Determinants of Urban Land Supply in the - IA de Investigación especializada en el MIT

Determinants of Urban Land Supply in the
People’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-promotion
motives affect urban land supply in the People’s Republic of China. Conditional
on demand-side factors, we ﬁnd that corruption is highly correlated with an
increase in land supply. The corruption effects are strongest for commercial
land, followed by residential land, and then industrial land. To shed light
on the competition motives among prefectural leaders, we examine how the
number of years in ofﬁce affects land supply and distinguish among different
hypotheses. Our empirical results show robust rising trends in land sales. Estos
results are consistent with the hypothesis that among prefectural leaders the
impatience and anxiety in later years from not being promoted may contribute
to an increase in land sales revenue in later years. We also ﬁnd that prefectural
leaders may aim for more land sales revenue over their ﬁrst few years in ofﬁce
instead of seeking higher revenue in their ﬁrst 1–2 years.

Palabras clave: institución, land supply, monocentric-city model, People’s Republic
of China, political factors
JEL codes: O18, P25, P26

I. Introducción

According to communist theory, and hence by law in the People’s Republic
of China (PRC), all urban land in the PRC is owned by the state. Sin embargo, como el
PRC has opened up and pursued economic reform toward a more market-oriented
economía, land in the PRC is also being gradually marketized. The current system is
based on leaseholding; the state is still the ultimate owner of all land, but private

∗Wen-Tai Hsu (Autor correspondiente): Associate Professor of Economics, School of Economics, Singapur
Management University. Correo electrónico: wentaihsu@smu.edu.sg; Xiaolu Li: Assistant Professor, Institute of Urban
Desarrollo, Nanjing Audit University. Correo electrónico: lixiaolu.sg@gmail.com; Yang Tang: Assistant Professor, División
of Economics, Nanyang Technological University. Correo electrónico: tangyang@ntu.edu.sg; Jing Wu: Associate Professor,
Hang Lung Center for Real Estate and Department of Construction Management, Universidad de Tsinghua. Correo electrónico:
ireswujing@tsinghua.edu.cn. We would like to thank Tomoki Fujii, Jing Li, participants at the Asian Development
Review 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 his
outstanding research assistance. Wen-Tai Hsu gratefully acknowledges the ﬁnancial support provided by the Sing Lun
Fellowship of Singapore Management University. en este documento, the Asian Development Bank recognizes “China” as
the People’s Republic of China. The usual disclaimer applies.

Asian Development Review, volumen. 34, No. 2, páginas. 152–183

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Determinants of Urban Land Supply in the PRC 153

parties can purchase land use rights for a certain period (usually 70 years for
residential use and 40 years for commercial use). Before 2004, the way in which
these leaseholds were sold or transferred was not transparent. Reform to the law in
2004 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 urbanization
rate increased from around 20% en 1979 to about 62% en 2010. As city populations
crecer, demand for urban land increases. In other economies where free markets
are the norm, economic forces—such as those illustrated in Alonzo–Muth–Mills
monocentric-city models—may explain most of the urban land supply expansion.
Local governments around the globe have inﬂuence over land supply through
zoning, land use regulations, and housing permits. But such political inﬂuence is
presumably dwarfed by the large power that local governments in the PRC enjoy
when determining their land supply. In the Asian context, two notable exceptions
are Hong Kong, China and Singapore.

We obtained data for all transactions of land parcels by the various levels of
local government in the PRC since 2007. We aggregated variables at the prefectural
nivel. 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; ellos son
typically rural areas. Prefectural-level city is the ofﬁcial name for such jurisdictions,
but this can be somewhat confusing since their geographic scope is usually much
larger than a metropolitan area’s and typically covers large tracts of rural land. Para
this reason, we prefer to simply call them prefectures. We use the total ﬂoor space
sold by local governments in each prefecture to measure the change in land supply.
This information is available for each year from 2007 a 2013. Corresponding
measures for each of the three major types of usage—residential, comercial, y
industrial—are also available. Our land and population data together cover the urban
parts of 286 prefectures.1

The purpose of this paper is to empirically probe the political determinants
of land supply. In this context, we will investigate two distinct issues. el primero
is corruption, which is believed to be one of the motives in local governments’
selling of land (ver, Por ejemplo, Cai, Henderson, and Zhang 2013). Segundo, a
shed light on the inﬂuence of competition among prefectural ofﬁcials, we study
the effect of years in ofﬁce—the number of years that a particular leader has been
in ofﬁce in that prefecture in the year of sale—on land sales.2 The literature has
documented 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 rural
residents. De este modo, land sales by local governments are urban by their nature. We also have the urban population of each
prefecture. See section II.A for details.

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

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154 Asian Development Review

posts and the Communist party has various effects.3 The institutional background
is that there are no speciﬁc term periods or limits for prefectural leaders; su
appointments are reviewed and decided by provincial leaders within the Communist
party.4 The key criteria is local economic performance, measured in terms of gross
domestic product (PIB) growth, and hence competition among prefectural leaders
encourages them to sell land or pursue higher land sales revenue in order to fund
infrastructure or other mega projects that may help boost the local economy. El
fact that local governments were not allowed to borrow by issuing debt until very
recently reinforced this incentive.

Based on these competition motives, we propose three hypotheses regarding
how years in ofﬁce may affect land supply. Hipótesis 1 is that a local leader tends
to sell many land parcels or earn large land sales revenue in the beginning of his or
her term in order to promote local economic growth. Primero, higher land sales revenue
may provide more funds for infrastructure or mega projects that enhance economic
actuación. Segundo, even if land sales revenue is not used to fund infrastructure,
the increase in land supply is conducive to economic growth because it lowers the
cost of land for both business and manufacturing operations. It also lowers housing
costos, which attracts larger population inﬂows. If Hypothesis 1 is true, land sales
quantity and/or revenue tend to be larger in the initial years in ofﬁce for prefectural
líderes. Hipótesis 2 is that when a prefectural leader stays too long, comparado
with the distribution of years in ofﬁce among prefectural leaders, he or she might
feel pressure to perform and hence increase land sales, in terms of quantity and/or
revenue, in order to increase the chance of getting promoted. Hipótesis 3 is also
related to the later years of a prefectural leader’s tenure. Hipótesis 2 assumes
that when one is not promoted after staying in a prefecture for too long, he or
she gets anxious for promotion. Another possible explanation is that he or she
may simply “give up,” and thus engage in fewer land sales for the purpose of
getting promoted. Note that a prefectural leader might still increase land supply
for the purpose of corruption. A priori, there are various possibilities regarding
whether each hypothesis is supported by data. But logically, if there is any pattern
in later years due to the competition-for-promotion motive, it cannot be that both
Hypotheses 2 y 3 hold simultaneously.

Al mismo tiempo, governments may supply more land simply because
people need it; this response to demand pressure should be considered also. De este modo,
before probing the effects of political factors, we ﬁrst determine whether standard
urban-economic determinants have had an inﬂuence on urban land supply in the
PRC. 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.

3Ver, Por ejemplo, Li and Zhou (2005) for GDP-growth competition among provincial leaders and Zheng

et al. (2014) for how political competition among prefectural leaders impacts local environmental quality.

4Ofﬁcially, a regular term is 5 años, but this is hardly binding as the actual length of term varies substantially.

See section III.C for details.

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Determinants of Urban Land Supply in the PRC 155

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

Our next step is to study whether the two above-mentioned political
factors possess additional signiﬁcant inﬂuence on land supply, conditional on
urban-economic determinants. In terms of corruption, direct evidence is hard to
come by. Por eso, 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 sales
by governments is an indication of corruption.5 We ﬁnd a very strong positive
association between corruption and changes in land supply. This association is
strongest for commercial land, followed by residential land, and then industrial
land. We also run a set of regressions with land sales revenue as the dependent
variable. We ﬁnd that the effects of corruption on sales revenue for residential and
commercial 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. El
contrast between these two sets of results show that the land sales revenue increases
less than proportionally than the quantity of land sales. Este, Sucesivamente, suggests that
as corruption leads to local governments selling more land, they sell them at lower
prices compared with other sales methods. Residential and commercial land seem
to be the main sources of corruption.

The above results suggest that land sales revenue and quantity may entail
different information. Por lo tanto, in terms of the years-in-ofﬁce effect, we also run
two sets of regressions: one using the change in land supply and one using land
sales revenue. Note that the leadership in the PRC is two track. At the prefectural
nivel, the mayor is the ofﬁcial leader of the government, but the “real boss” is the
highest party ofﬁcial in that prefecture, the party (jefe) secretary. De este modo, we run
these regressions for both party secretaries and mayors to examine whether there
is any difference between the two. To identify the years-in-ofﬁce effect, we use a
quadratic speciﬁcation and tease out the speciﬁcity of prefectures and individual
leaders by estimating prefecture-leader ﬁxed effects. As the story is based on the
political competition for promotion, we also control for corruption.

We ﬁnd that there is a very robust rising trend. Eso es, the longer a prefectural
leader is in ofﬁce, the more land he or she sells, both in terms of quantity and
revenue. 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 be
given in section III.C. Primero, the result is consistent with Hypothesis 2: impatience
or anxiety at not getting promoted in later years may contribute to the rising trend.
Como 77% of party secretaries’ length of term was not more than 4 años, those who
stay longer than 4 years may feel anxiety or impatience.

Segundo, based on the results on corruption, a prefectural leader may increase
land supply for more pocket money. De este modo, if Hypothesis 3 is true and a prefectural

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

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156 Asian Development Review

leader “gives up” in later years, the rising pattern implies that these prefectural
leaders become more corrupt in later years. Sin embargo, we ﬁnd that the effect of
corruption on the increase of land supply decreases with the number of years in
ofﬁce. Además, we also see that conditioned on other factors that may potentially
affect corruption, the correlation between years in ofﬁce and corruption are negative
and insigniﬁcant. De este modo, our results do not support Hypothesis 3.

results seem to contradict Hypothesis 1 because we do
Tercero, nuestro
not see large initial
land sales, but whether this is inconsistent with the
competition-for-promotion motive or not needs to be carefully examined. As most
cities in the PRC are growing in terms of both population and income during the
review period, by restricting the quantity of land earlier, local government may raise
more revenue later because land prices have been pushed up. De este modo, an ambitious
prefectural leader might trade revenue early in his or her term with higher overall
land sales revenue over a longer time span, which may increase the chance of getting
promoted (even a little later than the average number of years in ofﬁce). If this
conjecture is true, then there should be a rising trend in land prices. encontramos que
this is indeed the case. We also ﬁnd that the increases in land sales revenue are
larger from year 2 through year 5, whereas the increases become smaller after 5
años. De este modo, the rising pattern in the ﬁrst few years may still be consistent with a
competition-for-promotion motive because prefectural leaders may aim for higher
land sales revenue overall in the ﬁrst few (alrededor 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 attracted
global interest (Wu, Gyourko, and Deng 2016; Fang et al. 2015), few attempts have
been made to understand the urban land market from the supply perspective until
very recently. While some research highlights the role of the central government’s
land use quota allocation system (Liang, Lu, and Zhang 2016), most existing
literature focuses on local governments’ land supply behavior. Based on empirical
analyses of 35 major cities, Deng, Gyourko, and Wu (2012) point out that besides a
strong common trend at the national level, local governments’ land supply behavior
is substantially affected by the size of budget deﬁcits; local chief ofﬁcers’ desires
for promotion also play an important role. Du and Peiser (2014) ﬁnd that local
governments intentionally control the land supply to maximize their revenue from
the land market. Empirical research by Wu, feng, and Li (2015) concludes that
the budgetary deﬁcits of local governments can affect their land supply behavior;
increases in land prices, sin embargo, are mainly driven by demand-side factors.
Their ﬁnding supports our assertion that urban-economic determinants such as
income and population increases must be controlled before political factors can
be investigated.

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

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Determinants of Urban Land Supply in the PRC 157

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

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

II. Data and Demand Factors

Datos

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 of
this level is the so-called prefectural-level city (di-ji-shi). Although direct-control
cities (Beijing, Chongqing, Shanghai, and Tianjin) are politically at the same
level of provinces, they are geographically similar to prefectural-level cities and
hence included in this level. Sin embargo, prefectural-level cities are usually too big,
often much bigger than a metropolitan area in international standards based on
commuting ﬂows (Fujita et al. 2004). One can easily tell this by inspecting a map
de, Por ejemplo, Chongqing, Beijing, or Xuzhou. To remedy this problem, we use
urban 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 at
all urban areas within a prefecture collectively; some of these urban areas may be
outside the metropolitan area in which the prefectural government is located. Nuestro
prefectural population data is from censuses taken in 2000 y 2010. For the years
in between, we either interpolate or extrapolate. We do not use the population data
from the China City Statistical Yearbook because the population ﬁgures therein are
only for the hukou (household registration) sistema; tal como, they do not include
migrant workers and do not reﬂect the true size of a city.

From the ofﬁcial website of the Ministry of Land and Resources, we collect
information on all transactions of land parcels by various levels of local (urban)
governments in the PRC. During the period 2007–2013, there were about 1.8
million land parcel transactions conducted by local governments. For the purpose
of our research, we aggregate variables to the prefectural level. The change in the
land supply measure that we use is the total ﬂoor space sold by governments in
each prefecture in each year from 2007 a 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 or
neighborhood committee (ju-wei-hui) that is similar to the concept of a neighborhood. Similar jurisdictions in rural
areas are village committees (cun-wei-hui).

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158 Asian Development Review

government ofﬁcials may care more about land revenue than increasing land supply.
De este modo, we also obtain data on land sales revenue.7 We also know the type of land use
per land transaction (residential, comercial, or industrial). From the revenue and
quantity information on land sales, we calculate the average price per unit of land
area or per unit of ﬂoor space. We also know the method by which land is sold. Como
mentioned in the introduction, the fraction of two-stage auctions is used to capture
the degree of corruption. All land that has been sold in our data is urban land. Este
is because rural land is owned collectively by villagers and any rural land must be
converted to urban land (via acquisition by the urban government) before it can be
sold. Por eso, our land sales data is consistent with the level of geography in our
estudiar.

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

Standard Predictions of Urban Economics

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

Although there is a strong correlation between the two variables, the slope
is less than 45 degrees. This indicates that the increase in land supply is less than
the increase in population. Sin embargo, this is consistent with the predictions from
a standard monocentric-city model when land and housing demand are elastic.
Eso es, land demand grows less than proportionally with respect to population
growth because new city residents are willing to reduce their consumption of land
and 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 regression
especificaciones. Du and Peiser (2014) make the point that local governments may want to hoard land to seek higher land
sales revenue later. Por eso, land sales revenues and changes in land supply might show different trends. Sin embargo,
as we will see, these two do show different patterns in terms of corruption, but in terms of the years-in-ofﬁce effect
they 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 ﬁxed
effects if we deﬂate GDP per capita.

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Determinants of Urban Land Supply in the PRC 159

Cifra 1. Population Increment and Change in Land Supply

Fuentes: 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 and
housing for business and industrial activities.

columnas (1)–(4) de mesa 1 show the regression results of the logarithms
of the change in land supply—total and by type (residential, comercial, o
industrial)—on the above-mentioned standard urban economic explanations of
land-demand increases. In all the regressions in this paper, we control for year ﬁxed
effects 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 effect
of population growth. Sin embargo, since our population data are either interpolated
or extrapolated, the logarithms of population increments become constant over
the breakdown of land supply and
tiempo. As industrial structures may affect
potentially affect the total land supply as well, we use the ratio of tertiary industries
to secondary industries (in GDP terms) to proxy industrial structure. columnas
(5)–(8) show results when we control for this ratio.

As expected, the effect of population increments and the increase in GDP
per capita are strongly signiﬁcant. The elasticities of the change in land supply
to population increase are around 0.42–0.48. The elasticities of the change
in land supply to the increase in GDP per capita are around 0.04–0.07. El
services-to-manufacturing ratio has a strongly signiﬁcant negative impact on the
change in industrial land supply. We also observe that this ratio has a positive effect
on the change in commercial land supply; sin embargo, this effect is insigniﬁcant. Como
the two main sectors produce opposite effects, it is conceivable that the effect on the
change in residential land supply would be insigniﬁcant. Finalmente, this ratio also has
a signiﬁcant negative effect on the change in total land supply, lo que indica que

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160 Asian Development Review

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< p = * , 5 0 . < p = * * , 1 0 . < p = n i n w o h s d n a l e v e l e c n i v o r p e h t t a d e r e t s u l c e r a s r o r r e d r a d n a t S . s e c r u o s e R d n a d n a L f o y r t s i n i M e h t * * * . l o r t n o c a s a d e d u l c n i s i P D G y r a d n o c e s o t y r a i t r e t f o o i t a r e h t r e h t e h w . s n o i t a l u c l a c ’ s r o h t u A : e c r u o S l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / 3 4 2 1 5 2 1 6 4 4 3 6 2 a d e v _ a _ 0 0 0 9 8 p d / . f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 Determinants of Urban Land Supply in the PRC 161 the effects on the change in industrial and residential land supply dominate that for commercial land supply. III. How Do Political Factors Matter? As veriﬁed in the previous section, increases in land supply may be due to government responses to demand factors such as increases in population or income. In this section, we study the effect of corruption and the years in ofﬁce of prefectural leaders, conditional on the above-mentioned demand factors. Before we proceed, we ﬁrst examine the relationship between land sales revenue and budget deﬁcits. A. Budget Deﬁcits There are institutional reasons that closely link land sales by local governments with budget deﬁcits. Since reform of the tax-sharing system in 1994, the central government has kept a major part of tax revenues, while local governments remain burdened with increasing local expenditures. However, unlike local governments in advanced economies, local governments in the PRC were until very recently prevented from directly issuing debt to fund budget deﬁcits or other investments such as mega projects. Accordingly, local governments in the PRC 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” minus expenditure. Here, in-budget revenue includes tax and miscellaneous revenue, but not land sales. In 2007, aggregating over all prefectures, land sales revenue was 58% of total government expenditure. The corresponding percentages for years 2008–2013 are 22%, 37%, 50%, 44%, 34%, and 49%, respectively. These percentages indicate that land sales are indeed an important part of local government ﬁnances, although they vary substantially from year to year. The ratio of land sales revenue to government expenditure also varies substantially by prefecture. Figure 2 shows the distribution of this ratio; the unweighted mean and standard deviation are 0.35 and 0.15, respectively. Not surprisingly, we also observe in Figure 3 a strong positive linkage between budget deﬁcits and land sales. However, despite the positive and clear association between deﬁcits and land sales, there is a great deal of variation in land sales for any given budget deﬁcit. Needless to say, budget deﬁcits are highly endogenous to various political factors and it is difﬁcult to identify and disentangle all the relevant factors. For example, budget deﬁcits may be higher because governments want to spend more on infrastructure to promote economic growth, which is related to the above-mentioned political competition motives. There may also be reasons other than political competition and corruption that inﬂuence budget deﬁcits. To address this issue, in the regressions below we compare l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / 3 4 2 1 5 2 1 6 4 4 3 6 2 a d e v _ a _ 0 0 0 9 8 p d . / f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 162 Asian Development Review Figure 2. Ratio of Land Sales Revenue to Government Expenditure 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. Figure 3. Budget Deﬁcits and Land Sales Revenue l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / 3 4 2 1 5 2 1 6 4 4 3 6 2 a d e v _ a _ 0 0 0 9 8 p d / . f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 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. the results when including budget deﬁcits as a regressor to the results without including 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. We also show results with this regressor in the robustness checks. B. Corruption Corruption is widely believed to be prevalent in the PRC, and land sales are often considered one of the main sources. However, direct evidence on corruption Determinants of Urban Land Supply in the PRC 163 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 court system (or similarly, the inner control of the Communist party). Nevertheless, Cai, Henderson, and Zhang (2013) provide indirect but powerful proof that corruption in land sales does exist via a comparison between different auction systems. They show strong evidence that among the three available methods of land sales (sealed bidding, English auction, two-stage auction), corruption is highly associated with the two-stage auction. The second stage of a two-stage auction is an English auction that occurs if more than one bidder is still competing for the land parcel at the end of the ﬁrst stage. The key difference in a two-stage auction from an English auction is that the entry occurs sequentially in the ﬁrst stage and is public information, whereas the entry to the English auction is simultaneous. The sequential nature of this auction form allows both government ofﬁcials and developers room to signal one another, hence preventing additional competitors from entering.9 Cai, Henderson, and Zhang (2013) show empirically that land sale prices and competition are signiﬁcantly less for two-stage auctions and that ofﬁcials divert coveted properties to two-stage auctions. These results are consistent with the theory of corruption and bidding that the authors develop. Given their results, we take the fraction of two-stage auctions among all land sales as a proxy for corruption. Columns (1)–(4) in Table 2 show the results. Here, we regress the change in each type of land supply (in logarithmic form) on the fraction of two-stage auctions in that type, with the same set of controls in Table 1. Overall, a two-stage auction has a positive and signiﬁcant effect on the increase of total land supply, as well as of each type of land. In Columns (5)–(8), we show the results when the dependent variable becomes the logarithm of land sales revenue. In comparison to the results based on quantity of land sales, the coefﬁcients on corruption are dramatically reduced for residential and commercial land, whereas the reductions for total and industrial land are smaller. The effect on industrial land sales revenue becomes 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 price cut when there is a two-stage auction compared with other methods. The price cut is 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 is announced about 20 working days in advance, the exact date of the start of the ﬁrst stage of the auction may not be speciﬁed. Second, although bidders can apply during the announcement period before the ﬁrst stage starts, approvals to participate, or qualiﬁcation, can be delayed until after the ﬁrst stage is under way. Thus, the insider bidder alone may 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 likely that 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 to reserve price is much more condensed around the lower bound (1) for two-stage auctions than English auctions. The l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / 3 4 2 1 5 2 1 6 4 4 3 6 2 a d e v _ a _ 0 0 0 9 8 p d / . f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 164 Asian Development Review l a i r t s u d n I l a i c r e m m o C l a i t n e d i s e R l a t o T ) 5 ( l a i r t s u d n I l a i c r e m m o C l a i t n e d i s e R ) 4 ( ) 3 ( ) 2 ( l a t o T ) 1 ( ) e u n e v e r s e l a s d n a l ( n l ) y l p p u s d n a l n i e s a e r c n i ( n l y l p p u S d n a L d n a n o i t p u r r o C . 2 e l b a T ) 8 ( * * * 5 2 7 0 0 . * * * 1 0 6 0 . ) 7 8 0 0 ( . ) 7 1 0 0 ( . * * * 6 4 5 0 − . ) 1 9 1 0 ( . 3 8 5 0 . ) 3 2 4 0 ( . * * * 4 4 0 8 . ) 4 6 4 0 ( . s e Y 9 2 4 1 , 1 9 4 0 . ) 7 ( * * * 0 0 7 0 . * * * 2 0 1 0 . ) 2 8 0 0 ( . * * 5 7 4 0 . ) 5 1 0 0 ( . ) 2 2 2 0 ( . * * 4 9 4 0 . ) 5 0 2 0 ( . * * * 4 1 6 3 . ) 6 1 3 0 ( . s e Y 5 2 4 1 , 9 5 4 0 . ) 6 ( * * * 6 3 7 0 . 0 * * * 2 0 7 . 0 ) 5 8 0 . 0 ( ) 5 1 0 . 0 ( 7 2 1 . 0 ) 4 7 1 . 0 ( * 8 6 2 . 0 ) 7 5 1 . 0 ( * * * 4 6 6 0 . 0 * * * 8 5 6 . 0 ) 2 8 0 . 0 ( 8 5 8 0 0 . 0 ) 4 1 0 . 0 ( * * 6 8 5 . 0 ) 4 7 1 . 0 ( ) 3 7 2 . 0 ( * * * 8 4 5 0 . 0 * * * 7 7 4 . 0 ) 2 6 0 . 0 ( ) 4 1 0 . 0 ( * * * 3 9 5 . 0 − ) 5 8 1 . 0 ( * * * 1 0 7 0 . 0 * * * 0 6 4 . 0 ) 6 5 0 . 0 ( ) 2 1 0 . 0 ( 4 2 1 . 0 ) 4 3 1 . 0 ( * * * 6 1 5 . 9 ) 2 7 2 . 0 ( s e Y 7 3 4 , 1 5 1 5 . 0 * * * 6 9 6 . 9 ) 2 1 3 . 0 ( s e Y 9 0 4 , 1 5 4 5 . 0 * 1 9 7 . 0 ) 6 5 4 . 0 ( * * * 9 8 0 . 3 ) 7 8 4 . 0 ( s e Y 9 2 4 , 1 2 6 4 . 0 * * * 0 1 2 . 1 ) 0 1 2 . 0 ( * * * 6 9 2 . 2 − ) 9 6 2 . 0 ( s e Y 7 3 4 , 1 1 8 3 . 0 * * * 1 1 4 0 . 0 * * * 7 2 4 . 0 ) 6 6 0 . 0 ( ) 3 1 0 . 0 ( 1 6 1 . 0 − ) 2 2 1 . 0 ( * * * 5 6 7 . 0 ) 8 3 1 . 0 ( * * * 5 8 5 0 . 0 * * * 2 4 4 . 0 ) 5 5 0 . 0 ( * * * 8 3 8 . 0 ) 3 0 1 . 0 ( ) 5 1 2 . 0 ( ) 1 1 0 . 0 ( * * 9 3 2 . 0 − * * * 0 7 8 . 3 ) 8 2 2 . 0 ( s e Y 7 3 4 , 1 1 2 4 . 0 * * * 1 1 2 . 5 ) 6 2 2 . 0 ( s e Y 8 3 4 , 1 9 1 5 . 0 ) y r a d n o c e s o t y r a i t r e t ( s P D G f o o i t a r ) a t i p a c r e p P D G n i e s a e r c n i ( n l ) l a i t n e d i s e r ( e g a t s - 2 f o n o i t c a r f ) l a i c r e m m o c ( e g a t s - 2 f o n o i t c a r f ) l a i r t s u d n i ( e g a t s - 2 f o n o i t c a r f ) l a t o t ( e g a t s - 2 f o n o i t c a r f ) e s a e r c n i n o i t a l u p o p ( n l s e l b a i r a V s n o i t a v r e s b O E F r a e Y 2 R t n a t s n o C f o e t i s b e w e h t m o r f e r a a t a d d n a L . k o o b r a e Y s c i t s i t a t S y t i C m o r f e r a a t a d P D G . s n o i t a l u c l a c ’ s r o h t u a d n a , 0 1 0 2 d n a 0 0 0 2 n i a t a d s u s n e c n o i t a l u p o p m o r f e r a a t a d n o i t a l u p o P : s e t o N . t c u d o r p c i t s e m o d s s o r g = P D G , s t c e f f e d e x ﬁ = E F s n o i t c a s n a r t l l a f o n o i t c a r f e h t s i ) e p y t ( . 1 . 0 < p = * , 5 0 . < p = * * , 1 0 . < p = e g a t s - 2 f o n o i t c a r f e h T . s e s e h t n e r a p n i n w o h s d n a l e v e l e c n i v o r p e h t t a d e r e t s u l c e r a s r o r r e d r a d n a t S . s e c r u o s e R d n a d n a L f o y r t s i n i M e h t * * * . n o i t p u r r o c y x o r p o t d e s u s i t I . n o i t c u a e g a t s - 2 a e s u t a h t ) l a t o t r o ( d n a l f o e p y t t a h t r o f e r u t c e f e r p t a h t n i h t i w . s n o i t a l u c l a c ’ s r o h t u A : e c r u o S l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / 3 4 2 1 5 2 1 6 4 4 3 6 2 a d e v _ a _ 0 0 0 9 8 p d / . f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 Determinants of Urban Land Supply in the PRC 165 major source of corruption. This is likely because residential and commercial land have 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 is likely to be minor. First, there could be a channel through which the increase in land supply leads to more corruption because of some sort of positive externality that spurs the development of the city and hence increases land demand, which gives room to more corruption via land sales. However, this channel is unlikely to be simultaneous with the channel from corruption to changes in land supply, as infrastructure takes time to build and mega projects such as special economic zones take time to realize.12 Even when this time lag is short, the controls of current population and income alleviate this concern. However, can income and population themselves be endogenous via the same channel? Again, this is unlikely because of the time needed for the effect of land sales on GDP and population increases to take effect (e.g., migration restrictions in the PRC). Second, in terms of potential omitted variables, we do a robustness check by controlling for prefecture ﬁxed effects. Here, we do not control for prefecture ﬁxed effects because corruption may be highly related to local culture and tradition. We investigate prefectural ﬁxed effects in section III.D. C. The Effect of Years in Ofﬁce Here, we study how land sales revenue and the changes in land supply are affected by the number of years that a prefectural leader has been in ofﬁce. This information about the trajectories of land sales, in terms of both quantity and revenue, within a prefectural leader’s tenure, other things being equal, helps us test the three hypotheses described in the introduction. Methodology and Results In the PRC, the promotion of local government ofﬁcials is determined by provincial-level party committees. Considering that for provincial-level party (chief) secretaries, the GDP growth rate serves as a key performance indicator in evaluating the performance of their subordinates (prefectural-level leaders), it has been 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 more than 30% for English auctions. They also show that the price cut in two-stage auction is robust when conditional on other 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 are CNY200.2, CNY182, and CNY384.6 per square meter, respectively. The corresponding numbers for residential land are CNY574.2, CNY339.8, and CNY1,774.6; and those for commercial land are CNY605.9, CNY368.5, and CNY1,665.3. 12A more complete modeling of this argument would require a dynamic model. l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / 3 4 2 1 5 2 1 6 4 4 3 6 2 a d e v _ a _ 0 0 0 9 8 p d . / f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 166 Asian Development Review boost local GDP growth, especially by investing in infrastructure and mega projects. Then, since a substantial amount of funding for such investments comes from land sales and since land sales are more ﬂexible than other revenue sources, it is likely for local leaders to try to increase land sales revenue to support such development in infrastructure and mega projects. If land prices are very elastic, then selling more land entails more revenue, but if land prices are inelastic, restricting land supply may lead to higher revenue. In addition, local governments also have incentives to supply more land to accommodate growing urban populations, which of course is another important source of expanding GDP. Therefore, as land sales quantity and revenue renders different information, we include both as dependent variables. To test the three hypotheses regarding the effect of years in ofﬁce, we adopt a quadratic speciﬁcation and run regressions for both prefectural party secretaries and mayors. As the conjecture is based on prefectural leaders’ incentive to promote GDP, we control for both demand factors and corruption. Instead of simply controlling prefecture ﬁxed effects to account for prefectural-level invariant factors, we control for prefecture-speciﬁc, party-secretary–mayor ﬁxed effects. That is, for each prefecture, there is a coefﬁcient for each prefectural leader to account for its speciﬁcity. In such a speciﬁcation, the sum of such prefecture-leader ﬁxed 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 of population increase is time invariant, as we explained earlier in section II. But to avoid completely losing any information on population increases, we change the income 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 the word “urban” in the regression tables, as we focus on the urban areas throughout the entire paper. Before discussing the results, we note that the endogeneity concerns are minor in this case as well. First, there is unlikely to be a reverse causality from land sales (in either quantity or revenue) on the years-in-ofﬁce variable. The concern here is that land sales may come back to affect the increase in GDP. Again, this effect is unlikely to be simultaneous with the effect from years in ofﬁce to land sales as it takes some time for the impacts of infrastructure and/or housing developments to be realized. Even when the time lag is short, in our robustness checks we show that the presence of the GDP-increase variable does not change the results. Last, the inclusion of prefecture-leader ﬁxed effects should alleviate any concern on potential omitted 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 ﬁnd in 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. l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / 3 4 2 1 5 2 1 6 4 4 3 6 2 a d e v _ a _ 0 0 0 9 8 p d . / f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 Determinants of Urban Land Supply in the PRC 167 l a i r t s u d n I l a i c r e m m o C l a i t n e d i s e R ) 8 ( ) 7 ( ) 6 ( l a t o T ) 5 ( l a i r t s u d n I l a i c r e m m o C l a i t n e d i s e R ) 4 ( ) 3 ( ) 2 ( l a t o T ) 1 ( ) e u n e v e r s e l a s d n a l ( n l ) y l p p u s d n a l n i e s a e r c n i ( n l y l p p u S d n a L d n a ) s e i r a t e r c e S y t r a P ( e c ﬁ f O n i s r a e Y . 3 e l b a T * * * 0 0 9 0 . * * * 1 4 1 1 . ) 4 3 2 0 ( . 0 6 9 0 0 0 − . ) 6 7 9 0 0 0 ( . 1 3 1 0 0 − . ) 2 3 6 0 0 ( . 4 1 6 0 − . ) 0 8 1 0 ( . 6 6 1 0 0 − . ) 7 0 1 0 0 ( . 3 6 3 0 0 0 − . ) 4 6 7 0 0 ( . 1 5 4 0 − . ) 5 1 5 0 ( . ) 2 0 4 0 ( . 9 5 3 0 . ) 3 7 3 0 ( . * * * 5 1 6 8 . ) 6 0 8 0 ( . s e Y s e Y 0 4 3 1 , 5 9 8 0 . 6 9 2 0 . ) 8 8 1 0 ( . * * * 6 0 5 5 . ) 8 3 7 0 ( . s e Y s e Y 1 3 3 1 , 5 7 8 0 . * * * 0 4 3 . 1 ) 9 7 1 . 0 ( 8 1 7 0 0 . 0 − ) 7 3 9 0 0 . 0 ( 7 3 2 0 . 0 − ) 1 1 8 0 . 0 ( 4 6 2 . 0 − ) 0 9 3 . 0 ( 4 5 3 0 . 0 ) 9 3 2 . 0 ( * * * 5 7 1 . 1 ) 3 3 1 . 0 ( 7 8 6 0 0 . 0 − ) 3 5 7 0 0 . 0 ( 3 7 3 0 . 0 − ) 9 6 5 0 . 0 ( 9 4 3 . 0 − ) 3 8 2 . 0 ( 7 8 2 . 0 ) 0 1 3 . 0 ( * * * 2 0 0 . 9 ) 8 5 7 . 0 ( s e Y s e Y 4 4 3 , 1 7 0 9 . 0 * * * 5 0 . 0 1 ) 2 8 5 . 0 ( s e Y s e Y 5 2 3 , 1 3 3 9 . 0 * * * 7 0 8 . 0 ) 0 8 2 . 0 ( 2 6 5 0 0 . 0 − ) 4 1 1 0 . 0 ( 1 6 2 0 . 0 − ) 3 8 7 0 . 0 ( 0 0 7 . 0 − ) 4 1 6 . 0 ( * * * 0 6 8 . 0 ) 4 7 1 . 0 ( 5 3 1 0 . 0 − 5 5 2 0 0 . 0 ) 1 0 1 0 . 0 ( ) 4 4 7 0 . 0 ( * * 3 7 7 . 0 − ) 4 8 3 . 0 ( 8 6 5 . 0 ) 4 5 3 . 0 ( * * * 1 9 6 . 3 ) 0 0 1 . 1 ( s e Y s e Y 0 4 3 , 1 9 3 8 . 0 * * * 5 7 6 . 0 ) 5 1 2 . 0 ( ) 3 2 6 . 0 ( 4 9 6 . 0 − s e Y s e Y 1 4 3 , 1 3 1 8 . 0 * * * 8 9 6 . 0 ) 3 9 1 . 0 ( 7 0 4 0 0 0 . 0 ) 9 5 9 0 0 . 0 ( 9 6 5 0 . 0 − ) 6 1 6 0 . 0 ( 8 3 3 0 . 0 − ) 0 2 4 . 0 ( * 4 8 3 . 0 ) 7 2 2 . 0 ( * * * 1 4 5 . 0 ) 4 3 1 . 0 ( 6 4 1 0 0 . 0 − ) 9 8 6 0 0 . 0 ( 3 4 2 0 0 . 0 ) 0 8 3 0 . 0 ( 9 2 2 . 0 − ) 2 8 2 . 0 ( 0 6 2 . 0 ) 5 2 2 . 0 ( * * * 7 6 0 . 3 ) 4 8 6 . 0 ( * * * 1 1 4 . 5 ) 8 8 4 . 0 ( s e Y s e Y 4 4 3 , 1 7 6 8 . 0 s e Y s e Y 3 4 3 , 1 3 9 8 . 0 ) y r a d n o c e s o t y r a i t r e t ( s P D G f o o i t a r ) l a i t n e d i s e r ( e g a t s - 2 f o n o i t c a r f ) l a i c r e m m o c ( e g a t s - 2 f o n o i t c a r f ) l a i r t s u d n i ( e g a t s - 2 f o n o i t c a r f ) l a t o t ( e g a t s - 2 f o n o i t c a r f E F ) y r a t e r c e S - y t r a P ( - e r u t c e f e r P t n a t s n o C s n o i t a v r e s b O E F r a e Y 2 R 2 ) e c ﬁ f o n i s r a e y ( ) e s a e r c n i P D G ( n l e c ﬁ f o n i s r a e y s e l b a i r a V , 1 0 . < p = e h t m o r f e r a a t a d d n a L . k o o b r a e Y s c i t s i t a t S y t i C m o r f e r a a t a d P D G . n o i t c e l l o c ’ s r o h t u a m o r f y l l a u t c a s i t c e f f e d e x ﬁ ) y r a t e r c e s - y t r a p ( e r u t c e f e r p A . s e s e h t n e r a p n i n w o h s d n a l e v e l e c n i v o r p * * * . y t i c ﬁ i c e p s s t i r o f t n u o c c a o t y r a t e r c e s y t r a p l a r u t c e f e r p h c a e r o f t n e i c ﬁ f e o c a s i e r e h t , e r u t c e f e r p h c a e r o f , s i t a h t ; t c e f f e d e x ﬁ y r a t e r c e s - y t r a p , c ﬁ i c e p s - e r u t c e f e r p . 1 . 0 < p = * , 5 0 . < p = a * * l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / 3 4 2 1 5 2 1 6 4 4 3 6 2 a d e v _ a _ 0 0 0 9 8 p d . / f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 . s n o i t a l u c l a c ’ s r o h t u A : e c r u o S e r a e h t s r o y a m d n a s e i r a t e r c e s y t r a p l a r u t c e f e r p r o f s m r e t f o h t g n e l e h t n o a t a D : s e t o N t a d e r e t s u l c e r a s r o r r e d r a d n a t S . s e c r u o s e R d n a d n a L f o y r t s i n i M e h t f o e t i s b e w . t c u d o r p c i t s e m o d s s o r g = P D G , s t c e f f e d e x ﬁ = E F 168 Asian Development Review those for land sales revenue. For all eight columns, we ﬁnd strongly signiﬁcant upward trends during the party secretaries’ tenures. All quadratic-term coefﬁcients are negative but insigniﬁcant, which suggests a very slight concavity in the rising trend. Table 4 shows the results for mayors. The increases in land supply also show signiﬁcant rising trends with the exception of industrial land. The results on land revenue is much weaker, except that the coefﬁcient for residential land remains signiﬁcant. All linear-term coefﬁcients for party secretaries are much larger than those for mayors. The coefﬁcients of the controls are mostly insigniﬁcant, likely because 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 1 states that a local leader tends to sell many land parcels or earn higher land sales revenue in the beginning of his or her term in order to promote local economic growth. Hence, for prefectural leaders, land sales quantity and/or revenue tend to be greater in the initial years in ofﬁce. Hypothesis 2 states that when a prefectural leader stays in the position for a long time, he or she might feel pressure to perform and hence increase land sales, in terms of quantity and/or revenue, to increase the chance of promotion. Hypothesis 3 states that when a prefectural leader is not promoted after being in the same position for an extended period, he or she may simply give up and engage in fewer land sales. First, the rising pattern is consistent with Hypothesis 2; namely, impatience or 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 party secretaries and mayors for all 1,773 party secretaries and 1,970 mayors in the 287 prefectural cities between 1983 and 2013. The average length of term for party secretaries was 3.6 years with a standard deviation of 1.9 years, and for mayors the 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 population data cover 7 years, but the longest length of term was 9 years for both party secretaries and mayors. (Some leaders were already in ofﬁce in 2007 and stayed in that position throughout our data period.) As a large share of leaders stayed in ofﬁce no more than 4 years, those who stayed more than 4 years may have felt anxiety or impatience. The main difference between Hypotheses 2 and 3 is the different psychological reactions to not being promoted in later years. Based on the results on corruption, it is safe to say that a prefectural leader may increase land sales for more pocket money. Thus, if Hypothesis 3 is true and a prefectural leader gives up in l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / 3 4 2 1 5 2 1 6 4 4 3 6 2 a d e v _ a _ 0 0 0 9 8 p d . / f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 Determinants of Urban Land Supply in the PRC 169 ) 8 ( 6 1 5 0 0 0 . ) 7 8 6 0 0 ( . 6 7 7 0 0 − . ) 4 0 1 0 0 ( . 1 7 1 0 0 − . ) 7 4 7 0 0 ( . 3 3 9 0 − . ) 7 ( 4 1 2 0 0 0 − . ) 5 7 7 0 0 ( . 4 6 1 0 0 − . ) 0 2 1 0 0 ( . 9 7 1 0 0 . ) 1 9 7 0 0 ( . 1 1 4 0 − . ) 2 7 6 0 ( . ) 4 3 6 0 ( . 8 9 9 0 . ) 1 1 6 0 ( . * * * 8 0 2 6 . ) 6 0 8 0 ( . s e Y s e Y 0 4 3 1 , 5 9 8 0 . 9 6 2 0 . ) 4 9 1 0 ( . * * * 3 1 3 3 . ) 0 8 5 0 ( . s e Y s e Y 1 3 3 1 , 2 8 8 0 . a y l l a u t c a s i , 5 0 . < p = t c e f f e * * , 1 0 . < p = l a i r t s u d n I l a i c r e m m o C l a i t n e d i s e R l a i r t s u d n I l a i c r e m m o C l a i t n e d i s e R ) e u n e v e r s e l a s d n a l ( n l ) y l p p u s d n a l n i e s a e r c n i ( n l y l p p u S d n a L d n a ) s r o y a M ( e c ﬁ f O n i s r a e Y . 4 e l b a T ) 6 ( * 9 1 1 . 0 ) 5 1 6 0 . 0 ( 0 3 9 0 0 . 0 − ) 8 7 8 0 0 . 0 ( 7 4 2 0 . 0 − ) 0 0 9 0 . 0 ( 6 2 2 . 0 − ) 3 1 5 . 0 ( 7 9 2 0 . 0 − ) 8 3 2 . 0 ( l a t o T ) 5 ( 2 2 1 0 . 0 ) 2 0 5 0 . 0 ( 9 7 3 0 0 . 0 − ) 3 5 7 0 0 . 0 ( 1 6 2 0 . 0 − ) 0 2 6 0 . 0 ( 5 1 4 . 0 − ) 9 9 3 . 0 ( 3 9 8 0 . 0 ) 0 9 2 . 0 ( * * * 7 2 5 . 6 ) 0 4 6 . 0 ( * * * 5 7 8 . 7 ) 7 5 4 . 0 ( s e Y s e Y 4 4 3 , 1 1 1 9 . 0 s e Y s e Y 5 2 3 , 1 8 3 9 . 0 ) 4 ( 0 1 2 0 0 . 0 ) 1 1 7 0 . 0 ( 9 7 4 0 . 0 − ) 6 0 1 0 . 0 ( 7 0 2 0 . 0 − ) 5 3 8 0 . 0 ( 1 5 0 . 1 − ) 0 4 7 . 0 ( * * 4 0 2 . 1 ) 6 0 6 . 0 ( 5 8 2 . 1 ) 7 5 8 . 0 ( s e Y s e Y 0 4 3 , 1 4 4 8 . 0 ) 3 ( ) 2 ( * * 4 2 1 . 0 ) 8 9 5 0 . 0 ( * * * 6 7 2 0 . 0 − ) 6 0 9 0 0 . 0 ( 8 7 1 0 . 0 ) 5 6 7 0 . 0 ( 0 8 4 . 0 − ) 9 5 5 . 0 ( * * * 9 4 6 . 0 ) 6 0 2 . 0 ( * 7 1 1 . 0 ) 7 6 6 0 . 0 ( 9 5 6 0 0 . 0 − ) 4 8 9 0 0 . 0 ( 3 5 5 0 . 0 − ) 6 1 7 0 . 0 ( 2 9 2 . 0 ) 8 6 4 . 0 ( 9 9 2 . 0 ) 5 4 2 . 0 ( l a t o T ) 1 ( * * * 3 6 1 . 0 ) 2 1 5 0 . 0 ( 7 8 1 0 0 . 0 − ) 6 0 8 0 0 . 0 ( 2 1 1 0 . 0 ) 4 1 4 0 . 0 ( 8 6 9 0 . 0 − 0 0 8 0 . 0 ) 1 4 3 . 0 ( ) 4 4 2 . 0 ( ) y r a d n o c e s o t y r a i t r e t ( s P D G f o o i t a r ) l a i t n e d i s e r ( e g a t s - 2 f o n o i t c a r f ) l a i c r e m m o c ( e g a t s - 2 f o n o i t c a r f ) l a i r t s u d n i ( e g a t s - 2 f o n o i t c a r f ) l a t o t ( e g a t s - 2 f o n o i t c a r f 2 ) e c ﬁ f o n i s r a e y ( ) e s a e r c n i P D G ( n l e c ﬁ f o n i s r a e y s e l b a i r a V * * * 9 8 3 . 2 − ) 0 1 5 . 0 ( * * * 8 1 7 . 1 ) 3 3 5 . 0 ( * * * 3 0 8 . 4 ) 5 5 3 . 0 ( s e Y s e Y 1 4 3 , 1 5 2 8 . 0 s e Y s e Y 4 4 3 , 1 7 6 8 . 0 s e Y s e Y 3 4 3 , 1 2 9 8 . 0 E F r o y a M - e r u t c e f e r P s n o i t a v r e s b O E F r a e Y 2 R t n a t s n o C m o r f e r a a t a d d n a L . k o o b r a e Y s c i t s i t a t S y t i C m o r f e r a a t a d P D G . n o i t c e l l o c ’ s r o h t u a m o r f e r a s r o y a m d n a s e i r a t e r c e s y t r a p l a r u t c e f e r p r o f s m r e t f o h t g n e l e h t n o a t a D : s e t o N . t c u d o r p c i t s e m o d s s o r g = P D G , s t c e f f e d e x ﬁ = E F d e x ﬁ ) r o y a m ( e r u t c e f e r p A . s e s e h t n e r a p n i n w o h s d n a l e v e l e c n i v o r p e h t t a d e r e t s u l c e r a s r o r r e d r a d n a t S . s e c r u o s e R d n a d n a L f o y r t s i n i M e h t f o e t i s b e w e h t * * * . y t i c ﬁ i c e p s s t i r o f t n u o c c a o t r o y a m l a r u t c e f e r p h c a e r o f t n e i c ﬁ f e o c a s i e r e h t , e r u t c e f e r p h c a e r o f , s i t a h t ; t c e f f e d e x ﬁ r o y a m , c ﬁ i c e p s - e r u t c e f e r p . 1 . 0 < p = * l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / 3 4 2 1 5 2 1 6 4 4 3 6 2 a d e v _ a _ 0 0 0 9 8 p d / . f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 . s n o i t a l u c l a c ’ s r o h t u A : e c r u o S 170 Asian Development Review later years, then the rising pattern in the years-in-ofﬁce effect on land sales implies that these prefectural leaders become more corrupt over time. To check this, we examine the effect of interacting corruption with years in ofﬁce in the regressions in Tables 3 and 4. As seen in Table 2, corruption exerts more inﬂuence on land sales quantity than land sales revenue for the reasons explained in section III.B; we examine 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 party secretaries as the rising trend is most pronounced for them. For total, residential, and commercial land, the coefﬁcients on the interaction terms imply that the effect of corruption on the increase of land supply is smaller the greater the number of years in ofﬁce.15 If a prefectural leader gives up in later years and becomes more corrupt, he or she should sell more land instead of less. Hence, the results here suggest that Hypothesis 3 is not supported. An alternative way to examine this is to run a regression of corruption on years in ofﬁce and the same set of other regressors in Table 3. Here again we focus on party secretaries. The results are shown in Panel 2 of Table 5. We see that conditioned on other factors that may potentially affect corruption, the correlation between years in ofﬁce and corruption is negative for residential land, positive for commercial land, and insigniﬁcant for industrial land. Overall, there is a negative but insigniﬁcant correlation. We can conclude that the empirical results do not support Hypothesis 3. The rising pattern throughout the years in ofﬁce implies that Hypothesis 1 is not supported, but whether this is inconsistent with the promotion-for-competition motive or not should be carefully examined. There are potentially two distinct arguments that may reconcile the competition-for-promotion motive with the fact that Hypothesis 1 does not hold. The ﬁrst argument is that when urban areas expand cities need to convert rural land into urban land. However, rural land is owned collectively 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 realizing his or her ambition. In the PRC, various uses of urban land are collectively called construction land, in contrast with land not slated for development. The increase in construction land can be used as a proxy for how much rural land is converted to urban land in a year. Focusing on party secretaries, Panel 1 of Table 6 shows the results when regressing the increase in construction land (in logarithmic form) on the same set of regressors as in Table 3.16 Here, we do not ﬁnd any signiﬁcant effects for years in ofﬁce. This suggests that the proportion of increase in construction land is 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 signiﬁcant coefﬁcients of the quadratic interaction terms of the other types of land indicate a slight convexity, but as these coefﬁcients are much smaller than those of the linear interaction terms, the overall pattern is still that the effect of corruption 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-increase variable is excluded. See section III.D for the reasons behind these variations. l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / 3 4 2 1 5 2 1 6 4 4 3 6 2 a d e v _ a _ 0 0 0 9 8 p d / . f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 Determinants of Urban Land Supply in the PRC 171 e c ﬁ f O n i s r a e Y d n a n o i t p u r r o C : 2 l e n a P ) n o i t c u a e g a t s - o w t f o n o i t c a r f ( n o i t p u r r o C y l p p u S d n a L d n a e c ﬁ f O n i s r a e Y : 1 l e n a P ) y l p p u s d n a l n i e s a e r c n i ( n l y l p p u S d n a L n i e g n a h C d n a , e c ﬁ f O n i s r a e Y , n o i t p u r r o C . 5 e l b a T 5 2 6 0 0 0 . ) 7 2 1 0 0 ( . 5 0 – e 3 2 9 − . ) 2 4 8 0 0 0 0 ( . 4 1 9 0 0 0 . ) 0 2 8 0 0 0 ( . 8 8 1 0 0 − . 5 2 4 0 0 0 0 . * 5 0 1 0 . ) 1 5 5 0 0 ( . ) 8 8 2 0 0 0 ( . 2 3 4 0 0 0 0 − . ) 9 3 1 0 0 ( . 6 6 1 0 − . ) 6 2 2 0 0 ( . 1 6 7 0 0 . ) 8 6 7 0 ( . ) 6 2 1 0 ( . 5 2 1 0 . ) 9 6 7 0 ( . * * * 7 6 8 0 . 0 − 0 3 1 0 . 0 − 4 1 1 0 0 . 0 ) 0 3 3 0 . 0 ( ) 9 0 2 0 0 . 0 ( 1 3 7 0 0 . 0 − ) 3 1 1 0 . 0 ( * 8 1 1 . 0 ) 7 6 6 0 . 0 ( 9 6 4 0 . 0 − ) 2 1 7 . 0 ( 0 0 5 0 0 0 . 0 ) 8 1 1 0 0 . 0 ( ) 2 0 2 0 . 0 ( 7 2 2 0 0 . 0 ) 7 9 5 0 0 . 0 ( 9 6 2 0 0 0 . 0 − ) 8 2 4 0 . 0 ( 9 5 1 0 . 0 ) 2 1 7 . 0 ( 7 2 2 0 . 0 ) 6 8 3 . 0 ( ) 5 2 4 0 . 0 ( 4 7 2 0 . 0 5 5 3 . 0 ) 2 0 6 0 . 0 ( 5 7 6 . 0 − ) 7 4 6 . 0 ( * * * 1 7 6 . 1 ) 2 5 3 . 0 ( * * * 2 1 1 . 0 − ) 3 4 3 0 . 0 ( 0 5 4 0 0 . 0 − ) 9 4 7 0 . 0 ( * 5 6 6 . 0 − ) 4 7 3 . 0 ( * * * 9 2 0 . 1 ) 7 3 2 . 0 ( * 9 5 3 0 . 0 − ) 7 8 1 0 . 0 ( 3 0 6 0 . 0 − ) 8 1 6 0 . 0 ( 0 1 4 0 . 0 ) 4 3 4 . 0 ( * * * 0 8 2 . 2 ) 9 1 6 . 0 ( * * * 1 8 9 . 0 − * * * 2 1 1 . 0 ) 4 8 3 0 . 0 ( ) 7 3 3 . 0 ( * * * 4 0 2 . 1 ) 3 8 3 . 0 ( * * 6 2 4 . 0 − * 0 4 4 0 . 0 ) 4 2 2 0 . 0 ( ) 2 9 1 . 0 ( ) 4 ( ) 3 ( ) 2 ( ) 1 ( ) 4 ( ) 3 ( ) 2 ( l a i r t s u d n I l a i c r e m m o C l a i t n e d i s e R l a t o T ) 1 ( * * * 7 3 1 . 1 ) 5 4 3 . 0 ( * 2 9 6 0 . 0 − ) 7 7 3 0 . 0 ( 8 9 2 0 0 . 0 − ) 3 8 3 0 . 0 ( 0 8 1 . 0 − ) 6 8 2 . 0 ( * 7 5 4 . 1 ) 8 7 7 . 0 ( * 8 5 6 . 0 − * 4 2 7 0 . 0 ) 8 0 4 0 . 0 ( ) 6 5 3 . 0 ( ) e c ﬁ f o n i s r a e y ( * ) l a t o t ( e g a t s - 2 f o n o i t c a r f 2 ) e c ﬁ f o n i s r a e y ( * ) l a t o t ( e g a t s - 2 f o n o i t c a r f ) l a i t n e d i s e r ( e g a t s - 2 f o n o i t c a r f ) y r a d n o c e s o t y r a i t r e t ( s P D G f o o i t a r ) l a t o t ( e g a t s - 2 f o n o i t c a r f ) e c ﬁ f o n i s r a e y ( * ) l a i t n e d i s e r ( e g a t s - 2 f o n o i t c a r f 2 ) e c ﬁ f o n i s r a e y ( * ) l a i t n e d i s e r ( e g a t s - 2 f o n o i t c a r f ) l a i c r e m m o c ( e g a t s - 2 f o n o i t c a r f ) e c ﬁ f o n i s r a e y ( * ) l a i c r e m m o c ( e g a t s - 2 f o n o i t c a r f 2 ) e c ﬁ f o n i s r a e y ( * ) l a i c r e m m o c ( e g a t s - 2 f o n o i t c a r f 2 ) e c ﬁ f o n i s r a e y ( ) e s a e r c n i P D G ( n l e c ﬁ f o n i s r a e y s e l b a i r a V . d e u n i t n o C l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / 3 4 2 1 5 2 1 6 4 4 3 6 2 a d e v _ a _ 0 0 0 9 8 p d . / f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 172 Asian Development Review e c ﬁ f O n i s r a e Y d n a n o i t p u r r o C : 2 l e n a P ) n o i t c u a e g a t s - o w t f o n o i t c a r f ( n o i t p u r r o C y l p p u S d n a L d n a e c ﬁ f O n i s r a e Y : 1 l e n a P ) y l p p u s d n a l n i e s a e r c n i ( n l ) 4 ( ) 3 ( ) 2 ( ) 1 ( ) 4 ( ) 3 ( ) 2 ( l a i r t s u d n I l a i c r e m m o C l a i t n e d i s e R l a t o T ) 1 ( . d e u n i t n o C . 5 e l b a T * * * 6 4 9 0 . ) 5 9 5 0 0 ( . * * * 9 6 6 0 . ) 1 6 1 0 ( . * * * 1 0 7 . 0 ) 1 0 8 0 0 ( . * * * 2 0 9 . 0 ) 1 0 6 0 . 0 ( s e Y s e Y 1 4 3 1 , 2 0 7 0 . s e Y s e Y 1 4 3 1 , 7 3 6 0 . s e Y s e Y 6 4 3 1 , 5 4 8 0 . s e Y s e Y 8 4 3 , 1 1 0 8 . 0 2 7 8 . 0 8 1 9 0 . 0 ) 0 0 8 . 0 ( ) 4 2 4 . 0 ( 0 2 3 0 . 0 − ) 4 8 4 0 . 0 ( * * 1 5 8 . 4 ) 7 1 3 . 2 ( s e Y s e Y 0 4 3 , 1 7 2 8 . 0 * * * 6 5 0 . 2 − ) 9 8 7 . 0 ( * * * 1 2 3 . 2 ) 5 5 7 . 0 ( * * * 3 6 2 . 4 ) 1 4 8 . 0 ( s e Y s e Y 1 4 3 , 1 7 1 8 . 0 s e Y s e Y 4 4 3 , 1 8 6 8 . 0 s e Y s e Y 3 4 3 , 1 4 9 8 . 0 ) e c ﬁ f o n i s r a e y ( * ) l a i r t s u d n i ( e g a t s - 2 f o n o i t c a r f 2 ) e c ﬁ f o n i s r a e y ( * ) l a i r t s u d n i ( e g a t s - 2 f o n o i t c a r f ) l a i r t s u d n i ( e g a t s - 2 f o n o i t c a r f s e l b a i r a V . t c u d o r p c i t s e m o d s s o r g = P D G , s t c e f f e d e x ﬁ = E F E F ) y r a t e r c e S - y t r a P ( - e r u t c e f e r P t n a t s n o C s n o i t a v r e s b O E F r a e Y 2 R e h t m o r f e r a a t a d d n a L . k o o b r a e Y s c i t s i t a t S y t i C m o r f e r a a t a d P D G . n o i t c e l l o c ’ s r o h t u a m o r f e r a s r o y a m d n a s e i r a t e r c e s y t r a p l a r u t c e f e r p r o f s m r e t f o h t g n e l e h t n o a t a D : s e t o N . s e i r a t e r c e s y t r a p r o f e r a e l b a t s i h t n i s n o i s s e r g e r e h t l l A . s e s e h t n e r a p n i n w o h s d n a l e v e l e c n i v o r p e h t t a d e r e t s u l c e r a s r o r r e d r a d n a t S . s e c r u o s e R d n a d n a L f o y r t s i n i M . 1 . 0 < p = * , 5 0 . < p = * * e h t , 1 0 . f o e t i s b e w < p = * * * l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / 3 4 2 1 5 2 1 6 4 4 3 6 2 a d e v _ a _ 0 0 0 9 8 p d . / f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 . s n o i t a l u c l a c ’ s r o h t u A : e c r u o S l a i r t s u d n I l a i c r e m m o C l a i t n e d i s e R s e c i r P d n a L d n a e c ﬁ f O n i s r a e Y : 2 l e n a P d n a L n o i t c u r t s n o C d n a e c ﬁ f O n i s r a e Y : 1 l e n a P ) e c i r p e g a r e v a ( n l ) d n a l n o i t c u r t s n o c n i e s a e r c n i ( n l s e c i r P d n a L d n a , d n a L n o i t c u r t s n o C , e c ﬁ f O n i s r a e Y . 6 e l b a T Determinants of Urban Land Supply in the PRC 173 ) 4 ( 5 4 1 0 0 . ) 3 ( 2 5 5 0 0 . ) 3 6 1 0 ( . 9 8 3 0 0 0 − . ) 1 2 2 0 ( . 9 0 2 0 0 0 − . ) 5 2 5 0 0 0 ( . ) 4 7 5 0 0 0 ( . 9 2 1 0 . ) 0 8 2 0 ( . 1 4 1 0 . ) 9 2 2 0 ( . 7 4 5 0 . ) 8 0 4 0 ( . 1 7 2 0 . ) 7 1 2 0 ( . 9 8 5 0 0 − . ) 8 3 2 0 ( . * * 8 5 1 4 . ) 2 8 9 1 ( . s e Y s e Y 2 8 5 1 , 9 5 7 0 . * * 1 3 3 0 − . ) 3 4 1 0 ( . 4 9 3 2 . ) 1 7 8 2 ( . s e Y s e Y 0 8 5 1 , 9 0 8 0 . , 5 0 . < p = * * , 1 0 . < p = ) 2 ( * 9 6 3 . 0 ) 9 8 1 . 0 ( 0 2 6 0 0 . 0 − ) 6 4 4 0 0 . 0 ( 2 4 3 . 0 0 5 1 0 . 0 ) 6 1 3 . 0 ( ) 4 9 1 . 0 ( * * * 6 1 3 . 0 − ) 8 0 1 . 0 ( l a t o T ) 1 ( * 3 2 3 . 0 ) 4 7 1 . 0 ( 8 2 5 0 0 . 0 − ) 3 1 4 0 0 . 0 ( 2 8 2 . 0 ) 3 8 2 . 0 ( 9 0 2 0 0 . 0 − ) 6 0 2 . 0 ( * * 6 0 4 . 0 − ) 7 9 1 . 0 ( ) 4 ( ) 3 ( ) 2 ( ) 1 ( 9 8 1 0 . 0 ) 9 5 4 . 0 ( 8 2 1 0 . 0 ) 0 8 1 0 . 0 ( 9 8 4 0 0 . 0 0 6 1 0 . 0 ) 8 3 4 . 0 ( ) 8 6 1 0 . 0 ( 9 1 8 0 0 . 0 ) 8 8 1 0 . 0 ( ) 5 1 4 . 0 ( 2 9 1 . 0 ) 9 6 1 . 0 ( 4 3 1 0 . 0 ) 0 0 4 . 0 ( ) 6 7 1 0 . 0 ( 5 5 1 . 0 ) 9 5 1 . 0 ( 2 1 2 . 0 − 6 8 1 . 0 − 7 3 5 . 0 ) 8 8 0 . 1 ( 1 6 7 0 . 0 ) 8 6 7 . 0 ( 0 1 5 . 0 ) 9 1 0 . 1 ( 5 2 1 . 0 ) 9 6 7 . 0 ( 6 9 8 . 0 ) 1 4 9 . 0 ( 9 6 4 0 . 0 − ) 2 1 7 . 0 ( 0 4 8 . 0 ) 2 0 9 . 0 ( 9 5 1 0 . 0 ) 2 1 7 . 0 ( 6 8 5 . 3 ) 3 5 2 . 2 ( s e Y s e Y 0 0 6 , 1 3 0 9 . 0 * * 4 2 2 . 4 ) 3 0 0 . 2 ( s e Y s e Y 7 5 5 , 1 2 7 8 . 0 * * 0 4 0 . 3 ) 8 8 6 . 0 ( ) 5 1 5 . 1 ( 9 2 1 0 . 0 − s e Y 6 1 9 s e Y 3 6 7 . 0 * * 9 4 0 . 3 ) 8 4 3 . 1 ( s e Y s e Y 1 4 9 0 6 7 . 0 5 9 1 . 0 ) 1 5 8 . 0 ( 5 4 9 . 1 ) 5 6 7 . 1 ( s e Y 7 6 8 s e Y 2 7 7 . 0 7 0 3 . 2 ) 8 8 4 . 1 ( s e Y 2 9 8 s e Y 9 6 7 . 0 ) y r a d n o c e s o t y r a i t r e t ( s P D G f o o i t a r ) l a i t n e d i s e r ( e g a t s - 2 f o n o i t c a r f ) l a i c r e m m o c ( e g a t s - 2 f o n o i t c a r f ) l a i r t s u d n i ( e g a t s - 2 f o n o i t c a r f ) l a t o t ( e g a t s - 2 f o n o i t c a r f E F ) y r a t e r c e S - y t r a P ( - e r u t c e f e r P E F r o y a M - e r u t c e f e r P s n o i t a v r e s b O E F r a e Y 2 R ) t i c ﬁ e d t e g d u b ( n l t n a t s n o C 2 ) e c ﬁ f o n i s r a e y ( ) e s a e r c n i P D G ( n l e c ﬁ f o n i s r a e y s e l b a i r a V ) P D G ( n l e h t m o r f e r a a t a d d n a L . k o o b r a e Y s c i t s i t a t S y t i C m o r f e r a a t a d P D G . n o i t c e l l o c ’ s r o h t u a m o r f e r a s r o y a m d n a s e i r a t e r c e s y t r a p l a r u t c e f e r p r o f s m r e t f o h t g n e l e h t n o a t a D : s e t o N . t c u d o r p c i t s e m o d s s o r g = P D G , s t c e f f e d e x ﬁ = E F . s e i r a t e r c e s y t r a p r o f e r a e l b a t s i h t n i s n o i s s e r g e r e h t l l A . s e s e h t n e r a p n i n w o h s d n a l e v e l e c n i v o r p e h t t a d e r e t s u l c e r a s r o r r e d r a d n a t S . s e c r u o s e R d n a d n a L f o y r t s i n i M e h t f o e t i s b e w * * * . t n e m p o l e v e d r o f t o n s i t a h t d n a l h t i w t s a r t n o c n i d n a l n o i t c u r t s n o c d e l l a c y l e v i t c e l l o c e r a d n a l n a b r u f o s e s u s u o i r a v , a n i h C f o c i l b u p e R s ’ e l p o e P e h t n I . 1 . 0 < p = * l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / 3 4 2 1 5 2 1 6 4 4 3 6 2 a d e v _ a _ 0 0 0 9 8 p d / . f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 . s n o i t a l u c l a c ’ s r o h t u A : e c r u o S 174 Asian Development Review is ambitious in land sales, we should probably see a larger increase in construction land initially, but we do not see this here. The second argument is that as most cities in the PRC were growing in terms of both population and income during the review period, by restricting the quantity of land sales early a local government might raise more revenue later since land prices have been driven up. Thus, an ambitious prefectural leader might trade higher revenue early on in his or her tenure for larger overall land sales revenue over a longer time span, which could still increase the chance of getting promoted (even if 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 party secretaries, Panel 2 in Table 6 shows the results when regressing land prices (in logarithmic form) on the same set of regressors as in Table 3, except that we replace the GDP-increase variable with GDP itself.17 Here we ﬁnd that land prices rose over the years in ofﬁce for the party secretaries (Column [1]) and that the effect is mainly driven by residential land prices (Column [2]). Further evidence for this argument comes 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 set of 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 for party secretaries; hence, there are eight increments). Thus, the increase in revenue is larger from year 2 to year 5, and the increase becomes smaller after 5 years. This is also consistent with the above-mentioned distribution of term lengths. Thus, we can conclude that the rising pattern in the ﬁrst few years may still be consistent with the competition-for-promotion motive because prefectural leaders may aim for higher land 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 two subsections. First, we conducted two sets of robustness checks for the results on corruption. For the reasons explained in section III.A, we ran the same set of regressions in Table 2 but are now controlling for budget deﬁcits (in logarithmic form) 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 prefecture ﬁxed 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 should depend 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 provincial level. l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / 3 4 2 1 5 2 1 6 4 4 3 6 2 a d e v _ a _ 0 0 0 9 8 p d / . f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 Determinants of Urban Land Supply in the PRC 175 l a i r t s u d n I l a i c r e m m o C l a i t n e d i s e R l a t o T ) 5 ( l a i r t s u d n I l a i c r e m m o C l a i t n e d i s e R ) 4 ( ) 3 ( ) 2 ( l a t o T ) 1 ( ) e u n e v e r s e l a s d n a l ( n l ) y l p p u s d n a l n i e s a e r c n i ( n l s t i c ﬁ e D t e g d u B h t i w — n o i t p u r r o C — k c e h C s s e n t s u b o R . 7 e l b a T ) 8 ( * * * 7 3 7 0 0 . * * * 5 6 5 0 . ) 4 0 9 0 0 ( . ) 5 7 1 0 0 ( . * * * 1 8 5 0 − . ) 7 ( * * * 6 7 6 0 . * * * 1 0 1 0 . ) 9 7 8 0 0 ( . ) 4 5 1 0 0 ( . * * 6 7 4 0 . ) 6 9 1 0 ( . 5 0 4 0 . ) 7 5 3 0 ( . 0 6 5 0 . ) 3 2 4 0 ( . * * * 8 1 6 7 . ) 1 1 6 0 ( . s e Y 4 9 3 1 , 7 8 4 0 . ) 6 5 3 0 ( . * * 4 7 4 0 . ) 6 0 2 0 ( . * * * 2 1 7 3 . ) 3 8 4 0 ( . s e Y 1 9 3 1 , 6 4 4 0 . ) 2 3 2 0 ( . 1 6 5 0 0 − . ) 6 ( * * * 7 7 6 . 0 ) 8 0 9 0 . 0 ( * * * 6 3 7 0 . 0 ) 0 6 1 0 . 0 ( 9 3 1 . 0 ) 2 7 1 . 0 ( 5 1 1 . 0 ) 1 8 3 . 0 ( * 4 7 2 . 0 ) 8 5 1 . 0 ( * * * 4 6 6 0 . 0 * * * 2 3 6 . 0 ) 8 7 8 0 . 0 ( 7 7 8 0 0 . 0 ) 3 4 1 0 . 0 ( ) 4 7 1 . 0 ( 3 4 1 . 0 * * 7 7 5 . 0 ) 1 7 3 . 0 ( ) 6 7 2 . 0 ( * * * 8 2 5 0 . 0 * * * 3 4 4 . 0 ) 5 3 6 0 . 0 ( ) 0 4 1 0 . 0 ( * * * 7 4 6 . 0 − * * 5 1 5 . 0 ) 4 9 1 . 0 ( ) 9 5 2 . 0 ( * * * 7 6 6 0 . 0 * * * 6 2 4 . 0 ) 9 6 5 0 . 0 ( ) 8 1 1 0 . 0 ( 1 8 9 0 . 0 * * 9 6 5 . 0 ) 0 3 1 . 0 ( ) 6 3 2 . 0 ( * * * 2 8 3 . 9 ) 7 3 5 . 0 ( s e Y 2 0 4 , 1 9 0 5 . 0 * * * 8 4 5 . 9 ) 1 6 5 . 0 ( s e Y 5 7 3 , 1 8 3 5 . 0 * 2 6 7 . 0 ) 6 5 4 . 0 ( * * * 2 9 4 . 2 ) 5 5 5 . 0 ( s e Y 4 9 3 , 1 6 6 4 . 0 * * * 6 0 2 . 1 ) 6 1 2 . 0 ( * * * 0 7 9 . 2 − ) 3 5 3 . 0 ( s e Y 2 0 4 , 1 1 8 3 . 0 * * * 1 9 3 0 . 0 * * * 8 8 3 . 0 ) 7 4 6 0 . 0 ( ) 4 3 1 0 . 0 ( * 4 9 1 . 0 − * * * 2 5 8 . 0 ) 3 0 1 . 0 ( ) 6 8 2 . 0 ( * * * 6 1 8 . 0 ) 3 3 1 . 0 ( * * * 1 7 5 0 . 0 * * * 9 0 4 . 0 ) 5 5 5 0 . 0 ( ) 9 0 1 0 . 0 ( * * * 6 7 2 . 0 − * * * 7 9 5 . 0 ) 6 4 9 0 . 0 ( ) 6 2 2 . 0 ( * * * 1 7 8 . 0 ) 6 1 2 . 0 ( * * * 3 2 8 . 2 ) 7 8 3 . 0 ( s e Y 2 0 4 , 1 9 3 4 . 0 * * * 6 8 4 . 4 ) 6 3 3 . 0 ( s e Y 3 0 4 , 1 5 2 5 . 0 ) y r a d n o c e s o t y r a i t r e t ( s P D G f o o i t a r ) a t i p a c r e p P D G n i e s a e r c n i ( n l ) e s a e r c n i n o i t a l u p o p ( n l s e l b a i r a V ) l a i t n e d i s e r ( e g a t s - 2 f o n o i t c a r f ) l a i c r e m m o c ( e g a t s - 2 f o n o i t c a r f ) l a i r t s u d n i ( e g a t s - 2 f o n o i t c a r f ) l a t o t ( e g a t s - 2 f o n o i t c a r f ) t i c ﬁ e d t e g d u b ( n l s n o i t a v r e s b O E F r a e Y 2 R t n a t s n o C f o e t i s b e w e h t m o r f e r a a t a d d n a L . k o o b r a e Y s c i t s i t a t S y t i C m o r f e r a a t a d P D G . s n o i t a l u c l a c ’ s r o h t u a d n a 0 1 0 2 d n a 0 0 0 2 n i a t a d s u s n e c n o i t a l u p o p m o r f e r a a t a d n o i t a l u p o P : s e t o N . t c u d o r p c i t s e m o d s s o r g = P D G , s t c e f f e d e x ﬁ = E F s n o i t c a s n a r t l l a f o n o i t c a r f e h t s i ) e p y t ( e g a t s - 2 f o n o i t c a r f e h T . s e s e h t n e r a p n i n w o h s d n a l e v e l e c n i v o r p e h t t a d e r e t s u l c e r a s r o r r e d r a d n a t S . s e c r u o s e R d n a d n a L f o y r t s i n i M e h t . 1 . 0 < p = * , 5 0 . < p = * * , 1 0 . < p = * * * . n o i t p u r r o c y x o r p o t d e s u s i t I . n o i t c u a e g a t s - 2 a e s u t a h t ) l a t o t r o ( d n a l f o e p y t t a h t r o f e r u t c e f e r p t a h t n i h t i w . s n o i t a l u c l a c ’ s r o h t u A : e c r u o S l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / 3 4 2 1 5 2 1 6 4 4 3 6 2 a d e v _ a _ 0 0 0 9 8 p d . / f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 176 Asian Development Review l a i r t s u d n I l a i c r e m m o C l a i t n e d i s e R ) 8 ( ) 7 ( ) 6 ( l a i r t s u d n I l a i c r e m m o C l a i t n e d i s e R ) e u n e v e r s e l a s d n a l ( n l ) y l p p u s d n a l n i e s a e r c n i ( n l s t c e f f E d e x i F e r u t c e f e r P h t i w — n o i t p u r r o C — k c e h C s s e n t s u b o R . 8 e l b a T 0 5 5 0 0 0 . 2 9 1 0 0 . ) 5 8 5 0 0 ( . * 3 6 5 0 − . ) 0 4 6 0 0 ( . 9 2 4 0 − . ) 0 4 3 0 ( . ) 6 7 2 0 ( . * 1 5 0 1 . ) 0 4 5 0 ( . * * * 8 1 1 1 . ) 8 8 4 1 ( . s e Y s e Y 0 4 3 1 , 6 5 8 0 . * 3 0 3 0 . ) 1 6 1 0 ( . * * * 2 4 0 1 . ) 3 7 0 1 ( . s e Y s e Y 1 3 3 1 , 4 4 8 0 . 3 0 6 0 0 . 0 ) 6 5 6 0 . 0 ( 7 8 4 . 0 − ) 6 6 3 . 0 ( 4 0 7 0 . 0 ) 7 8 1 . 0 ( l a t o T ) 5 ( 6 4 6 0 0 . 0 − ) 2 9 4 0 . 0 ( * 3 1 4 . 0 − ) 9 3 2 . 0 ( 0 5 2 . 0 ) 4 1 2 . 0 ( * * * 6 6 . 5 1 ) 7 5 3 . 1 ( s e Y s e Y 4 4 3 , 1 2 7 8 . 0 * * * 5 3 . 5 1 ) 2 8 9 . 0 ( s e Y s e Y 5 2 3 , 1 8 0 9 . 0 ) 4 ( ) 3 ( 9 2 5 0 0 . 0 ) 0 0 7 0 . 0 ( * 7 2 6 . 0 − ) 4 7 3 . 0 ( 2 9 4 0 . 0 ) 4 6 6 0 . 0 ( * * 5 3 7 . 0 − ) 6 8 2 . 0 ( * * 6 2 1 . 1 ) 9 3 5 . 0 ( * * * 0 1 9 . 5 ) 1 7 7 . 1 ( s e Y s e Y 0 4 3 , 1 6 8 7 . 0 * * * 5 3 6 . 0 ) 8 6 1 . 0 ( * * 8 1 6 . 2 ) 9 8 0 . 1 ( s e Y s e Y 1 4 3 , 1 0 6 7 . 0 ) 2 ( 0 8 7 0 0 . 0 − ) 0 6 5 0 . 0 ( 9 1 2 . 0 − ) 2 2 3 . 0 ( * * 1 4 4 . 0 ) 9 8 1 . 0 ( l a t o T ) 1 ( 6 5 4 0 . 0 ) 3 1 4 0 . 0 ( 4 4 3 . 0 − ) 6 1 2 . 0 ( 8 3 2 . 0 ) 8 5 1 . 0 ( ) y r a d n o c e s o t y r a i t r e t ( s P D G f o o i t a r ) l a i t n e d i s e r ( e g a t s - 2 f o n o i t c a r f ) l a i c r e m m o c ( e g a t s - 2 f o n o i t c a r f ) l a i r t s u d n i ( e g a t s - 2 f o n o i t c a r f ) l a t o t ( e g a t s - 2 f o n o i t c a r f ) e s a e r c n i P D G ( n l s e l b a i r a V * * * 2 5 4 . 6 ) 6 5 2 . 1 ( * * * 8 3 9 . 7 ) 9 3 9 . 0 ( s e Y s e Y 4 4 3 , 1 4 1 8 . 0 s e Y s e Y 3 4 3 , 1 9 4 8 . 0 E F e r u t c e f e r P s n o i t a v r e s b O E F r a e Y 2 R t n a t s n o C f o e t i s b e w e h t m o r f e r a a t a d d n a L . k o o b r a e Y s c i t s i t a t S y t i C m o r f e r a a t a d P D G . s n o i t a l u c l a c ’ s r o h t u a d n a 0 1 0 2 d n a 0 0 0 2 n i a t a d s u s n e c n o i t a l u p o p m o r f e r a a t a d n o i t a l u p o P : s e t o N . t c u d o r p c i t s e m o d s s o r g = P D G , s t c e f f e d e x ﬁ = E F s n o i t c a s n a r t l l a f o n o i t c a r f e h t s i ) e p y t ( . 1 . 0 < p = * , 5 0 . < p = * * , 1 0 . < p = e g a t s - 2 f o n o i t c a r f e h T . s e s e h t n e r a p n i n w o h s d n a l e v e l e c n i v o r p e h t t a d e r e t s u l c e r a s r o r r e d r a d n a t S . s e c r u o s e R d n a d n a L f o y r t s i n i M e h t * * * . n o i t p u r r o c y x o r p o t d e s u s i t I . n o i t c u a e g a t s - 2 a e s u t a h t ) l a t o t r o ( d n a l f o e p y t t a h t r o f e r u t c e f e r p t a h t n i h t i w . s n o i t a l u c l a c ’ s r o h t u A : e c r u o S l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / 3 4 2 1 5 2 1 6 4 4 3 6 2 a d e v _ a _ 0 0 0 9 8 p d . / f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 Determinants of Urban Land Supply in the PRC 177 with these prefecture ﬁxed effects. Here, we see that the coefﬁcients on corruption are reduced sharply, except that the coefﬁcients on industrial land increases. Comparison with Table 2 suggests that corruption may indeed be related to local time-invariant factors but, even so, the increases in residential and commercial land supply still exhibit very strong correlations with corruption. Also intriguing is the fact that the effect of corruption on industrial land increases (and even becomes signiﬁcant for industrial land revenue) compared with Table 2. This suggests that industrial activity is less correlated with local time-invariant factors, likely because industrial activities are quite mobile across locations. For the robustness checks for the years-in-ofﬁce effect, we also examine cases where budget deﬁcits are controlled. To address endogeneity issues, we rely on the assumption that the channel from land sales, in terms of either quantity or revenue, 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 check when GDP increase is not included in case the time lag of the above-mentioned channel is short. For these robustness checks, we focus on the total quantity and revenue 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 of land sales, respectively. Columns (2)–(4) show the results for quantity of land sales with budget deﬁcits controlled (Column [2]), when GDP-increase variable is not controlled (Column [3]), and a combination of the previous two cases (Column [4]). Columns (6)–(8) repeat the same cases for land sales revenue. The results are all very similar to the benchmark. Table 10 repeats the same exercises for mayors. 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 to see a rising trend for land revenue when GDP-increase variable is not controlled. In Table 9, we do not see such large changes in coefﬁcients. This is likely because the explanatory power of mayors’ years in ofﬁce is weaker when compared with Tables 3 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 capture the variations in land sales. The last robustness check is to include prefecture-speciﬁc time trends. This is based on the following concern. Since land sales quantity, price, and revenue all rise over time, could our observed result of the rising trends for years in ofﬁce simply result from the various controls not being able to fully explain these rising trends in terms of quantity, price, and sales? This concern is not fully addressed by controlling for year ﬁxed effects, which capture overall rising trends in land sales quantity, price, and revenue, but not those speciﬁc to prefectures. Thus, we add prefecture-speciﬁc linear time trends to the regressions in Tables 3 and 4 and show the results in Table 11 for total land supply only. Linear time trends translate to exponential time trends in levels as our dependent variables are all in logarithmic l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / 3 4 2 1 5 2 1 6 4 4 3 6 2 a d e v _ a _ 0 0 0 9 8 p d / . f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 178 Asian Development Review * * * 1 6 1 1 . * * * 1 3 1 1 . ) 4 6 1 0 ( . 8 9 5 0 0 0 − . ) 0 5 1 0 ( . 8 8 5 0 0 0 − . ) 8 8 6 0 0 0 ( . ) 3 6 6 0 0 0 ( . 5 1 3 0 − . ) 6 7 3 0 ( . 8 7 1 0 − . ) 2 5 3 0 ( . 3 6 2 0 . ) 3 5 2 0 ( . * * * 5 0 0 1 . ) 4 9 5 0 ( . s e Y s e Y 8 1 5 1 , 1 3 9 0 . ) 4 5 3 0 ( . 8 9 2 0 − . 3 5 2 0 . ) 1 5 2 0 ( . * * * 6 6 8 9 . ) 2 5 4 0 ( . s e Y s e Y 7 5 5 1 , 3 3 9 0 . * * * 5 8 1 . 1 ) 3 4 1 . 0 ( 2 5 6 0 0 . 0 − ) 2 8 7 0 0 . 0 ( 1 7 3 0 . 0 − ) 4 9 5 0 . 0 ( 6 8 3 . 0 − ) 8 0 3 . 0 ( 2 0 2 0 . 0 ) 3 5 2 . 0 ( 3 9 2 . 0 ) 2 1 3 . 0 ( * * * 7 0 . 0 1 ) 4 1 7 . 0 ( s e Y s e Y 2 9 2 , 1 1 3 9 . 0 * * * 5 7 1 . 1 ) 3 3 1 . 0 ( 7 8 6 0 0 . 0 − ) 3 5 7 0 0 . 0 ( 3 7 3 0 . 0 − ) 9 6 5 0 . 0 ( 9 4 3 . 0 − ) 3 8 2 . 0 ( 7 8 2 . 0 ) 0 1 3 . 0 ( * * * 5 0 . 0 1 ) 2 8 5 . 0 ( s e Y s e Y 5 2 3 , 1 3 3 9 . 0 * * * 9 9 5 . 0 ) 6 1 1 . 0 ( 0 1 2 0 0 . 0 − * * * 6 9 5 . 0 ) 7 1 1 . 0 ( 5 5 1 0 0 . 0 − ) 5 6 6 0 0 . 0 ( ) 6 4 6 0 0 . 0 ( ) 9 5 2 . 0 ( 4 6 8 0 . 0 − 7 7 2 . 0 − ) 9 1 2 . 0 ( 6 6 2 . 0 ) 7 8 1 . 0 ( * * * 8 4 3 . 5 ) 1 6 3 . 0 ( s e Y s e Y 9 5 5 , 1 0 9 8 . 0 ) 7 5 2 . 0 ( 6 0 3 . 0 − 7 6 2 . 0 ) 5 8 1 . 0 ( * * * 0 1 3 . 5 ) 1 2 3 . 0 ( s e Y s e Y 9 9 5 , 1 1 9 8 . 0 * * * 4 2 5 . 0 ) 6 3 1 . 0 ( 5 8 1 0 0 . 0 − ) 5 0 7 0 0 . 0 ( 7 5 7 0 0 . 0 ) 6 8 3 0 . 0 ( 9 0 2 . 0 − ) 3 9 2 . 0 ( 0 8 7 0 . 0 ) 1 9 1 . 0 ( 0 6 2 . 0 ) 6 2 2 . 0 ( * * * 8 1 2 . 5 ) 6 3 5 . 0 ( s e Y s e Y 9 0 3 , 1 2 9 8 . 0 * * * 1 4 5 . 0 ) 4 3 1 . 0 ( 6 4 1 0 0 . 0 − ) 9 8 6 0 0 . 0 ( 3 4 2 0 0 . 0 ) 0 8 3 0 . 0 ( 9 2 2 . 0 − ) 2 8 2 . 0 ( 0 6 2 . 0 ) 5 2 2 . 0 ( * * * 1 1 4 . 5 ) 8 8 4 . 0 ( s e Y s e Y 3 4 3 , 1 3 9 8 . 0 ) 8 ( ) 7 ( ) 6 ( ) 5 ( ) 4 ( ) 3 ( ) 2 ( ) 1 ( ) e u n e v e r s e l a s d n a l l a t o t ( n l ) y l p p u s d n a l l a t o t n i e s a e r c n i ( n l y l p p u S d n a L d n a ) s e i r a t e r c e S y t r a P ( e c ﬁ f O n i s r a e Y — k c e h C s s e n t s u b o R . 9 e l b a T ) y r a d n o c e s o t y r a i t r e t ( s P D G f o o i t a r E F ) y r a t e r c e S - y t r a P ( - e r u t c e f e r P s n o i t a v r e s b O E F r a e Y 2 R ) l a t o t ( e g a t s - 2 f o n o i t c a r f ) t i c ﬁ e d t e g d u b ( n l t n a t s n o C 2 ) e c ﬁ f o n i s r a e y ( ) e s a e r c n i P D G ( n l e c ﬁ f o n i s r a e y s e l b a i r a V , 1 0 . < p = e h t m o r f e r a a t a d d n a L . k o o b r a e Y s c i t s i t a t S y t i C m o r f e r a a t a d P D G . n o i t c e l l o c ’ s r o h t u a m o r f y l l a u t c a s i t c e f f e d e x ﬁ ) y r a t e r c e s - y t r a p ( e r u t c e f e r p A . s e s e h t n e r a p n i n w o h s d n a l e v e l e c n i v o r p e r a e h t s r o y a m d n a s e i r a t e r c e s y t r a p l a r u t c e f e r p r o f s m r e t f o h t g n e l e h t n o a t a D : s e t o N t a d e r e t s u l c e r a s r o r r e d r a d n a t S . s e c r u o s e R d n a d n a L f o y r t s i n i M e h t f o e t i s b e w . t c u d o r p c i t s e m o d s s o r g = P D G , s t c e f f e d e x ﬁ = E F * * * . y t i c ﬁ i c e p s s t i r o f t n u o c c a o t y r a t e r c e s y t r a p l a r u t c e f e r p h c a e r o f t n e i c ﬁ f e o c a s i e r e h t , e r u t c e f e r p h c a e r o f , s i t a h t ; t c e f f e d e x ﬁ y r a t e r c e s - y t r a p , c ﬁ i c e p s - e r u t c e f e r p . 1 . 0 < p = * , 5 0 . < p = a * * l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / 3 4 2 1 5 2 1 6 4 4 3 6 2 a d e v _ a _ 0 0 0 9 8 p d / . f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 . s n o i t a l u c l a c ’ s r o h t u A : e c r u o S Determinants of Urban Land Supply in the PRC 179 a y l l a u t c a s i , 5 0 . < p = t c e f f e * * , 1 0 . < p = * * * 9 6 1 1 . * * * 5 1 1 1 . 5 4 8 0 0 0 0 − . ) 2 1 2 0 ( . ) 7 8 1 0 ( . 1 1 1 0 0 0 − . ) 1 0 1 0 0 ( . ) 0 6 9 0 0 0 ( . 2 9 3 0 − . ) 5 4 5 0 ( . 3 6 2 0 − . 2 0 2 0 0 . ) 6 9 3 0 ( . ) 8 4 2 0 ( . * * * 9 3 0 1 . ) 3 2 7 0 ( . s e Y s e Y 8 1 5 1 , 6 3 9 0 . ) 6 9 4 0 ( . 9 3 3 0 − . 4 3 5 0 0 0 . ) 7 4 2 0 ( . * * * 9 0 0 1 . ) 8 3 5 0 ( . s e Y s e Y 7 5 5 1 , 7 3 9 0 . 6 9 4 0 0 . 0 ) 0 2 5 0 . 0 ( 4 1 3 0 0 . 0 − ) 6 7 7 0 0 . 0 ( 1 3 3 0 . 0 − ) 3 4 6 0 . 0 ( 1 3 5 . 0 − ) 1 5 4 . 0 ( 9 1 8 0 . 0 − ) 4 7 2 . 0 ( 7 0 1 . 0 ) 1 9 2 . 0 ( * * * 9 7 9 . 7 ) 5 3 5 . 0 ( s e Y s e Y 2 9 2 , 1 7 3 9 . 0 2 2 1 0 . 0 ) 2 0 5 0 . 0 ( 9 7 3 0 0 . 0 − ) 3 5 7 0 0 . 0 ( 1 6 2 0 . 0 − ) 0 2 6 0 . 0 ( 5 1 4 . 0 − ) 9 9 3 . 0 ( 3 9 8 0 . 0 ) 0 9 2 . 0 ( * * * 5 7 8 . 7 ) 7 5 4 . 0 ( s e Y s e Y 5 2 3 , 1 8 3 9 . 0 * * * 0 0 6 . 0 9 6 8 0 0 0 . 0 − ) 8 3 1 . 0 ( ) 5 5 8 0 0 . 0 ( * * * 3 6 5 . 0 9 2 1 0 0 . 0 ) 0 9 8 0 0 . 0 ( ) 4 3 1 . 0 ( * * * 1 7 1 . 0 ) 8 9 4 0 . 0 ( 3 4 3 0 0 . 0 − * * * 3 6 1 . 0 ) 2 1 5 0 . 0 ( 7 8 1 0 0 . 0 − ) 4 7 7 0 0 . 0 ( ) 6 0 8 0 0 . 0 ( 7 2 1 . 0 − ) 8 4 3 . 0 ( 4 4 1 . 0 − ) 1 6 2 . 0 ( 1 1 1 . 0 ) 6 8 1 . 0 ( * * * 3 0 6 . 5 ) 4 3 4 . 0 ( s e Y s e Y 9 5 5 , 1 9 8 8 . 0 ) 4 3 3 . 0 ( 6 1 1 . 0 − 0 1 1 . 0 ) 6 8 1 . 0 ( * * * 8 6 4 . 5 ) 4 6 3 . 0 ( s e Y s e Y 9 9 5 , 1 0 9 8 . 0 7 4 1 0 . 0 ) 4 2 4 0 . 0 ( 2 3 1 . 0 − ) 9 5 3 . 0 ( 7 9 6 0 . 0 8 8 7 0 . 0 ) 4 1 2 . 0 ( ) 4 4 2 . 0 ( * * * 9 9 6 . 4 ) 5 0 4 . 0 ( s e Y s e Y 9 0 3 , 1 0 9 8 . 0 2 1 1 0 . 0 ) 4 1 4 0 . 0 ( 8 6 9 0 . 0 − ) 1 4 3 . 0 ( 0 0 8 0 . 0 ) 4 4 2 . 0 ( * * * 3 0 8 . 4 ) 5 5 3 . 0 ( s e Y s e Y 3 4 3 , 1 2 9 8 . 0 ) 8 ( ) 7 ( ) 6 ( ) 5 ( ) 4 ( ) 3 ( ) 2 ( ) 1 ( ) e u n e v e r s e l a s d n a l l a t o t ( n l ) y l p p u s d n a l l a t o t n i e s a e r c n i ( n l y l p p u S d n a L d n a ) s r o y a M ( e c ﬁ f O n i s r a e Y — k c e h C s s e n t s u b o R . 0 1 e l b a T ) y r a d n o c e s o t y r a i t r e t ( s P D G f o o i t a r ) l a t o t ( e g a t s - 2 f o n o i t c a r f ) t i c ﬁ e d t e g d u b ( n l E F r o y a M - e r u t c e f e r P s n o i t a v r e s b O E F r a e Y 2 R t n a t s n o C 2 ) e c ﬁ f o n i s r a e y ( ) e s a e r c n i P D G ( n l e c ﬁ f o n i s r a e y s e l b a i r a V m o r f e r a a t a d d n a L . k o o b r a e Y s c i t s i t a t S y t i C m o r f e r a a t a d P D G . n o i t c e l l o c ’ s r o h t u a m o r f e r a s r o y a m d n a s e i r a t e r c e s y t r a p l a r u t c e f e r p r o f s m r e t f o h t g n e l e h t n o a t a D : s e t o N . t c u d o r p c i t s e m o d s s o r g = P D G , s t c e f f e d e x ﬁ = E F d e x ﬁ ) r o y a m ( e r u t c e f e r p A . s e s e h t n e r a p n i n w o h s d n a l e v e l e c n i v o r p e h t t a d e r e t s u l c e r a s r o r r e d r a d n a t S . s e c r u o s e R d n a d n a L f o y r t s i n i M e h t f o e t i s b e w e h t * * * . y t i c ﬁ i c e p s s t i r o f t n u o c c a o t r o y a m l a r u t c e f e r p h c a e r o f t n e i c ﬁ f e o c a s i e r e h t , e r u t c e f e r p h c a e r o f , s i t a h t ; t c e f f e d e x ﬁ r o y a m , c ﬁ i c e p s - e r u t c e f e r p . 1 . 0 < p = * l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / 3 4 2 1 5 2 1 6 4 4 3 6 2 a d e v _ a _ 0 0 0 9 8 p d . / f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 . s n o i t a l u c l a c ’ s r o h t u A : e c r u o S 180 Asian Development Review s r o y a M s e i r a t e r c e S y t r a P i ) d n e r T e m T c ﬁ i c e p S - e r u t c e f e r P ( s r e d a e L l a r u t c e f e r P r o f e c ﬁ f O n i s r a e Y — k c e h C s s e n t s u b o R . 1 1 e l b a T l a t o t ( n l s e l a s d n a l ) e u n e v e r ) 8 ( * * * 8 0 3 0 . 1 8 4 0 0 0 . ) 3 5 9 0 0 ( . ) 5 9 1 0 0 ( . 1 8 9 0 0 0 − . e s a e r c n i ( n l l a t o t n i ) y l p p u s d n a l ) 7 ( * * * 5 3 4 0 . ) 6 2 7 0 0 ( . 7 2 1 0 0 . ) 3 5 1 0 0 ( . 3 4 5 0 0 0 0 . ) 8 4 8 0 0 ( . 3 3 5 0 − . ) 4 7 4 0 0 ( . 8 5 1 0 − . ) 0 7 8 0 ( . 2 3 2 0 − . ) 4 3 6 0 ( . 7 2 2 0 − . * * * 2 5 7 5 . ) 7 5 4 0 ( . ) 7 0 8 0 ( . * * * 9 0 8 2 . ) 0 7 4 0 ( . ) 2 3 6 0 ( . s e Y s e Y s e Y 5 2 3 1 , 7 6 9 0 . s e Y s e Y s e Y 3 4 3 1 , 9 3 9 0 . l a t o t ( n l s e l a s d n a l ) e u n e v e r ) 6 ( * * 4 5 2 . 0 ) 7 2 1 . 0 ( 4 1 1 0 . 0 − ) 9 3 2 0 . 0 ( 1 4 6 0 . 0 ) 9 0 7 0 . 0 ( 8 8 2 . 0 − ) 7 0 8 . 0 ( 7 0 3 . 0 − ) 0 1 5 . 0 ( * * * 9 3 1 . 5 ) 4 6 7 . 0 ( s e Y s e Y o N 5 2 3 , 1 9 5 9 . 0 e s a e r c n i ( n l l a t o t n i ) y l p p u s d n a l ) 5 ( * * * 5 8 3 . 0 ) 1 6 9 0 . 0 ( 6 0 1 0 0 . 0 ) 8 8 1 0 . 0 ( 5 6 3 0 0 . 0 ) 7 8 4 0 . 0 ( 0 1 4 . 0 ) 4 2 6 . 0 ( 9 9 2 . 0 − ) 3 9 4 . 0 ( * * * 9 8 2 . 2 ) 1 1 6 . 0 ( s e Y s e Y o N 3 4 3 , 1 8 2 9 . 0 l a t o t ( n l s e l a s d n a l ) e u n e v e r ) 4 ( * * * 0 8 8 . 1 5 2 1 0 . 0 ) 9 2 3 . 0 ( ) 7 5 1 0 . 0 ( 2 7 2 0 0 . 0 − ) 4 5 7 0 . 0 ( 5 4 4 . 0 − ) 1 3 7 . 0 ( 3 4 3 . 0 − ) 6 2 4 . 0 ( * * * 2 9 7 . 8 ) 8 5 9 . 0 ( s e Y s e Y s e Y 5 2 3 , 1 6 6 9 . 0 e s a e r c n i ( n l l a t o t n i ) y l p p u s d n a l ) 3 ( * * * 5 6 0 . 1 1 6 1 0 . 0 ) 0 2 2 . 0 ( 7 4 2 0 0 . 0 ) 8 2 1 0 . 0 ( ) 3 4 4 0 . 0 ( 1 6 1 0 0 . 0 − ) 1 8 4 . 0 ( 3 9 1 . 0 − ) 4 5 4 . 0 ( * * * 4 6 7 . 3 ) 1 5 6 . 0 ( s e Y s e Y s e Y 3 4 3 , 1 5 4 9 . 0 l a t o t ( n l s e l a s d n a l ) e u n e v e r ) 2 ( * * * 4 3 6 . 1 ) 3 1 3 . 0 ( 9 3 1 0 . 0 − ) 9 9 1 0 . 0 ( 8 4 8 0 . 0 ) 2 3 6 0 . 0 ( 6 6 2 . 0 − ) 4 9 6 . 0 ( 0 3 5 . 0 − ) 4 9 4 . 0 ( * * * 1 5 2 . 9 ) 8 0 2 . 1 ( s e Y s e Y o N 5 2 3 , 1 5 5 9 . 0 e s a e r c n i ( n l l a t o t n i ) y l p p u s d n a l ) 1 ( * * * 8 3 7 . 0 ) 5 2 2 . 0 ( 9 8 1 0 0 . 0 − ) 0 5 1 0 . 0 ( 0 9 2 0 . 0 ) 3 1 4 0 . 0 ( 7 6 4 . 0 ) 6 8 4 . 0 ( 3 1 3 . 0 − ) 0 7 4 . 0 ( * * * 8 8 0 . 3 ) 5 2 8 . 0 ( s e Y s e Y o N 3 4 3 , 1 4 3 9 . 0 ) y r a d n o c e s o t y r a i t r e t ( s P D G f o o i t a r i d n e r T e m T c ﬁ i c e p S - e r u t c e f e r P E F ) y r a t e r c e S - y t r a P ( - e r u t c e f e r P E F r o y a M - e r u t c e f e r P ) l a t o t ( e g a t s - 2 f o n o i t c a r f t n a t s n o C s n o i t a v r e s b O E F r a e Y 2 R 2 ) e c ﬁ f o n i s r a e y ( ) e s a e r c n i P D G ( n l e c ﬁ f o n i s r a e y s e l b a i r a V e h t m o r f e r a a t a d d n a L . k o o b r a e Y s c i t s i t a t S y t i C m o r f e r a a t a d P D G . n o i t c e l l o c ’ s r o h t u a m o r f e r a s r o y a m d n a s e i r a t e r c e s y t r a p l a r u t c e f e r p r o f s m r e t f o h t g n e l e h t n o a t a D : s e t o N - e r u t c e f e r p a y l l a u t c a s i t c e f f e d e x ﬁ r e d a e l - e r u t c e f e r p A s e s e h t n e r a p n i n w o h s d n a l e v e l e c n i v o r p e h t t a d e r e t s u l c e r a s r o r r e d r a d n a t S . s e c r u o s e R d n a d n a L f o y r t s i n i M e h t f o e t i s b e w . t c u d o r p c i t s e m o d s s o r g = P D G , s t c e f f e d e x ﬁ = E F . y t i c ﬁ i c e p s s t i r o f t n u o c c a o t r e d a e l l a r u t c e f e r p h c a e r o f t n e i c ﬁ f e o c a s i e r e h t , e r u t c e f e r p h c a e r o f , s i t a h T . r o y a m a r o y r a t e r c e s y t r a p a e b n a c r e d a e l a d n a , t c e f f e d e x ﬁ r e d a e l c ﬁ i c e p s . 1 . 0 < p = * , 5 0 . < p = * * , 1 0 . < p = * * * l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / 3 4 2 1 5 2 1 6 4 4 3 6 2 a d e v _ a _ 0 0 0 9 8 p d / . f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 . s n o i t a l u c l a c ’ s r o h t u A : e c r u o S Determinants of Urban Land Supply in the PRC 181 form. Here, we see that even with the linear time trend, the rising trend in years in ofﬁce remains quite robust.19 IV. Conclusions In this paper, we hypothesize that the two key features in the PRC’s political economy of land supply are corruption and competition for promotion. We thus explore the effects of corruption on the increase in urban land supply, as well as the effect of prefectural leaders’ years in ofﬁce to shed light on the competition motives. Conditional on standard urban-economic (demand-side) determinants and industrial 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 sales revenue. This suggests that while increased corruption leads to larger increases in land supply, it reduces prices compared with other methods of land sales. The effects of corruption are strongest for commercial land, followed by residential land. The effects for industrial land are insigniﬁcant. This indicates that industrial land sales are not a major source of corruption, perhaps because of its relatively lower land value. For the years-in-ofﬁce effect, we formulate three hypotheses regarding how the competition-for-promotion motive can matter. Our empirical results show very robust rising trends in land sales both in terms of quantity and revenue. These results are consistent with the hypothesis that the impatience and anxiety of not having been 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 later years. By investigating how land prices and the increment of land sales revenue change with years in ofﬁce, we ﬁnd that the rising pattern in the ﬁrst few years can still be consistent with the competition-for-promotion motive because prefectural leaders 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-promotion motive is likely to affect land sales through a combination of maximizing overall revenues in the ﬁrst few years in ofﬁce by restraining early sales and the subsequent impatience or anxiety of not getting promoted in later years. The questions that we ask and study in this paper seem quite speciﬁc to the PRC since these activities occur in an environment where local governments have dominant land ownership and strong control over land use, and where the selection of government ofﬁcials is a top-down process rather than a bottom-up process as in a 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 reform and 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 now become signiﬁcant with the prefecture-speciﬁc time trend (Columns [6]–[8] in Table 11). l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / 3 4 2 1 5 2 1 6 4 4 3 6 2 a d e v _ a _ 0 0 0 9 8 p d / . f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 182 Asian Development Review Hong Kong, China and Singapore also share some similar features in terms of the control over land use that these two governments have, but obviously there is a lack of political competition with other cities within a city’s hierarchy. Nevertheless, these lessons from the PRC are precious and interesting precisely because of their speciﬁcity as we get to see the effects of institutions. Although this paper provides no ground for making normative statements about the PRC’s urban land supply, this ﬁrst-cut evidence provides us with clues to think about normative issues. In particular, as the institutional environment seems to resemble one where Henry George’s idea of a single tax on land might be put into effect, one can ask to what extent the Henry George theorem (Arnott and Stiglitz 1979) holds in the PRC. As mentioned by Cai, Henderson, and Zhang (2013), the prevalence of corruption has squandered the opportunity for the PRC to realize the ideals of Henry George. Our results indicate that corruption is relevant. Yet, the quantitative question remains unanswered here and the qualitative question of how political competition matters in terms of welfare must also be clariﬁed. 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