determinants of urban land supply in the people’s republic...

Determinants of Urban Land Supply in thePeople’s Republic of China: How Do

Political Factors Matter?Wen-Tai Hsu, Xiaolu Li, Yang Tang, and Jing Wu∗

This paper explores whether and how corruption and competition-for-promotionmotives affect urban land supply in the People’s Republic of China. Conditionalon demand-side factors, we find that corruption is highly correlated with anincrease in land supply. The corruption effects are strongest for commercialland, followed by residential land, and then industrial land. To shed lighton the competition motives among prefectural leaders, we examine how thenumber of years in office affects land supply and distinguish among differenthypotheses. Our empirical results show robust rising trends in land sales. Theseresults are consistent with the hypothesis that among prefectural leaders theimpatience and anxiety in later years from not being promoted may contributeto an increase in land sales revenue in later years. We also find that prefecturalleaders may aim for more land sales revenue over their first few years in officeinstead of seeking higher revenue in their first 1–2 years.

Keywords: institution, land supply, monocentric-city model, People’s Republicof China, political factorsJEL codes: O18, P25, P26

I. Introduction

According to communist theory, and hence by law in the People’s Republicof China (PRC), all urban land in the PRC is owned by the state. However, as thePRC has opened up and pursued economic reform toward a more market-orientedeconomy, land in the PRC is also being gradually marketized. The current system isbased on leaseholding; the state is still the ultimate owner of all land, but private

∗Wen-Tai Hsu (corresponding author): Associate Professor of Economics, School of Economics, SingaporeManagement University. E-mail: [email protected]; Xiaolu Li: Assistant Professor, Institute of UrbanDevelopment, Nanjing Audit University. Email: [email protected]; Yang Tang: Assistant Professor, Divisionof Economics, Nanyang Technological University. E-mail: [email protected]; Jing Wu: Associate Professor,Hang Lung Center for Real Estate and Department of Construction Management, Tsinghua University. E-mail:[email protected]. We would like to thank Tomoki Fujii, Jing Li, participants at the Asian DevelopmentReview Conference on Urban and Regional Development in Asia held in Seoul in July 2016, the managing editor,and two anonymous referees for helpful comments and suggestions. We would also like to thank Xin Yi for hisoutstanding research assistance. Wen-Tai Hsu gratefully acknowledges the financial support provided by the Sing LunFellowship of Singapore Management University. In this paper, the Asian Development Bank recognizes “China” asthe People’s Republic of China. The usual disclaimer applies.

Asian Development Review, vol. 34, no. 2, pp. 152–183 © 2017 Asian Development Bankand Asian Development Bank Institute

Determinants of Urban Land Supply in the PRC 153

parties can purchase land use rights for a certain period (usually 70 years forresidential use and 40 years for commercial use). Before 2004, the way in whichthese leaseholds were sold or transferred was not transparent. Reform to the law in2004 required that all government land transactions be published on the Internet.This has allowed researchers to collect land transaction data from 2004 onward,making the current study possible.

Cities in the PRC have grown rapidly in recent decades. The urbanizationrate increased from around 20% in 1979 to about 62% in 2010. As city populationsgrow, demand for urban land increases. In other economies where free marketsare the norm, economic forces—such as those illustrated in Alonzo–Muth–Millsmonocentric-city models—may explain most of the urban land supply expansion.Local governments around the globe have influence over land supply throughzoning, land use regulations, and housing permits. But such political influence ispresumably dwarfed by the large power that local governments in the PRC enjoywhen determining their land supply. In the Asian context, two notable exceptionsare Hong Kong, China and Singapore.

We obtained data for all transactions of land parcels by the various levels oflocal government in the PRC since 2007. We aggregated variables at the prefecturallevel. The most common form of this level is the so-called “prefectural-level city”(di-ji-shi). There are also prefectures that are not prefectural-level cities; they aretypically rural areas. Prefectural-level city is the official name for such jurisdictions,but this can be somewhat confusing since their geographic scope is usually muchlarger than a metropolitan area’s and typically covers large tracts of rural land. Forthis reason, we prefer to simply call them prefectures. We use the total floor spacesold by local governments in each prefecture to measure the change in land supply.This information is available for each year from 2007 to 2013. Correspondingmeasures for each of the three major types of usage—residential, commercial, andindustrial—are also available. Our land and population data together cover the urbanparts of 286 prefectures.1

The purpose of this paper is to empirically probe the political determinantsof land supply. In this context, we will investigate two distinct issues. The firstis corruption, which is believed to be one of the motives in local governments’selling of land (see, for example, Cai, Henderson, and Zhang 2013). Second, toshed light on the influence of competition among prefectural officials, we studythe effect of years in office—the number of years that a particular leader has beenin office in that prefecture in the year of sale—on land sales.2 The literature hasdocumented that competition for promotion within the hierarchy in government

1Only urban land is owned by the state; rural land in each prefecture is owned collectively by the ruralresidents. Thus, land sales by local governments are urban by their nature. We also have the urban population of eachprefecture. See section II.A for details.

2See Hsu and Tang (2016) for a theory of political economy that incorporates these two factors.

154 Asian Development Review

posts and the Communist party has various effects.3 The institutional backgroundis that there are no specific term periods or limits for prefectural leaders; theirappointments are reviewed and decided by provincial leaders within the Communistparty.4 The key criteria is local economic performance, measured in terms of grossdomestic product (GDP) growth, and hence competition among prefectural leadersencourages them to sell land or pursue higher land sales revenue in order to fundinfrastructure or other mega projects that may help boost the local economy. Thefact that local governments were not allowed to borrow by issuing debt until veryrecently reinforced this incentive.

Based on these competition motives, we propose three hypotheses regardinghow years in office may affect land supply. Hypothesis 1 is that a local leader tendsto sell many land parcels or earn large land sales revenue in the beginning of his orher term in order to promote local economic growth. First, higher land sales revenuemay provide more funds for infrastructure or mega projects that enhance economicperformance. Second, even if land sales revenue is not used to fund infrastructure,the increase in land supply is conducive to economic growth because it lowers thecost of land for both business and manufacturing operations. It also lowers housingcosts, which attracts larger population inflows. If Hypothesis 1 is true, land salesquantity and/or revenue tend to be larger in the initial years in office for prefecturalleaders. Hypothesis 2 is that when a prefectural leader stays too long, comparedwith the distribution of years in office among prefectural leaders, he or she mightfeel pressure to perform and hence increase land sales, in terms of quantity and/orrevenue, in order to increase the chance of getting promoted. Hypothesis 3 is alsorelated to the later years of a prefectural leader’s tenure. Hypothesis 2 assumesthat when one is not promoted after staying in a prefecture for too long, he orshe gets anxious for promotion. Another possible explanation is that he or shemay simply “give up,” and thus engage in fewer land sales for the purpose ofgetting promoted. Note that a prefectural leader might still increase land supplyfor the purpose of corruption. A priori, there are various possibilities regardingwhether each hypothesis is supported by data. But logically, if there is any patternin later years due to the competition-for-promotion motive, it cannot be that bothHypotheses 2 and 3 hold simultaneously.

At the same time, governments may supply more land simply becausepeople need it; this response to demand pressure should be considered also. Thus,before probing the effects of political factors, we first determine whether standardurban-economic determinants have had an influence on urban land supply in thePRC. As in the Alonzo–Muth–Mills monocentric-city models and their variants,gains in population and income are two key sources of increases in land demand.

3See, for example, Li and Zhou (2005) for GDP-growth competition among provincial leaders and Zhenget al. (2014) for how political competition among prefectural leaders impacts local environmental quality.

4Officially, a regular term is 5 years, but this is hardly binding as the actual length of term varies substantially.See section III.C for details.


Our first set of results confirms that increments in both population and incomesignificantly influence the change in land supply in positive ways.

Our next step is to study whether the two above-mentioned politicalfactors possess additional significant influence on land supply, conditional onurban-economic determinants. In terms of corruption, direct evidence is hard tocome by. Hence, we resort to the powerful finding of Cai, Henderson, and Zhang(2013) that the usage of two-stage auctions among the three methods of land salesby governments is an indication of corruption.5 We find a very strong positiveassociation between corruption and changes in land supply. This association isstrongest for commercial land, followed by residential land, and then industrialland. We also run a set of regressions with land sales revenue as the dependentvariable. We find that the effects of corruption on sales revenue for residential andcommercial land are much smaller than the effects on the change in land supply,whereas the reduction of the effect for industrial land is relatively moderate. Thecontrast between these two sets of results show that the land sales revenue increasesless than proportionally than the quantity of land sales. This, in turn, suggests thatas corruption leads to local governments selling more land, they sell them at lowerprices compared with other sales methods. Residential and commercial land seemto be the main sources of corruption.

The above results suggest that land sales revenue and quantity may entaildifferent information. Therefore, in terms of the years-in-office effect, we also runtwo sets of regressions: one using the change in land supply and one using landsales revenue. Note that the leadership in the PRC is two track. At the prefecturallevel, the mayor is the official leader of the government, but the “real boss” is thehighest party official in that prefecture, the party (chief) secretary. Thus, we runthese regressions for both party secretaries and mayors to examine whether thereis any difference between the two. To identify the years-in-office effect, we use aquadratic specification and tease out the specificity of prefectures and individualleaders by estimating prefecture-leader fixed effects. As the story is based on thepolitical competition for promotion, we also control for corruption.

We find that there is a very robust rising trend. That is, the longer a prefecturalleader is in office, the more land he or she sells, both in terms of quantity andrevenue. The effect for party secretaries is much stronger than that for mayors.Here we only briefly sketch the reasons behind this rising pattern as details will begiven in section III.C. First, the result is consistent with Hypothesis 2: impatienceor anxiety at not getting promoted in later years may contribute to the rising trend.As 77% of party secretaries’ length of term was not more than 4 years, those whostay longer than 4 years may feel anxiety or impatience.

Second, based on the results on corruption, a prefectural leader may increaseland supply for more pocket money. Thus, if Hypothesis 3 is true and a prefectural

5For more details, see section III.C in this paper and Cai, Henderson, and Zhang (2013).


leader “gives up” in later years, the rising pattern implies that these prefecturalleaders become more corrupt in later years. However, we find that the effect ofcorruption on the increase of land supply decreases with the number of years inoffice. In addition, we also see that conditioned on other factors that may potentiallyaffect corruption, the correlation between years in office and corruption are negativeand insignificant. Thus, our results do not support Hypothesis 3.

Third, our results seem to contradict Hypothesis 1 because we donot see large initial land sales, but whether this is inconsistent with thecompetition-for-promotion motive or not needs to be carefully examined. As mostcities in the PRC are growing in terms of both population and income during thereview period, by restricting the quantity of land earlier, local government may raisemore revenue later because land prices have been pushed up. Thus, an ambitiousprefectural leader might trade revenue early in his or her term with higher overallland sales revenue over a longer time span, which may increase the chance of gettingpromoted (even a little later than the average number of years in office). If thisconjecture is true, then there should be a rising trend in land prices. We find thatthis is indeed the case. We also find that the increases in land sales revenue arelarger from year 2 through year 5, whereas the increases become smaller after 5years. Thus, the rising pattern in the first few years may still be consistent with acompetition-for-promotion motive because prefectural leaders may aim for higherland sales revenue overall in the first few (around 5) years in office.

We briefly review the related literature on the PRC’s land market as follows.Although the surge of land and housing prices in cities in the PRC has attractedglobal interest (Wu, Gyourko, and Deng 2016; Fang et al. 2015), few attempts havebeen made to understand the urban land market from the supply perspective untilvery recently. While some research highlights the role of the central government’sland use quota allocation system (Liang, Lu, and Zhang 2016), most existingliterature focuses on local governments’ land supply behavior. Based on empiricalanalyses of 35 major cities, Deng, Gyourko, and Wu (2012) point out that besides astrong common trend at the national level, local governments’ land supply behavioris substantially affected by the size of budget deficits; local chief officers’ desiresfor promotion also play an important role. Du and Peiser (2014) find that localgovernments intentionally control the land supply to maximize their revenue fromthe land market. Empirical research by Wu, Feng, and Li (2015) concludes thatthe budgetary deficits of local governments can affect their land supply behavior;increases in land prices, however, are mainly driven by demand-side factors.Their finding supports our assertion that urban-economic determinants such asincome and population increases must be controlled before political factors canbe investigated.

In comparison to the above-mentioned studies, we are the first to study theeffect of years in office on land sales behavior. In addition, we also provide analysiscovering all three major types of land usage instead of limiting the analysis to


residential land parcels. In terms of corruption, whereas Cai, Henderson, and Zhang(2013) focus on the differential effects between two-stage and English auctionsat the individual-parcel level, we focus on how this distinction affects prefecturalaggregate variables.

The rest of the paper is organized as follows. In section II, we detail thebasic sources of data and examine the standard urban-economic predictions, whichis mainly a demand-side explanation. Section III explores whether and how variouspolitical factors matter, conditional on the demand factors. Section IV concludes.

II. Data and Demand Factors

A. Data

It is important to first clarify the level of geography used in our analysis.Most of the data we collect are at the prefecture level. The most common form ofthis level is the so-called prefectural-level city (di-ji-shi). Although direct-controlcities (Beijing, Chongqing, Shanghai, and Tianjin) are politically at the samelevel of provinces, they are geographically similar to prefectural-level cities andhence included in this level. However, prefectural-level cities are usually too big,often much bigger than a metropolitan area in international standards based oncommuting flows (Fujita et al. 2004). One can easily tell this by inspecting a mapof, for example, Chongqing, Beijing, or Xuzhou. To remedy this problem, we useurban population, which is defined as residents of the urban areas in the prefecture,rather than the population of the entire prefecture.6 Doing so means we look atall urban areas within a prefecture collectively; some of these urban areas may beoutside the metropolitan area in which the prefectural government is located. Ourprefectural population data is from censuses taken in 2000 and 2010. For the yearsin between, we either interpolate or extrapolate. We do not use the population datafrom the China City Statistical Yearbook because the population figures therein areonly for the hukou (household registration) system; as such, they do not includemigrant workers and do not reflect the true size of a city.

From the official website of the Ministry of Land and Resources, we collectinformation on all transactions of land parcels by various levels of local (urban)governments in the PRC. During the period 2007–2013, there were about 1.8million land parcel transactions conducted by local governments. For the purposeof our research, we aggregate variables to the prefectural level. The change in theland supply measure that we use is the total floor space sold by governments ineach prefecture in each year from 2007 to 2013. For some of our questions of study,

6There is a very fine and specific definition of an urban area in the PRC, which is a small jurisdiction orneighborhood committee (ju-wei-hui) that is similar to the concept of a neighborhood. Similar jurisdictions in ruralareas are village committees (cun-wei-hui).


government officials may care more about land revenue than increasing land supply.Thus, we also obtain data on land sales revenue.7 We also know the type of land useper land transaction (residential, commercial, or industrial). From the revenue andquantity information on land sales, we calculate the average price per unit of landarea or per unit of floor space. We also know the method by which land is sold. Asmentioned in the introduction, the fraction of two-stage auctions is used to capturethe degree of corruption. All land that has been sold in our data is urban land. Thisis because rural land is owned collectively by villagers and any rural land must beconverted to urban land (via acquisition by the urban government) before it can besold. Hence, our land sales data is consistent with the level of geography in ourstudy.

From the China City Statistical Yearbook we also obtain otherprefectural-level characteristics such as GDP and its breakdown.8 In particular,we make the analysis consistent with our level of geography (all urban areaswithin a prefecture), we exclude the GDP of the primary sector and aggregatethe GDP of the secondary and tertiary sectors to represent urban GDP (i.e., GDPproduced in urban areas). Our population, land, and GDP data together cover 286prefectural-level cities. The sources of other data are detailed in their respectivesections or subsections.

B. Standard Predictions of Urban Economics

To start thinking about the determinants of urban land supply, imagine astrict version of the monocentric-city model: land and housing demand is inelastic.Suppose the city government’s supply of land meets demand. Then one wouldexpect a 45-degree line representing the relation between population increment andthe change in land supply on a log scale. Figure 1 shows a scatter plot of populationincrement and change in land supply by prefecture on a log scale.

Although there is a strong correlation between the two variables, the slopeis less than 45 degrees. This indicates that the increase in land supply is less thanthe increase in population. However, this is consistent with the predictions froma standard monocentric-city model when land and housing demand are elastic.That is, land demand grows less than proportionally with respect to populationgrowth because new city residents are willing to reduce their consumption of landand housing to save on commuting costs. Standard models also predict that rising

7Both land sales revenue and the change in land supply are used as dependent variables in our regressionspecifications. Du and Peiser (2014) make the point that local governments may want to hoard land to seek higher landsales revenue later. Hence, land sales revenues and changes in land supply might show different trends. Nevertheless,as we will see, these two do show different patterns in terms of corruption, but in terms of the years-in-office effectthey often go the same way. See section III for more details.

8The prefectural GDP per capita data used here are nominal. In the regressions that we run in the next section,we control for year fixed effects. Since monetary policy is the same economywide, we cannot control for year fixedeffects if we deflate GDP per capita.


Figure 1. Population Increment and Change in Land Supply

Sources: Ministry of Land and Resources. http://www.mlr.gov.cn/mlrenglish/; National Bureau of Statistics.http://www.stats.gov.cn/english/; and authors’ calculations.

incomes increase demand for residential land and housing, as well as land andhousing for business and industrial activities.

Columns (1)–(4) of Table 1 show the regression results of the logarithmsof the change in land supply—total and by type (residential, commercial, orindustrial)—on the above-mentioned standard urban economic explanations ofland-demand increases. In all the regressions in this paper, we control for year fixedeffects and cluster standard errors at the province level. In the regression in Table 1,we do not include prefecture fixed effects because we want to investigate the effectof population growth. However, since our population data are either interpolatedor extrapolated, the logarithms of population increments become constant overtime. As industrial structures may affect the breakdown of land supply andpotentially affect the total land supply as well, we use the ratio of tertiary industriesto secondary industries (in GDP terms) to proxy industrial structure. Columns(5)–(8) show results when we control for this ratio.

As expected, the effect of population increments and the increase in GDPper capita are strongly significant. The elasticities of the change in land supplyto population increase are around 0.42–0.48. The elasticities of the changein land supply to the increase in GDP per capita are around 0.04–0.07. Theservices-to-manufacturing ratio has a strongly significant negative impact on thechange in industrial land supply. We also observe that this ratio has a positive effecton the change in commercial land supply; however, this effect is insignificant. Asthe two main sectors produce opposite effects, it is conceivable that the effect on thechange in residential land supply would be insignificant. Finally, this ratio also hasa significant negative effect on the change in total land supply, which indicates that


Tabl

e1.

Stan

dard

Urb

an-E

cono

mic

Pre

dict

ions

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k

ln(i

ncre

ase

inla

ndsu

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)

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rial

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dust

rial

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iabl

es(1

)(2

)(3

)(4

)(5

)(6

)(7

)(8

)

ln(p

opul

atio

nin

crea

se)

0.43

7***

0.42

3***

0.47

8***

0.44

4***

0.44

9***

0.42

9***

0.47

0***

0.47

9***

(0.0

54)

(0.0

67)

(0.0

59)

(0.0

58)

(0.0

57)

(0.0

69)

(0.0

59)

(0.0

63)

ln(i

ncre

ase

inG

DP

per

capi

ta)

0.05

78**

*0.

0402

***

0.07

06**

*0.

0557

***

0.05

76**

*0.

0401

***

0.07

08**

*0.

0556

***

(0.0

11)

(0.0

14)

(0.0

13)

(0.0

14)

(0.0

11)

(0.0

14)

(0.0

13)

(0.0

14)

rati

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0.13

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(0.1

23)

(0.1

33)

(0.1

87)

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3.38

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5.89

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4.39

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815**

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(0.1

67)

(0.2

11)

(0.1

87)

(0.2

06)

(0.1

82)

(0.2

30)

(0.2

12)

(0.2

62)

Yea

rF

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1,43

81,

437

1,44

11,

429

1,43

81,

437

1,44

11,

429

R2

0.49

90.

389

0.32

90.

431

0.50

40.

390

0.33

10.

458

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betw

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Col

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(1)–

(4)

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(8)

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the effects on the change in industrial and residential land supply dominate that forcommercial land supply.

III. How Do Political Factors Matter?

As verified in the previous section, increases in land supply may be dueto government responses to demand factors such as increases in population orincome. In this section, we study the effect of corruption and the years in officeof prefectural leaders, conditional on the above-mentioned demand factors. Beforewe proceed, we first examine the relationship between land sales revenue and budgetdeficits.

A. Budget Deficits

There are institutional reasons that closely link land sales by localgovernments with budget deficits. Since reform of the tax-sharing system in1994, the central government has kept a major part of tax revenues, while localgovernments remain burdened with increasing local expenditures. However, unlikelocal governments in advanced economies, local governments in the PRC wereuntil very recently prevented from directly issuing debt to fund budget deficits orother investments such as mega projects. Accordingly, local governments in thePRC have relied heavily on land sale revenues as an important source of financing.Nominally, what we mean by deficit is the so-called “in-budget revenue” minusexpenditure. Here, in-budget revenue includes tax and miscellaneous revenue, butnot land sales. In 2007, aggregating over all prefectures, land sales revenue was58% of total government expenditure. The corresponding percentages for years2008–2013 are 22%, 37%, 50%, 44%, 34%, and 49%, respectively. Thesepercentages indicate that land sales are indeed an important part of localgovernment finances, although they vary substantially from year to year. Theratio of land sales revenue to government expenditure also varies substantially byprefecture. Figure 2 shows the distribution of this ratio; the unweighted mean andstandard deviation are 0.35 and 0.15, respectively.

Not surprisingly, we also observe in Figure 3 a strong positive linkagebetween budget deficits and land sales. However, despite the positive and clearassociation between deficits and land sales, there is a great deal of variationin land sales for any given budget deficit. Needless to say, budget deficits arehighly endogenous to various political factors and it is difficult to identify anddisentangle all the relevant factors. For example, budget deficits may be higherbecause governments want to spend more on infrastructure to promote economicgrowth, which is related to the above-mentioned political competition motives.There may also be reasons other than political competition and corruption thatinfluence budget deficits. To address this issue, in the regressions below we compare


Figure 2. Ratio of Land Sales Revenue to Government Expenditure


Figure 3. Budget Deficits and Land Sales Revenue


the results when including budget deficits as a regressor to the results withoutincluding budget deficits as a regressor. The two sets of results are quite similar.In our benchmark regressions, we do not include budget deficits as a regressor. Wealso show results with this regressor in the robustness checks.

B. Corruption

Corruption is widely believed to be prevalent in the PRC, and land sales areoften considered one of the main sources. However, direct evidence on corruption


is hard to come by. Court, media, and party records reflect only part of corruption,and these records often reflect political rivalries and the (in)efficiency of the courtsystem (or similarly, the inner control of the Communist party).

Nevertheless, Cai, Henderson, and Zhang (2013) provide indirect butpowerful proof that corruption in land sales does exist via a comparison betweendifferent auction systems. They show strong evidence that among the threeavailable methods of land sales (sealed bidding, English auction, two-stage auction),corruption is highly associated with the two-stage auction. The second stage of atwo-stage auction is an English auction that occurs if more than one bidder is stillcompeting for the land parcel at the end of the first stage. The key difference in atwo-stage auction from an English auction is that the entry occurs sequentially inthe first stage and is public information, whereas the entry to the English auctionis simultaneous. The sequential nature of this auction form allows both governmentofficials and developers room to signal one another, hence preventing additionalcompetitors from entering.9 Cai, Henderson, and Zhang (2013) show empiricallythat land sale prices and competition are significantly less for two-stage auctionsand that officials divert coveted properties to two-stage auctions. These results areconsistent with the theory of corruption and bidding that the authors develop. Giventheir results, we take the fraction of two-stage auctions among all land sales as aproxy for corruption.

Columns (1)–(4) in Table 2 show the results. Here, we regress the changein each type of land supply (in logarithmic form) on the fraction of two-stageauctions in that type, with the same set of controls in Table 1. Overall, a two-stageauction has a positive and significant effect on the increase of total land supply, aswell as of each type of land. In Columns (5)–(8), we show the results when thedependent variable becomes the logarithm of land sales revenue. In comparisonto the results based on quantity of land sales, the coefficients on corruption aredramatically reduced for residential and commercial land, whereas the reductionsfor total and industrial land are smaller. The effect on industrial land sales revenuebecomes insignificant. Whereas corruption leads to a greater quantity of land sold,the increase in land revenue is less than proportional, suggesting that there is a pricecut when there is a two-stage auction compared with other methods. The price cutis also evidenced in Cai, Henderson, and Zhang (2013), which is consistent with the“bribers’ benefits.”10 These results suggest that residential and commercial land is a

9Cai, Henderson, and Zhang (2013, 494) explain how the signaling is done: “Although the auction isannounced about 20 working days in advance, the exact date of the start of the first stage of the auction may not bespecified. Second, although bidders can apply during the announcement period before the first stage starts, approvalsto participate, or qualification, can be delayed until after the first stage is under way. Thus, the insider bidder alonemay know the exact time the first stage starts and he alone may be qualified to submit a bid at that time. As a result,if there is a bid at reserve price as soon as the auction opens, other bidders can infer from that signal that it is likelythat the auction has been corrupted.”

10As is clear from Figure 1 in Cai, Henderson, and Zhang (2013), the distribution of the ratio of sales price toreserve price is much more condensed around the lower bound (1) for two-stage auctions than English auctions. The


Tabl

e2.

Cor

rupt

ion

and

Lan

dSu

pply

ln(i

ncre

ase

inla

ndsu

pply

)ln

(lan

dsa

les

reve

nue)

Tot

alR

esid

enti

alC

omm

erci

alIn

dust

rial

Tot

alR

esid

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major source of corruption. This is likely because residential and commercial landhave much higher land prices than industrial land.11

Is it possible that these regressions suffer from endogeneity issues?Generally, we cannot rule out the possibility, but we argue that this concern islikely to be minor. First, there could be a channel through which the increase inland supply leads to more corruption because of some sort of positive externalitythat spurs the development of the city and hence increases land demand, whichgives room to more corruption via land sales. However, this channel is unlikely tobe simultaneous with the channel from corruption to changes in land supply, asinfrastructure takes time to build and mega projects such as special economic zonestake time to realize.12 Even when this time lag is short, the controls of currentpopulation and income alleviate this concern. However, can income and populationthemselves be endogenous via the same channel? Again, this is unlikely because ofthe time needed for the effect of land sales on GDP and population increases to takeeffect (e.g., migration restrictions in the PRC). Second, in terms of potential omittedvariables, we do a robustness check by controlling for prefecture fixed effects. Here,we do not control for prefecture fixed effects because corruption may be highlyrelated to local culture and tradition. We investigate prefectural fixed effects insection III.D.

C. The Effect of Years in Office

Here, we study how land sales revenue and the changes in land supply areaffected by the number of years that a prefectural leader has been in office. Thisinformation about the trajectories of land sales, in terms of both quantity andrevenue, within a prefectural leader’s tenure, other things being equal, helps us testthe three hypotheses described in the introduction.

Methodology and Results

In the PRC, the promotion of local government officials is determinedby provincial-level party committees. Considering that for provincial-level party(chief) secretaries, the GDP growth rate serves as a key performance indicator inevaluating the performance of their subordinates (prefectural-level leaders), it hasbeen widely believed that these prefectural-level leaders have a strong incentive to

sales prices of more than 70% of two-stage auctions are at the reserve price, whereas this fraction is slightly morethan 30% for English auctions. They also show that the price cut in two-stage auction is robust when conditional onother factors. This is in line with the bribers’ benefit as they pay less to acquire land parcels using this method.

11The mean, median, and 95th percentile of the prices of industrial land in our pooled sample areCNY200.2, CNY182, and CNY384.6 per square meter, respectively. The corresponding numbers for residentialland are CNY574.2, CNY339.8, and CNY1,774.6; and those for commercial land are CNY605.9, CNY368.5, andCNY1,665.3.

12A more complete modeling of this argument would require a dynamic model.


boost local GDP growth, especially by investing in infrastructure and mega projects.Then, since a substantial amount of funding for such investments comes from landsales and since land sales are more flexible than other revenue sources, it is likelyfor local leaders to try to increase land sales revenue to support such developmentin infrastructure and mega projects. If land prices are very elastic, then selling moreland entails more revenue, but if land prices are inelastic, restricting land supplymay lead to higher revenue. In addition, local governments also have incentives tosupply more land to accommodate growing urban populations, which of course isanother important source of expanding GDP. Therefore, as land sales quantity andrevenue renders different information, we include both as dependent variables.

To test the three hypotheses regarding the effect of years in office, weadopt a quadratic specification and run regressions for both prefectural partysecretaries and mayors. As the conjecture is based on prefectural leaders’ incentiveto promote GDP, we control for both demand factors and corruption. Instead ofsimply controlling prefecture fixed effects to account for prefectural-level invariantfactors, we control for prefecture-specific, party-secretary–mayor fixed effects.That is, for each prefecture, there is a coefficient for each prefectural leader toaccount for its specificity. In such a specification, the sum of such prefecture-leaderfixed effects can be viewed as prefectural fixed effects.13 For the demand factors,we can no longer use the population growth variable because the logarithm ofpopulation increase is time invariant, as we explained earlier in section II. But toavoid completely losing any information on population increases, we change theincome variable (logarithm of the increase in urban GDP per capita) to GDP itself(logarithm of the increase in urban GDP).14 From this point on, we do not repeat theword “urban” in the regression tables, as we focus on the urban areas throughoutthe entire paper.

Before discussing the results, we note that the endogeneity concerns areminor in this case as well. First, there is unlikely to be a reverse causality from landsales (in either quantity or revenue) on the years-in-office variable. The concern hereis that land sales may come back to affect the increase in GDP. Again, this effectis unlikely to be simultaneous with the effect from years in office to land sales asit takes some time for the impacts of infrastructure and/or housing developmentsto be realized. Even when the time lag is short, in our robustness checks we showthat the presence of the GDP-increase variable does not change the results. Last, theinclusion of prefecture-leader fixed effects should alleviate any concern on potentialomitted variables.

Table 3 shows the results for party secretaries. Columns (1)–(4) show the(logarithmic) results for the increase in land supply and Columns (5)–(8) show

13An alternative way is to control for both prefecture fixed effects and prefecture-leader fixed effects. We findin this case the prefecture-leader fixed effects are less precisely estimated (i.e., larger p-values).

14Because we are looking at increases, population information does not automatically drop out.

Determinants of Urban Land Supply in the PRC 167Ta

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those for land sales revenue. For all eight columns, we find strongly significantupward trends during the party secretaries’ tenures. All quadratic-term coefficientsare negative but insignificant, which suggests a very slight concavity in the risingtrend. Table 4 shows the results for mayors. The increases in land supply also showsignificant rising trends with the exception of industrial land. The results on landrevenue is much weaker, except that the coefficient for residential land remainssignificant. All linear-term coefficients for party secretaries are much larger thanthose for mayors. The coefficients of the controls are mostly insignificant, likelybecause the prefecture-leader fixed effects soak up much of the variations.

In sum, we find a rising trend both in the quantity and revenue of land sales,and note that this pattern is much stronger for party secretaries than mayors.

Behind the Rising Pattern

Recalling the three hypotheses laid out in the introduction, Hypothesis 1states that a local leader tends to sell many land parcels or earn higher land salesrevenue in the beginning of his or her term in order to promote local economicgrowth. Hence, for prefectural leaders, land sales quantity and/or revenue tend tobe greater in the initial years in office. Hypothesis 2 states that when a prefecturalleader stays in the position for a long time, he or she might feel pressure to performand hence increase land sales, in terms of quantity and/or revenue, to increase thechance of promotion. Hypothesis 3 states that when a prefectural leader is notpromoted after being in the same position for an extended period, he or she maysimply give up and engage in fewer land sales.

First, the rising pattern is consistent with Hypothesis 2; namely, impatienceor anxiety at not being promoted in later years may contribute to the rising trend.To support this result, we collected data on the length of terms for prefectural partysecretaries and mayors for all 1,773 party secretaries and 1,970 mayors in the 287prefectural cities between 1983 and 2013. The average length of term for partysecretaries was 3.6 years with a standard deviation of 1.9 years, and for mayorsthe average length of term was 3.3 years with a standard deviation of 1.8 years.Moreover, 77% of party secretaries’ length of term was not more than 4 years,whereas the corresponding number for mayors was 70%. Our land and populationdata cover 7 years, but the longest length of term was 9 years for both partysecretaries and mayors. (Some leaders were already in office in 2007 and stayedin that position throughout our data period.) As a large share of leaders stayed inoffice no more than 4 years, those who stayed more than 4 years may have feltanxiety or impatience.

The main difference between Hypotheses 2 and 3 is the differentpsychological reactions to not being promoted in later years. Based on the resultson corruption, it is safe to say that a prefectural leader may increase land sales formore pocket money. Thus, if Hypothesis 3 is true and a prefectural leader gives up in


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.


later years, then the rising pattern in the years-in-office effect on land sales impliesthat these prefectural leaders become more corrupt over time. To check this, weexamine the effect of interacting corruption with years in office in the regressionsin Tables 3 and 4. As seen in Table 2, corruption exerts more influence on landsales quantity than land sales revenue for the reasons explained in section III.B; weexamine the regressions with the change in land supply as the dependent variable.The results are shown in Panel 1 of Table 5. Here we focus on the effect of partysecretaries as the rising trend is most pronounced for them. For total, residential,and commercial land, the coefficients on the interaction terms imply that the effectof corruption on the increase of land supply is smaller the greater the number ofyears in office.15 If a prefectural leader gives up in later years and becomes morecorrupt, he or she should sell more land instead of less. Hence, the results heresuggest that Hypothesis 3 is not supported. An alternative way to examine this is torun a regression of corruption on years in office and the same set of other regressorsin Table 3. Here again we focus on party secretaries. The results are shown in Panel2 of Table 5. We see that conditioned on other factors that may potentially affectcorruption, the correlation between years in office and corruption is negative forresidential land, positive for commercial land, and insignificant for industrial land.Overall, there is a negative but insignificant correlation. We can conclude that theempirical results do not support Hypothesis 3.

The rising pattern throughout the years in office implies that Hypothesis 1 isnot supported, but whether this is inconsistent with the promotion-for-competitionmotive or not should be carefully examined. There are potentially two distinctarguments that may reconcile the competition-for-promotion motive with the factthat Hypothesis 1 does not hold. The first argument is that when urban areas expandcities need to convert rural land into urban land. However, rural land is ownedcollectively by rural residents. Therefore, even if a prefectural leader is ambitious,he or she may need to convert more rural land to urban land first before realizinghis or her ambition. In the PRC, various uses of urban land are collectively calledconstruction land, in contrast with land not slated for development. The increase inconstruction land can be used as a proxy for how much rural land is converted tourban land in a year. Focusing on party secretaries, Panel 1 of Table 6 shows theresults when regressing the increase in construction land (in logarithmic form) onthe same set of regressors as in Table 3.16 Here, we do not find any significant effectsfor years in office. This suggests that the proportion of increase in construction landis roughly constant within a prefectural leader’s tenure. Hence, if a prefectural leader

15The coefficients on the interaction terms for industrial land are insignificant. The positive and significantcoefficients of the quadratic interaction terms of the other types of land indicate a slight convexity, but as thesecoefficients are much smaller than those of the linear interaction terms, the overall pattern is still that the effect ofcorruption on the increase of land supply is less the greater the years in office.

16Different columns show the variation when budget deficits are included and when the GDP-increasevariable is excluded. See section III.D for the reasons behind these variations.


ble

5.C

orru

ptio

n,Y

ears

inO

ffice

,and

Cha

nge

inL

and

Supp

ly

Pan

el1:

Yea

rsin

Offi

cean

dL

and

Supp

lyP

anel

2:C

orru

ptio

nan

dY

ears

inO

ffice

ln(i

ncre

ase

inla

ndsu

pply

)C

orru

ptio

n(f

ract

ion

oftw

o-st

age

auct

ion)

Tot

alR

esid

enti

alC

omm

erci

alIn

dust

rial

Var

iabl

es(1

)(2

)(3

)(4

)(1

)(2

)(3

)(4

)

year

sin

offi

ce1.

137**

*1.

029**

*1.

671**

*0.

355

−0.0

130

−0.0

867**

*0.

105*

0.00

625

(0.3

45)

(0.2

37)

(0.3

52)

(0.3

86)

(0.0

202)

(0.0

330)

(0.0

551)

(0.0

127)

(yea

rsin

offi

ce)2

−0.0

692*

−0.0

359*

−0.1

12**

*0.

0227

0.00

0500

0.00

114

0.00

0425

−9.2

3e–0

5(0

.037

7)(0

.018

7)(0

.034

3)(0

.042

5)(0

.001

18)

(0.0

0209

)(0

.002

88)

(0.0

0084

2)ln

(GD

Pin

crea

se)

−0.0

0298

−0.0

603

−0.0

0450

0.02

740.

0022

7−0

.007

31−0

.000

432

0.00

914

(0.0

383)

(0.0

618)

(0.0

749)

(0.0

602)

(0.0

0597

)(0

.011

3)(0

.013

9)(0

.008

20)

rati

oof

GD

Ps

(ter

tiar

yto

seco

ndar

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0.04

10−0

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*−0

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−0.0

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90.

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−0.1

66−0

.018

8(0

.286

)(0

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)(0

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)(0

.647

)(0

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8)(0

.066

7)(0

.126

)(0

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6)fr

acti

onof

2-st

age

(tot

al)

1.45

7*0.

0159

−0.0

469

0.12

50.

0761

(0.7

78)

(0.7

12)

(0.7

12)

(0.7

69)

(0.7

68)

frac

tion

of2-

stag

e(t

otal

)*(y

ears

inof

fice

)−0

.658

*

(0.3

56)

frac

tion

of2-

stag

e(t

otal

)*(y

ears

inof

fice

)20.

0724

*

(0.0

408)

frac

tion

of2-

stag

e(r

esid

enti

al)

1.20

4***

(0.3

83)

frac

tion

of2-

stag

e(r

esid

enti

al)*

(yea

rsin

offi

ce)

−0.4

26**

(0.1

92)

frac

tion

of2-

stag

e(r

esid

enti

al)*

(yea

rsin

offi

ce)2

0.04

40*

(0.0

224)

frac

tion

of2-

stag

e(c

omm

erci

al)

2.28

0***

(0.6

19)

frac

tion

of2-

stag

e(c

omm

erci

al)*

(yea

rsin

offi

ce)

−0.9

81**

*

(0.3

37)

frac

tion

of2-

stag

e(c

omm

erci

al)*

(yea

rsin

offi

ce)2

0.11

2***

(0.0

384)

Con

tinu

ed.


Tabl

e5.

Con

tinu

ed.

Pan

el1:

Yea

rsin

Offi

cean

dL

and

Supp

lyP

anel

2:C

orru

ptio

nan

dY

ears

inO

ffice

ln(i

ncre

ase

inla

ndsu

pply

)C

orru

ptio

n(f

ract

ion

oftw

o-st

age

auct

ion)

Tot

alR

esid

enti

alC

omm

erci

alIn

dust

rial

Var

iabl

es(1

)(2

)(3

)(4

)(1

)(2

)(3

)(4

)

frac

tion

of2-

stag

e(i

ndus

tria

l)0.

872

(0.8

00)

frac

tion

of2-

stag

e(i

ndus

tria

l)*(

year

sin

offi

ce)

0.09

18(0

.424

)fr

acti

onof

2-st

age

(ind

ustr

ial)

*(ye

ars

inof

fice

)2−0

.032

0(0

.048

4)C

onst

ant

4.26

3***

2.32

1***

−2.0

56**

*4.

851**

0.90

2***

0.70

1***

0.66

9***

0.94

6***

(0.8

41)

(0.7

55)

(0.7

89)

(2.3

17)

(0.0

601)

(0.0

801)

(0.1

61)

(0.0

595)

Pre

fect

ure-

(Par

ty-S

ecre

tary

)F

EY

esY

esY

esY

esY

esY

esY

esY

esY

ear

FE

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Obs

erva

tion

s1,

343

1,34

41,

341

1,34

01,

348

1,34

61,

341

1,34

1R

20.

894

0.86

80.

817

0.82

70.

801

0.84

50.

637

0.70

2

FE

=fi

xed

effe

cts,

GD

P=

gros

sdo

mes

tic

prod

uct.

Not

es:

Dat

aon

the

leng

thof

term

sfo

rpr

efec

tura

lpa

rty

secr

etar

ies

and

may

ors

are

from

auth

ors’

coll

ecti

on.G

DP

data

are

from

Cit

yS

tati

stic

sY

earb

ook.

Lan

dda

taar

efr

omth

ew

ebsi

teof

the

Min

istr

yof

Lan

dan

dR

esou

rces

.Sta

ndar

der

rors

are

clus

tere

dat

the

prov

ince

leve

land

show

nin

pare

nthe

ses.

All

the

regr

essi

ons

inth

ista

ble

are

for

part

yse

cret

arie

s.**

*=

p<

.01,

**=

p<

.05,

*=

p<

0.1.

Sou

rce:

Aut

hors

’ca

lcul

atio

ns.


Tabl

e6.

Yea

rsin

Offi

ce,C

onst

ruct

ion

Lan

d,an

dL

and

Pri

ces

Pan

el1:

Yea

rsin

Offi

cean

dC

onst

ruct

ion

Lan

dP

anel

2:Y

ears

inO

ffice

and

Lan

dP

rice

sln

(inc

reas

ein

cons

truc

tion

land

)ln

(ave

rage

pric

e)

Tot

alR

esid

enti

alC

omm

erci

alIn

dust

rial

Var

iabl

es(1

)(2

)(3

)(4

)(1

)(2

)(3

)(4

)

year

sin

offi

ce−0

.186

−0.2

120.

0048

90.

0189

0.32

3*0.

369*

0.05

520.

0145

(0.4

00)

(0.4

15)

(0.4

38)

(0.4

59)

(0.1

74)

(0.1

89)

(0.2

21)

(0.1

63)

(yea

rsin

offi

ce)2

0.01

340.

0081

90.

0160

0.01

28−0

.005

28−0

.006

20−0

.002

09−0

.003

89(0

.017

6)(0

.018

8)(0

.016

8)(0

.018

0)(0

.004

13)

(0.0

0446

)(0

.005

74)

(0.0

0525

)ln

(GD

Pin

crea

se)

0.15

50.

192

(0.1

59)

(0.1

69)

ln(G

DP

)0.

282

0.34

20.

547

0.12

9(0

.283

)(0

.316

)(0

.408

)(0

.280

)ra

tio

ofG

DP

s(t

erti

ary

tose

cond

ary)

0.84

00.

896

0.51

00.

537

−0.0

0209

0.01

500.

271

0.14

1(0

.902

)(0

.941

)(1

.019

)(1

.088

)(0

.206

)(0

.194

)(0

.217

)(0

.229

)fr

acti

onof

2-st

age

(tot

al)

0.01

59−0

.046

90.

125

0.07

61−0

.406

**

(0.7

12)

(0.7

12)

(0.7

69)

(0.7

68)

(0.1

97)

frac

tion

of2-

stag

e(r

esid

enti

al)

−0.3

16**

*

(0.1

08)

frac

tion

of2-

stag

e(c

omm

erci

al)

−0.3

31**

(0.1

43)

frac

tion

of2-

stag

e(i

ndus

tria

l)−0

.058

9(0

.238

)ln

(bud

getd

efici

t)0.

195

−0.0

129

(0.8

51)

(0.6

88)

Con

stan

t2.

307

1.94

53.

049**

3.04

0**4.

224**

3.58

62.

394

4.15

8**

(1.4

88)

(1.7

65)

(1.3

48)

(1.5

15)

(2.0

03)

(2.2

53)

(2.8

71)

(1.9

82)

Pre

fect

ure-

(Par

ty-S

ecre

tary

)F

EY

esY

esY

esY

esY

esY

esY

esY

esP

refe

ctur

e-M

ayor

FE

Yea

rF

EY

esY

esY

esY

esY

esY

esY

esY

esO

bser

vati

ons

892

867

941

916

1,55

71,

600

1,58

01,

582

R2

0.76

90.

772

0.76

00.

763

0.87

20.

903

0.80

90.

759

FE

=fi

xed

effe

cts,

GD

P=

gros

sdo

mes

tic

prod

uct.

Not

es:

Dat

aon

the

leng

thof

term

sfo

rpr

efec

tura

lpa

rty

secr

etar

ies

and

may

ors

are

from

auth

ors’

coll

ecti

on.G

DP

data

are

from

Cit

yS

tati

stic

sY

earb

ook.

Lan

dda

taar

efr

omth

ew

ebsi

teof

the

Min

istr

yof

Lan

dan

dR

esou

rces

.Sta

ndar

der

rors

are

clus

tere

dat

the

prov

ince

leve

land

show

nin

pare

nthe

ses.

All

the

regr

essi

ons

inth

ista

ble

are

for

part

yse

cret

arie

s.In

the

Peop

le’s

Rep

ubli

cof

Chi

na,v

ario

usus

esof

urba

nla

ndar

eco

llec

tivel

yca

lled

cons

truc

tion

land

inco

ntra

stw

ith

land

that

isno

tfor

deve

lopm

ent.

***

=p

<.0

1,**

=p

<.0

5,*

=p

<0.

1.S

ourc

e:A

utho

rs’

calc

ulat

ions

.


is ambitious in land sales, we should probably see a larger increase in constructionland initially, but we do not see this here.

The second argument is that as most cities in the PRC were growing in termsof both population and income during the review period, by restricting the quantityof land sales early a local government might raise more revenue later since landprices have been driven up. Thus, an ambitious prefectural leader might trade higherrevenue early on in his or her tenure for larger overall land sales revenue over alonger time span, which could still increase the chance of getting promoted (evenif a little later than the average number of years in office). If this conjecture is true,then we should expect to see a rising trend of land prices as well. Focusing on partysecretaries, Panel 2 in Table 6 shows the results when regressing land prices (inlogarithmic form) on the same set of regressors as in Table 3, except that we replacethe GDP-increase variable with GDP itself.17 Here we find that land prices rose overthe years in office for the party secretaries (Column [1]) and that the effect is mainlydriven by residential land prices (Column [2]). Further evidence for this argumentcomes from regressing land sales revenue on each nth year in office as a dummy(instead of the quadratic specification) with n = 1, 2, … , 9, and with the same setof other controls. Again, there is a clear rising trend in the effect of years in office,and the increments from one year to the next are 1.13, 1.15, 1.15, 1.23, 1.09, 1.05,0.96, and 0.84, respectively. (The maximum number of years in office is 9 years forparty secretaries; hence, there are eight increments). Thus, the increase in revenue islarger from year 2 to year 5, and the increase becomes smaller after 5 years. This isalso consistent with the above-mentioned distribution of term lengths. Thus, we canconclude that the rising pattern in the first few years may still be consistent with thecompetition-for-promotion motive because prefectural leaders may aim for higherland sales revenue overall in the first few (around 5) years in office.18

D. Robustness Checks

We conducted various robustness checks for our results in the previous twosubsections. First, we conducted two sets of robustness checks for the results oncorruption. For the reasons explained in section III.A, we ran the same set ofregressions in Table 2 but are now controlling for budget deficits (in logarithmicform) in Table 7. The results remain quite similar to those in Table 2.

In the benchmark regressions in Table 2, we do not control for prefecturefixed effects, as corruption may be related to local culture. Table 8 shows the results

17In the previous regressions, both the dependent and independent variables are flows. But as prices shoulddepend on overall supply and demand, we use GDP instead of its increase as the demand factor.

18With different data coverage, Du and Peiser (2014) show evidence for this hypothesis at the provinciallevel.


ble

7.R

obus

tnes

sC

heck

—C

orru

ptio

n—w

ith

Bud

get

Defi

cits

ln(i

ncre

ase

inla

ndsu

pply

)ln

(lan

dsa

les

reve

nue)

Tot

alR

esid

enti

alC

omm

erci

alIn

dust

rial

Tot

alR

esid

enti

alC

omm

erci

alIn

dust

rial

Var

iabl

es(1

)(2

)(3

)(4

)(5

)(6

)(7

)(8

)

ln(p

opul

atio

nin

crea

se)

0.40

9***

0.38

8***

0.42

6***

0.44

3***

0.63

2***

0.67

7***

0.67

6***

0.56

5***

(0.0

555)

(0.0

647)

(0.0

569)

(0.0

635)

(0.0

878)

(0.0

908)

(0.0

879)

(0.0

904)

ln(i

ncre

ase

inG

DP

per

capi

ta)

0.05

71**

*0.

0391

***

0.06

67**

*0.

0528

***

0.06

64**

*0.

0736

***

0.10

1***

0.07

37**

*

(0.0

109)

(0.0

134)

(0.0

118)

(0.0

140)

(0.0

143)

(0.0

160)

(0.0

154)

(0.0

175)

rati

oof

GD

Ps

(ter

tiar

yto

seco

ndar

y)−0

.276

***

−0.1

94*

0.09

81−0

.647

***

0.00

877

0.13

90.

476**

−0.5

81**

*

(0.0

946)

(0.1

03)

(0.1

30)

(0.1

94)

(0.1

74)

(0.1

72)

(0.2

32)

(0.1

96)

ln(b

udge

tdefi

cit)

0.59

7***

0.85

2***

0.56

9**0.

515**

0.14

30.

115

−0.0

561

0.40

5(0

.226

)(0

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)(0

.236

)(0

.259

)(0

.371

)(0

.381

)(0

.356

)(0

.357

)fr

acti

onof

2-st

age

(tot

al)

0.87

1***

0.57

7**

(0.2

16)

(0.2

76)

frac

tion

of2-

stag

e(r

esid

enti

al)

0.81

6***

0.27

4*

(0.1

33)

(0.1

58)

frac

tion

of2-

stag

e(c

omm

erci

al)

1.20

6***

0.47

4**

(0.2

16)

(0.2

06)

frac

tion

of2-

stag

e(i

ndus

tria

l)0.

762*

0.56

0(0

.456

)(0

.423

)C

onst

ant

4.48

6***

2.82

3***

−2.9

70**

*2.

492**

*9.

548**

*9.

382**

*3.

712**

*7.

618**

*

(0.3

36)

(0.3

87)

(0.3

53)

(0.5

55)

(0.5

61)

(0.5

37)

(0.4

83)

(0.6

11)

Yea

rF

EY

esY

esY

esY

esY

esY

esY

esY

esO

bser

vati

ons

1,40

31,

402

1,40

21,

394

1,37

51,

402

1,39

11,

394

R2

0.52

50.

439

0.38

10.

466

0.53

80.

509

0.44

60.

487

FE

=fi

xed

effe

cts,

GD

P=

gros

sdo

mes

tic

prod

uct.

Not

es:P

opul

atio

nda

taar

efr

ompo

pula

tion

cens

usda

tain

2000

and

2010

and

auth

ors’

calc

ulat

ions

.GD

Pda

taar

efr

omC

ity

Sta

tist

ics

Yea

rboo

k.L

and

data

are

from

the

web

site

ofth

eM

inis

try

ofL

and

and

Res

ourc

es.S

tand

ard

erro

rsar

ecl

uste

red

atth

epr

ovin

cele

vel

and

show

nin

pare

nthe

ses.

The

frac

tion

of2-

stag

e(t

ype)

isth

efr

acti

onof

all

tran

sact

ions

wit

hin

that

pref

ectu

refo

rth

atty

peof

land

(or

tota

l)th

atus

ea

2-st

age

auct

ion.

Itis

used

topr

oxy

corr

upti

on.**

*=

p<

.01,

**=

p<

.05,

*=

p<

0.1.

Sou

rce:

Aut

hors

’ca

lcul

atio

ns.


Tabl

e8.

Rob

ustn

ess

Che

ck—

Cor

rupt

ion—

wit

hP

refe

ctur

eF

ixed

Eff

ects

ln(i

ncre

ase

inla

ndsu

pply

)ln

(lan

dsa

les

reve

nue)

Tot

alR

esid

enti

alC

omm

erci

alIn

dust

rial

Tot

alR

esid

enti

alC

omm

erci

alIn

dust

rial

Var

iabl

es(1

)(2

)(3

)(4

)(5

)(6

)(7

)(8

)

ln(G

DP

incr

ease

)0.

0456

−0.0

0780

0.04

920.

0052

9−0

.006

460.

0060

30.

0192

0.00

550

(0.0

413)

(0.0

560)

(0.0

664)

(0.0

700)

(0.0

492)

(0.0

656)

(0.0

640)

(0.0

585)

rati

oof

GD

Ps

(ter

tiar

yto

seco

ndar

y)−0

.344

−0.2

19−0

.735

**−0

.627

*−0

.413

*−0

.487

−0.4

29−0

.563

*

(0.2

16)

(0.3

22)

(0.2

86)

(0.3

74)

(0.2

39)

(0.3

66)

(0.2

76)

(0.3

40)

frac

tion

of2-

stag

e(t

otal

)0.

238

0.25

0(0

.158

)(0

.214

)fr

acti

onof

2-st

age

(res

iden

tial

)0.

441**

0.07

04(0

.189

)(0

.187

)fr

acti

onof

2-st

age

(com

mer

cial

)0.

635**

*0.

303*

(0.1

68)

(0.1

61)

frac

tion

of2-

stag

e(i

ndus

tria

l)1.

126**

1.05

1*

(0.5

39)

(0.5

40)

Con

stan

t7.

938**

*6.

452**

*2.

618**

5.91

0***

15.3

5***

15.6

6***

10.4

2***

11.1

8***

(0.9

39)

(1.2

56)

(1.0

89)

(1.7

71)

(0.9

82)

(1.3

57)

(1.0

73)

(1.4

88)

Pre

fect

ure

FE

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yea

rF

EY

esY

esY

esY

esY

esY

esY

esY

esO

bser

vati

ons

1,34

31,

344

1,34

11,

340

1,32

51,

344

1,33

11,

340

R2

0.84

90.

814

0.76

00.

786

0.90

80.

872

0.84

40.

856

FE

=fi

xed

effe

cts,

GD

P=

gros

sdo

mes

tic

prod

uct.

Not

es:P

opul

atio

nda

taar

efr

ompo

pula

tion

cens

usda

tain

2000

and

2010

and

auth

ors’

calc

ulat

ions

.GD

Pda

taar

efr

omC

ity

Sta

tist

ics

Yea

rboo

k.L

and

data

are

from

the

web

site

ofth

eM

inis

try

ofL

and

and

Res

ourc

es.S

tand

ard

erro

rsar

ecl

uste

red

atth

epr

ovin

cele

vel

and

show

nin

pare

nthe

ses.

The

frac

tion

of2-

stag

e(t

ype)

isth

efr

acti

onof

all

tran

sact

ions

wit

hin

that

pref

ectu

refo

rth

atty

peof

land

(or

tota

l)th

atus

ea

2-st

age

auct

ion.

Itis

used

topr

oxy

corr

upti

on.**

*=

p<

.01,

**=

p<

.05,

*=

p<

0.1.

Sou

rce:

Aut

hors

’ca

lcul

atio

ns.


with these prefecture fixed effects. Here, we see that the coefficients on corruptionare reduced sharply, except that the coefficients on industrial land increases.Comparison with Table 2 suggests that corruption may indeed be related to localtime-invariant factors but, even so, the increases in residential and commercial landsupply still exhibit very strong correlations with corruption. Also intriguing is thefact that the effect of corruption on industrial land increases (and even becomessignificant for industrial land revenue) compared with Table 2. This suggests thatindustrial activity is less correlated with local time-invariant factors, likely becauseindustrial activities are quite mobile across locations.

For the robustness checks for the years-in-office effect, we also examinecases where budget deficits are controlled. To address endogeneity issues, we relyon the assumption that the channel from land sales, in terms of either quantity orrevenue, to boost GDP is not simultaneous; hence, we simply treat the current-year increase in GDP as a demand factor variable. We also do a robustness checkwhen GDP increase is not included in case the time lag of the above-mentionedchannel is short. For these robustness checks, we focus on the total quantity andrevenue of land sales. Table 9 shows the results for party secretaries. Columns(1)–(5) repeat the benchmark results from Table 3 for quantity and revenue ofland sales, respectively. Columns (2)–(4) show the results for quantity of land saleswith budget deficits controlled (Column [2]), when GDP-increase variable is notcontrolled (Column [3]), and a combination of the previous two cases (Column[4]). Columns (6)–(8) repeat the same cases for land sales revenue. The resultsare all very similar to the benchmark. Table 10 repeats the same exercises formayors.

The rising trend for the increase in land supply remains quite robust. Again,we do not see much influence from controlling for budget deficits, but we start tosee a rising trend for land revenue when GDP-increase variable is not controlled. InTable 9, we do not see such large changes in coefficients. This is likely because theexplanatory power of mayors’ years in office is weaker when compared with Tables3 and 4; the GDP-increase variable accounts for some variation in land sales. Thus,when GDP-increase variable is taken out, mayors’ years in office starts to capturethe variations in land sales.

The last robustness check is to include prefecture-specific time trends. Thisis based on the following concern. Since land sales quantity, price, and revenueall rise over time, could our observed result of the rising trends for years in officesimply result from the various controls not being able to fully explain these risingtrends in terms of quantity, price, and sales? This concern is not fully addressed bycontrolling for year fixed effects, which capture overall rising trends in land salesquantity, price, and revenue, but not those specific to prefectures. Thus, we addprefecture-specific linear time trends to the regressions in Tables 3 and 4 and showthe results in Table 11 for total land supply only. Linear time trends translate toexponential time trends in levels as our dependent variables are all in logarithmic


Tabl

e9.

Rob

ustn

ess

Che

ck—

Yea

rsin

Offi

ce(P

arty

Secr

etar

ies)

and

Lan

dSu

pply

ln(i

ncre

ase

into

tall

and

supp

ly)

ln(t

otal

land

sale

sre

venu

e)

Var

iabl

es(1

)(2

)(3

)(4

)(5

)(6

)(7

)(8

)

year

sin

offi

ce0.

541**

*0.

524**

*0.

596**

*0.

599**

*1.

175**

*1.

185**

*1.

131**

*1.

161**

*

(0.1

34)

(0.1

36)

(0.1

17)

(0.1

16)

(0.1

33)

(0.1

43)

(0.1

50)

(0.1

64)

(yea

rsin

offi

ce)2

−0.0

0146

−0.0

0185

−0.0

0155

−0.0

0210

−0.0

0687

−0.0

0652

−0.0

0588

−0.0

0598

(0.0

0689

)(0

.007

05)

(0.0

0646

)(0

.006

65)

(0.0

0753

)(0

.007

82)

(0.0

0663

)(0

.006

88)

ln(G

DP

incr

ease

)0.

0024

30.

0075

7−0

.037

3−0

.037

1(0

.038

0)(0

.038

6)(0

.056

9)(0

.059

4)ra

tio

ofG

DP

s(t

erti

ary

tose

cond

ary)

−0.2

29−0

.209

−0.3

06−0

.277

−0.3

49−0

.386

−0.2

98−0

.315

(0.2

82)

(0.2

93)

(0.2

57)

(0.2

59)

(0.2

83)

(0.3

08)

(0.3

54)

(0.3

76)

ln(b

udge

tdefi

cit)

0.07

80−0

.086

40.

0202

−0.1

78(0

.191

)(0

.219

)(0

.253

)(0

.352

)fr

acti

onof

2-st

age

(tot

al)

0.26

00.

260

0.26

70.

266

0.28

70.

293

0.25

30.

263

(0.2

25)

(0.2

26)

(0.1

85)

(0.1

87)

(0.3

10)

(0.3

12)

(0.2

51)

(0.2

53)

Con

stan

t5.

411**

*5.

218**

*5.

310**

*5.

348**

*10

.05**

*10

.07**

*9.

866**

*10

.05**

*

(0.4

88)

(0.5

36)

(0.3

21)

(0.3

61)

(0.5

82)

(0.7

14)

(0.4

52)

(0.5

94)

Pre

fect

ure-

(Par

ty-S

ecre

tary

)F

EY

esY

esY

esY

esY

esY

esY

esY

esY

ear

FE

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Obs

erva

tion

s1,

343

1,30

91,

599

1,55

91,

325

1,29

21,

557

1,51

8R

20.

893

0.89

20.

891

0.89

00.

933

0.93

10.

933

0.93

1

FE

=fi

xed

effe

cts,

GD

P=

gros

sdo

mes

tic

prod

uct.

Not

es:

Dat

aon

the

leng

thof

term

sfo

rpr

efec

tura

lpa

rty

secr

etar

ies

and

may

ors

are

from

auth

ors’

coll

ecti

on.G

DP

data

are

from

Cit

yS

tati

stic

sY

earb

ook.

Lan

dda

taar

efr

omth

ew

ebsi

teof

the

Min

istr

yof

Lan

dan

dR

esou

rces

.Sta

ndar

der

rors

are

clus

tere

dat

the

prov

ince

leve

lan

dsh

own

inpa

rent

hese

s.A

pref

ectu

re(p

arty

-sec

reta

ry)

fixe

def

fect

isac

tual

lya

pref

ectu

re-s

peci

fic,

part

y-se

cret

ary

fixe

def

fect

;th

atis

,for

each

pref

ectu

re,t

here

isa

coef

fici

ent

for

each

pref

ectu

ral

part

yse

cret

ary

toac

coun

tfo

rit

ssp

ecifi

city

.***

=p

<.0

1,**

=p

<.0

5,*

=p

<0.

1.S

ourc

e:A

utho

rs’

calc

ulat

ions

.


Tabl

e10

.R

obus

tnes

sC

heck

—Y

ears

inO

ffice

(May

ors)

and

Lan

dSu

pply

ln(i

ncre

ase

into

tall

and

supp

ly)

ln(t

otal

land

sale

sre

venu

e)

Var

iabl

es(1

)(2

)(3

)(4

)(5

)(6

)(7

)(8

)

year

sin

offi

ce0.

163**

*0.

171**

*0.

563**

*0.

600**

*0.

0122

0.00

496

1.11

5***

1.16

9***

(0.0

512)

(0.0

498)

(0.1

34)

(0.1

38)

(0.0

502)

(0.0

520)

(0.1

87)

(0.2

12)

(yea

rsin

offi

ce)2

−0.0

0187

−0.0

0343

0.00

129

−0.0

0086

9−0

.003

79−0

.003

14−0

.001

11−0

.000

845

(0.0

0806

)(0

.007

74)

(0.0

0890

)(0

.008

55)

(0.0

0753

)(0

.007

76)

(0.0

0960

)(0

.010

1)ln

(GD

Pin

crea

se)

0.01

120.

0147

−0.0

261

−0.0

331

(0.0

414)

(0.0

424)

(0.0

620)

(0.0

643)

rati

oof

GD

Ps

(ter

tiar

yto

seco

ndar

y)−0

.096

8−0

.132

−0.1

16−0

.127

−0.4

15−0

.531

−0.3

39−0

.392

(0.3

41)

(0.3

59)

(0.3

34)

(0.3

48)

(0.3

99)

(0.4

51)

(0.4

96)

(0.5

45)

ln(b

udge

tdefi

cit)

0.06

97−0

.144

−0.0

819

−0.2

63(0

.214

)(0

.261

)(0

.274

)(0

.396

)fr

acti

onof

2-st

age

(tot

al)

0.08

000.

0788

0.11

00.

111

0.08

930.

107

0.00

534

0.02

02(0

.244

)(0

.244

)(0

.186

)(0

.186

)(0

.290

)(0

.291

)(0

.247

)(0

.248

)C

onst

ant

4.80

3***

4.69

9***

5.46

8***

5.60

3***

7.87

5***

7.97

9***

10.0

9***

10.3

9***

(0.3

55)

(0.4

05)

(0.3

64)

(0.4

34)

(0.4

57)

(0.5

35)

(0.5

38)

(0.7

23)

Pre

fect

ure-

May

orF

EY

esY

esY

esY

esY

esY

esY

esY

esY

ear

FE

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Obs

erva

tion

s1,

343

1,30

91,

599

1,55

91,

325

1,29

21,

557

1,51

8R

20.

892

0.89

00.

890

0.88

90.

938

0.93

70.

937

0.93

6

FE

=fi

xed

effe

cts,

GD

P=

gros

sdo

mes

tic

prod

uct.

Not

es:

Dat

aon

the

leng

thof

term

sfo

rpr

efec

tura

lpa

rty

secr

etar

ies

and

may

ors

are

from

auth

ors’

coll

ecti

on.

GD

Pda

taar

efr

omC

ity

Sta

tist

ics

Yea

rboo

k.L

and

data

are

from

the

web

site

ofth

eM

inis

try

ofL

and

and

Res

ourc

es.

Sta

ndar

der

rors

are

clus

tere

dat

the

prov

ince

leve

lan

dsh

own

inpa

rent

hese

s.A

pref

ectu

re(m

ayor

)fi

xed

effe

ctis

actu

ally

apr

efec

ture

-spe

cifi

c,m

ayor

fixe

def

fect

;th

atis

,fo

rea

chpr

efec

ture

,th

ere

isa

coef

fici

ent

for

each

pref

ectu

ral

may

orto

acco

unt

for

its

spec

ifici

ty.

***

=p

<.0

1,**

=p

<.0

5,*

=p

<0.

1.S

ourc

e:A

utho

rs’

calc

ulat

ions

.


Tabl

e11

.R

obus

tnes

sC

heck

—Y

ears

inO

ffice

for

Pre

fect

ural

Lea

ders

(Pre

fect

ure-

Spec

ific

Tim

eT

rend

)

Par

tySe

cret

arie

sM

ayor

s

ln(i

ncre

ase

ln(t

otal

ln(i

ncre

ase

ln(t

otal

ln(i

ncre

ase

ln(t

otal

ln(i

ncre

ase

ln(t

otal

into

tal

land

sale

sin

tota

lla

ndsa

les

into

tal

land

sale

sin

tota

lla

ndsa

les

land

supp

ly)

reve

nue)

land

supp

ly)

reve

nue)

land

supp

ly)

reve

nue)

land

supp

ly)

reve

nue)

Var

iabl

es(1

)(2

)(3

)(4

)(5

)(6

)(7

)(8

)

year

sin

offi

ce0.

738**

*1.

634**

*1.

065**

*1.

880**

*0.

385**

*0.

254**

0.43

5***

0.30

8***

(0.2

25)

(0.3

13)

(0.2

20)

(0.3

29)

(0.0

961)

(0.1

27)

(0.0

726)

(0.0

953)

(yea

rsin

offi

ce)2

−0.0

0189

−0.0

139

0.01

610.

0125

0.00

106

−0.0

114

0.01

270.

0048

1(0

.015

0)(0

.019

9)(0

.012

8)(0

.015

7)(0

.018

8)(0

.023

9)(0

.015

3)(0

.019

5)ln

(GD

Pin

crea

se)

0.02

900.

0848

0.00

247

−0.0

0272

0.00

365

0.06

410.

0005

43−0

.009

81(0

.041

3)(0

.063

2)(0

.044

3)(0

.075

4)(0

.048

7)(0

.070

9)(0

.047

4)(0

.084

8)ra

tio

ofG

DP

s(t

erti

ary

tose

cond

ary)

0.46

7−0

.266

−0.0

0161

−0.4

450.

410

−0.2

88−0

.158

−0.5

33(0

.486

)(0

.694

)(0

.481

)(0

.731

)(0

.624

)(0

.807

)(0

.634

)(0

.870

)fr

acti

onof

2-st

age

(tot

al)

−0.3

13−0

.530

−0.1

93−0

.343

−0.2

99−0

.307

−0.2

27−0

.232

(0.4

70)

(0.4

94)

(0.4

54)

(0.4

26)

(0.4

93)

(0.5

10)

(0.4

70)

(0.4

57)

Con

stan

t3.

088**

*9.

251**

*3.

764**

*8.

792**

*2.

289**

*5.

139**

*2.

809**

*5.

752**

*

(0.8

25)

(1.2

08)

(0.6

51)

(0.9

58)

(0.6

11)

(0.7

64)

(0.6

32)

(0.8

07)

Pre

fect

ure-

Spe

cifi

cT

ime

Tre

ndY

esY

esY

esY

esY

esY

esY

esY

esP

refe

ctur

e-(P

arty

-Sec

reta

ry)

FE

Yes

Yes

Yes

Yes

Pre

fect

ure-

May

orF

EY

esY

esY

esY

esY

ear

FE

No

No

Yes

Yes

No

No

Yes

Yes

Obs

erva

tion

s1,

343

1,32

51,

343

1,32

51,

343

1,32

51,

343

1,32

5R

20.

934

0.95

50.

945

0.96

60.

928

0.95

90.

939

0.96

7

FE

=fi

xed

effe

cts,

GD

P=

gros

sdo

mes

tic

prod

uct.

Not

es:

Dat

aon

the

leng

thof

term

sfo

rpr

efec

tura

lpa

rty

secr

etar

ies

and

may

ors

are

from

auth

ors’

coll

ecti

on.G

DP

data

are

from

Cit

yS

tati

stic

sY

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form. Here, we see that even with the linear time trend, the rising trend in years inoffice remains quite robust.19

IV. Conclusions

In this paper, we hypothesize that the two key features in the PRC’s politicaleconomy of land supply are corruption and competition for promotion. We thusexplore the effects of corruption on the increase in urban land supply, as well asthe effect of prefectural leaders’ years in office to shed light on the competitionmotives. Conditional on standard urban-economic (demand-side) determinants andindustrial structure, the usage of two-stage auctions (as the indicator of corruption)is strongly associated with the quantity of land sales, but less so for land salesrevenue. This suggests that while increased corruption leads to larger increases inland supply, it reduces prices compared with other methods of land sales. The effectsof corruption are strongest for commercial land, followed by residential land. Theeffects for industrial land are insignificant. This indicates that industrial land salesare not a major source of corruption, perhaps because of its relatively lower landvalue.

For the years-in-office effect, we formulate three hypotheses regarding howthe competition-for-promotion motive can matter. Our empirical results show veryrobust rising trends in land sales both in terms of quantity and revenue. These resultsare consistent with the hypothesis that the impatience and anxiety of not havingbeen promoted may contribute to the increase in land sales revenue in later years,and are inconsistent with the hypothesis that prefectural leaders may give up in lateryears. By investigating how land prices and the increment of land sales revenuechange with years in office, we find that the rising pattern in the first few years canstill be consistent with the competition-for-promotion motive because prefecturalleaders may aim for larger land sales revenue overall in the first few (around 5)years in office. Altogether, these results suggest that the competition-for-promotionmotive is likely to affect land sales through a combination of maximizing overallrevenues in the first few years in office by restraining early sales and the subsequentimpatience or anxiety of not getting promoted in later years.

The questions that we ask and study in this paper seem quite specific to thePRC since these activities occur in an environment where local governments havedominant land ownership and strong control over land use, and where the selectionof government officials is a top-down process rather than a bottom-up process as ina democracy. There are a few other communist countries that have similar features,but none of them is going through or has gone through the kind of economic reformand growth that has spurred urbanization in the PRC, except perhaps Viet Nam.

19In particular, whereas the years-in-office effect on total land revenue is not significant in Table 4, it has nowbecome significant with the prefecture-specific time trend (Columns [6]–[8] in Table 11).


Hong Kong, China and Singapore also share some similar features in terms of thecontrol over land use that these two governments have, but obviously there is a lackof political competition with other cities within a city’s hierarchy.

Nevertheless, these lessons from the PRC are precious and interestingprecisely because of their specificity as we get to see the effects of institutions.Although this paper provides no ground for making normative statements about thePRC’s urban land supply, this first-cut evidence provides us with clues to think aboutnormative issues. In particular, as the institutional environment seems to resembleone where Henry George’s idea of a single tax on land might be put into effect, onecan ask to what extent the Henry George theorem (Arnott and Stiglitz 1979) holdsin the PRC. As mentioned by Cai, Henderson, and Zhang (2013), the prevalenceof corruption has squandered the opportunity for the PRC to realize the ideals ofHenry George. Our results indicate that corruption is relevant. Yet, the quantitativequestion remains unanswered here and the qualitative question of how politicalcompetition matters in terms of welfare must also be clarified. We tackle thesequestions in Hsu and Tang (2016).

References

Arnott, Richard J., and Joseph E. Stiglitz. 1979. “Aggregate Land Rents, Expenditure onPublic Goods, and Optimal City Size.” Quarterly Journal of Economics 93 (4): 471–500.

Cai, Hongbin, J. Vernon Henderson, and Qinghua Zhang. 2013. “China’s Land Market Auctions:Evidence of Corruption?” The Rand Journal of Economics 44 (3): 488–521.

Deng, Yongheng, Joseph Gyourko, and Jing Wu. 2012. “Land and House Price Measurementin China.” In Property Markets and Financial Stability, edited by Alexandra Heath, FrankPacker, and Callan Windsor. Basel: Bank of International Settlement and Reserve Bank ofAustralia.

Du, Jinfeng, and Richard Peiser. 2014. “Land Supply, Pricing and Local Governments’ LandHoarding in China.” Regional Science and Urban Economics 48: 180–89.

Fang, Hangming, Quanlin Gu, Wei Xiong, and Li-An Zhou. 2015. “Demystifying the ChineseHousing Boom.” NBER Working Paper No. 21112.

Fujita, Masahisa, Tomoya Mori, J. Vernon Henderson, and Yoshitsugu Kanemoto. 2004. “SpatialDistribution of Economic Activities in Japan and China.” In Handbook of Regional andUrban Economics, edited by J. Vernon Henderson and Jacques-François Thisse, 2911–77.Amsterdam: Elsevier.

Hsu, Wen-Tai, and Yang Tang. 2016. “The Political Economy of Land Supply in China.”Singapore Management University Working Paper.

Li, Hongbin, and Li-An Zhou. 2005. “Political Turnover and Economic Performance: TheIncentive Role of Personnel Control in China.” Journal of Public Economics 89 (9/10):1743–72.

Liang, Wenquan, Ming Lu, and Hang Zhang. 2016. “Housing Prices Threaten Competitiveness:How Do China’s Inland-Favoring Land Policies Raise Wages.” Journal of HousingEconomics. Forthcoming.


Wu, Guiying, Qu Feng, and Pei Li. 2015. “Does Local Governments’ Budget Deficit Push UpHousing Prices in China?” China Economic Review 35: 183–96.

Wu, Jing, Joseph Gyourko, and Yongheng Deng. 2016. “Evaluating the Risk of Chinese HousingMarkets: What We Know and What We Need to Know.” China Economic Review 39: 91–114.

Zheng, Siqi, Matthew E. Kahn, Weizeng Sun, and Danglun Luo. 2014. “Incentives for China’sUrban Mayors to Mitigate Pollution Externalities: The Role of the Central Government andPublic Environmentalism.” Regional Science and Urban Economics 47: 61–71.

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