory variable, since the analysis finds it to be insignificant for predicting FDI. 102 The tail 5% could be very large, for instance, some outlier percentage could be as highas around 10,000%, which when averaging out, could alone determine how large that contribution of this particular category is, a scenario that we'd like to avoid. Hence, it i s better to focus on the magnitude of potential TFP and FDI gains and objective performance, rather than on rankings. TableB-3.ExpectedTFPGainsFromImprovementsin OverallInvestmentClimateto 90thPercentile Total Gains11 Citychar Private Foreign Burden Lmkt Finance Hsociety Edutech beijing 0.04 -130.54 79.95 62.9 193.6 74.49 150.11 62.27 -449.3E hangzhou 0.1 -1.89 21.34 26.96 11.23 -5.71 -11.09 20.43 15.64 suzhou 0.1 -2.89 50.08 -48.6 -8.79 -3.76 12.64 6.84 68.54 guangzhou 0.15 -37.37 32.39 -8.23 27.31 12.07 23.03 14.2 10.98 dalian 0.21 28.58 22.72 -4.22 12.99 4.62 12.92 -0.45 9.2 shanghai 0.23 -39.36 14.91 -1.59 33.15 6.88 33.57 3.56 27.48 shenzhen 0.27 -16.37 20.99 -19.72 -1.05 -1.56 40.88 10.94 41.83 dongguan 0.29 -15.21 21.84 -26.49 -7.6 -1.87 39.98 6.73 64.88 tianjin 0.33 5.75 12.37 5.63 33.75 17.06 13.8 3.56 0.81 yantai 0.34 31.46 3.93 11.16 4.77 6.97 -0.79 0.31 32.88 jiangmen 0.35 51.85 12.6 -10.3.1 -17.57 1.12 12.87 5.68 40.43 qingdao 0.36 22.52 3.69 9.92 5.18 3.48 6.68 2.75 37.4 nanjing 0.36 -9.17 7.44 9.44 28.38 14.45 10.34 12.85 18.01 chengdu 0.37 21.13 2.85 13.49 24.06 8.16 6.47 5.55 -10.13 xiamen 0.37 31.24 12.24 -8.47 0.93 -1.37 15.8 2.97 35.1 ningbo 0.37 9.45 2.46 9.27 25.03 0.27 -6.46 0.38 53.03 zibo 0.39 35.35 1.53 15.25 11.06 9.03 -9.49 7.54 21.5 wuxi 0.4 9.97 2.26 10.33 17.66 0.45 1.34 6.65 43.89 wuhan 0.4 28.94 6.55 13.39 26.91 14.43 2 11.18 -23.62 foshan 0.4 18.58 7.52 2.11 14.44 1.1 7.96 3.73 39.24 hefei 0.42 47.6 6.36 8.42 12.52 5.2 7.64 7.36 -2.52 shenyang 0.44 25.44 7.33 7.67 29.67 9.38 13.15 4.81 -6.31 shaoxing 0.45 19.08 -0.46 13.05 13.36 3.76 -2.36 3.29 43.45 fuzhou 0.45 24.25 6.36 0.1 7.8 -0.91 9.35 9.76 35.47 jinan 0.45 20.55 2.67 13.79 13.07 7.1 8.76 10.48 14.29 chongqing 0.46 3.5 2.75 13.59 15.94 6.7 4.63 11.11 18.55 nanchang 0.46 37.39 5.19 11.93 4.63 7.94 8.68 8.83 -0.92 nantong 0.46 29.69 1.89 8.46 12.46 -0.04 1.16 4.33 35.22 jinhua 0.46 20.57 -0.98 13.75 17.58 0.16 -6.32 9.85 41.62 weihai 0.46 35.39 1.85 9.05 -4.3 -0.5 4 5.85 41.85 huizhou 0.47 40.42 11.91 -12.09 -1.85 0.1 23.21 2.04 37.53 haerbing 0.47 31.08 4.11 12.17 24.93 16.46 21.81 3.87 -22.48 rian 0.48 29.38 5.5 12.32 26.8 16.03 9.41 8.09 -19.62 :hangchun 0.48 24.9 4.98 9.75 19.29 5.69 20.41 7.19 -2.21 .aian 0.5 43.32 1.57 12.13 5.49 4.6 2.67 11.15 10.33 'inyi 0.5 40.73 1.03 9.87 0.62 0.65 -4.8 13.68 31.23 :hangzhou 0.5 21.12 0.63 10.19 17.41 0.27 2.63 6.35 37.93 iuzhou 0.51 29.34 0.4 10.58 10.98 0.38 -3.15 4.28 42.6 ~ ~~ 103 Total Gains11 Citychar Private Foreign Burden Lmkt Finance Hsociety Edutech zhengzhou 0.51 26.41 1.38 12.06 8.22 5.5 10.49 8.37 17.8: shijiazhuang 0.52 26.3 1 3.54 12.07 14.22 4.25 7.73 5.98 18.: taizhou 0.53 15.81 -0.12 11.98 24.29 0.04 -1.52 7.14 34.2( weifang 0.53 31.79 0.78 10.16 9.92 1.51 0.5 5.45 34.3L tangshan 0.53 23.99 1.3 9.41 7.5 1.92 6.98 7.78 36.55 zhuhai 0.55 29.21 10.6 -10.21 10.55 0.2 23.78 2.19 27.0: changsha 0.55 21.69 2.83 10.67 30.29 10.81 2.91 7.63 0.5; quanzhou 0.55 25.09 6.51 -3.17 13.67 -0.16 7.26 4.71 44.3L jiaxing 0.55 19.37 1.75 6.85 8.76 0.82 -3.37 4.87 53.1; qinhuangdao 0.55 38.9 3.88 6.71 11.61 3.48 9.15 0.44 21.1( zhangzhou 0.56 39.54 4.95 -0.08 6.52 -0.46 3.32 9.21 34.2( langfang 0.56 40.52 1.91 7.73 3.28 -0.86 13.23 4.4 23.95 jining 0.57 29.02 0.63 10.65 7.99 5.3 2.33 8.34 2E guiyang 0.58 40.45 3.36 10.4 23.02 9.43 2.76 2.77 -0.7C haikou 0.58 27.2 4.97 5.38 28.9 9.9 19.29 1.06 -4.1; lianyungang 0.59 29.29 2.19 6.07 8.67 5.2 9.94 8.15 26.I kunming 0.6 27.15 2.93 10.85 11.47 5.32 6.24 2.02 15.75 wenzhou 0.6 16.81 -0.99 10.56 21.89 -0.45 0 13.15 31.9; huhehaote 0.61 33.16 0.23 10.37 12.11 3.25 18.86 4.93 11.; liuzhou 0.62 35.8 1.74 9.76 11.57 6.95 6.03 3.63 17.71 maoming 0.63 30.26 1.17 7.88 6.64 1.91 11.03 8.51 29.25 xuzhou 0.63 24.08 1.11 9.48 21.24 7.61 9.33 7.62 14.11 xianyang 0.63 33.75 2.07 10.22 14.67 4.85 11.86 6.54 5.5s baoding 0.63 29.9 2.39 9.81 11.49 6.98 7.99 6.82 17.29 guilin 0.63 37.26 2.86 9.17 17.6 5.06 8.83 3.45 9.68 shantou 0.63 34.83 2.71 3.5 5.17 1.52 8.38 2.21 30.29 yichang 0.64 38.18 2.39 8.39 8.66 4.74 7.91 4.85 18.06 jinzhou 0.64 42.14 1.74 8.26 17.64 3.53 4.15 5.64 11.88 nanning 0.64 31.9 4 9.1 15.88 11.74 12.4 1.7 8.08 taiyuan 0.64 29.47 2.88 10.84 20.69 7.8 7.01 6.17 7.91 daqing 0.65 12.6 0.31 10.96 22.01 6.57 24.21 2.04 16.19 deyang 0.65 33.05 -0.06 9.99 9.49 2.32 0 0.77 26.88 yangzhou 0.66 26.11 1.06 7.94 11.43 2 5.67 7.47 34.16 anshan 0.66 27.64 1.07 9.99 14.9 2.17 8.45 7.76 23.26 wuhu 0.66 36.75 1.61 8.23 10.87 1.08 8.01 6.06 23.94 mianyang 0.67 36.63 0.87 9.32 9.55 1.91 7.11 2.69 14.04 zhoukou 0.68 47.2 0.14 9.75 2.1 1.08 9.4 2.62 18.73 baotou 0.68 18.76 1.46 9.27 13.14 14.59 11.34 8.39 17.25 yinchuan 0.68 43.48 -0.04 9.56 5.53 2.99 8.15 5.47 19.09 yancheng 0.69 29.53 -0.59 9.19 17.71 0.84 2.3 8.19 28.47 YueYang 0.7 30.88 1.75 9.01 4.22 10.19 5.66 15.42 16.73 sanming 0.71 33.5 0.51 8.3 9.26 1.01 5.25 8.41 36.21 jingzhou 0.71 42.37 0.33 8.8 9.61 2.13 4.86 4.87 17.56 jilin 0.71 29.55 1.71 8.37 12.15 6 16.4 4.41 17.86 xiangfan 0.72 41.85 2.1 8.74 10.44 4.81 5.94 4.54 16.83 yuxi 0.73 32.69 0.9 8.05 16.66 2.56 5.83 6.51 27.19 jingmen 0.73 39.05 1 7.08 10.78 2.23 5.44 3.34 23.43 zangzhou 0.73 29.94 -0.53 8.9 9.26 1.23 10.13 6.83 29.13 shangqiu 0.73 41.04 -0.95 9.74 -1.47 0.51 12.28 3.58 30.74 104 Total Gains11 Citychar Private Foreign Burden Lmkt Finance Hsociety Edutech nanyang 0.74 29.95 1.68 9.14 8.49 7.59 5.42 9.27 19.89 shangrao 0.74 41.78 0.18 9.19 5.89 1.98 5.72 2.55 28.23 leshan 0.75 40.28 -0.34 8.93 5.31 3.81 -1.06 4.18 22.46 fushun 0.75 32.45 2.91 7.81 13.33 5.57 14.16 6.07 14.83 changde 0.75 26.35 0.14 8.25 16.21 6.72 8.82 4.03 22.85 wulumuqi 0.76 25.29 1.03 8.79 15.52 3.92 15.79 3.36 2.59 qujing 0.76 32.87 0.76 8.81 13.7 6.02 4.56 4.03 28.58 baoji 0.76 37.49 1.53 9.29 10 6.46 2.8 6.85 17.54 luoyang 0.76 24.28 1.21 9.39 17.28 4.6 10.8 8.12 17.22 yuncheng 0.76 37.18 0.38 9.3 14.97 3.53 0 8.21 21.6 qiqihaer 0.76 35.87 0.99 9.25 6.9 11.31 10.41 3.74 14.71 handan 0.77 24.02 1.2 8.39 16.51 6.75 7.25 6.55 22.73 ganzhou 0.78 37.61 2.96 2.25 7.25 2 12.61 5.72 23.35 lanzhou 0.79 27.61 3.05 8.72 11.24 9.83 5.38 7.34 14.33 xuchang 0.8 33.94 -0.55 8.22 7.14 2.09 6.98 6.31 29.1 zhuzhou 0.8 30.49 0.72 7.97 21.92 10.28 7.31 7.91 5.13 chuzhou 0.8 37.8 0.39 7.59 11.53 1.59 6.63 5.29 26.11 chenzhou 0.8 33.03 0.09 8.42 12.74 2.41 10.26 1.75 27.98 zhangjiakou 0.81 32.57 2.44 6.59 6.47 10.51 9.86 6.68 24.49 anqing 0.81 37.18 1.13 7.26 3.11 7.35 6.23 5.82 26.19 zunyi 0.81 32.81 2.46 8.37 15.39 7.4 4.57 4.47 14.43 xiaogan 0.82 38.3 0.52 7.45 14.34 2.14 7.08 6.56 16.51 xining 0.83 35.05 0.82 8.08 11.09 5.39 12.21 4.97 14.99 xinxiang 0.83 33.48 1.07 7.72 10.72 6.26 7.03 10.45 15.92 jiujiang 0.85 34.27 2.3 7.01 8.69 4.68 10.6 3.34 23.17 datong 0.85 32.16 1.98 7.38 12.74 3.7 14.01 6.79 18.91 wuzhong 0.86 40.5 1 0.04 8.49 7.03 2.39 5.53 5.39 29.34 yibin 0.87 30.99 -0.25 8.21 11.3 3.5 0.61 4.77 25.27 yichun 0.88 34.6 0.64 7.5 16.5 4.19 5.76 5.23 23.55 hengyang 0.88 27.34 1.72 8.22 10.06 14.64 8.46 4.51 16.68 benxi 0.92 32.78 0.57 7.17 11.8 7.23 13.82 3.66 20.78 tianshui 1 38.06 1.86 6.84 13.22 9.48 3.99 0.37 12.89 huanggang 1.09 31.91 0.96 5.52 14.6 6.84 8.78 6.9 18.83 -11TFPgainsexpressedinpercentagepoints Table B-4.ExpectedForeignOwnership GainsFromImprovementsin OverallInvestmentClimateto 90thPercentile Total Gains11 Citychar Burden Lmkt Hsociety Edutech dongguan -0.3 38.33 74.48 1.19 -14.98 0.22 shenzhen -0.3 18.07 73.37 0.91 -25.43 32.16 suzhou -0.19 42.93 39.2 1.38 -29.78 46.84 zhuhai -0.19 18.01 61.57 -0.39 -12.03 36.04 huizhou -0.18 -34.44 135.8 -0.17 -9.48 9.04 foshan -0.07 79.91 40.26 -4.11 -59.48 41.85 qingdao -0.02 -296.28 960.6 -48.16 -256.08 -268.54 105 Total Gains,lJ Citychar Burden Lmkt Hsociety Edutech jiangmen 0 -2575.34 6911.5 -86.68 -1660.66 -2409.61 xiamen 0.02 -140.05 286.37 -21.92 133.91 -145.69 guangzhou 0.02 -260.07 141.37 68.87 510.06 -350.07 dalian 0.02 69.3 1 263.53 37.14 30.92 -292.81 weihai 0.02 -284.88 -51.06 -6.96 253.13 109.13 hangzhou 0.03 81.83 97.73 -12.81 330.24 -415.26 shantou 0.14 135.96 -107.44 4.36 18.62 37.07 yantai 0.18 37.41 45 8.46 -0.78 2.12 shaoxing 0.19 25.14 -27.47 5.97 40.3 53.06 shanghai 0.19 -27.66 86.86 5.56 28.58 7.81 ningbo 0.27 0.96 80.08 0.24 7.73 11.2 tianjin 0.3 16.07 56.04 12.35 23.81 -7.97 fuzhou 0.33 20.26 27.37 -0.82 29.28 20.46 nanjing 0.39 0.18 56.69 8.65 37.89 -7.12 haikou 0.4 21.78 66.05 9.37 2.92 0.36 beijing 0.41 10.57 48.18 4.55 36.19 -1.82 wuxi 0.42 -17.37 75.15 0.28 25.11 15.87 zibo 0.43 3.27 57.01 5.44 24.42 7.04 weifang 0.43 37.22 39.78 1.22 17.08 4.07 nantong 0.46 36.06 31.81 -0.03 15.48 15.53 tangshan 0.46 16.72 47.78 1.47 32.89 0.33 zhangzhou 0.46 32.04 24.83 -0.37 24.1 19.53 daqing 0.47 -9.87 77.64 5.88 7.08 2.16 langfang 0.48 28.84 41.54 -0.65 15.56 15.03 maoming 0.49 45.99 6.26 1.58 23.08 22.59 huzhou 0.5 19.73 47.16 0.25 14.43 17.1 nanchang 0.51 27.44 25.25 4.72 21.63 9.09 quanzhou 0.51 18.53 45.02 -0.11 13.23 22.78 ganzhou 0.52 72.65 3.48 1.97 20.4 -4.83 qinhuangdao 0.53 27.43 48.72 2.39 2.3 16.13 lianyungang 0.53 42.58 22.28 3.77 24.58 7.03 changzhou 0.53 5.51 56.61 0.17 21.11 15.02 jiaxing 0.56 5.63 63.03 0.53 10.86 19.44 shijiazhuang 0.56 23.05 43.7 2.57 22.39 4.28 xuchang 0.56 32.83 21.83 1.95 23.51 12.65 shenyang 0.57 6.33 61.29 4.83 15.8 7.5 taian 0.57 30.11 34.15 2.63 21.19 7.37 jinan 0.59 9.58 38.9 3.61 36.28 8.01 kunming 0.59 20.82 55.95 3.52 3.77 1.74 jining 0.61 29.78 31.41 3.27 22.36 9.88 taizhou 0.61 22.24 48.7 0.02 16.31 11.11 hefei 0.61 30.36 48.31 2.32 16.47 -2.1 baotou 0.63 -7.1 43.92 10.24 40.23 2.02 linyi 0.64 36.51 27.04 0.33 27.01 8.62 jinhua 0.64 18.89 44.85 0.07 28.21 5.32 zhangchun 0.65 15.79 54.32 2.78 13.33 6.05 jingmen 0.66 25.42 40.89 1.63 13.53 3.22 snshan 0.67 -0.8 64.99 1.4 27.81 4 huhehaote 0.67 14.76 51.07 1.92 14.24 9.98 106 Total GainsI/ Citychar Burden Lmkt Hsociety Edutech shangrao 0.67 55.58 43.48 1.43 5.77 -11.84 yangzhou 0.68 20.51 38.34 1.27 24.09 13.2 wuhan 0.68 8.68 45.7 5.57 26.64 1.65 zhengzhou 0.68 16.76 45.68 2.67 20.02 8.52 wuhu 0.7 20.65 57.05 0.67 14.16 4.35 jinzhou 0.7 29.56 40.21 2.12 18.55 5.88 fushun 0.72 18 40.96 3.84 24.39 9.03 guilin 0.74 34.9 40.92 2.82 5.83 8.75 shangqiu 0.74 43.81 31.51 0.33 10.58 10.35 sanming 0.75 22.62 33.94 0.63 27.99 11-65 nanning 0.76 36.47 47.99 6.54 4.41 3.74 chengdu 0.76 15.88 40.42 2.61 10.84 2.53 jiujiang 0.77 35.89 42.73 3.41 10.98 -1.48 xian 0.77 21.58 33.01 6.56 20.49 6.74 deyang 0.77 29.57 39.14 1.28 2.09 0.87 zhoukou 0.78 44.57 31.09 0.62 10.86 8.26 yuxi 0.78 19.84 42.41 1.57 13.6 10.55 guiyang 0.79 24.35 51.27 4.54 6.7 1 4.75 baoding 0.79 29.72 32.71 3.67 19.98 12.69 xuzhou 0.8 28.21 36.75 3.93 25.96 3.68 xianyang 0.8 12.14 56.53 2.48 17.08 0.47 xiangfan 0.81 25.78 40.1 2.81 16.61 3.35 anqing 0.84 36.76 27.3 4.66 13.16 13.52 cangzhou 0.84 23.64 40.27 0.71 19.35 15.28 haerbing 0.84 14.49 52.51 6.09 10.12 8.58 wulumuqi 0.84 10.11 28.25 2.31 15.46 6.14 chuzhou 0.86 29.92 52.81 0.97 11.91 1.96 liuzhou 0.87 25.92 54.03 3.22 11.57 1.63 jilin 0.88 15.32 50.58 3.19 19.05 5.23 yichun 0.88 39.55 34.55 2.72 12.08 3.06 yichang 0.89 15.32 56.11 2.22 15.54 -0.77 qiqihaer 0.9 31.08 39.45 6.28 9.99 3 jingzhou 0.91 32.74 38.06 1.09 13.04 4.01 wenzhou 0.91 15.75 37.56 -0.19 39.13 6.11 hengyang 0.93 29.98 36.67 9.07 12.73 6.79 taiyuan 0.93 12.37 54.32 3.54 19.95 5.4 handan 0.95 24.33 41.34 3.6 19.84 6.69 xiaogan 0.95 28.17 40.24 1.22 13.31 8.79 yancheng 0.95 23.01 50.48 0.4 19.23 5.86 zhangjiakou 0.95 27.11 40.82 5.85 20.37 2.78 changsha 0.97 14.69 46.96 4.02 20.16 7.84 chongqing 0.97 24.99 32.7 2.09 20.33 1.16 wuzhong ,0.97 26.93 29.35 1.39 16.38 15.19 YueYang 0.99 20.04 34.86 4.77 29.93 2.74 baoji 0.99 25.18 41.57 3.24 15.89 3.54 qujing 1 35.67 43.2 3.01 6.87 3.43 lanzhou 1.02 13.82 35.66 5.02 23.42 7.18 xinxiang 1.02 24.31 41.22 3.34 23.1 3.7 changde 1.03 20.79 49.61 3.22 8.85 9.9 107 Total GainsI/ Citychar Burden Lmkt Hsociety Edutech luoyang 1.os 15.95 42.23 2.18 26.92 7.53 mianyang 1.06 25.52 48 0.8 5.12 1.31 zhuzhou 1.07 16.91 44.12 5.05 24.06 4.23 yinchuan 1.12 13.91 54.53 1.2 11.94 9.19 yuncheng 1.12 26.2 41.52 1.58 24.68 -0.56 leshan 1.14 23.98 44.05 1.65 7.2 3.9 xining 1.14 24.66 40.8 2.57 13.01 3.9 datong 1.15 20.08 47.88 1.79 23.58 3.74 chenzhou 1.17 21.06 63.11 1.08 2.88 9.06 benxi 1.2 6.19 69.07 3.66 13.25 5.55 tianshui 1.27 33.98 41.97 4.9 4.5 1 5.09 nanyang 1.27 20.61 43.04 2.88 26.85 2.37 zunyi 1.31 29.87 47.51 3.02 10.23 3.46 yibin 1.35 23.84 41.42 1.49 14.43 3.48 huanggang 1.39 21.43 46.95 3.52 19.36 3.41 -1/Gainsinstandardizedvariable. For actualexpectedpercentagepoint increaseinforeign ownership, multiply by standard deviation (0.317) in foreign ownership. Table B-5. Expected Gains inTFP and ForeignOwnership From Improving Government Effectiveness Gains in TFP Gains in Foreign Ownership Total Govt Labor Total Govt Labor City Gains 11 Burden Market Finance City Gains 21 Burden Market linyi -0.02 -17.4 -18.33 135.73 huizhou -0.25 100.12 -0.12 jiangmen -0.01 491.24 -31.4 -359.84 dongguan -0.23 98.43 1.57 hangzhou -0.01 -201.98 102.62 199.36 shenzhen -0.22 98.77 1.23 weihai 0 540.63 62.39 -503.01 jiangmen -0.2 101.27 -1-27 suzhou 0 9364.61 -4011.33 13475.94 qingdao -0.15 105.28 -5.28 jiaxing 0.03 141.16 13.17 -54.33 shantou -0.15 104.23 -4.23 yantai 0.04 43.59 63.64 -7.23 zhuhai -0.11 100.64 -0.64 huzhou 0.04 133.8 4.6 -38.4 suzhou -0.08 96.6 3.4 zibo 0.04 104.34 85.18 -89.52 shaoxing -0.04 127.8 -27.8 zhangzhou 0.05 69.51 -4.89 35.38 foshan -0.03 111.37 -11.37 jinhua 0.05 154.05 1.37 -55.42 weihai -0.01 88.01 11.99 qingdao 0.05 33.77 22.68 43.55 hangzhou 0.02 115.09 -15.09 xiamen 0.06 6.09 -8.93 102.84 ganzhou 0.03 63.81 36.19 leshan 0.06 65.95 47.24 -13.2 guangzhou 0.04 67.24 32.76 nantong 0.06 91.8 -0.32 8.52 maoming 0.04 79.8 20.2 weifang 0.06 83.13 12.66 4.2 xiamen 0.04 108.29 -8.29 taian 0.06 43.03 36.03 20.93 dalian, 0.05 87.65 12.35 dalian 0.06 42.56 15.12 42.32 fuzhou 0.09 103.07 -3.07 shaoxing 0.07 90.52 25.46 -15.97 yantai 0.1 84.18 15.82 ningbo 0.07 132.88 1.42 -34.3 zhangzhou 0.11 101.5 -1.5 fuzhou 0.07 48.04 -5.62 57.59 xuchang 0.13 91.81 8.19 deyang 0.08 80.36 19.64 0 lianyungang 0.14 85.54 14.46 wuxi 0.08 90.81 2.31 6.88 nantong 0.15 100.09 -0.09 108 Gains in TFP Gains in Foreign Ownership Total Govt Labor Total Govt Labor City Gains I! Burden Market Finance City Gains 21 Burden Market shangqiu 0.08 -13.01 4.51 108.5 nanchang 0.15 84.24 15.76 zhoukou 0.09 16.72 8.57 74.71 linyi 0.17 98.79 1.21 tangshan 0.09 45.72 11.7 42.58 shanghai 0.18 93.99 6.01 langfang 0.09 20.95 -5.47 84.51 weifang 0.18 97.02 2.98 jining 0.09 51.16 33.94 14.9 langfang 0.2 1015 9 -1-59 dongguan 0.09 -24.92 -6.13 131.05 tianjin 0.2 81.95 18.05 guangzhou 0.09 43.76 19.35 36.9 taian 0.21 92.84 7.16 foshan 0.09 61.43 4.69 33.88 jining 0.21 90.57 9.43 shantou 0.1 34.33 10.06 55.61 beijing 0.22 91.37 8.63 nanchang 0.1 21.79 37.36 40.85 ningbo 0.22 99.71 0.29 huizhou 0.1 -8.6 0.46 108.14 tangshan 0.22 97.02 2.98 shangrao 0.1 43.31 14.56 42.12 quanzhou 0.23 100.25 -0.25 shenzhen 0.1 -2.74 -4.07 106.82 shangqiu 0.24 98.96 1.04 changzhou 0.1 85.69 1.35 12.96 huzhou 0.24 99.47 0.53 hefei 0.11 49.36 20.5 30.14 zhoukou 0.25 98.06 1.94 sanming 0.11 59.68 6.53 33.8 jinan 0.25 91.5 8.5 quanzhou 0.11 65.8 -0.75 34.95 nanjing 0.26 86.76 13.24 yinchuan 0.11 33.16 17.96 48.88 wulumuqi 0.26 92.45 7.55 jingzhou 0.12 57.91 12.8 29.29 shijiazhuang 0.26 94.45 5.55 taizhou 0.12 06.48 0.16 -6.65 sanming 0.26 98.19 1.81 maoming 0.12 33.92 9.73 56.35 zibo 0.27 91.28 8.72 zhengzhou 0.12 33.96 22.71 43.33 yangzhou 0.27 96.78 3.22 mianyang 0.12 51.44 10.29 38.27 anqing 0.27 85.41 14.59 chongqing 0.13 58.43 24.58 16.99 qinhuangdao 0.27 95.32 4.68 yangzhou 0.13 59.83 10.47 29.7 jingmen 0.28 96.17 3.83 wuzhong 0.13 47 15.97 37.03 baoding 0.29 89.92 10.08 xuchang 0.13 44.05 12.87 43.08 jinhua 0.29 99.84 0.16 wenzhou 0.13 02.08 -2.08 0 jinzhou 0.3 95 5 jinan 0.13 45.18 24.54 30.27 taizhou 0.3 99.96 0.04 wuhu 0.13 54.43 5.43 40.15 wuzhong 0.3 95.46 4.54 qinhuangdao 0.13 47.9 14.35 37.75 haikou 0.3 87.58 12.42 yibin 0.13 73.34 22.71 3.95 changzhou 0.3 99.7 0.3 shijiazhuang 0.14 54.28 16.24 29.48 shangrao 0.3 96.82 3.18 jingmen 0.14 58.44 12.07 29.48 xian 0.3 83.43 16.57 anqing 0.14 18.65 44.02 37.34 hefei 0.31 95.43 4.57 yichang 0.14 40.65 22.24 37.11 deyang 0.31 96.82 3.18 kunming 0.14 49.82 23.09 27.09 wuxi 0.32 99.63 0.37 lianyungang 0.14 36.41 21-82 41.77 fushun 0.32 91-43 8.57 yuncheng 0.14 80.9 19.1 0 xuzhou 0.32 90.33 9.67 YueYang 0.14 21.04 50.78 28.18 guilin 0.32 93.55 6.45 chengdu 0.14 62.17 21.1 16.73 chengdu 0.33 93.93 6.07 yancheng 0.14 84.94 4.04 11.02 yichun 0.33 92.69 7.31 baoji 0.15 51-93 33.54 14.54 zhengzhou 0.33 94.48 5.52 cangzhou 0.15 44.9 5.96 49.14 chongqing 0.34 94 6 liuzhou 0.15 47.13 28.32 24.55 wenzhou 0.34 100.52 -0.52 xiangfan 0.15 49.27 22.72 28.02 yuxi 0.34 96.44 3.56 nanyang 0.16 39.49 35.3 25.2 canazhou " 0.34 98.28 1.72 109 Gains in TFP Gains in ForeignOwnership Total Govt Labor Total Govt Labor City Gainsi! Burden Market Finance City Gains21 Burden Market chuzhou 0.16 58.39 8.04 33.57 baotou 0.34 81.1 18.9 beijing 0.16 46.29 17.81 35.89 xiangfan 0.35 93.46 6.54 jinzhou 0.16 69.67 13.94 16.39 wuhan 0.35 89.13 10.87 baoding 0.17 43.43 26.38 30.19 kunming 0.35 94.09 5.91 anshan 0.17 58.4 8.49 33.11 jiujiang 0.35 92.6 7.4 ganzhou 0.17 33.15 9.17 57.68 jingzhou 0.36 97.21 2.79 shanghai 0.17 45.04 9.34 45.61 jiaxing 0.36 99.17 0.83 wuhan 0.17 62.09 33.29 4.62 huhehaote 0.36 96.37 3.63 yuxi 0.18 66.5 10.22 23.28 changchun 0.37 95.13 4.87 qujing 0.18 56.44 24.79 18.77 shenyang 0.37 92.69 7.31 zhuhai 0.19 30.55 0.59 68.86 YueYang 0.39 87.96 12.04 nanjing 0.19 53.38 27.17 19.45 xiaogan 0.39 97.06 2.94 xiaogan 0.19 60.86 9.08 30.07 daqing 0.4 92.96 7.04 xianyang 0.2 46.75 15.44 37.81 wuhu 0.4 98.83 1.17 guilin 0.2 55.9 16.06 28.04 nanning 0.41 88 12 xinxiang 0.2 44.66 26.05 29.29 lanzhou 0.41 87.66 12.34 guiyang 0.2 65.38 26.79 7.83 qiqihaer 0.41 86.27 13.73 chenzhou 0.2 50.13 9.47 40.4 handan 0.43 91.99 8.01 jiujiang 0.2 36.24 19.53 44.24 hengyang 0.43 80.17 19.83 huhehaote 0.21 35.38 9.51 55.12 guiyang 0.44 91-87 8.13 lanzhou 0.21 42.48 37.17 20.35 anshan 0.44 97.89 2.11 tianjin 0.21 52.24 26.4 21.35 baoji 0.44 92.77 7.23 zhangjiakou 0.22 24.11 39.16 36.74 zhangjiakou 0.44 87.47 12.53 changchun 0.22 42.5 12.54 44.96 xinxiang 0.45 92.5 7.5 qiqihaer 0.22 24.11 39.5 36.38 qujing 0.46 93.49 6.51 zunyi 0.22 56.26 27.03 16.71 chuzhou 0.46 98.2 1.8 taiyuan 0.23 58.27 21.98 19.75 luoyang 0.47 95.08 4.92 yichun 0.23 62.4 15.83 21-77 jilin 0.47 94.06 5.94 shenyang 0.23 56.83 17.98 25.19 xianyang 0.47 95.79 4.21 handan 0.23 54.12 22.13 23.75 yuncheng 0.48 96.34 3.66 xining 0.24 38.65 18.8 42.54 yancheng 0.48 99.21 0.79 changde 0.24 51.05 21.15 27.8 changsha 0.49 92.11 7.89 xuzhou 0.24 55.65 19.92 24.43 xining 0.49 94.07 5.93 changsha 0.24 68.83 24.56 6.61 haerbing 0.49 89.62 10.38 jilin 0.25 35.17 17.37 47.46 liuzhou 0.5 94.38 5.62 fushun 0.25 40.31 16.86 42.82 mianyang 0.52 98.37 1.63 luoyang 0.25 52.87 14.08 33.05 leshan 0.52 96.4 3.6 xian 0.25 51.31 30.68 18.01 yichang 0.52 96.2 3.8 nanning 0.26 39.68 29.34 30.99 zhuzhou 0.53 89.73 10.27 datong 0.26 41-84 12.15 46.01 taiyuan 0.54 93.88 6.12 baotou 0.27 33.63 37.34 29.03 changde 0.54 93.9 6.1 tianshui 0.27 49.54 35.52 14.94 datong 0.57 96.39 3.61 wulumuqi 0.27 44.06 11.13 44.81 yibin 0.58 96.53 3.47 hengyang 0.29 30.34 44.14 25.52 nanyang 0.58 93.72 6.28 iaerbing 0.3 39.44 26.05 34.51 tianshui 0.59 89.55 10.45 Denxi 0.3 35.92 22.02 42.06 yinchuan 0.62 97.84 2.16 zhuzhou 0.32 55.48 26.03 18.5 zunyi 0.66 94.02 5.98 110 Gains in TFP Gains in ForeignOwnership Total Govt Labor Total Govt Labor City Gains11 Burden Market Finance City Gains 21 Burden Market huanggang 0.33 48.3 22.64 29.06 huanggang 0.7 93.02 6.98 haikou 0.34 49.75 17.04 33.21 chenzhou 0.75 98.32 1.68 daqing 0.34 41.69 12.44 45.87 benxi 0.87 94.97 5.03 -1/TFPgainsexpressedinpercentagepoints -21Gains instandardized variable. For actual expected percentage point increase inforeign ownership, multiply by standard deviation (0.317) inforeign ownership. Table B-6. Overall Rankings for Government Effectiveness TFP- FDI- based based Combined City rank rank Average rank jiangmen 2 4 3.0 1 suzhou 5 8 6.5 2 hangzhou 3 12 7.5 3 weihai 4 11 7.5 3 qingdao 12 5 8.5 5 linyi 1 25 13.0 6 yantai 7 19 13.0 6 shaoxing 19 9 14.0 8 xiamen 13 16 14.5 9 zhangzhou 10 20 15.0 10 dongguan 29 2 15.5 11 dalian 18 17 17.5 12 huizhou 34 1 17.5 12 nantong 15 23 19.0 14 shantou 32 6 19.0 14 fuzhou 21 18 ~19.5 16 shenzhen 36 3 19.5 16 foshan 31 10 20.5 18 weifang 16 27 21.5 19 guangzhou 30 14 22.0 20 huzhou 8 37 22.5 21 taian 17 30 23.5 22 zibo 9 44 26.5 23 ningbo 20 33 26.5 23 langfang 27 28 27.5 25 nanchang 33 24 28.5 26 jining 28 31 29.5 27 maoming 44 15 29.5 27 shangqiu 24 36 30.0 29 tangshan 26 34 30.0 29 111 TFP- FDI- based based Combined City rank rank Average rank Jinhua 11 50 30.5 31 zhoukou 25 38 31.5 32 xuchang 50 21 35.5 33 quanzhou 40 35 37.5 34 deyang 22 59 40.5 35 sanming 39 43 41.O 36 jiaxing 6 77 41.5 37 wuxi 23 60 41.5 37 lianyungang 61 22 41.5 37 Zhuhai 81 7 44.0 40 Ganzhou 76 13 44.5 41 shangrao 35 56 45.5 42 jinan 52 39 45.5 42 changzhou 37 55 46.0 44 Yangzhou 48 45 46.5 45 Taizhou 43 52 47.5 46 Hefei 38 58 48.0 47 Shijiazhuang 56 42 49.0 48 Qinhuangdao 54 47 50.5 49 wuzhong 49 53 51.O 50 Shanghai 77 26 51.5 51 anqing 58 46 52.0 52 Beijing 72 32 52.0 52 jingmen 57 48 52.5 54 zhengzhou 45 66 55.5 55 chongqing 47 67 57.0 56 Jingzhou 42 76 59.0 57 Wenzhou 51 68 59.5 58 Leshan 14 107 60.5 59 Tianjin 92 29 60.5 59 Nanjing 82 40 61.O 61 Baoding 74 49 61.5 62 Jinzhou 73 51 62.0 63 chengdu 64 64 64.0 64 Kunming 60 74 67.0 65 wuhu 53 84 68.5 66 cangzhou 67 70 68.5 66 Xiangfan 69 72 70.5 68 YueYang 63 81 72.0 69 Yuxi 79 . _ 69 74.0 70 112 TFP- FDI- based based Combined City rank rank Average rank Guilin 85 63 74.0 70 Wuhan 78 73 75.5 72 mianyang 46 106 76.0 73 wulumuqi 113 41 77.0 74 Yinchuan 41 116 78.5 75 baoji 66 92 79.0 76 yuncheng 62 100 81.O 77 yichun 98 65 81.5 78 jiujiang 89 75 82.0 79 xiaogan 83 82 82.5 80 xuzhou 103 62 82.5 80 Xi'an 108 57 82.5 80 yancheng 65 101 83.0 83 Anshan 75 91 83.0 83 yichang 59 108 83.5 85 Chuzhou 71 96 83.5 85 fushun 106 61 83.5 85 yibin 55 113 84.0 88 huhehaote 90 78 84.0 88 liuzhou 68 105 86.5 90 changchun 94 79 86.5 90 haikou 119 54 86.5 90 qujing 80 95 87.5 93 guiyang 87 90 88.5 94 lanzhou 91 86 88.5 94 shenyang 99 80 89.5 96 Xinxiang 86 94 90.0 97 Qiqihaer 95 87 91.o 98 baotou 111 71 91.o 98 xianyang 84 99 91.5 100 nanyang 70 114 92.0 101 zhangjiakou 93 93 93.0 102 Handan 100 88 94.0 103 naming 109 85 97.0 104 jilin 105 98 101.5 105 hengyang 114 89 101.5 105 daqing 120 83 101.5 105 xining 101 103 102.0 108 luoyang 107 97 102.0 108 chenzhou 88 119 103.5 110 113 TFP- FDI- based based Combined City rank rank Average rank taiyuan 97 110 103.5 110 changsha 104 103 103.5 110 Zunyi 96 117 106.5 113 changde 102 111 106.5 13 Haerbin 115 104 109.5 15 datong 110 112 111.0 16 zhuzhou 117 109 113.0 17 tianshui 112 115 113.5 18 benxi 116 120 118.0 19 huanggang 118 118 118.0 119 For Tables B-3, B-4, and B-5, the interested reader should able to interpret the tables themselves to figure out potential gains from improvingbothbroad and narrow ICs to the level of Nice. One thing to note is that when the total gain i s negative, it means that the city has already reached the level of Nice. The percent accounted for by each category then should be interpreted accordingly: if government burdenaccounts for -30% of the total gains in TFP of -0.02, then it implies that the reducing government burden to the level ofNice would increaseTFP by -0.02"-30%), Le., 0.006. How important is each IC element for achieving the overall results? To answer this question, we compute the average for Tables B-7. The exercise suggests that the most importantIC elements, inorder of importance, are as follows: 1. The most importantIC elements are the generalmarket attractiveness, which accounts for 29% of IC gains inTFP, and 22% of gains inForeign; and government efficiency, which accounts for 13% of IC gains in TFP and 46% of IC gains in Foreign. Inthe short-run, the number one thing that the government can do i s to improve government efficiency; it accounts for 51% of short-run IC gains in TFP, and 94% short-run IC gains inForeign. 2. Of secondary importance are: a. to improve the access to finance: 8% of broad IC gains in TFP, and 1/3 of short-run IC gains inTFP; b. to progress toward a harmonious society: 6% of broad IC gains in TFP, 17% of broad IC gains inForeign. c. to improve on humancapital: 12% of broad IC gains in TFP, and 7% of broad IC gains inForeign.62 3. Of tertiary importance are: a. Domestic privatization: 3% of broad IC gains inTFP; 62Though the education and technology category accounts for a negative proportion of broad IC gains in Foreign, it merely reflects the temporary nature of FDI concentratingon labor-intensive industries. Inthe long-run, it is unclear how the skill-technology would help FDI. We thus give this negative accounting results a less weight than the number indicates. 114 b. foreign participation: 8% of broadI C gains inTFP; c. labor market flexibility: 5% of broad IC gains inTFF, 3% o f broad I C gains in Foreign; 17% of short-run I C gains in TFP, and 6% of short-run I C gains in Foreign. Table B-7.Average Expected ResultsFrom Improving Investment Climateor Government Effectiveness to 90thPercentile BroadInvestmentClimate GovernmentEffectiveness Variable Outcome=TFP outcome=FOREIGN Outcome=TFP outcome=FOREIGN PotentialGains InOutcomes 0.60 0.66I/ 0.15 0.30 11 City Characteristics ' I 29.4% 21.8% Harmonious Society 5.9% 17.0% Education And Technology 21.7% 6.6% Domestic Privatization 2.8% International Integration 8.1% Government Burden 12.5% 46.3% 50.7% 94.5% Labor Market Flexibility 4.5% 2.6% 17.3% 5.5% Access ToLoans 7.9% 30.6% The bold categories are more under direct control under the government in the short run, and are therefore my action-able. For all the share of contributions, we drop the top and bottom 5% to eliminate the influence of outliers. 115 Data 'able B-8.City Variables, Group 1 Log(Numb Labor er Of Effective Redundanc City Nonstate Foreign Employees) Tax Rate Y Anqing 0.784 0.078 4.747 0.098 0.049 Anshan 0.807 0.040 5.667 0.148 0.015 Baoding 0.719 0.061 5.971 0.114 0.038 Baoji 0.757 0.015 5.701 0.120 0.041 Baotou 0.776 0.056 5.598 0.126 0.079 Beijing 0.553 0.265 6.036 0.114 0.026 Benxi 0.826 0.039 4.860 0.133 0.055 Cangzhou 0.926 0.043 5.321 0.123 0.011 Changchun 0.623 0.141 5.503 0.125 0.025 Changde 0.872 0.060 4.847 0.135 0.043 Changsha 0.715 0.080 5.594 0.151 0.049 Changzhou 0.849 0.118 5.352 0.139 0.005 Chengdu 0.769 0.126 5.826 0.115 0.025 Chenzhou 0.876 0.031 4.437 0.153 0.019 Chongqing 0.747 0.059 6.266 0.135 0.027 Chuzhou 0.850 0.067 5.161 0.115 0.014 Dalian 0.358 0.441 6.637 0.094 0.012 Daqing 0.862 0.013 4.559 0.182 0.036 Datong 0.700 0.056 5.206 0.140 0.028 Deyang 0.888 0.044 5.612 0.138 0.016 Dongguan 0.189 0.807 6.483 0.081 0.000 Foshan 0.556 0.348 6.65 1 0.125 0.008 Fushun 0.646 0.079 5.181 0.142 0.036 Fuzhou 0.569 0.390 5.397 0.116 0.001 Ganzhou 0.632 0.299 5.172 0.128 0.016 Guangzhou 0.355 0.459 6.463 0.106 0.018 Guilin 0.687 0.082 5.023 0.150 0.028 Guiyang 0.673 0.071 5.850 0.137 0.046 Haerbing 0.671 0.083 5.382 0.111 0.063 Haikou 0.571 0.226 4.837 0.149 0.048 Handan 0.784 0.047 5.777 0.174 0.044 Hangzhou 0.661 0.254 6.251 0.112 0.000 Hefei 0.595 0.205 5.859 0.101 0.021 Hengyang 0.719 0.006 5.344 0.105 0.102 Huanggang 0.770 0.071 5.045 0.184 0.061 Huhehaote 0.869 0.056 4.727 0.098 0.019 Huizhou 0.275 0.696 6.465 0.081 0.005 Huzhou 0.862 0.106 5.409 0.121 0.006 Jiangmen 0.403 0.586 5.908 0.038 0.007 Jiaxing 0.779 0.190 5.589 0.119 0.008 Jilin 0.75 1 0.073 5.130 0.125 0.037 Jinan 0.75 1 0.057 5.389 0.124 0.029 Jingmen 0.804 0.115 5.107 0.125 0.017 Jingzhou 0.858 0.058 5.808 0.130 0.016 Jinhua 0.933 0.053 5.945 0.157 0.005 116 Logwumb _. Labor er Of Effective Redundanc City Nonstate Foreign Employees) Tax Rate Y Jining 0.845 0.067 6.008 0.116 0.027 Jinzhou 0.762 0.110 4.913 0.163 0.021 Jiujiang 0.670 0.073 5.338 0.127 0.034 Kunming 0.693 0.046 5.550 0.107 0.028 Langfang 0.767 0.160 5.229 0.100 0.001 Lanzhou 0.622 0.024 5.73 1 0.130 0.063 Leshan 0.912 0.034 5.658 0.118 0.026 Lianyungang 0.744 0.202 5.078 0.111 0.027 Linyi 0.828 0.130 5.157 0.088 0.007 Liuzhou 0.767 0.071 5.604 0.131 0.037 Luoyang 0.783 0.010 5.250 0.144 0.03 1 Maoming 0.804 0.129 4.589 0.126 0.013 Mianyang 0.820 0.057 5.492 0.118 0.014 Nanchang 0.625 0.099 5.670 0.096 0.032 Nanjing 0.593 0.211 6.200 0.138 0.044 Nanning 0.604 0.080 5.011 0.136 0.061 Nantong 0.789 0.184 5.465 0.104 0.004 Nanyang 0.750 0.033 5.867 0.130 0.047 Ningbo 0.785 0.209 5.945 0.140 0.005 Qingdao 0.740 0.203 5.528 0.090 0.014 Qinhuangdao 0.651 0.194 5.385 0.098 0.019 Qiqihaer 0.802 0.014 5.403 0.097 0.070 Quanzhou 0.495 0.486 5.529 0.139 0.004 Qujing 0.821 0.035 5.523 0.149 0.039 Sanming 0.844 0.078 4.908 0.136 0.010 Shanghai 0.505 0.413 5.707 0.123 0.016 Shangqiu 0.960 0.009 4.356 0.082 0.007 Shangrao 0.869 0.027 4.680 0.134 0.015 Shantou 0.697 0.274 5.104 0.119 0.011 Shaoxing 0.906 0.078 5.530 0.132 0.017 Shenyang 0.529 0.210 5.898 0.141 0.036 Shenzhen 0.275 0.674 6.581 0.075 0.001 Shijiazhuang 0.685 0.059 6.196 0.129 0.021 Suzhou 0.311 0.666 6.986 0.082 0.001 raian 0.799 0.070 6.288 0.103 0.022 raiyuan 0.682 0.019 5.873 0.156 0.042 raizhou 0.890 0.056 5.572 0.158 0.004 rangshan 0.808 0.124 6.455 0.113 0.012 Tianjin 0.443 0.294 6.100 0.134 0.047 rianshui 0.682 0.027 5.405 0.151 0.076 Weifang 0.839 0.105 5.940 0.116 0.010 Weihai 0.790 0.167 6.180 0.079 0.002 Wenzhou 0.949 0.051 5.689 0.176 0.002 Wuhan 0.600 0.108 6.151 0.118 0.048 Wuhu 0.767 0.101 5.446 0.143 0.010 Wulumuqi 0.799 0.036 4.310 0.146 0.027 Wuxi 0.786 0.173 5.800 0.126 0.006 Wuzhong 0.880 0.000 4.306 0.128 0.020 117 Log(Numb Labor er Of Effective Redundanc City Nonstate Foreign Employees) Tax Rate Y Xiamen 0.391 0.561 5.630 0.086 0.000 Xian 0.597 0.076 5.929 0.128 0.063 Xiangfan 0.720 0.058 6.180 0.138 0.030 Xianyang 0.743 0.050 5.421 0.114 0.027 Xiaogan 0.837 0.064 5.126 0.166 0.018 Xining 0.810 0.035 4.705 0.121 0.038 Xinxiang 0.787 0.049 5.633 0.120 0.044 Xuchang 0.93 1 0.041 4.994 0.139 0.017 xuzhou 0.808 0.075 5.614 0.153 0.040 Yancheng 0.928 0.051 5.479 0.138 0.009 Yangzhou 0.808 0.114 5.266 0.120 0.014 Yantai 0.740 0.192 6.084 0.097 0.022 Yibin 0.907 0.009 5.571 0.147 0.027 Yichang 0.718 0.106 5.783 0.109 0.027 Yichun 0.823 0.041 5.108 0.182 0.032 Yinchuan 0.887 0.042 4.745 0.091 0.020 Yueyang 0.749 0.053 5.123 0.087 0.059 Yuncheng 0.852 0.013 5.888 0.175 0.025 Yuxi 0.812 0.079 5.310 0.179 0.018 Zhangiiakou 0.669 0.107 5.235 0.104 0.069 Zhangzhou 0.582 0.395 4.813 0.100 0.002 Zhengzhou 0.808 0.066 6.199 0.115 0.025 Zhoukou 0.874 0.039 5.236 0.101 0.010 Zhuhai 0.253 0.692 5.587 0.093 0.005 Zhuzhou 0.821 ' 0.051 5.478 0.173 0.067 Zibo 0.819 0.073 6.501 0.110 0.03 1 Zunyi 0.666 0.028 5.524 0.138 0.050 Total 0.719 0.146 5.553 0.125 0.026 118 Table B-9.City Variables, Group2 % Employees Property W G d P Entertainment With Rights Per Capita And Travel It % Access Ceo University Protection Log(City For The City Costs / Sales Index To Loans Schooling Education Index Population) City) Anqing 0.010 0.167 0.590 14.350 0.119 0.809 6.404 8.103 Anshan 0.009 0.202 0.570 15.520 0.153 0.673 5.849 9.604 Baoding 0.013 0.213 0.590 15.050 0.200 0.713 6.992 8.563 Baoji 0.012 0.251 0.700 15.430 0.172 0.612 5.911 8.398 Baotou 0.009 0.226 0.490 15.840 0.180 0.418 5.484 9.913 Beijing 0.013 0.653 0.565 16.765 0.421 0.429 7.059 9.847 Benxi 0.018 0.140 0.300 15.040 0.126 0.548 5.053 9.166 Cangzhou 0.010 0.I62 0.500 14.200 0.120 0.466 6.521 8.690 Changchun 0.019 0.339 0.410 16.120 0.290 0.697 6.585 9.292 Changde 0.020 0.173 0.530 14.720 0.149 0.411 6.399 8.630 Changsha 0.025 0.443 0.720 15.350 0.289 0.651 6.414 9.163 Changzhou 0.014 0.199 0.730 14.730 0.131 0.661 5.855 9.690 Chengdu 0.017 0.446 0.690 16.182 0.312 0.724 6.966 9.268 Chenzhou 0.012 0.171 0.470 14.320 0.108 0.809 6.126 8.458 Chongqing 0.008 0.311 0.700 15.825 0.209 0.709 7.927 8.497 Chuzhou 0.021 0.221 0.580 15.180 0.108 0.780 6.072 8.340 Dalian 0.007 0.440 0.673 16.520 0.255 0.649 6.33 1 9.789 Daqing 0.013 0.324 0.190 15.660 0.193 0.369 5.569 10.099 Datong 0.014 0.185 0.330 15.350 0.149 0.296 5.703 8.524 Deyang 0.007 0.216 0.780 15.530 0.133 0.833 5.943 8.646 Dongguan 0.003 0.252 0.340 15.400 0.122 0.387 6.475 10.511 Foshan 0.008 0.33 1 0.660 15.680 0.146 0.528 5.860 10.095 Fushun 0.014 0.208 0.380 15.410 0.192 0.398 5.416 9.046 Fuzhou 0.007 0.245 0.620 14.920 0.155 0.758 6.412 9.468 Ganzhou 0.009 0.282 0.410 15.610 0.125 0.558 6.740 7.815 Guangzhou 0.007 0.507 0.650 16.440 0.259 0.612 6.603 10.264 Guilin 0.016 0.304 0.570 15.490 0.236 0.724 6.202 8.458 Guiyang 0.020 0.401 0.720 15.910 0.287 0.471 5.852 8.775 Haerbing 0.025 0.453 0.390 16.110 0.376 0.498 6.878 9.094 Haikou 0.027 0.427 0.360 16.330 0.298 0.475 4.963 9.153 Handan 0.006 0.189 0.570 15.040 0.141 0.408 6.761 8.622 Hangzhou 0.006 0.759 0.820 16.270 0.261 0.982 6.480 9.894 Hefei 0.012 0.438 0.660 16.350 0.285 0.542 6.097 8.828 Hengyang 0.022 0.202 0.500 15.200 0.164 0.389 6.578 8.339 Huanggang 0.017 0.150 0.420 15.110 0.113 0.700 6.588 8.019 Huhehaote 0.018 0.331 0.350 15.300 0.229 0.269 5.369 9.246 Huizhou 0.008 0.293 0.370 15.530 0.I32 0.753 5.681 9.397 Huzhou 0.011 0.175 0.840 14.580 0.112 0.630 5.550 9.368 Jiangmen 0.007 0.190 0.610 15.180 0.169 0.936 5.955 9.319 Jiaxing 0.008 0.155 0.850 13.870 0.054 0.790 5.811 9.684 Jilin 0.015 0.275 0.340 15.290 0.179 0.655 6.062 9.022 Jinan 0.010 0.332 0.630 15.540 0.23 1 0.561 6.380 9.552 Jingmen 0.013 0.243 0.630 15.330 0.139 0.703 5.704 8.771 Jingzhou 0.009 0.223 0.650 15.560 0.178 0.552 6.456 8.145 Jinhua 0.006 0.211 0.890 15.150 0.124 0.658 6.113 9.312 119 % Employees Property Log(Gdp Entertainment With Rights Per Capita And Travel It % Access Ceo University Protection Log(City For The City Costs/ Sales Index To Loans Schooling Education Index Population) City) Jining 0.008 0.219 0.730 15.090 0.154 0.849 6.687 8.857 Jinzhou 0.012 0.289 0.680 15.650 0.220 0.786 5.730 8.628 Jiujiang 0.013 0.375 0.440 15.090 0.119 0.882 6.138 8.279 Kunming 0.012 0.322 0.640 15.740 0.202 0.435 6.220 9.167 Langfang 0.009 0.302 0.500 14.890 0.179 0.741 5.966 8.979 Lanzhou 0.011 0.233 0.620 15.240 0.193 0.377 5.730 9.036 Leshan 0.007 0.177 0.810 15.310 0.143 0.700 5.852 8.302 Lianyungang 0.012 0.264 0.560 15.230 0.157 0.696 6.150 8.419 Linyi 0.005 0.239 0.870 15.110 0.157 0.709 6.923 8.537 Liuzhou 0.010 0.327 0.640 15.580 0.191 0.675 5.871 8.672 Luoyang 0.019 0.233 0.470 15.050 0.180 0.484 6.472 8.889 Maoming 0.007 0.182 0.520 14.520 0.142 0.706 6.508 8.651 Mianyang 0.013 0.275 0.600 15.840 0.200 0.687 6.271 8.386 Nanchang 0.009 0.412 0.630 15.790 0.289 0.750 6.133 9.081 Nanjing 0.012 0.399 0.640 16.100 0.222 0.617 6.369 9.732 Nanning 0.017 0.372 0.480 15.700 0.240 0.357 6.475 8.445 Nantong 0.013 0.248 0.760 14.880 0.155 0.746 6.651 8.995 NanYW 0.007 0.214 0.630 15.500 0.159 0.562 6.975 8.359 Ningbo 0.013 0.298 0.870 14.900 0.123 0.758 6.315 9.902 Qingdao 0.011 0.299 0.690 15.180 0.175 0.950 6.595 9.572 Qinhuangdao 0.016 0.238 0.590 15.080 0.192 0.683 5.620 9.039 Qiqihaer 0.013 0.235 0.480 15.700 0.186 0.539 6.314 8.267 Quanzhou 0.010 0.191 0.630 14.040 0.096 0.777 6.485 9.291 Qujing 0.011 0.192 0.650 15.040 0.104 0.654 6.356 8.030 Sanming 0.009 0.183 0.640 14.250 0.080 0.794 5.591 8.895 Shanghai 0.014 0.447 0.485 15.533 0.231 0.449 7.463 10.247 Shangqiu 0.007 0.274 0.440 14.300 0.106 0.868 6.701 7.959 Shangrao 0.007 0.602 0.620 15.030 0.107 0.946 6.447 7.800 Shantou 0.007 0.213 0.580 14.950 0.127 0.960 6.189 8.754 Shaoxing 0.008 0.186 0.820 14.550 0.130 0.781 6.075 9.644 Shenyang 0.021 0.374 0.560 16.100 0.305 0.543 6.542 9.548 Shenzhen 0.006 0.413 0.370 16.420 0.175 0.740 6.393 10.316 Shijiazhuang 0.009 0.251 0.630 15.810 0.201 0.793 6.822 9.117 Suzhou 0.004 0.473 0.730 16.430 0.210 0.934 6.395 10.295 Taian 0.007 0.276 0.730 15.850 0.239 0.782 6.310 8.825 Taiyuan 0.013 0.330 0.610 15.610 0.243 0.688 5.805 9.172 Taizhou 0.017 0.247 0.810 14.790 0.140 0.404 6.321 9.287 Tangshan 0.006 0.208 0.640 15.470 0.116 0.522 6.565 9.368 Tianjin 0.015 0.430 0.610 16.392 0.273 0.611 6.931 9.686 Tianshui 0.018 0.229 0.630 15.230 0.179 0.447 5.852 7.517 Weifang 0.008 0.232 0.770 15.330 0.130 0.751 6.746 8.921 Weihai 0.004 0.157 0.710 15.400 0.118 0.826 5.515 9.940 Wenzhou 0.013 0.275 0.780 14.740 0.129 0.443 6.615 9.171 Wuhan 0.019 0.462 0.750 16.610 0.356 0.630 6.667 9.452 Wuhu 0.008 0.234 0.580 15.350 0.151 0.825 5.414 8.968 Wulumuqi 0.016 0.343 0.330 15.770 0.268 0.496 5.226 9.362 120 Employees Property Log(Gdp Entertainment With Rights Per Capita And Travel It % Access Ceo University Protection Log(City For The City Costs/ Sales Index To Loans Schooling Education Index Population) City) Wuxi 0.012 0.260 0.760 14.808 0.145 0.482 6.103 10.201 Wuzhong 0.010 0.147 0.600 13.780 0.091 0.391 4.840 8.372 Xiamen 0.006 0.339 0.560 15.770 0.169 0.832 5.394 9.927 Xian 0.019 0.473 0.610 16.170 0.363 0.420 6.586 8.959 Xiangfan 0.008 0.241 0.620 15.630 0.180 0.396 6.361 8.498 Xianyang 0.018 0.395 0.500 16.000 0.250 0.506 6.188 9.146 Xiaogan 0.010 0.163 0.560 15.180 0.174 0.793 6.229 8.251 Xining 0.019 0.237 0.400 15.440 0.180 0.356 5.333 8.372 Xinxiang 0.017 0.173 0.560 15.640 0.170 0.402 6.333 8.364 Xuchang 0.009 0.151 0.570 14.680 0.095 0.569 6.106 8.678 xuzhou 0.018 0.324 0.560 15.580 0.209 0.610 6.821 8.720 Yancheng 0.020 0.156 0.720 15.060 0.121 0.535 6.682 8.626 Yangzhou 0.015 0.218 0.640 14.360 0.108 0.698 6.119 9.088 Yantai 0.007 0.367 0.790 15.560 0.190 0.832 6.472 9.456 Yibin 0.010 0.159 0.760 14.980 0.101 0.560 6.250 8.145 Yichang 0.012 0.311 0.590 15.800 0.182 0.835 5.988 8.929 Yichun 0.015 0.179 0.590 15.220 0.110 0.702 6.264 7.944 Yinchuan 0.013 0.202 0.570 14.910 0.183 0.529 4.926 8.870 Yueyang 0.014 0.250 0.630 15.620 0.183 0.616 6.272 8.697 Yuncheng 0.006 0.261 0.780 15.600 0.141 0.768 6.205 8.219 Yuxi 0.007 0.267 0.620 14.460 0.129 0.728 5.340 8.994 Zhangiiakou 0.010 0.183 0.480 15.270 0.117 0.674 6.109 8.419 Zhangzhou 0.012 0.226 0.710 14.550 0.133 0.690 6.122 8.946 Zhengzhou 0.009 0.244 0.580 15.510 0.209 0.746 6.560 9.210 Zhoukou 0.008 0.186 0.540 15.370 0.179 0.926 6.971 7.826 Zhuhai 0.019 0.404 0.290 15.910 0.150 0.683 4.892 9.948 Zhuzhou 0.023 0.302 0.560 15.810 0.248 0.361 5.916 8.771 Zibo 0.008 0.223 0.920 15.900 0.209 0.864 6.028 9.626 Zunyi 0.018 0.282 0.640 15.343 0.187 0.430 6.584 7.846 Total 0.012 0.287 0.600 15.394 0.183 0.634 6.233 9.000 121 'able B-10.City Variables, Group 3 % City Gdp City Permanent Time Per Road Portcosts Price For Employees Spent YO Of %Girl Capita Mileage Industrial- Log(City Having With Four Time With EnrollmentIn Growth Use Average Medical Gov't Ok Air For Elementary City Rate Electricity Wage) Insurance Regulators The City Schools Anqing 12.500 2.257 420.000 0.500 9.176 0.460 0.028 0.954 45.800 Anshan 26.900 2.418 222.400 0.620 9.309 0.512 0.058 0.704 47.550 Baoding 14.500 1.933 157.600 0.590 9.375 0.561 0.054 0.745 48.100 Baoji 22.000 2.627 1034.400 0.560 9.330 0.560 0.051 0.817 45.100 Baotou 33.700 2.779 687.200 0.470 9.691 0.823 0.089 0.494 44.550 Beijing 15.600 2.594 147.200 0.590 10.298 0.970 0.056 0.627 48.100 Benxi 14.000 2.834 3 10.400 0.700 9.225 0.687 0.040 0.666 49.460 Cangzhou 14.600 2.131 124.000 0.420 9.325 0.43 1 0.051 0.784 47.900 Changchun 12.600 2.343 526.400 0.750 9.663 0.560 0.027 0.943 47.300 Changde 35.200 2.621 788.000 0.370 9.468 0.580 0.041 0.890 48.710 Changsha 13.600 1.910 636.000 0.600 9.849 0.776 0.047 0.598 47.250 Changzhou 15.500 2.691 136.000 0.740 9.903 0.780 0.019 0.825 46.270 Chengdu 13.600 2.639 2006.400 0.500 9.773 0.908 0.046 0.846 46.710 Chenzhou 12.400 2.723 363.200 0.500 9.376 0.450 0.03 1 0.971 53.210 Chongqing 112.700 2.270 1744.000 0.430 9.572 0.568 0.051 0.664 47.340 Chuzhou 10.120 2.642 252.800 0.600 9.211 0.520 0.030 0.944 46.110 D a h 19.000 2.268 50.000 0.530 9.889 0.990 0.062 0.959 48.800 Daqing 9.200 3.102 805.600 0.660 10.027 0.865 0.035 0.968 47.300 Datong 20.200 2.915 368.800 0.380 9.454 0.640 0.052 0.438 47.800 Deyang 19.380 2.373 2004.800 0.420 9.688 0.858 0.032 0.966 49.300 Dongguan 17.400 1.648 50.000 0.680 10.140 0.990 0.033 0.978 43.860 Foshan 14.500 2.480 50.000 0.750 9.881 0.938 0.046 0.951 46.080 Fushun 15.000 2.793 307.200 0.610 9.541 0.577 0.034 0.702 47.910 Fuzhou 13.000 2.239 170.400 0.480 9.716 0.446 0.031 0.978 46.290 Ganzhou 11.300 2.133 370.400 0.630 9.242 0.583 0.027 0.950 44.610 Guangzhou 13.750 2.013 50.000 0.590 10.343 0.959 0.061 0.831 45.980 Guilin 16.370 2.605 526.400 0.510 9.532 0.660 0.025 1.000 47.940 Guiyang 13.700 2.489 674.400 0.310 9.554 0.804 0.058 0.918 48.260 Haerbing 17.430 2.827 699.200 0.490 9.383 0.813 0.053 0.816 49.000 Haikou 110.700 1.889 50.000 0.620 9.776 0.980 0.043 0.993 Handan 13.600 2.154 434.400 0.590 9.505 0.436 0.072 0.690 49.050 Hangzhou 13.700 2.528 107.200 0.560 10.247 0.940 0.006 0.800 47.080 Hefei 19.200 2.560 324.800 0.490 9.611 0.920 0.053 0.858 44.050 Hengyang 10.800 1.593 483.200 0.500 9.394 0.546 0.035 0.863 47.480 Huanggang 9.000 2.256 750.400 0.650 8.792 0.410 0.032 0.590 45.200 Huhehaote 21.200 2.802 556.800 0.460 9.721 0.720 0.047 0.870 47.030 Huizhou 13.100 3.241 63.200 0.600 9.578 0.903 0.022 1.000 47.130 Huzhou 15.300 2.515 121.600 0.740 9.975 0.690 0.030 0.891 49.040 Jiangmen 14.440 2.778 78.400 0.590 9.522 0.740 0.030 0.962 47.710 Jiaxing 16.200 1.946 93.600 0.600 9.925 0.570 0.039 0.975 48.470 Jilin 13.000 3.053 593.600 0.510 9.449 0.586 0.039 0.614 52.190 Jinan 14.100 2.173 261.600 0.600 9.800 0.716 0.038 0.577 47.100 Jingmen 11.500 2.709 992.800 0.580 9.255 0.717 0.041 0.851 48.560 Jingzhou 10.270 2.404 992.800 0.460 9.134 0.740 0.047 0.824 46.300 122 % City Gdp City Permanent Time Per Road Port Costs Price For Employees Spent % Of %Girl Capita Mileage Industrial- Log(City Having With Four Time With Enrollment In Growth Use Average Medical Gov't Ok Air For Elementary City Rate Electricity Wage) Insurance Regulators The City Schools Jinhua 21.700 2.789 214.400 0.760 10.055 0.760 0.03 1 0.678 45.540 Jining 16.700 2.106 251.200 0.500 9.518 0.524 0.039 0.863 45.990 Jinzhou 19.500 2.497 299.200 0.430 9.409 0.585 0.033 0.792 48.690 Jiujiang 14.300 2.446 663.200 0.450 9.270 0.800 0.029 0.902 45.730 Kunming 11.100 1.099 857.600 0.360 9.646 0.820 0.066 1.ooo 47.800 Langfang 11.600 2.165 50.000 0.400 9.410 0.656 0.023 0.904 48.670 Lanzhou 10.100 2.138 1468.800 0.330 9.592 0.507 0.066 0.566 48.000 Leshan 14.800 2.248 2091.200 0.490 9.340 0.604 0.037 0.933 47.540 Lianyungang 113.400 2.358 50.000 0.700 9.450 0.627 0.031 0.885 44.100 Linyi 16.600 2.165 93.600 0.600 9.313 0.648 0.051 0.893 39.100 Liuzhou 13.500 2.298 356.800 0.410 9.711 0.820 0.041 0.790 48.360 Luoyang 16.200 2.235 570.400 0.400 9.475 0.597 0.052 0.427 47.700 Maoming 15.600 2.529 82.400 0.640 9.584 0.702 0.020 0.992 40.300 Mianyang 14.400 2.167 1961.600 0.540 9.469 0.800 0.036 0.930 47.680 Nanchang 15.100 1.736 628.000 0.610 9.652 0.560 0.033 0.904 46.500 Nanjing 115.000 2.434 196.800 0.680 10.007 0.790 0.060 0.803 43.210 Nanning 11.900 2.238 122.400 0.470 9.645 0.870 0.031 0.950 48.000 Nantong 16.100 2.279 111.200 0.560 9.674 0.775 0.050 0.883 48.300 Nanyang 22.830 1.986 567.200 0.440 9.227 0.700 0.051 0.265 47.000 Ningbo 14.800 2.404 56.000 0.650 10.048 0.870 0.036 0.918 50.600 Qingdao 15.600 2.243 50.000 0.680 9.752 0.910 0.025 0.904 48.290 Qinhuangdao 11.700 2.554 208.000 0.550 9.670 0.653 0.050 0.959 53.000 Qiqihaer 16.450 2.717 914.400 0.680 9.296 0.598 0.059 0.879 48.980 Quanzhou 13.500 2.837 81.600 0.640 9.579 0.821 0.031 0.945 45.410 Qujing 19.520 3.817 766.400 0.510 9.606 0.772 0.052 0.978 44.600 Sanming 10.000 3.800 265.600 0.500 9.6 19 0.540 0.029 0.680 45.570 Shanghai 18.380 1.699 50.000 0.560 10.102 0.970 0.040 0.852 48.470 Shangqiu 21.900 2.461 296.800 0.380 9.033 0.697 0.020 0.904 47.700 Shangrao 18.260 2.602 408.800 0.640 9.246 0.780 0.009 0.994 46.900 Shantou 10.100 1.316 228.800 0.570 9.494 0.938 0.021 1.000 45.600 Shaoxing 15.200 2.302 114.400 0.600 9.966 0.953 0.032 0.827 47.600 Shenyang 18.120 2.282 286.400 0.490 9.534 0.800 0.040 0.825 48.400 Shenzhen 8.000 1.312 50.000 0.710 10.371 0.890 0.061 0.940 43.710 Shijiazhuang 13.200 2.269 271.200 0.420 9.518 0.758 0.058 0.767 47.670 Suzhou 16.100 2.345 72.000 0.650 10.022 0.980 0.048 0.836 48.800 Taian 16.400 2.195 304.800 0.580 9.379 0.690 0.043 0.984 40.100 Taiyuan 15.200 2.285 445.600 0.460 9.642 0.720 0.074 0.614 48.330 Taizhou 13.000 2.032 157.600 0.600 10.099 0.630 0.031 0.918 45.950 Tangshan 14.300 2.459 95.200 0.390 9.552 0.527 0.056 0.759 48.790 Tianjin 14.900 2.414 52.800 0.460 9.959 0.919 0.063 0.817 48.950 Tianshui 13.050 0.880 1207.200 0.380 9.276 0.580 0.044 0.800 56.000 Weifang 16.840 2.305 88.000 0.620 9.420 0.721 0.055 0.945 46.200 Weihai 16.930 2.414 224.000 0.570 9.478 0.709 0.034 1.000 46.000 Wenzhou 13.800 2.089 204.000 0.600 9.860 0.623 0.028 0.299 45.120 Wuhan 14.500 1.813 806.400 0.620 9.534 0.870 0.060 0.675 44.200 123 City Gdp City Permanent Time Per Road PortCosts Price For Employees Spent % Of %Girl Capita Mileage Industrial- Log(City Having With Four Time With Enrollment In Growth Use Average Medical Gov't Ok Air For Elementary City Rate Electricity Wage) Insurance Regulators The City Schools Wuhu 20.380 2.702 260.000 0.530 9.559 0.560 0.032 0.940 46.080 Wulumuqi 14.680 1.880 3028.000 0.350 9.840 0.870 0.042 0.705 48.100 Wuxi 16.300 2.315 100.000 0.690 9.925 0.900 0.038 0.809 46.000 Wuzhong 13.000 3.954 1010.400 0.500 9.521 0.420 0.029 0.767 49.100 Xiamen 14.400 1.895 50.000 0.590 9.949 0.950 0.045 1.ooo 46.110 Xian 24.600 2.528 891.200 0.270 9.647 0.827 0.077 0.712 45.600 Xiangfan 11.000 3.196 900.000 0.380 8.958 0.700 0.049 0.749 48.400 Xianyang 12.200 2.341 904.000 0.590 9.290 0.658 0.051 0.806 46.300 Xiaogan 10.100 2.388 791.200 0.470 9.078 0.470 0.048 0.950 44.120 Xining 13.900 3.376 1640.000 0.280 9.691 0.663 0.032 0.765 Xinxiang 21.240 1.927 476.800 0.450 9.160 0.830 0.043 0.810 33.320 Xuchang 14.100 2.073 442.400 0.530 9.181 0.444 0.011 0.900 46.020 xuzhou 13.200 2.285 171.200 0.430 9.668 0.652 0.045 0.601 47.700 Yancheng 14.500 2.362 152.000 0.740 9.389 0.525 0.043 0.762 45.300 Yangzhou 14.500 2.53 1 221.600 0.540 9.664 0.562 0.031 0.774 46.360 Yantai 17.500 2.387 192.800 1.500 9.697 0.910 0.041 1.000 48.960 Yibin 12.600 1.768 1988.800 0.500 9.469 0.800 0.051 0.598 46.930 Yichang 17.300 3.074 1014.400 0.640 9.272 0.740 0.041 0.717 48.600 Yichun 16.300 3.392 708.000 0.590 9.320 0.503 0.025 0.957 45.400 Yinchuan 18.400 3.182 1008.800 0.600 9.506 0.680 0.041 0.790 47.000 Yueyang 24.800 2.765 759.200 0.600 9.415 0.542 0.034 0.762 33.300 Yuncheng 14.200 2.915 739.200 0.480 9.336 0.570 0.047 0.459 47.500 Yuxi 7.400 4.2 10 910.400 0.440 9.708 0.650 0.045 0.973 42.660 Zhangjiakou 13.000 3.310 320.800 0.460 9.421 0.415 0.060 0.696 48.460 Zhangzhou 11.400 2.643 52.800 0.500 9.390 0.310 0.028 0.989 46.500 Zhengzhou 14.000 2.240 467.200 0.440 9.617 0.510 0.028 0.816 48.030 Zhoukou 13.500 1.841 402.400 0.400 8.882 0.920 0.018 0.814 47.600 Zhuhai 10.800 2.158 120.000 0.790 9.908 0.990 0.031 1,000 45.300 Zhuzhou 18.000 1.973 624.800 0.430 9.414 0.656 0.036 0.538 45.000 Zibo 22.250 2.334 173.600 0.550 9.556 0.684 0.047 0.836 48.100 Zunyi 12.800 1.743 787.200 0.340 9.504 0.644 0.058 0.781 47.890 Total 19.355 2.406 509.942 0.544 9.589 0.709 0.042 0.818 46.858 124 BIBLIOGRAPHY American Chamber of Commerce in Shanghai, White Paper 2005: American Business in China, 2005. 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