metadata: authors: 0: author: Xu,Gang
1: author: Xu,L. Colin
2: author: Si,Ruichao
count: China
docna: 0: docna: Bureaucrats, Tournament Competition, and
            Performance Manipulation : Evidence from Chinese Cities
docty: Policy Research Working Paper
subtopic: Labor Markets,Economic Growth,Industrial Economics,Economic Theory & Research,Regional Economic Development,Economic Development,Spatial and Local Economic Development,Subnational Economic Development,Gender and Development,Energy Policies & Economics
teratopic: Social Protections and Labor,Industry,Energy,Macroeconomics and Economic Growth,Gender
abstracts: cdata!: Tournament competition is viewed as
            motivating bureaucrats in promoting growth. This paper
            examines how this incentive leads to economic performance
            manipulation. Using data from Chinese cities, the analysis
            shows that performance exaggeration increases over the
            course of the first term of the top bureaucrat, peaking in
            the last year of his or her term. Winning a tournament
            competition is behind this performance manipulation:
            political rivals reinforce each other in exaggerating
            performance, and political competition intensifies the
            tendency for manipulation. Performance exaggeration leads to
            higher chances of promotion, but the ratchet effect (that
            is, better performance today leading to a higher target
            tomorrow) and the potential to blame predecessors induce
            restraint. A good local institutional environment also
            restrains performance manipulation.
geo_reg_key: 517198,119225,517191
repnme: repnme: Bureaucrats, Tournament Competition, and
            Performance Manipulation : Evidence from Chinese Cities
display_title: Bureaucrats, Tournament Competition,
            and Performance Manipulation : Evidence from Chinese Cities
listing_relative_url: /research/2022/02/33731558/bureaucrats-tournament-competition-performance-manipulation-evidence-chinese-cities
topicv3: Economic Growth m1326241 471,Local and Regional Economic Development
            m1326938 798,Power and Electricity Sector m1327094 515,Gender m1327621 615,Jobs m1503931 2670
publishtoextweb_dt: 2022-02-15T00:00:00Z
dois: 10.1596/1813-9450-9938
historic_topic: Social Protections and Labor,Industry,Energy,Macroeconomics and Economic Growth,Gender
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
no_of_pages: 61
author_keys: 0: author_key: 540135
1: author_key: 125549
2: author_key: NA
model_extractions: Policy Research Working Paper 9938 Bureaucrats, Tournament Competition, and Performance Manipulation Evidence from Chinese Cities _Gang Xu L. Colin Xu Ruichao Si_ Development Economics Development Research Group February 2022
Policy Research Working Paper 9938

# **Abstract**

Tournament competition is viewed as motivating bureaucrats in promoting growth. This paper examines how this incentive leads to economic performance manipulation. Using data from Chinese cities, the analysis shows that performance exaggeration increases over the course of the first term of the top bureaucrat, peaking in the last year of his or her term. Winning a tournament competition is behind this performance manipulation: political rivals reinforce each other in exaggerating performance, and political competition intensifies the tendency for manipulation. Performance exaggeration leads to higher chances of promotion, but the ratchet effect (that is, better performance today leading to a higher target tomorrow) and the potential to blame predecessors induce restraint. A good local institutional environment also restrains performance manipulation.

This paper is a product of the Development Research Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at lxu1@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Bureaucrats, Tournament Competition, and Performance Manipulation: Evidence from Chinese Cities**<sup>*****</sup>

Gang Xu<sup>†</sup> L. Colin Xu<sup>‡</sup> Ruichao Si<sup>§</sup>

**Keywords:** Performance manipulation, nighttime lights, promotion incentives, political cycles, tournament competition.

**JEL Codes:** H11, H77, O43, P26, R12

> * We thank Junyan Jiang, Lina Song, Xi Weng and participants at various seminars and conferences for their constructive comments. We also thank Martin Raiser for thoughtful and detailed comments. The authors declare that they have no relevant or material financial interests that relate to the research described in this paper. The views and errors are the authors’ own, and do not implicate the World Bank or the countries it represents.

> † Gang Xu, Center for Transnationals’ Studies & School of Economics, Nankai University, Tianjin, China (email: xgdl2005@hotmail.com).

> ‡ Lixin Colin Xu: World Bank, the Research Group, 1818 H Street, NW, Washington, DC 20433 (email: lxu1@worldbank.org).

> § Ruichao Si, School of Economics, Nankai University, Tianjin, China (email: siruichao@hotmail.com).
# **1 Introduction**

While agreeing on the importance of bureaucracy for a country’s prosperity, scholars disagree on how the quality of bureaucracy affects the economy (Besley et al. 2021). According to the Weberian view, bureaucrats are mission-driven professionals obeying rules and delivering expert public services and goods (Weber 1922). Their selection is based on mission, expertise, and competence, and their career tracks are based on merits. There is support for this view: better bureaucratic quality at the country level is associated with a higher GDP level, and its improvement is associated with higher GDP growth (Evans and Rauch 1999; Besley et al. 2021). According to the public choice school view, in contrast, bureaucrats are rent seekers that use public offices for private benefits, such as collecting bribes for delivering services, or using the public sector jobs for patronage (Tullock 1967; Stigler 1971; Peltzman 1976). Given the amount of resources under bureaucrat allocation, as well as their impact in determining the regulatory framework, it is important to shed light on the validity of these opposite views. To do this, we must understand how bureaucrats are selected and incentivized, how bureaucratic performance is measured, and how bureaucrats respond to bureaucratic performance criteria.

In this paper, we examine how the incentives of Chinese local bureaucrats shape their behavior in reporting GDP growth, commonly viewed as their most important performance measure (Li and Zhou 2005). This context highlights the three key elements behind understanding bureaucracy: incentives, selection, and measurement (Besley et al. 2021). In particular, how does the upper government design bureaucrats’ career tracks based on their GDP growth performance? How do they report their GDP performance and what are their incentives? Is the reported performance reliable? Does the institutional context matter for bureaucrat behavior in performance reporting?

Besides shedding light on bureaucrat behavior, understanding GDP measurement is important on its own. As the key indicator of economic activity, GDP is at the core of macroeconomic analysis and policy making. The veracity of GDP data is vital to international and interregional comparisons as well as policy decision-making. It is believed that the quality of GDP figures in developing countries is lower mainly for technical reasons such as lower statistical capacity (Johnson et al. 2013; Hu and Yao 2019). However, perhaps due to the lack of full accountability of the government, GDP figures in developing countries are also more susceptible to manipulation.

The Chinese context is particularly relevant for understanding bureaucratic performance for several reasons. _First_ , China has had millenniums of history of centralized states, as well as relatively strong state capacity. Though there might be some accountability in China (Besley and Kudamatsu 2
2007, Fang et al. 2019, Gehlbach and Keefer 2011), such accountability is likely limited and non-stable. Without strong accountability, local officials retain large discretion, making data manipulation feasible. _Second_ **,** the literature agrees that local governments face strong political incentives in promoting local economic growth (Li and Zhou 2005; Xu 2011). Such incentives could lead to exaggerating GDP growth to gain advantages in promotions. Given the information asymmetry along the administrative hierarchies (Hayek 1945), when facing the choices between growing the local economy and inflating growth figures, the career-concerned subnational leaders find it easier and tempting to choose the latter over the former.<sup>1</sup> _Finally_ , Weberian merit-based promotion incentives of local officials are thought to be a key factor behind China’s strong growth over decades (Maskin, Qian and Xu 2000; Li and Zhou 2005; Xu 2011). If anything, China has been viewed as the prototype of strong bureaucracy throughout history (Fukuyama 2014, p. 370). Whether such merit-based promotions are reality or myth is thus critically important for understanding China’s growth in particular and the nature of bureaucracy in general.

In this paper, we study how the performances of top local bureaucrats are measured and manipulated, and the underlying incentives of such behavior, using data of Chinese cities from 2001 to 2013. We first provide evidence that local GDP growth data are susceptible to manipulation. Using data of growth-target setting in economic plans, an annual practice for Chinese governments at all levels, we graphically show a sharp discontinuity in the distribution of over-performance above the growth target—i.e., the gap between the reported growth and the targeted growth--at the threshold of just achieving the target, which indicates “sorting” of reported figures around the target. This, combined with increasingly frequent adjustments of official GDP figures, is suggestive of potential manipulations of annual GDP growth rates. Following Henderson, Storeygard, and Weil (2012),<sup>2</sup> we then estimate the city-level true economic growth rates and the level of manipulation with nighttime lights data. We find large variations in the level of manipulation (i.e., the difference between the reported GDP growth and the predicted true growth) across cities and years during 2001-2013.<sup>3</sup> To understand the motivations behind bureaucrats exaggerating GDP performance, we examine how it is related to their career incentives. We first show whether career incentives related to political

> 1 The evidence on GDP manipulation in China is inconclusive. On the one hand, many have cast doubt on the credibility of Chinese official GDP data, offering evidence of manipulation (Adams and Chen 1996; Rawski 2001; Maddison and Wu 2006; Ma et al. 2014; Lyu et al. 2018; Chen et al. 2019). On the other hand, there are also studies that fail to find evidence of data manipulation, and claim that the Chinese data are accurate (Perkins and Rawski 2008; Mehrotra and Pääkkönen 2011; Holz 2014).

> 2 Researchers now often use nighttime luminosity as a measure of real economic activity. For example, Martinez (2019) demonstrates that night-lights elasticity of GDP is systematically larger in non-democratic countries relative to democratic countries, suggestive of manipulation. See Donaldson and Storeygard (2016) for a comprehensive discussion. 3 However, as detailed in section 4, due to its derivation from a regression-based framework, our measure of manipulation should be interpreted in _relative_ terms. We do not focus on the absolute extent of GDP over-reporting or under-reporting; we instead focus on the relative extent of GDP manipulation across regions, and the underlying incentives.

3
cycles, as measured by the number of years in office for local top bureaucrats, may influence growth manipulation. Political cycles in China have been shown to matter for economic decisions such as local government budgets (Guo 2009) and IPO decisions (Piotroski and Zhang 2014). Because promotions of local officials rarely occur beyond their first term of office, we expect the level of growth exaggeration to increase over their course of the first term, and to peak around the 5th year, the last year of the term. Indeed, we find a robustly strong curvilinear relationship between the time in office for city party secretaries and the extent of growth exaggeration. Based on the empirical results, the city secretary would inflate growth rates by 0.26 percentage point in the fifth year relative to the initial year, which amounts to 210 million RMB at constant 2000 prices.<sup>4</sup> Interestingly, the association between bureaucrat career incentives and performance manipulation is present only in regions with a weak institutional environment.

We then provide evidence that the pattern of performance manipulation is consistent with the tournament competition framework. In this framework, local top bureaucrats compete for promotions based on their relative performance (Li and Zhou 2005; Shi and Xi 2018). Supporting this framework, we find a political rival effect among the cities within the province, that is, the city’s top bureaucrat would exaggerate performance more when his political rivals exaggerate more. This effect is especially pronounced for economically comparable rivals. On the other hand, no similar patterns are found among geographically neighboring cities _across_ provinces. Furthermore, stronger inter-city competition among city rivals—i.e., when city party secretaries have similar promotion schedules— is associated with stronger incentives to exaggerate, and induces the party secretaries to do it earlier during their terms. Overall, our findings suggest that the inter-city competition for promotion within the same province is an important factor in shaping the political cycle effects for GDP manipulation.

Since exaggerating performance is supposed to be driven by promotion incentives, we then explore whether this practice pays off. The existing literature studying performance-based political selection in China offers inconclusive results. Constructing a more comprehensive measure of career advancements based on real power rather than just hierarchical ranks, we show that while higher reported economic performance is not robustly related to promotions, over-reporting is robustly associated with a higher likelihood of promotion. Our findings thus suggest that this performancebased selection of leaders has adverse effects in encouraging performance manipulation, which could distort data quality.

Why then would some officials under-report performance? It turns out that the “ratchet principle”

> 4 While our paper does not directly address the issue of the absolute extent of GDP over-reporting in China, the moderate extent of over-reporting (i.e., 0.26 percentage point) in the high-career-incentive year (i.e., the fifth year of the term) relative to the low incentive year (i.e., the first year), as well as the presence of over-reporting only in regions with relatively weak institutions, is suggestive of moderate GDP over-reporting at the country level.

4
is at play: cities with strong current performance—surpassing the growth target by a wider margin— tend to withhold effort now to avoid facing a higher target in the future (Weitzman 1980). Indeed, using a regression discontinuity design (RDD), we find cities that truthfully achieved their targets are likely to report a growth smaller than the true growth, compared with those that just failed the target. In addition, they are more likely to report lower growth in their first year in the job when they could scapegoat their predecessors. The Chinese cadre management system thus has a built-in mechanism to contain GDP reporting, at least in some years.

Our paper contributes to two strands of literature. It is related to the literature on bureaucracy and development--see Besley et al. (2021) for a comprehensive survey.<sup>5</sup> Despite the large literature, the top area needing further research is aspects related to the measurement of bureaucratic performance (Besley et al. 2021).<sup>6</sup> Existing studies on senior bureaucrats often measure bureaucratic outputs as certain aggregate outcomes such as GDP growth (Li and Zhou 2005; Jia, Kudamatsu and Seim 2015), revenue generation (Xu 2018), or local development outcomes (Gulzar and Pasquale 2017). We add value by examining how the key performance measure of top local bureaucrats in China, GDP growth, is subject to bureaucratic manipulation, under prevailing career incentives. Such performance manipulation makes it harder to motivate bureaucrats, and could render the incentive design of tournament competition, often viewed as the key design behind China’s growth, less effective. We also show how the ratchet principle also limits the extent of performance manipulation in the Chinese bureaucratic system, and thus partially restores the effectiveness of the tournament competition system.

We also contribute to the literature on local government behaviors and local development in China, one of the most prolific areas of research for studying the contemporary Chinese economy. This literature has emphasized the role of incentives and the capacity of local government officials in accounting for local economic development in China. The prevailing view is that the promotional incentives shaped by tournament competition among regions strongly facilitated regional and therefore China’s growth (Maskin, Qian and Xu 2000; Li and Zhou 2005; Xu 2011). Others question this conclusion by pointing out the non-robustness of the link between promotional incentives and local growth (Landry 2003), and/or modifying the view by emphasizing complementary factors such as connections and factions (Shih et al. 2012; Jia, Kudamatsu and Seim 2015), or selection of talents (Yao and Zhang 2015). All these studies assume the reported growth rates are unbiased. We cast doubt

> 5 See also Dixit (1997); Evans and Rauch (1999, 2000); Khan, Khwaja and Oklen (2016, 2019); Finan, Olken and Pande (2017); Ashraf and Bandiera (2018); Rasul and Rogger (2018); Xu (2018); Bandiera et al. (2020); Bertrand et al. (2020); and Colonnelli, Prem and Teso (2020).

> 6 See also Asher and Novosad (2020), and Donaldson and Storeygard (2016).

5
on this assumption.<sup>7</sup> We find a large, positive and significant effect of over-reporting on promotion, while the effect of reported growth is less pronounced and robust. Our results suggest that if performance metrics are susceptible to manipulation, performance-based competition among China’s sub-national governments can facilitate GDP manipulation, and reduce the effectiveness of the tournament competition system for regional development. We also point out and offer evidence that the ratchet principle tends to limit the extent of over-reporting. Relatedly, our paper also contributes to the literature on political cycles,<sup>8</sup> especially those in China (Guo 2009; Nie, Jiang, and Wang 2013; Xi, Yao, and Zhang 2018). While this literature on China attributes promotion incentives as arising from tournament competition, it does not empirically document such competition. We add value by empirically modeling the strategic competition among political peers using spatial econometric models, as well as considering the degree of political competition.<sup>9</sup> Finally, we provide the first piece of evidence on ratchet effects in tournament competition among local governments, adding to the emerging literature on growth target setting (Li et al. 2019).

In the rest of the paper, Section 2 introduces China’s bureaucratic system and GDP accounting system. Section 3 describes the data, and section 4 introduces the empirical strategy. Section 5 presents the results on the political cycle effects. Section 6 investigates the political rival effects of performance manipulation. Section 7 discusses the effect of performance manipulation on promotion, and the circumstances for local bureaucrats to restrain performance exaggeration. Section 8 concludes.

# **2 Institutional Background**

## **2.1 China’s bureaucratic system**

7 Another study also examines politician incentives and GDP manipulation. Chen, Qiao, and Zhu (2021) focus on the effect of different stages of politicians’ careers on local officials’ GDP manipulation in China. We differ from this study in many ways. First, we focus on the political cycle effects. Second and more importantly, we explore how performance manipulation is related to political rivals’ manipulation, especially comparable rivals, as well as the degree of political competition, but not geographical neighbors. We are thus able to offer evidence of direct political mechanisms of tournament competition in inducing performance manipulation. Third, we painstakingly construct more comprehensive measures of career track based on _real power_ rather than just administrative ranks, and use it to provide evidence that performance manipulation pays for career advancements. Fourth, we show that the ratchet effects and the opportunity to scapegoat predecessors explain why some bureaucrats would restraint performance manipulation. Finally, we offer improvements in empirical details: (1) we use a relatively long period of data, which improves the credibility of the results as studies have shown that the studies on local political incentives are fragile with data with different time coverage (Shih et al. 2012; Landry 2003); (2) we employ prefecture-level data in contrast to the county-level data of night light data to predict GDP, which represents an improvement in predicting real GDP since Gibson et al. (2021) caution that the predictive performance of the night light data (i.e., DMSP) is less accurate for lower-level spatial units, for lower density areas, and for smaller areas.

8 For the general literature in this area, see Nordhaus (1975), MacRae (1977), Rogoff (1990), Shi and Svensson (2006), and Labonne (2016).

9 Yu, Zhou, and Zhu (2016) and Shi and Xi (2018) employ similar spatial econometric models to estimate the strategic competition among city governments for GDP growth and coal mine deaths respectively, and find positive neighborhood effects among cities in the same province, particularly those with similar economic ranks.

6
China is a unitary state. Cities are classified into four hierarchical levels: provincial, deputy-provincial, prefectural and county. In this paper, the word “city” is used to denote both deputy-provincial- and prefecture-level cities, which constitute the majority of all prefecture-level administrative units.<sup>10</sup> Each city has two top bureaucrats: the city party secretary (PS), the top official of the city communist party, and the mayor, the head of the city government.<sup>11</sup> Though the PS and the mayor have the same administrative ranking, the PS is _de facto_ dominant, enjoying more power over policy making, personnel control and resource allocation within their jurisdictions under the regionally decentralized political system (Landry 2008). While a typical PS is ranked at the prefecture level,<sup>12</sup> some are ranked at the deputy-provincial level either because the city is particularly important or because they hold deputy-provincial level positions at the same time.

The PS is appointed and supervised by the provincial party committee.<sup>13</sup> A full term of office is five years. In practice, most PSs stay in office for fewer than five years. Although some may be appointed for a second term, they rarely stay in the same office for more than 10 years (Wang, Zhang, and Zhou 2020). Figure 1 shows the distribution of tenure lengths in office for the PSs in our data. A large proportion of PSs stay in office for around 3 years, with few having a second term.

Before the reform began in 1978, city leaders were evaluated by their political conformity. After the reform, regional economic performance measures (such as the growth of total output and fiscal revenues) became key performance indicators for their career advancement (Li and Zhou 2005; Wang, Zhang, and Zhou 2020). For example, Li and Zhou (2005) show that between 1979 and 1995, the chance of promotion for provincial PSs increased with their economic performance. At the prefecturecity level, promotion opportunities of PSs mainly include membership in the provincial Party Standing Committee, the vice provincial governorship, and the deputy ministership in the central government.<sup>14</sup>

# **2.2 GDP accounting system and target setting in China**

# **2.2.1 GDP compilation and statistical institutions**

> 10 There are 333 prefecture-level administrative units in China as of 2020: 15 deputy-provincial level cities, 278 prefecture-level cities, 7 prefectures ( _diqu_ ), 30 autonomous prefectures ( _zizhizhou_ ) and 3 leagues ( _meng_ ). We do not consider provincial- and county-level cities because county-level cities are already embedded in prefecture-level cities, while province-level cities are too large to be comparable.

> 11 Top Chinese local officials could of course be viewed as both politicians and bureaucrats. Since they are typically professional life-time careerists in the Chinese bureaucratic system, who are selected for their capacity and evaluated professionally, and aim at rising in rank and importance, it is likely better to view them as bureaucrats rather than politicians, which has the connotation of catering to voters, non-existent in China.

> 12 The ranks of China’s officials are similar to those of administrative units.

> 13 Appointments of PSs and mayors of deputy-provincial level cities are also subject to the oversight of the Central Committee. These deputy-provincial level cities are governed by provincial-level governments though.

> 14 For deputy-provincial-level ranked PSs, the positions for promotion include provincial secretaryship and governorship as well as ministership in the central government.

7
Here we introduce the process of GDP compilation by statistical bureaus in China, and the potential for local government maneuver, drawing heavily from Ma et al. (2014) and Holz (2005). The National Bureau of Statistics (NBS), an agency directly under the State Council, is in charge of the collection, research and publication of economic statistics throughout China. Local governments at the level of county and above have their own statistical division, which is under the joint oversight of the local governments and the statistical counterpart one level up.<sup>15</sup> Take the provincial bureaus of statistics (provincial Bureaus for short) as an example. The provincial governments hold the appointment authority over major statistical personnel and make key decisions on the funding allocation to provincial Bureaus. Meanwhile, provincial Bureaus must conduct statistical works following the rules and guidelines issued by the NBS.

Statistics collected by local Bureaus are reported to the next higher level. Provincial GDP statistics thus depend on the GDP figures reported by each prefecture, as well as on the independent sampling and data processing from a higher-level statistical bureau. The local Bureaus compile local GDP by combining all related data from the next lower-level bureaus, from other departments, and from the NBS survey teams. The local Bureaus are mainly in charge of raw data collection from firms above the cutoff level in industry,<sup>16</sup> wholesale and retail, and hotels and catering sectors as well as from construction enterprises with qualified certification and independent accounting. The NBS survey teams are responsible for sampling surveys on firms below the cutoff level in the corresponding sectors, but they are not involved in collecting economic statistics in the construction and banking, securities and insurance sectors.

This process of GDP compilation may open doors to GDP manipulation. Since local leaders are rewarded for economic growth, they have incentives to exaggerate local growth. First, because the local government has appointment authority over major personnel of the local Bureaus, the local leader’s “requests” for a higher GDP growth figure, albeit in violation of the Statistics Law, are unlikely to be rejected by the subordinate statistics bureau. In addition, it is stipulated that key data compiled by the local Bureaus, such as GDP data, should be approved by local government leaders before being submitted to the upper statistical bureau. Thus, the GDP calculation by local Bureaus is inevitably subject to administrative interference under the existing oversight structure. Second, as mentioned above, the NBS survey teams conduct sampling surveys only for enterprises _below_ the cutoff level in industry, wholesale and retail, and hotels and catering sectors, i.e., relatively small firms. Based on the available data from 11 provinces in 2010, the below-scale industrial firms only accounted

> 15 In this paper, we mainly focus on the provincial and prefectural governments and their statistical bureaus. 16 The cutoff was 5 million yuan for the revenue from principal business until 2010, and since then, it was changed to 20 million yuan.

8
for an average of 8.3% of the total industrial value added. Such a small proportion precludes the NBS survey teams from effective checks on the rest of the statistics from the local statistical agencies.

Furthermore, data on industrial firms suffer from limited information disclosure, further undermining monitoring from the public. As of now, only the aggregate industrial value-added data are publicly available at the province and the prefecture level, and these data cannot even be decomposed into the one- or two-digit subsectors.<sup>17</sup> Indeed, the largest discrepancy between the total national value added and the sum of the corresponding provincial values is found in the industrial sector. Finally, neither the census nor other data of the NBS are capable of adequately adjusting these skewed statistics. For example, while the 2008 Benchmark Revisions following the census reduced the overall value-added discrepancy as well as discrepancies in the tertiary sector, the data adjustments only eliminated a small proportion of the discrepancy in the industrial sector.

# **2.2.2 Setting growth target**

Chinese governments at all levels make their economic plans in advance, setting key economic targets, including GDP growth rates, industry value added, fiscal revenues and expenditures. The drafting of plans involves a top-down process: In December, the Central Economic Working Conference, jointly held by the Central Committee of Communist Party and the State Council, reviews economic performance in the current year, and then sets the national growth target for the next year. Provincial and prefecture governments then set their own targets on the basis of the national targets, taking into account the local conditions (Lyu et al. 2018).<sup>18</sup> These annual plans are released in the government work reports, and presented in the local People’s Congress at the beginning of each year.

# **2.3 Evidence of manipulation at the subnational level**

The quality and credibility of China’s officially released economic data have been scrutinized despite the lack of direct evidence. In recent years, a growing number of Chinese local governments significantly adjusted their economic data. In 2017, the provincial governor of Liaoning province acknowledged having inflated fiscal revenue by 20% per year in 2011-14. In 2018, the government of Tianjin released a revised GDP figure of 2016 that had been adjusted downward by one-third of the previously published number, while Inner Mongolia has reduced the 2016 data of fiscal revenue and industrial output by 25% and 40% respectively.<sup>19</sup>

> 17 This stands in stark contrast to the tertiary sector, whose value-added data can be divided into six subsectors. 18 The targets set by the upper-level governments, which can serve an important function of transmitting superiors’ preferences on the relative importance of economic growth to subordinates, are taken quite seriously by the lower-level governments. This also results in the top-down amplification of GDP growth targets (Li et al. 2019). 19 Similar cases have been found in 10 cities and districts in Hunan, Yunnan and Jilin provinces as well as in Chongqing. For detailed reports and analyses, see https://www.ft.com/content/0361c1a4-bcfe-11e6-8b45b8b81dd5d080; https://asia.nikkei.com/Economy/Chinese-local-governments-rush-to-admit-fake-data, and

9
To shed light on whether GDP manipulation is widespread among Chinese cities, we first look into the distribution of a city’s over-performance, defined here as the gap between the reported growth and the target growth. We then conduct the manipulation test proposed by Cattaneo, Jansson, and Ma (2018) to test for a discontinuity in the distribution of the over-performance around 0, the threshold for meeting the target. As shown in Figure 2, the gap between the density estimate on the two sides of zero is large and statistically significant ( _p_ =0.001). At the discontinuity point, the density is significantly higher on the positive side than on the negative side.

A similar test using the cumulative _quarterly_ GDP growth data is also revealing. Since the evaluation of bureaucrat performance is based on annual rather than quarterly data, if local governments indeed manipulate GDP data, the need for manipulation emerges only as the year-end approaches. Thus, the discontinuity in over-performance should emerge mainly in the latter half of the year (Lyu et al. 2018). To test this, we use the provincial-level data here since the city-level quarterly GDP data are not widely available. As shown in Appendix Figure A.1, the break in density of overperformance in growth is significant only in the third and fourth quarters, and the magnitude steadily increases over the course of the year. This result is again suggestive of performance manipulation among the Chinese cities.

# **3 Data and Variables**

## **3.1 Nighttime lights data**

The nighttime lights data were collected by the U.S. Air Force Defense Meteorological Satellite Program (DMSP) using the Operational Linescan System (OLS) sensors. They were processed and distributed by the scientists at the National Oceanic and Atmospheric Administration’s (NOAA) National Geophysical Data Center (NGDC), available from 1992 to 2013. The DMSP satellites observe every location on the planet each night at the local time from 8:30 pm to 10 pm. In processing, some restrictions are imposed to remove observations affected by sunlight, glare, moonlight, aurora as well as cloud cover, and readings that likely reflect ephemeral events (such as fire) are set to zero, leaving mostly man-made lights from human settlements.<sup>20</sup> In each satellite-year data set, light intensity is recorded as a Digital Number (DN) ranging from 0 (no lights) to 63 (top-coded), for every 30 arc-second output pixels (about 0.86 square kilometers at

> https://www.scmp.com/comment/insight-opinion/article/2130132/now-china-must-come-clean-about-its-nationaleconomic-data.

> 20 See Elvidge et al. (2009), Chen and Nordhaus (2011) and Henderson, Storeygard, and Weil (2012) for details.

10
the equator) spanning -65° to 75° in latitude and -180° to 180° in longitude.<sup>21</sup> To derive the city-level nighttime light intensity, we first clip the DMSP-OLS nighttime lights data by China’s municipal administrative boundaries, and calculate an area-weighted average of lights across pixels within a municipality for each of our sample years.

# **3.2 Biographical database for Chinese leaders**

The data on city officials are from the China Political Elite Database (CPED), constructed by Jiang (2018) and Jiang and Zhang (2020). The database consists of more than 4,000 Chinese leaders, covering all city leaders in office since 2000 and all provincial and national leaders since the mid1990s. Information for these officials is drawn from their biographies through authoritative internet sources. For each official, the database contains basic demographic characteristics as well as the time, place, organization and rank of every job assignment listed in his curriculum vitae. We match each city-year spell in our dataset with a party secretary and a mayor. In turnover years when multiple officials held the same post, we keep the official who held the position at the end of the year. Panel B of Table 1 shows the summary statistics for the city party secretaries.<sup>22</sup> Our database contains 1,041 city PSs in office between 2000 and 2013. Among the city PSs, 130 served in more than one city (mostly two); 4.2% are female, and 6% are ethnic minorities. Their average tenure is 3.9 years, with only 25% staying in the office for more than 5 years. Their mean age when assuming office is 50.6 years old. More than 68% of them are from their native provinces, and around 7% serve in cities that are their native cities.<sup>23</sup> We define promotion and demotion significantly more comprehensively than the literature. The literature defines demotions as either being investigated for corruption or violation of the party disciplines, or being relegated based on changes in ranks. This classification fails to identify all demotions: it would, for instance, improperly categorize many demotions as lateral moves since publicly announced demotions were very rare, and only under rare circumstances do demotions take the form of a direct rank relegation (Li and Zhou 2005).<sup>24</sup> Following Aghion and Tirole (1997), and consistent with what is commonly believed among the bureaucrats and Chinese citizens, we believe

> 21 For some years two satellites collected data and thus two composites were produced, and we average these two composites.

> 22 The unit of observation used in the calculation of summary statistics for time-invariant demographic characteristics (i.e., gender and ethnicity) is the leader, while that for time-varying variables is the city-leader spell. For variables that vary across years within the city-leader spell such as age and the number of years as the party member, the summary statistics are calculated based on the year when the leader assumed office.

> 23 The curriculum vitae does not list the official’s birthplace. Rather, it shows his native province and city.

> 24 For example, Mr. Xu Jingyan, who used to be the party secretary of Liaocheng, a western prefecture-level city in Shandong Province, was appointed as the party secretary of Jinan University, a university administered by the provincial government, at the end of 2018. Although both positions are of the same rank, the transfer is publicly viewed as a demotion due to the perceived drop in power and importance.

11
what motivates politicians is real power, not just formal rank. Indeed, within the same rank, the amount of power and rents differ greatly among official positions. A rich and populous prefecture-level city could have a total GDP that is 50-100 times as large as that in another poor city. The same PS position in a larger and more prosperous city, thus, implies greater power, and a lateral move (i.e., with the same hierarchical rank) to such a position should be counted and is widely viewed as a promotion. The importance of positional power (without regard to rank) is a worldwide phenomenon, as demonstrated by the relatively low “rank” of Robert Moses (“park commissioner of New York” among other similarly-ranked posts) despite his vast power comparable to even governors (Caro 1974). In coding, we thus take into account not only their hierarchical ranks but also changes in real power between the city secretary’s current position and the destination position. See Appendix B for details of how we code promotions, lateral moves, and demotions.

Among all the 1,178 city-leader pairs, less than a quarter were promoted and more than a third were demoted.<sup>25</sup> The demotion rate is much higher than that of Li and Zhou (2005) for provincial leaders between 1979 and 1995, possibly due to our more thorough and exhaustive way of categorizing promotions, lateral moves and demotions.<sup>26</sup> The patterns also indicate stronger career incentives for local politicians: both upside and downside potentials are substantial and real.

Figure 1 illustrates promotion rates by the number of years in the PS position. Overall, the likelihood of promotion decreases as the number of years in office increases. When the PS stays in the position for more than 5 years, the chances of promotion drop sharply. The fifth year can be viewed as the last fast-track train to promotions. Therefore, we assume that the promotion incentive should be the strongest as the number of years in office approaches five when the full term expires and the typical wholesale changes of government cadres happen.

# **3.3 Other data**

We also collect data of city-level socioeconomic conditions from 2000 to 2013. The variables include the level and growth rate of GDP, electricity consumption, population, and the shares of industrial and tertiary value added in GDP, all from _the China City Statistical Yearbooks_ , supplemented by _the Statistical Yearbooks on Regional Economy_ and the statistical yearbooks of provinces. Data on provincial and prefectural GDP growth targets are from _the Report on the Work of the Government_ of the corresponding administrative units. In our analysis, we focus on all the prefectural and subprovincial level cities, but exclude prefectures, autonomous prefectures and leagues.<sup>27</sup> The final panel contains observations for 281 localities for 13 years. Descriptive statistics of these variables are in

> 25 We fail to find information on destination positions for 22 city-leader pairs. 26 The promotion and demotion rates would differ for the provincial leaders and prefecture leaders as well. 27 This is because these prefecture-level administrative units lack data on some key socioeconomic variables. 12
Table 1.

# **4 Empirical Strategy**

## **4.1 Measuring GDP manipulation**

To infer true GDP, we rely on a proxy for economic activity that is objectively measured and highly correlated with true output. Since Henderson, Storeygard, and Weil (2012) developed an original statistical framework to use satellite data on night lights to improve official economic growth measures, nighttime lights have often been used as a proxy for economic activity (Hodler and Raschky 2014; Michalopoulos and Papaioannou 2014; Henderson et al. 2018). To predict the true GDP growth using nighttime lights data, Henderson, Storeygard, and Weil (2012) run the following regression: ln( _GDPi,t_ ) = _α_ 0 + _α_ 1 ln( _Night lightsi,t_ ) + _μi_ + _δt_ + _εi,t_ (1) where ln(GDPi,t) is the logarithm of real GDP, and ln(Night lightsi,t) the logarithm of the night light luminosity of the region, measured as the area-weighted average of DN values across pixels within the region. _μi_ and _δt_ are the region and the year fixed effects (FEs), respectively. To improve the precision of estimated economic growth, they combine the fitted values from equation (1) with the official GDP growth to arrive at a composite estimate that would have a lower error than that based on either measure separately. That is _Predicted true growthi,t_ = _λ Official growthi,t_ + (1 − _λ_ ) _Light-predicted growthi,t_ (2) where _λ_ is the optimal weight that minimizes the variance of measurement errors in the above estimate relative to the true value of economic growth. Under this framework, GDP manipulation is the difference between the officially reported growth and the predicted true growth in Eq. (2): (1 − _λ_ ) ( _Official growthi,t_ − _Light-predicted growthi,t_ ).<sup>28</sup> However, the relationship between nighttime lights and real GDP may differ for regions at different stages of development (Hu and Yao 2019). For example, regions at more advanced stages may focus more on technological innovation rather than infrastructure development, thus their GDP may be less associated with their nighttime lights. We thus apply the statistical framework of Henderson, Storeygard, and Weil (2012) and modify Eq. (1) to allow for region-specific slopes of lights to consider the different levels of development across regions. We classify China into eight economic regions based on their geographic proximity and levels of economic development following

> 28 Note that different values of _λ_ will only lead to changes in the absolute values of our measure, thereby not affecting the regression results except inflating the coefficients (or effects) by the same amount. In our case, although we allow _λ_ to be region-specific, since the variation is small and thus _λ_ can be viewed as almost constant, the above logic applies to our setting. Our estimated _λ_ is generally larger than those in the literature (e.g., Henderson, Storeygard, and Weil 2012).

13
the NBS,<sup>29</sup> and allow for region-specific optimal weights _λ_ . We find that the variation of the estimate of _λ_ is quite small, ranging from 0.576 to 0.594. Finally, the derived measure of over-reporting is winsorized at the 0.1th and 99.9th percentiles. See Appendix C for how we apply the modified version of Henderson, Storeygard, and Weil (2012) to our analysis of GDP manipulation of Chinese cities.<sup>30</sup> Figure 3 displays the relationship between ln(GDP) and ln(Lights) net of city and year FEs across the eight economic regions. A linear specification in the lights growth appears to be adequate. For all the regions except Northwest, real GDP’s elasticity with respect to nighttime lights is positive and statistically significant, ranging from 0.046 to 0.191. The adjusted _R_<sup>_2_</sup> indicates that the light intensity and city and year FEs explain more than 99.5% of the variation in official real GDP. The regional distribution of the estimated over-reporting of GDP growth ranges from -6.2 to 7.2 percentage points, with a standard deviation of 1.4 percentage points (Table 1).<sup>31</sup> By construction, our measure of over-reporting centers around zero, and can take both positive and negative values.<sup>32</sup> As a result, our measure of GDP manipulation should be viewed as capturing the extent of manipulation only in relative terms. If all the cities manipulate their growth rates upward, the slope will become steeper whereas cities lying on the fitted line will still get a zero over-reporting. In this sense, cities lying above (below) the fitted line are thus predicted to have more (less) overreporting than those lying on the fitted line. Therefore, while negative values of our measure could indicate under-reporting in some cases (especially when the magnitude is large), we should not take the negative sign literally as under-reporting.

To gain intuition for whether our measure is an appropriate proxy of the extent of manipulation, we link it with cities’ annual growth targets, typically set at the beginning of the year. We expect that cities that have achieved the yearly target (i.e., reported growth more than the target growth) are more likely to exaggerate their growth figures compared to those that have failed to do so—the former group should have more manipulators than the latter group. Figure 4 plots the distribution of our manipulation measure for these two groups. Indeed, the distribution of the target-reaching group (solid line) lies to the right of the distribution of the target-failing group (dashed line). In fact, nearly 60% of cities in the

> 29 The eight economic regions are: Northeast, North Coast, Eastern Coast, Middle Yellow River, Middle Yangtze River, Southwest, Northwest, and South Coast.

> 30 Gibson et al. (2021) show that the performance of DMSP data in predicting GDP is worse for lower-level spatial units, for lower density areas, and for smaller areas. Given the size and population density of Chinese cities, their results suggest our use of DMSP data to predict city-level GDP growth is appropriate and relatively accurate. 31 In robustness checks, we also use more sophisticated specifications (e.g., province-specific slopes or a quadratic specification) and add more controls when predicting GDP growth in Eq. (1), such as electricity consumption, bank loans and freight volume. However, we do not incorporate these into the baseline regression Eq. (1) since the current specification already explains more than 99.5% of variation in official real GDP. Including more terms leads to overfitting, which will make the light-predicted real GDP growth very close to officially reported one, resulting in the loss of useful information in nighttime lights.

> 32 It is essentially the first-differenced residuals from Eq. (1) multiplied by a constant between 0 and 1.

14
target-reaching group are presumed to have inflated their reported growth, compared with only 37% in the target-failing group.

# **4.2 Empirical specification**

Performance manipulation is likely shaped by promotion incentives, measured by the city leader’s years in office. From the tournament competition perspective, the primary gain of GDP manipulation for local officials is a higher probability of promotion. However, manipulation comes with a cost: if being caught cheating, he may face punishment, including demotion, job loss or criminal penalties (Chen, Qiao and Zhu 2021). Over-reporting could also, as we shall discuss later, increase future performance targets according to the ratchet principle (Weitzman, 1980), thus making the city leader’s future life more difficult. Since promotions rarely occur beyond the first term of office, efforts devoted to achieving promotion through GDP manipulation need to be timed strategically. Over-reporting in earlier years of the first term would have higher costs, and this is because future performance targets would rise, making the city leader’s subsequent years harder. This consideration implies rising overreporting during the years of the first term. Furthermore, since the probability of promotion drops sharply once passing the first term, as the term approaches completion, local leaders become more desperate to catch the last high-speed rail for promotions, again implying rising over-reporting during the years of the first term. In addition, it takes time for city leaders to have sufficient control over local government apparatus, making it again easier, or less costly, for them to manipulate GDP over time. This again has the effect of raising over-reporting over the course of the first term. For city leaders in their second term, the probability of promotion is significantly lower, and the incentives for overreporting would be lower than in the first term, especially the fifth year. We thus hypothesize that trading off the expected career reward against potential costs, the optimal extent of over-reporting increases over the course of the first term of the city leader, and peaks around the 5th year, the last year of the term, and declining after that. There should thus be an inverse-U relationship between GDP over-reporting and the city leader’s years in office, peaking around year five.

To test this hypothesis, we estimate the regression with the following specification: 1Y Y Y Y Y Y 2Y Y Y Y Y Y 2 ′ i ′ i (3) iioott iioott iioott ii ii pptt iioott whereyy = αα+ θθ _i_ , _o_ , _p_ and _t_ index city, party secretary, province and year respectively. The dependent variable YY + θθ YY + γγ XX + λλ WW + ηη + δδ + εε _yiot_ , i.e., the over-reporting of GDP growth of city _i_ in year _t_ , is the difference between reported GDP growth and predicted true GDP growth. _YearsInOfficeiot_ , the number of years in office for the city party secretary up to the end of year _t_ ,<sup>33</sup> and its quadratic term are the variables of interest. They capture

> 33 Specifically, it is calculated as the number of days between the date of assuming office and the last day of year _t_ , divided by 365. When multiple leaders held the same post in a year, the person with the latest entry date is chosen.

15
the curvilinear political cycle effects. The city FEs _ηi_ capture the time-invariant heterogeneity in the levels of over-reporting across cities, while the province-year FEs _δpt_ absorb any province-specific economic or political shocks on over-reporting in each year, such as those induced by the turnover of the provincial leadership or the policies of the provincial government. Later, we also allow for other specifications, including city-specific linear time trends or province-specific time trends combined with time-varying provincial level controls.

**_Xiot_** is a vector of variables measuring the city leader’s demographic characteristics, which include the gender, ethnicity, age and its squared term, the number of years since becoming a party member and educational attainment, as well as his career background including the prior working experience in the provincial-level governments,<sup>34</sup> in the Communist Youth League (CYL), or as the city party secretary (or the mayor) elsewhere. We allow the possibility that political connections with higher officials may determine their ability to manipulate the statistics. Following Jiang (2018), we define a city leader _C_ as a client of a provincial leader _P_ if and only if _C_ was first promoted to a prefecturelevel city leadership position (as city secretary or mayor) from within the province when _P_ was serving as the provincial secretary. Jiang (2018) argues that this measure can better capture the political connections of city-level leaders than the overlap-based alternative, and shows a significant increase in the official’s promotion probability after his patron moves to high-level decision-making bodies such as the Politburo. We also control for a dummy variable indicating whether the leader is a native, i.e., born in the same city as his jurisdiction. Native officials are more familiar with local conditions and could develop a more extensive personal network, which in turn results in lower costs of collusion (Jia and Nie 2017).

Since over-reporting could reflect the influence of not only manipulation but also other socioeconomic characteristics of the city, we include a large array of relevant city-level socioeconomic variables in _Wit_ . For example, manufacturing may be more light-intensive than other sectors, and higher population density also entails more light. We thus control for the share of value added in the industrial and tertiary sectors in GDP as well as population (in log) in all the regressions. We also add the growth rates of nighttime lights to proxy the underlying true growth potential.

Our identifying assumption is that, conditional on a large array of leader characteristics and citylevel economic variables, without artificial manipulation of the growth data, this measure of overreporting should not be systematically related with a city leader’s years in office. If finding a significant relationship between over-reporting and years in office, we infer manipulation based on

> 34 Since there are four sets of leadership at each administrative level except the townships, “government” here refers to all of them at the corresponding level, i.e., the government, the party committee, the People’s Congress and the Chinese People’s Political Consultative Conference.

16
career incentives of politicians.

# **5 Main Results**

## **5.1 Baseline results**

Table 2 presents the baseline results on the relationship between the political cycle and GDP overreporting. We begin with a parsimonious model with _YearsInOffice_ and its squared term as well as the city and year FEs. In columns 2 and 3 we incrementally add controls on leader traits and city socioeconomic characteristics. In column 4, we add the province-specific linear time trends and timevarying province-level controls. These controls include the characteristics of the provincial party secretaries, provincial growth target, and three dummies indicating the timing of the provincial Party Congress as well as the dummies of one year before and after the Congress.<sup>35</sup> Starting from column 5, we include province-year FEs to control all the province-specific economic or political shocks on overreporting in each year. In column 6, we control the city-level socioeconomic variables flexibly by allowing them to have province-specific coefficients. In column 7, we allow differential time trends across cities in over-reporting by including a large set of city-specific linear trends.

The results from these regressions support the political cycle effects on GDP over-reporting. Across the columns, the coefficient of _YearsInOffice_ is robustly positive and significant, and its quadratic term remains negative and statistically significant. There is thus an inverse U-shaped relationship between a city party secretary’s years in office and GDP over-reporting. The results uniformly imply that over-reporting peaks around the fifth year in office. For example, the vertex of the estimated quadratic function of years in office is 5.3 and 5.2 based on the results of columns 5 and 6, respectively. These results are consistent with our conjecture that an inverse-U shaped relationship between the political cycle and over-reporting would occur due to declining marginal costs of overreporting stemming from the ratchet principle, as well as to the “last train effects” stemming from a sharp drop in the promotion probability after the fifth year.

Our results represent a moderate economic significance. Based on column 5, relative to the overreporting in the first year and holding other things constant, the city leader will inflate the growth rates by 0.26 percentage point in the fifth year. Since the mean GDP of our sample cities is around 80 billion yuan (at constant 2000 prices), this translates into an over-reporting of 0.21 billion yuan in the fifth year. If we aggregate the over-reporting across the 5 years within the first term, the cumulative over-

> 35 The provincial Party Congress convenes every 5 years, accompanied by frequent turnovers. It thus captures a common political cycle. Note that the Congress convenes between October and December for half of the provinces, and between April and June in the following year for the other half. These variables are thus not absorbed by the year FEs.

17
reported growth is around 1 percentage point, amounting to 0.8 billion yuan for an average city, or 210 billion yuan for the whole country.

We briefly describe the results on the control variables. First, there is an inverse U-shaped relationship between over-reporting and the PS’s age, though both linear and quadratic terms are insignificant. Thus the political cycle effects are more important than politicians’ age for determining over-reporting. Second, a city PS connected with the current provincial PS manipulates the GDP growth significantly _less_ than those without such connections. A plausible interpretation is that with strong support from the upper government and an inherently higher base rate of promotion, the marginal return from over-reporting becomes lower. Since the literature suggests that factions and connections with the upper government play an important role in shaping local official incentives in China (Shih et al. 2012; Jia et al. 2015; Fang et al. 2019), our result suggests that our political cycle effect is robust after holding political connections constant. Third, city leaders with prior experience in the provincial governments and having been a party member for a longer time engage in more overreporting. Fourth, a native PS does not manipulate the growth rates significantly more or less than a non-native PS. Fifth, the share of industrial value added in GDP enters significantly with a positive sign, consistent with the fact that the discrepancy between national aggregate and provincial sums of value added is the largest in the industrial sector, probably due to limited information disclosure (Ma et al., 2014; Chen et al. 2019). Therefore, cities with more developed industrial sectors tend to overreport by more. Finally, the coefficient of lights growth is significantly negative.<sup>36</sup> In Table 3, we examine whether mayors’ characteristics matter for over-reporting. Since the PS is much more powerful than the mayor and faces tougher competition in the promotion tournament,<sup>37</sup> we expect that the effect, if any, should be limited to the PS. The first two columns examine whether the political cycle effects exist for mayors—by adding the demographic characteristics and career experience in place of those of the city party secretaries. Indeed, we do not find similar results for the mayors as for the PS. In columns 3 and 4, we add the controls for both the party secretary and the mayor to see if the political cycle effects remain robust for the PS once controlling for mayor characteristics. The inverse U-shaped political cycle effects are present only for the party secretary. The coefficients in columns 3 and 4 are almost identical to those in columns 5 and 6 of Table 2 without mayor controls. In the last column, we adopt a non-parametric specification on _YearsInOffice_ : we first round it down to the nearest integer and then group its values into six categories in years: 0-1 (the base

> 36 Due to the space constraints, we do not report the coefficients of city leader’s educational attainment and those of the city controls.

> 37 Ghanem, Shen, and Zhang (2020) find that the manipulation of city-level air pollution data is significantly associated with certain characteristics of party secretaries rather than those of the mayors. They also provide detailed explanations regarding the key role of party secretaries in the manipulation of those data.

18
category), 2, 3, 4, 5, and ≥ 6. As shown in column 5, in line with the baseline result, over-reporting generally follows an upward trend (except for the 4th year) within the first term of the party secretaries, and peaks in the 5th year. Importantly, the effect is small and statistically indistinguishable from 0 beyond the 5th year, consistent with the finding of Figure 1. In contrast, none of the years-in-office dummies for the mayors is large and statistically significant.

# **5.2 Further robustness checks**

# **5.2.1 Omitted political connections**

Although we have considered the role of political connections when examining the political cycle effects in GDP manipulation, our specific measure based on Jiang (2018) could be insufficient to capture all forms of potential connections. Yet showing that our key results are not driven by omitting political connections is important. Connections with the upper-level officials, for instance, may determine a PS’s ability to fabricate: he may be implicitly allowed to engage in more manipulation. Political connections could also put him on a fast track for promotion and thus affect the length of his tenure. We thus address this concern by including alternative measures of connections in Table 4.

We replicate our baseline result in column 1 for comparison. In column 2, we include the PS’s other important prior working experience, namely, being the city mayor, the vice governor, and a member of the standing committee of the provincial party, to capture political connections formed during the tenures. Since local favoritism by the upper-level officials may play an important role in shaping the local political economy (Chu et al. 2021), in column 3, we control two dummy variables indicating whether the city is the provincial party secretary’s hometown and was the location of his previous workplace respectively. In column 4, we investigate the role of connections that may stem from shared demographic characteristics between the city PS and the provincial party secretary (PPS). We use two variables to capture the potential connection: a dummy indicating that both the city PS and provincial PS are female, and a dummy for hometown ties (i.e., the city PS and provincial PS are from the same prefecture), which have been documented as being important in the political selection in China (Fisman et al. 2020). In column 5, we examine the connections between the city PS and the provincial PS that could be formed due to their shared local working experience, namely having worked in the same provincial government branches and in the same city. Finally, we include all of the above measures of connection in column 6.

Based on columns 2 to 6, a PS with experience as a mayor exaggerates GDP more, while a city that is the hometown of the provincial PS exhibits less manipulation. The coefficients on the other measures of connections are small and statistically insignificant. Most importantly, the coefficients on _YearsInOffice_ and its squared term remain similar to the baseline results in Table 2. Our result on the 19
political cycle effect is thus not driven by omitted political connections.

# **5.2.2 Alternative ways of predicting GDP growth using nighttime lights**

We conduct several robustness checks regarding alternative ways of predicting true GDP growth from the nighttime lights in Eq. (1) in Table A.1. In column 1, cities in Northwest China are excluded from the regression: in this region, the association between ln(GDP) and ln(Lights) is insignificant, as shown in Panel G in Figure 3. In column 2, we calibrate the raw nighttime lights data following the three procedures outlined in Liu et al. (2012), namely, inter-calibration, intra-annual composition, and interannual series correction. The calibrated lights data are then used to predict GDP growth. In columns 3 to 5, we try different specifications of the elasticities of GDP with respect to nighttime lights. We impose a common elasticity for all the cities in column 3, and allow for province-specific elasticities in column 4. In column 5, we allow the elasticity of each region to vary between the pre- and post2012 periods. The year 2012 is chosen since we find the biggest change in the lights-GDP relationship around 2012. When conducting the joint test of the equality of elasticities between two consecutive periods for each region, the cutoff year of 2012 yields the largest _F_ statistics. In the remaining columns, we use more controls to predict GDP in Eq. (1). We add a quadratic term of lights in column 6, and add city-level electricity consumption, total bank loans granted and total freight volume as additional controls in columns 7, 8 and 9, respectively. These three variables, used by Premier Li Keqiang to objectively measure economic performance (“Keqiang Index”), are highly correlated with real economic activity but less manipulatable, and thus suitable as additional predictors of GDP. Column 10 controls all these three variables. In column 11, following Clark, Pinkovskiy, and Sala-i-Martin (2020), we combine nighttime light and the “Keqiang Index” to predict the true GDP, which is then used to construct the over-reporting measure. All of the above results remain very similar to the baseline, revealing a significant inverse U-shaped relationship between the PS’s years in office and over-reporting. Importantly, the over-reporting always peaks around the 5th year.

# **5.2.3 Alternative specifications, estimation techniques and placebo tests**

We consider alternative specifications and estimation techniques of our baseline regression in Table A.2, and conduct several placebo tests. In columns 1 and 2, we adopt a one-step approach that uses a single regression to estimate the effect of political cycles on over-reporting while at the same time controlling for the relationship between night lights and GDP.<sup>38</sup> Since our baseline measure of overreporting is the difference between reported growth and light-predicted growth (scaled by the optimal weight on the light-predicted growth), we use the two components of this measure, i.e., reported

> 38 Magee and Doces (2015) use a similar specification to show that dictators exaggerate their true growth rates relative to the democratic regimes.

20
growth and light-predicted growth, as the dependent variables in columns 3 and 4, respectively. If our logic about the political cycle effects is correct, the previous results should be mostly driven by the reported growth. The light-predicted growth rates, being immune to manipulation, however, should be much less affected. The latter specification can thus be viewed as a placebo test. This is indeed corroborated by the estimation results. We find a significant inverse U-shaped pattern for the reported growth, whereas the coefficients on the linear and quadratic terms of _YearsInOffice_ are almost 0 for the light-predicted growth. Similarly, using lights growth as the dependent variable in column 5 also yields no political cycle effects. In addition, the political cycle effect is robust to controlling for the growth target. As expected, the higher the target is, the more a city overreports (column 8). Finally, in columns 9 and 10, we estimate a specification controlling for one-year lagged over-reporting by OLS and the system GMM, respectively.<sup>39</sup> The inverse U-shaped pattern continues to survive, the vertex of which stabilizes around 5.

# **5.2.4 Subsamples and alternative levels of clustering**

First, some city PSs stay in office for a short time. In column 1 of Table A.3, we thus drop city PSs whose total tenures are shorter than a full year. Column 2 drops observations for PSs with total tenures less than 2 years. In column 3, we drop deputy-provincial-level cities as these cities are regional centers, and evaluations on their secretaries might not be primarily based on economic performances.<sup>40</sup> Additionally, the period of our baseline results (2001—2013) overlaps with the tenure of three top leaders, among which Hu Jintao’s tenure is the longest (2003—2012). Column 4 thus restricts the sample to Hu’s tenure. In column 5, when matching the city leader data with annual data on night lights and economic variables, in cities where multiple leaders hold the same post within a given year, we choose the PS with the longest tenure rather than the last PS as in the baseline. We also allow standard errors to be clustered at the provincial-year level in column 6, and the provincial level in column 7. Reassuringly, the political cycle effect on GDP manipulation remains similar to the baseline.

# **5.3 Institutional environment and performance manipulation**

Institutional quality could shape local bureaucrats’ performance manipulation. In regions with more corruption and less marketization, local monitoring such as that by media would be less likely. The likelihood of being detected when local officials manipulate GDP should be smaller, making the costbenefit analysis of performance manipulation more likely to point to being beneficial. Marketization further increases the respect for the norm of trust, which might also restrain falsifying GDP. We thus

> 39 We treat all the regressors as pre-determined except the province-year FEs and use one and two lags of the regressors as instruments for the equation in first-differences. There is no evidence of serial correlation of order two in the differenced residuals and the Hansen’s _J_ test is also passed.

> 40 In 2019, the GDP combined of these cities accounted for nearly 20% of the country’s total GDP.

21
expect that GDP over-reporting would be higher in regions with worse institutional environment.

We thus divide the sample cities into two groups based on the median level of institutional quality in Table 5. Two proxies of institutional quality are chosen. One is measured by the provincial share of entertainment and traveling costs (ETC) over sales, which has been found to be a good proxy of corruption (Cai, Fang, and Xu 2011), and is widely used for capturing corruption in China. The other is the marketization index from the NERI report (Fan, Wang, and Zhu 2010). Lower ETC and a higher marketization index indicate better institutional environment.

As expected, we find the effect of political cycles on GDP manipulation is only limited to the regions with more severe corruption and less developed market economy. Judging by the contrast of the coefficients for the high and the low areas in both indices, the importance of marketization seems to be stronger than that of corruption.

# **5.4 Bureaucratic experience and performance manipulation**

Next, we explore the heterogeneity among officials by considering whether the PS has any prior experience as the city secretary or the mayor, as more experienced PSs could better understand and use the rules, and could more effectively manipulate GDP figures without causing problems.

In columns 5 and 6 of Table 5, the political cycle effect on GDP manipulation is indeed more relevant for experienced PSs. In contrast, reported growth from inexperienced secretaries is immune to this political cycle, with the estimated coefficients for both the linear and the quadratic terms small and statistically insignificant. Relevant political experience of the PS thus indeed facilitates performance manipulation.

# **5.5 Target and performance manipulation**

Cities differ in their ability to fulfill their growth targets. The difference could stem from bureaucrat capacity, or simply inherent growth potential. Whatever the cause, however, falling behind the target increases the appeal to inflate GDP to reach the target. We thus compare cities that achieve the growth target in the annual _Report on the Work of the Government_ with those that fail. We expect that for the PS truthfully accomplishing the plan in the sense that the predicted true growth surpasses the target, he would be less likely to exaggerate the reported figure.

Our results from the last two columns of Table 5 support the hypothesis that falling behind the target increases performance manipulation. The political cycle effect is observed and pronounced in cities whose true performance failed to reach the target. In contrast, those whose targets have been met truthfully do not exhibit the same pattern in over-reporting GDP figures.

22
# **6 Testing Tournament Competition**

We have found an inverse U-shaped relationship between GDP manipulation and a PS’s years in office, and interpreted it as being driven by promotion incentives in the tournament competition. However, we have yet to offer direct econometric evidence of the tournament competition. We now offer direct evidence.

Under tournament competition in the Chinese political system, the promotion of top city bureaucrats is determined by the provincial governments, which likely evaluate their performance based on economic growth (Li and Zhou 2005).<sup>41</sup> Employing the spatial econometrics framework, we now shed light on the plausibility of the tournament competition story by studying strategic interactions in GDP manipulation across cities within a province. The strategic interactions among rival bureaucrats in over-reporting are estimated by the following model: i 1Y Y Y Y Y Y 2Y Y Y Y Y Y 2 **_ʹ_** i + iioott i iitt−1 ii∈NN(ii). (4) iitt iioott iioott ii yy = αα+ ρρyy + ββ∑ ωω yy + θθ YY + θθ YY + γγ XX ii pptt iipptt λλThe variables and the fixed effects here are defined as those in the previous section.<sup>**_ʹ_**</sup> WW + ηη + δδ + εε<sup>42</sup> ∑ i , the key variable of interest in this section, is the average over-reporting of city _i_ ’s neighbors within the ii∈NN(ii) iitt ωω yy province, where _ωij_ is the element of the row-standardized spatial weighting matrix with equal weight for all the neighbors. Since the relevant players of tournament competition for a city secretary are other city secretaries within the province, neighbors of city _i_ are defined in the baseline specification as all the other cities from the same province, i.e., political neighbors. We estimate this dynamic, spatially autoregressive Durbin model by maximum likelihood following Yu, De Jong, and Lee (2008).

In addition to the incentives behind the tournament competition, we should also consider the neighborhood effects among the geographically proximate jurisdictions, which could stem from other mechanisms such as inter-region market interactions and information spillover (Henderson 2007). If these geographical spillover mechanisms are the dominant explanation, we expect the spatial effects (1) to be stronger among neighbors physically closer to each other, and (2) to extend beyond the provincial administrative boundaries. To distinguish the tournament competition and the geographical spillover mechanisms, we also estimate the neighborhood effects among both political and non-

> 41 Scholars disagree over whether growth is the primary _de facto_ evaluation criterion for promotion (Shih et al., 2012), and whether promotion criteria remain the same for different levels of bureaucrats in China (Landry, Lü and Duan 2017).

> 42 One exception is that we also add the neighborhood-average _Wjt_ to the control variables, as in the spatial Durbin model. The inclusion of city and province-year FEs (alternatively, province-specific linear trends plus time-varying provincial controls, discussed in the next footnote) can help alleviate the concern that the identification of endogenous interaction may suffer from the omitted variable bias due to contextual effects (Manski 1993). In our context, without properly controlling for the above effects, the revealed correlation of over-reporting among geographically adjacent cities in the same province could simply stem from some province-specific shocks induced by, e.g., the turnover of provincial leaders and the growth targets set by the provincial governments.

23
political neighbors within the radius of 350, 400 and 450km.

# **6.1 Political vs. geographical neighbors**

Column 1 of Table 6 estimates the political rival effects to test the tournament competition hypothesis, with neighbors being defined as all the other cities in the province.<sup>43</sup> To shed light on the relative explanatory power of the tournament competition and the geographical spillover hypotheses, we also limit neighbors to those being located within a certain distance in the province (columns 2–4) and in other provinces (columns 5 to 7).<sup>44</sup> Three findings emerge. First, in column 1, the coefficient for the within-province neighbors’ over-reporting (i.e., the spatial lag) is 0.64, and statistically significant. The estimated political-rival effect is thus substantial, which lends support to promotion incentives in the tournament competition as the underlying mechanism for performance manipulation. In other words, when promotion rivals over-report, local top bureaucrats compete in over-reporting to stay ahead. Second, in columns 2 to 4, the average over-reporting of political rivals located with radii between 350 and 450km all exerts a positive and significant effect on the level of own over-reporting. The magnitude of neighborhood effects increases with the expansion of the radius, inconsistent with the prediction of the geographical spillover hypothesis. Third, we barely find any evidence of neighborhood effects among geographically proximate cities that are located in different provinces (columns 5 to 7). In fact, the coefficient of the spatial lag is small and mostly insignificant, again lending no support to the geographical spillover hypothesis. The findings in Table 6 thus support the tournament competition hypothesis, but not the geographical spillover hypothesis.

# **6.2 Comparable rivals**

Since Chinese cities differ in administrative ranks and levels of development, to make tournament competition work efficiently, a relative performance evaluation has been conducted among leaders of prefecture-level cities with similar economic sizes (Yu, Zhou and Zhu 2016). If so, the positive political rival effect should be stronger among political rivals with similar per capita GDP than among

> 43 In this specification, since the main explanatory variable of interest, ∑ i is by construction highly correlated with province-year FEs, it is undesirable to directly include the province-year dummies. This is because ∑ i ii∈NN(ii) iitt is the average over-reporting for all the cities except city _i_ in the province, which, as a result, does not have enough of ωω yy ii∈NN(ii) iitt variation across different cities _i_ , and is thus highly correlated with province-average over-reporting, captured by the ωω yy province-year fixed effects. We thus instead control for the province-specific linear trends and a large array of timevarying provincial controls, including _YearsInOffice_ , _YearsInOffice_<sup>2</sup> and _Native_ for the provincial party secretaries, provincial growth target, three dummies indicating the timing of the provincial Party Congress, as well as one year before and after the Congress.

> 44 The number of sample cities used in this section (272) is smaller than that in the previous section (281) for two reasons: (1) Five cities are dropped since spatial econometrics models require a balanced panel; (2) Provinces with less than three cities used in the previous analysis are also dropped (two provinces each with only two cities).

24
those located closely but with very different levels of per capita GDP.

To test this, we define two cities as comparable rivals if their difference in per capita GDP in 2000 is within one standard deviation across cities following Shi and Xi (2018), and estimate Eq. (4) accordingly. The results are presented in Table 7. Consistent with our expectation, the result indicates that, _ceteris paribus_ , an average of one percentage point increase in the over-reporting of city _i_ ’s economically comparable political rivals increases its own over-reporting by 0.75 percentage points (column 1), compared to only 0.19 percentage points for the economically non-comparable political rivals (column 2). This coefficient (0.75) is also much larger than that among the geographically proximate political neighbors (see columns 2 to 4 in Table 6, with a similar specification). Again in line with the previous results, the over-reporting of non-political neighbors exerts small and mostly insignificant effects on their own over-reporting, regardless of their economic and geographic distance (columns 3 to 5). These results offer further support for the tournament competition story.

# **6.3 Degree of competition**

If the political cycle effects on performance manipulation are due to tournament competition, this effect should be mediated by the degree of competition among the political peers: the effect should be stronger as competition intensifies. Since PSs strategically manipulate performance based on their years in office, the competition should be more intensified if the political rivals have very similar years in office. This scenario of intensified competition is not uncommon since frequent turnover occurs around the Provincial Party Congress that convenes every 5 years. For example, in Gansu province, 11 out of 12 PSs assumed office in 2008 and one in February 2009.

To examine this logic, we measure the degree of competition (DoC) among political rivals using two proxies: the standard deviation of and the interquartile range (IQR) of _YearsInOffice_ for city PSs within each province for each year. For both measures, a small number implies more intense peer competition. Since the evolving competition in later years of a term could reflect the results of endogenous political structure (i.e., competition in performance manipulation could change the composition of the city secretaries and thus the degree of competition), for both DoC measures, we use their values in the initial year of each term (of five years). We then interact these two proxies with the linear and the quadratic terms of _YearsInOffice_ in columns 1 and 2 of Table 8, respectively.

Two patterns emerge. _First_ , the signs of the interactive linear and quadratic terms are opposite to those of the linear and quadratic terms. Thus, when the dispersion of tenure is large enough, or when peer competition is not tough, the political cycle effect disappears. Based on column 1, when the within-province standard deviation of _YearsInOffice_ exceeds 2.04 (the 80th percentile), neither of the linear and quadratic _YearsInOffice_ plays a role in GDP manipulation. This is in line with the 25
tournament competition hypothesis: when peer competition is not intense, the need for faking GDP data disappears. _Second_ , the fiercer the competition, the earlier the peak year of GDP manipulation arrives. Still based on column 1, when the competition is the fiercest—when the within-province standard deviation equals 0.32 (as in the example of Gansu Province above), the political cycle effect peaks around the 3rd year. In contrast, the peak appears as late as the 6th year when the degree of competition is reduced to 1.8 (the 65th percentile). This finding suggests that fiercer peer competition drives local leaders to speed up the schedules of GDP manipulation.<sup>45</sup> To summarize, our results lend strong support to tournament competition as the dominant mechanism for GDP manipulation. Over-reporting of political rivals increases the city secretary’s over-reporting, and the rival effect is more pronounced when the rivals are more comparable to the local secretary in terms of the income level of their jurisdictions, and when the degree of competition is stronger. We rule out the geographical spillover mechanism because over-reporting of non-rival geographical neighbors does not affect over-reporting of the city.

# **7 Over-Reporting: Payoff and Constraints**

In this section, we establish the career payoffs of overreporting, and discuss how it is constrained by the ratchet principle.

## **7.1 Does over-reporting pay?**

We have offered evidence that tournament competition is the likely mechanism for performance manipulation. Adding credibility to this story would be evidence that performance manipulations indeed have career payoffs. As discussed earlier, there is a large literature explaining China’s growth by the strong incentives of local government officials. In this subsection, we investigate the effect of performance over-reporting on promotion.

We first examine whether reported growth is associated with subsequent promotions. The dependent variable is an indicator of the PS being promoted in year _t_ + 1, and the key explanatory variable is the average reported GDP growth over his tenure in the city up to year _t_ . The average GDP growth over the tenure is found to matter more than annual GDP growth for promotion (Li and Zhou 2005). Control variables include leader characteristics and the city-level time-varying control variables measured in the year before the secretary assumes office. We also include province-year FEs to allow for province-specific changes in policies and leadership. This result is shown in column 1 of Table 9.

> 45 Neighbors in this table are defined as all the other cities in the same province. The above results remain robust if we further restrict neighbors to lie within a certain radius in the same province.

26
Consistent with the literature examining promotions of local officials using prefecture-level data in China (Landry, Lü and Duan 2017), we fail to find any economically and statistically significant effect of reported performance on promotion likelihood for PSs. Alternatively, we adopt an ordered probit model, as in Li and Zhou (2005), to distinguish lateral moves from demotions. In column 4, we show the marginal effect of average reported GDP growth on the likelihood of promotion. Though the coefficient now is more precisely estimated, the magnitude of this effect, similar to that of the linear probability model, is small. Increasing reported growth by one percentage point leads to an increase in the probability of promotion by only 0.46 percentage point, amounting to 6% of the mean.<sup>46</sup> Since the reported performance does not contribute much to promotion for city secretaries, does true performance matter? This scenario represents the case of the provincial government being able to distinguish data faking and real performance. In columns 2 and 5, we fail to find any significant effect of true GDP growth predicted by the nighttime lights on the likelihood of promotion. This is consistent with the fact that the true performance is hidden from provincial leaders.

Finally, to test whether manipulation matters, we examine whether promotion is related to GDP over-reporting after controlling for the predicted true economic growth. In column 3 of Table 9, we find a statistically significant and large coefficient of over-reporting. A one percentage point increase in growth over-reporting leads to an increase of 1.7 percentage points in the likelihood of promotion, or a 22 percent increase of the mean promotion rate. Since over-reporting is a generated variable, we also estimate the standard error using the bootstrap method with 1,000 replications. The result, reported in brackets in column 3, is similar, and the coefficient of the over-reporting variable stays significant at the 10% level. In column 6, we use the ordered probit model. Again, the magnitude is similar to the linear probability model, and the coefficient is now significant at the 1% level.

To summarize, consistent with the existing literature, our results show a weak association between reported economic growth and promotions for city secretaries. However, for cities with the same true growth, it pays in promotion for the city secretary to manipulate performance.

# **7.2 Why over-reporting less?**

If over-reporting has career payoffs, why do not all PSs over-report and do it all the time? Before proceeding, keep in mind that the over-reporting measure, by construction, has a mean around zero, and the reported performance being lower than the predicted true growth should be understood as relative under-reporting. Nevertheless, we still need to explain why some party secretaries would under-report (in a relative sense). We provide two possible explanations.

> 46 The mean and the standard deviation of promotion in our sample are 0.077 and 0.266, respectively.

27
# **7.2.1 Ratchet effects**

As mentioned, each city was given a growth target at the beginning of the year through a government work report. A well-established phenomenon in planning and target setting is the “ratchet effect,” that is, higher rewards from better current performance must be weighed against the cost of future assignment of more ambitious targets (Weitzman 1980). To the extent that agents are solid performers and could fulfill potential targets, they would withhold effort now to avoid a higher target in the future. Indeed, in column 7 of Table A.2, we find a large and significant effect of reported growth in the current year on the target in the next year, consistent with the ratchet principle.<sup>47</sup> With this finding in mind, we now examine whether solid performers withhold their true performance by under-reporting.

To implement, we adopt a regression discontinuity design: after controlling for the running variable of over-performance (i.e., the difference between the predicted true GDP growth and the city target), does being a solid performer (i.e., the predicted true GDP growth rate surpassing the target) induce under-reporting? We first test whether a solid performer is more likely to under-report, with the dependent variable being the indicator of the reported growth smaller than the predicted true growth. In the RDD framework, we use local linear regressions with a uniform kernel, and allow the slopes of the fitted lines to differ on either side of the threshold by interacting the running variable with the dummy variable of interest (i.e., being a solid performer). The optimal bandwidths are chosen by a robust data-driven procedure that allows for separate bandwidths on the two sides (Calonico, Cattaneo, and Titiunik, 2014). Results using a variety of bandwidths relative to the optimal one are shown in the first 4 columns of Table 10. In all specifications, we include city-level controls and cityleader controls as well as the province and year FEs.

We first conduct the manipulation test required for valid RDD identification by testing the continuity of the density function of over-performance (Cattaneo, Jansson, and Ma 2018). The result is plotted in Appendix Figure A.2. The _t_ -statistic ( _p_ -value) of the test is -0.491 (0.624), indicating a failure to find evidence of manipulation on the running variable. This is expected since local officials are not evaluated based on the unobservable true GDP growth.

From columns 1 to 4, we consistently find a positive and statistically significant coefficient of the indicator of being a solid performer. Cities that just crossed the threshold of being a solid performer are thus more likely to under-report the GDP growth, compared with those that could not meet their targets. The coefficient is robustly positive with similar magnitudes across different choices of bandwidths. This result confirms the ratchet principle under China’s GDP target setting: the city PS would be more likely to under-report growth when the city is a solid performer, with the true

> 47 However, we do not find the political cycle effect for the target of the following year (column 6 and 7 in Table A.2).

28
performance being above the planned target.

These solid performers, not surprisingly, still fulfill their targets. This is shown in columns 5 to 8, where the indicator of fulfilling the target (i.e., whether the reported growth surpasses the target) is the dependent variable. After controlling for a continuous function of true over-performance (i.e., predicted true GDP growth – target), the solid performers are more likely to fulfill their target.

A potential concern is that the city growth target, albeit predetermined, is not exogenous since the PS might influence the determination of the target for future manipulation. While the provincial government exerts an overwhelming influence in setting the growth target for cities in its jurisdiction, the city PS could influence the process. For example, the PS could lobby for a lower target so that the true growth just exceeds the target by a small margin. To address this concern, we repeat the above RDD exercise while restricting the sample to secretaries who had served in the current position for less than two years. Since a newcomer to a city is less likely to influence its target, the target and the running variable in this sample are more likely to be exogenous. The results shown in Panel B of Table 10 are similar to those in Panel A, again confirming our finding of ratchet effects.

# **7.2.2 Scapegoating**

Another explanation is that a new PS would under-report when he could scapegoat his predecessor in the turnover year. The new PS would report a low GDP number and scapegoat his predecessor for poor performance, making his performance benchmark low and thus his future years shine in terms of growth.<sup>48</sup> However, blaming the predecessor needs pre-conditions: (1) in the calendar year the city was mostly under the oversight of the predecessor; (2) the predecessor must be demoted so that scapegoating him would incur no political costs. After all, if his move is a lateral move or promotion, shifting blame is more subject to challenges and could cause future revenge. We thus expect that in city leader turnover years and when the departing PS is demoted, the new PS would under-report performance more.

To this end, we create an indicator _Demoted_ indicating that the departing PS was demoted, and another indicator _Scapegoatable_ indicating that in most of the calendar year the predecessor was in charge of the city. We then regress the indicator of under-reporting (i.e., the reported GDP growth being lower than the predicted true growth) on the turnover dummy, _Demoted_ , _Scapegoatable_ , and their two-way and three-way interactions. Our hypothesis only has implications for the three-way interaction: _Turnover_ × _Demoted_ × _Scapegoatable_ should positively predict under-reporting. In essence, this is again the ratchet effect at work when the PS could lower targets conveniently. We also

> 48 Such turnover effect is documented in Cohen, Coval, and Malloy (2011) who show that chairman turnover in the U.S. congress has an impact on state-level federal expenditures.

29
control for city characteristics, city FE, and province-year FE. We report two specifications, one without and one with additional controls of pre-turnover and post-turnover dummies to allow for differential patterns of under-reporting for those years. The results are in Table 11.

In columns 1 and 2, we find a large and statistically significant effect for the triple interaction term, implying that the incumbent would be more likely to under-report performance in turnover years when the performance could be attributable to the predecessor and when the predecessor had already been demoted. This result supports our conjecture.

# **8. Conclusions**

Bureaucrats are supposed to deliver performance, and they also have a large say in measuring performance. In this paper, we provide evidence that GDP, the key performance measure of local top bureaucrats, is sometimes subject to manipulation, which could be explained by political incentives stemming from the tournament competition system. Covering the period 2001-2013, we use nighttime lights to infer real GDP growth and detect GDP manipulation in cities. Our empirical analysis shows a robustly strong inverse U-shaped relationship between the extent of GDP growth over-reporting and the tenure of city party secretaries, with the peak stabilizing around the fifth year, their decisive career year. This is consistent with the career-incentive hypothesis that city leaders, when faced with an increasing chance of missing the last train for promotions, are more likely to exaggerate economic performance. Interestingly, the association between bureaucrat career incentives and performance manipulation exists only in regions with a weak institutional environment. We find several pieces of evidence in support of winning tournament competition as being the key motivation for GDP manipulation: when political rivals within the province over-report GDP more, the city over-reports more; this political rival effect is especially pronounced for economically comparable rivals; when the degree of competition among political rivals is particularly fierce, city party secretaries begin to overreport GDP earlier. Additional analysis suggests that over-reporting indeed leads to higher chances of promotion, but the ratchet effect and the chance to scapegoat predecessors limit the extent of overreporting.

Our findings have several implications. First, when bureaucrats’ performance is subject to manipulation, performance incentives could lead to performance manipulation rather than real performance improvement. Second, much can be done for improving the quality of GDP and other government economic statistics. Since GDP is at the core of formulating macroeconomic policies, skewed local growth numbers can mislead the upper-level governments into making wrong policies, resulting in substantial welfare loss. It thus makes sense to make the statistical agency independent 30
and immune to the influence of local governments, and the central government should complement GDP statistics with objective measures such as those based on night lights and other sources of nonmanipulatable data. Third, while there are likely positive effects from tournament competition among regional governments in China, as the literature has demonstrated, and the target-setting practices consistent with the ratchet principle tend to limit the extent of GDP manipulation, as we show here, our results suggest that we should also consider the negative side of tournament competition among local governments. Moreover, as we document that peer competition could make performance manipulation increase over time due to peers-reinforcement, even if the tournament competition design begins as a beneficial system for motivating bureaucrats, the dynamics of tournament competition could make adverse effects of performance manipulation rise over time. Thus, if we are to preserve the benefits of tournament competition, it is crucial to make performance non-manipulatable. All these implications are recognized by top business leaders. Legendary former Intel CEO, Andrew Groves, emphasizes the notion of paired statistics for management, such as pairing the number of deals closed with the retention rate of clients.<sup>49</sup> It appears that bureaucrat management could benefit from similar management disciplines, such as by pairing GDP growth with indicators of adverse effects of singleminded growth such as pollution and inequality.

# **References**

- Adams, F. Gerard, and Yimin Chen. 1996. “Skepticism about Chinese GDP Growth – the Chinese GDP Elasticity of Energy Consumption.” _Journal of Economic and Social Measurement_ 22 (4): 231–240.

- Aghion, Philippe, and Jean Tirole. 1997. “Formal and Real Authority in Organizations.” _Journal of Political Economy_ 105 (1): 1-29.

- Asher, Sam, and Paul Novosad. 2020. Rural Roads and Local Economic Development. _American Economic Review_ 110 (3): 797-823.

- Ashraf, Nava, and Oriana Bandiera. 2018. “Social Incentives in Organizations.” _Annual Review of Economics_ 10: 439-463.

- Bandiera, Oriana, Michael Carlos Best, Adnan Qadir Khan, and Andrea Prat. 2020. “The Allocation of Authority in Organizations: A Field Experiment with Bureaucrats.” _NBER Working Paper_ No. 26733.

- Bertrand, Marianne, Robin Burgess, Arunish Chawla, and Guo Xu. 2020. “The Glittering Prizes: Career Incentives and Bureaucratic Performance.” _Review of Economic Studies_ 87 (2): 626-655.

- Besley, Timothy J., Robin Burgess, Adnan Khan, Guo Xu. 2021. “Bureaucracy and Development.” _NBER Working Paper_ 29163.

- Besley, Timothy J., and Masayuki Kudamatsu. 2007. “Making Autocracy Work”. In _Institutions and Economic Performance_ , ed. by Elhanan Helpman. Cambridge, MA: Harvard University Press.

- Cai, Hongbin, Hanming Fang, and Lixin Colin Xu. 2011. “Eat, Drink, Firms, Government: An Investigation of Corruption from the Entertainment and Travel Costs of Chinese Firms.” _Journal of Law and Economics_ 54 (1): 55–78.

- Calonico, Sebastian, Matias D. Cattaneo, and Rocio Titiunik. 2014. “Robust Nonparametric Confidence

> 49 See the blog of Chin (2019): <u>Beware What You Measure: The Principle of Pairing Indicators (holistics.io).</u>

31
Intervals for Regression-discontinuity Designs.” _Econometrica_ 82 (6): 2295–2326.

- Caro, Robert. 1974. _The Power Broker: Robert Moses and the Fall of New York_ . New York: Vintage Books.

- Cattaneo, Matias D., Michael Jansson, and Xinwei Ma. 2018. “Manipulation Testing Based on Density Discontinuity.” _Stata Journal_ 18 (1): 234–261.

- Chen, Shuo, Xue Qiao, and Zhitao Zhu. 2021. “Chasing or Cheating? Theory and Evidence on China’s GDP Manipulation.” _Journal of Economic Behavior & Organization_ 189: 657–671.

- Chen, Wei, Xilu Chen, Chang-Tai Hsieh, and Zheng Song. 2019. “A Forensic Examination of China’s National Accounts.” _Brookings Papers on Economic Activity_ : 77–127.

- Chen, Xi and William D. Nordhaus. 2011. “Using Luminosity Data as a Proxy for Economic Statistics.” _PNAS_ 108 (21): 8589–94.

- Chu, Jian, Raymond Fisman, Songtao Tan, and Yongxiang Wang. 2021. “Hometown Ties and the Quality of Government Monitoring: Evidence from Rotation of Chinese Auditors.” _American Economic Journal: Applied Economics_ 13 (3): 176–201.

- Clark, Hunter, Maxim Pinkovskiy, and Xavier Sala-i-Martin. 2020. “China’s GDP Growth May Be Understated.” _China Economic Review_ : 101243.

- Cohen, Lauren, Joshua Coval, and Christopher Malloy. 2011. “Do Powerful Politicians Cause Corporate Downsizing?” _Journal of Political Economy_ 119 (6): 1015–1060.

- Colonnelli, Emanuele, Mounu Prem, and Edoardo Teso. 2020. “Patronage and Selection in Public Sector Organizations.” _American Economic Review_ 119 (10): 3071-3099.

- Dixit, Avinash. 1997. “Power of Incentives in Private versus Public Organizations.” _American Economic Review_ 87 (2): 378-382.

- Donaldson, Dave and Adam Storeygard. 2016. “The View from Above: Applications of Satellite Data in Economics.” _Journal of Economic Perspectives_ 30 (4): 171–98.

- Elvidge, Christopher, Daniel Ziskin, Kimberly Baugh, Benjamin Tuttle, Tilottama Ghosh, Dee Pack, Edward Erwin, and Mikhail Zhizhin. 2009. “A Fifteen-Year Record of Global Natural Gas Flaring Derived from Satellite Data.” _Energies_ 2 (3): 595–622.

- Evans, Peter B., and James E. Rauch. 1999. “Bureaucracy and Growth: A Cross-National Analysis of the Effects of ‘Weberian’ State Structures on Economic Growth.” _American Sociological Review_ 64 (5): 748-765.

- ----. 2000. “Bureaucratic Structure and Bureaucratic Performance in Less Developed Countries.” _Journal of Public Economics_ 75 (1): 49-71.

- Fan, Gang, Xiaolu Wang, and Hengpeng Zhu. 2010. _Marketization Index for China’s Provinces (2009)_ . Economic Science Press.

- Fang, Hanming, Quanlin Gu, and Li-An Zhou. 2019. "The Gradients of Power: Evidence from the Chinese Housing Market." _Journal of Public Economics_ 176: 32-52.

- Finan, Frederico, Benjamin A. Olken, and Rohini Pande. 2017. “The Personnel Economics of the Developing State.” In _Handbook of Economic Field Experiment,_ ed. By Aghijit V. Banerjee and Esther Duflo, 2: 467-514.

- Fisman, Raymond, Jing Shi, Yongxiang Wang, and Weixing Wu. 2020. “Social Ties and the Selection of China’s Political Elite.” _American Economic Review_ 110 (6): 1752–1781.

- Fukuyama, Francis. 2014. _Political Order and Political Decay: From the Industrial Revolution to the Globalization of Democracy_ . New York: Farrar, Straus and Giroux.

- Gehlbach, Scott, and Philip Keefer. 2011. “Investment without Democracy: Ruling-party Institutionalization and Credible Commitment in Autocracies.” _Journal of Comparative Economics_ 39 (2): 123-139.

- Ghanem, Dalia, Shu Shen, and Junjie Zhang. 2020. “A Censored Maximum Likelihood Approach to Quantifying Manipulation in China’s Air Pollution Data.” _Journal of the Association of Environmental and Resource Economists_ 7 (5): 965–1003.

- Gibson, John, Susan Olivia, Geua Boe-Gibson, and Chao Li. 2021. “Which Night Lights Data Should We Use in Economics, and Where?” _Journal of Development Economics_ 149: 102602.

- Gulzar, Saad, and Benjamin J. Pasquale. 2017. "Politicians, Bureaucrats, and Development: Evidence from

32
India." _American Political Science Review_ 111 (1): 162-183.

- Guo, Gang. 2009. “China’s Local Political Budget Cycles.” _American Journal of Political Science_ 53 (3): 621–632.

- Hayek, Friedrich. 1945. “The Use of Knowledge in Society.” _American Economic Review_ 35 (4): 519-530.

- Henderson, Vernon J. 2007. “Understanding Knowledge Spillovers.” _Regional Science and Urban Economics_ 37 (4): 497–508.

- Henderson, Vernon J., Tim Squires, Adam Storeygard, and David Weil. 2018. “The Global Distribution of Economic Activity: Nature, History, and the Role of Trade.” _Quarterly Journal of Economics_ 133 (1): 357–406.

- Henderson, Vernon J., Adam. Storeygard, and David N. Weil. 2012. “Measuring Economic Growth from Outer Space.” _American Economic Review_ 102 (2): 994–1028.

- Hodler, Roland and Paul A. Raschky. 2014. “Regional Favoritism.” _Quarterly Journal of Economics_ 129 (2): 995–1033.

- Holz, Carsten A. 2005. “OECD–China Governance Project: The Institutional Arrangements for the Production of Statistics.” _OECD Statistics Working Papers_ 2005/01.

- Holz, Carsten A. 2014. “The Quality of China’s GDP Statistics.” _China Economic Review_ 30: 309–338.

- Hu, Yingyao and Jiaxiong Yao. 2019. “Illuminating Economic Growth.” _IMF Working Paper_ .

- Jia, Ruixue, Masayuki Kudamatsu, and David Seim. 2015. “Political Selection in China: The Complementary Roles of Connections and Performance.” _Journal of the European Economic Association_ 13 (4): 631– 668.

- Jia, Ruixue and Huihua Nie. 2017. “Decentralization, Collusion, and Coal Mine Deaths.” _Review of Economics and Statistics_ 99 (1): 105–118.

- Jiang, Junyan. 2018. “Making Bureaucracy Work: Patronage Networks, Performance Incentives, and Economic Development in China.” _American Journal of Political Science_ 62 (4): 982– 999.

- Jiang, Junyan and Muyang Zhang. 2020. “Friends with Benefits: Patronage Networks and Distributive Politics in China.” _Journal of Public Economics_ 184: 104143.

- Johnson, Simon, William Larson, Chris Papageorgiou, and Arvind Subramanian. 2013. Is Newer Better? Penn World Table Revisions and Their Impact on Growth Estimates. _Journal of Monetary Economics_ 60 (2): 255-274.

- Khan, Adnan Q., Asim I. Khwaja, and Bejamin A. Olken. 2016. “Tax Farming Redux: Experimental Evidence on Performance Pay for Tax Collectors.” _Quarterly Journal of Economics_ 131 (1): 219-271.

- ----. 2019. “Making Moves Matters: Experimental Evidence on Incentivizing Bureaucrats through Performance-Based Postings.” _American Economic Review_ 109 (1): 237-70.

- Labonne, Julien. 2016. “Local Political Business Cycles: Evidence from Philippine Municipalities.” _Journal of Development Economics_ 121: 56–62.

- Landry, Pierre F. 2003. “The Political Management of Mayors in Post-Deng China.” _Copenhagen Journal of Asian Studies_ 17: 31–58.

- Landry, Pierre F. 2008. _Decentralized Authoritarianism in China: The Communist Party’s Control of Local Elites in the Post-Mao Era_ , Vol. 1. Cambridge University Press New York.

- Landry, Pierre F., Xiaobo Lü, and Haiyan Duan. 2017. “Does Performance Matter? Evaluating Political Selection Along the Chinese Administrative Ladder.” _Comparative Political Studies_ 51 (8): 1074– 1105.

- Li, Hongbin and Li-An Zhou. 2005. “Political Turnover and Economic Performance: The Incentive Role of Personnel Control in China.” _Journal of Public Economics_ 89 (9-10): 1743–1762.

- Li, Xing, Chong Liu, Xi Weng, and Li-An Zhou. 2019. “Target Setting in Tournaments: Theory and Evidence from China.” _Economic Journal_ 129 (623): 2888–2915.

- Liu, Zhifeng, Chunyang He, Qiaofeng Zhang, Qingxu Huang, and Yang Yang. 2012. “Extracting the Dynamics of Urban Expansion in China Using DMSP-OLS Nighttime Light Data from 1992 to 2008.” _Landscape and Urban Planning_ 106 (1): 62–72.

- Lyu, Changjiang, Kemin Wang, Frank Zhang, and Xin Zhang. 2018. “GDP Management to Meet or Beat

33
Growth Targets.” _Journal of Accounting and Economics_ 66 (1): 318–338.

- Ma, Ben, Guojun Song, Lei Zhang, and David A. Sonnenfeld. 2014. “Explaining Sectoral Discrepancies between National and Provincial Statistics in China.” _China Economic Review_ 30: 353–369.

- MacRae, C. Duncan. 1977. “A Political Model of the Business Cycle.” _Journal of Political Economy_ 85 (2): 239–263.

- Maddison, Angus, and Harry Wu. 2006. “China's Economic Performance: How Fast Has GDP Grown? How Big is It Compared with the USA?" _Working Paper at the University of Queensland_ .

- Magee, Christopher S. P., and John A. Doces. 2015. “Reconsidering Regime Type and Growth: Lies, Dictatorships, and Statistics.” _International Studies Quarterly_ 59 (2): 223–237.

- Manski, Charles F. 1993. “Identification of Endogenous Social Effects: The Reflection Problem.” _Review of Economic Studies_ 60 (3): 531-542.

- Maskin, Eric, Yingyi Qian, and Chenggang Xu. 2000. "Incentives, Information, and Organizational Form." _Review of Economic Studies_ 67 (2): 359-378.

- Martinez, Luis R. 2019. “How Much Should We Trust the Dictator’s GDP Growth Estimates?” _SSRN Working Paper_ .

- Mehrotra, Aaron and Jenni Pääkkönen. 2011. “Comparing China’s GDP Statistics with Coincident Indicators.” _Journal of Comparative Economics_ 39 (3): 406–411.

- Michalopoulos, S. and E. Papaioannou. 2014. “National Institutions and Subnational Development in Africa.” _Quarterly Journal of Economics_ 129 (1): 151–213.

- Nie, Huihua, Minjie Jiang, and Xianghong Wang. 2013. “The Impact of Political Cycle: Evidence from Coalmine Accidents in China.” _Journal of Comparative Economics_ 41 (4): 995– 1011.

- Nordhaus, William D. 1975. “The Political Business Cycle.” _Review of Economic Studies_ 42 (2): 169–190.

- Peltzman, Sam. 1976. "Toward a More General Theory of Regulation." _Journal of Law and Economics_ 19 (2): 211-240.

- Perkins, Dwight H. and Thomas G. Rawski. 2008. _Forecasting China’s Economic Growth to 2025_ . Cambridge: Cambridge University Press, 829–886.

- Piotroski, Joseph D and Tianyu Zhang. 2014. “Politicians and the IPO Decision: The Impact of Impending Political Promotions on IPO Activity in China.” _Journal of Financial Economics_ 111 (1): 111–136.

- Rasul, Imran, and Daniel Rogger. 2018. “Management of Bureaucrats and Public Services Delivery: Evidence from the Nigerian Civil Services.” _Economic Journal_ 128 (608): 413-446.

- Rawski, Thomas G. 2001. “What is Happening to China’s GDP Statistics?” _China Economic Review_ 12 (4): 347–354.

- Rogoff, Kenneth. 1990. “Equilibrium Political Budget Cycles.” _American Economic Review_ 80 (1): 21–36.

- Shi, Min and Jakob Svensson. 2006. “Political Budget Cycles: Do They Differ across Countries and Why?” _Journal of Public Economics_ 90 (8-9): 1367–1389.

- Shi, Xiangyu and Tianyang Xi. 2018. “Race to Safety: Political Competition, Neighborhood Effects, and Coal Mine Deaths in China.” _Journal of Development Economics_ 131: 79–95.

- Shih, Victor, Christopher Adolph, and Mingxing Liu. 2012. "Getting Ahead in the Communist Party: Explaining the Advancement of Central Committee Members in China." _American Political Science Review_ 106 (1): 166-187.

- Stigler, George J. 1971. "The Theory of Economic Regulation." _The Bell Journal of_ `E` _conomics and Management Science_ : 3-21.

- Tirole, Jean. 1994. “The Internal Organization of Government.” _Oxford Economic Papers_ 46 (1): 1-29.

- Tullock, Gordon. 1967. "The General Irrelevance of the General Impossibility Theorem." _Quarterly Journal of Economics_ 81 (2): 256-270.

- Wang, Zhi, Qinghua Zhang, and Li-An Zhou. 2020. “Career Incentives of City Leaders and Urban Spatial Expansion in China.” _Review of Economics and Statistics_ 102 (5): 1–45.

Weber, Max. 1922. _Economy and Society_ . 4th ed. Tübingen.

- Weitzman, Martin L. 1980. “The ‘Ratchet Principle’ and Performance Incentives.” _Bell Journal of_

34
_Economics_ : 302–308.

- Xi, Tianyang, Yang Yao, and Muyang Zhang. 2018. “Capability and Opportunism: Evidence from City Officials in China.” _Journal of Comparative Economics_ 46 (4): 1046–1061.

- Xu, Chenggang. 2011. “The Fundamental Institutions of China’s Reforms and Development.” _Journal of Economic Literature_ 49 (4): 1076–1151.

- Xu, Guo. 2018. “The Costs of Patronage: Evidence from the British Empire.” _American Economic Review_ 108 (11): 3170-98.

- Yao, Yang, Muyang Zhang. 2015. “Subnational Leaders and Economic Growth: Evidence from Chinese Cities.” _Journal of Economic Growth_ 20 (4), 405-436.

- Yu, Jihai, Robert De Jong, and Lung-fei Lee. 2008. “Quasi-Maximum Likelihood Estimators for Spatial Dynamic Panel Data with Fixed Effects when Both _n_ And _T_ Are Large.” _Journal of Econometrics_ 146 (1): 118–134.

- Yu, Jihai, Li-An Zhou, and Guozhong Zhu. 2016. “Strategic Interaction in Political Competition: Evidence from Spatial Effects across Chinese Cities.” _Regional Science and Urban Economics_ 57: 23–37.

35
<!-- Start of picture text --> wo<br>o oc<br>wo<br>N<br>2<br>S<br>8a<br>5<br>0 ®<br>><br>5° E<br>ne] ag<br>xo)<br>te<br>So<br>l=}<br>~2<br>8a =<br><=1 2 3 4 5 6 >=7<br># of years in office<br>ln # of leaders leaving office ——*— Promotion rate<br>St<br>|<br>Pn |<br>\<br>\<br>\<br>N 1 \<br>[| &<br>><br>eck 4<br>o : oe ~SSeeee,<br>-10 5 0 5 10<br>Reported GDP Growth — Target Growth<br>p=0.000<br><!-- End of picture text -->
<!-- Start of picture text --> Panel A: Northeast Panel B: North Coast<br>= e oe: e<br>S3223© it4 e 2 ene e 5B a XPe ¢ ee<br>Es=2 e Pi. |*% % 26gCege ef ) e oNix ee? 60 é e<br>a&° e Caper e,.e macsee iBQ - ¥, O e.?°<br>|3 * oe”es%° BRPe Foo8s e 2oe | MD) eth@ %e °<br>5oa | © 90 Seed Mee weee 3o onlye °<br>Leya e eeo 8 Perce6a® ee * . e gaae ee 3 °°?A<br>falg e e eo % fala e ce.@<br>°<br>ee e: reCrse g5. e 7 xe e<br>-6 -3 0 3 6 -5 -2 1 4<br>In(lights/area) net of city and year fixed effects In(lights/area) net of city and year fixed effects<br>coef = 18849016, (robust) se = 03916082, t= 4.81 coef= 12670163, (robust) se = 0387721, t= 3.27<br>Panel C: Eastern Coast Panel D: Middle Yellow River<br>= e ~ e<br>sg®.e32e° . . @<br>E y , 3a oie oN ee @ % "8 5<br>= ° ° E ° © We # fo fody °*<br>& ee cry eco © =go e F oo 3> 2%~~ 6- e<br>Bes5 eo rd eeeee 26 o teefo 5* e e e<br>=2 oe oo“e %. © 7 e Ss:Ba v % 0 4Le, 2Cr eee<br>et too) ee, cS e .e<br>c8 ee° ceeo ? ee aeg°2 . e e”eo.° e e<br>>=e 3 e = ee<br>=e ee = e<br>s °<br>6 3 0 3 6 -8 “5 -2 1 4 vf<br>In(lights/area) net of city and year fixed effects In(lights/area) net of city and year fixed effects<br>coef = 07798492, (robust) se = 02637225, t= 2.96 coef = 15643806, (robust) se = 05253952, t= 2.98<br>Panel E: Middle Reaches of the Yangtze River Panel F: Southwest China<br>~ . ~ <i<br>:225A ee :rs] "inte °°<br>of 5 V £. oa e 3% . ‘ e 7<br>eg5 ee e oe e0?e Fye5 © 28e oe e® ‘ee +}<br>g 2: Re Poo ° g © °<br>Be eee be he Be ee gt. . yt z<br>= oe ees > ® 3 °<br>ndid ° PFS See oe? e iS °° e tes oe<br>eo e@# :ee bie en s ‘<br>7 oF ge ? © a a x °<br>[o)E ° ee (o)= ° se<br>+ ad eee ~ : ‘eee<br>-8 -5 -2 1 4 7 -9 -6 -3 0 3 6 9<br>In(lights/area) net of city and year fixed effects In(lights/area) net of city and year fixed effects<br>coef= 04194825, (robust) se = 02290897, t = 1.83 coef= 06963885, (robust) se= 02508692,t = 2.78<br>Panel G: Big Northwest China Panel H: South Coast<br>= e ~ e<br>2#3<br>gofz OF e ogre ad rs]goN ° eFB& eeee 2<br>. e «°° 4 Pi % e Z 8 coy” , ok<br>&5 e© eee e %°e 6 0 . -5 . & ¥ ebad .<br>ae .ae ge Reeves<br>=SN e 'e a Se =4 ° ow ee<br>Psfa i ee 2 3 := e e . wee<br>(O)i e e fa(o) oe % e bl<br>8 x. =e a,<br>e , é e<br>-8 -5 -2 1 4 7 1 7 -4 ~1 2 5<br>In(lights/area) net of city and year fixed effects In(lights/area) net of city and year fixed effects<br>coef= 01978085, (robust) se= 02877767,t= .69 coef= .11956991, (robust) se= 0439898t= 2 .72,<br><!-- End of picture text -->
<!-- Start of picture text -->
> / :><br>& / :  \<br>oN f =<br>o ! an<br>(a) / : \<br>/ / :: \<br>/ \<br>=/\<br>/: \<br>7 : \<br>4 : \N<br>27 a“ :: NX Sos<br>-6 -3 0 3 6<br>Estimated Overreporting<br>———_ Target Achieved —-—-—-- Target Not Achieved<br><!-- End of picture text -->

kernel = epanechnikov, bandwidth = 0.30
Table 1: Summary statistics of key variables

|Variable|Mean|S.D.|Min|Max|Obs|
|---|---|---|---|---|---|
|**Panel A: City-level GDP, lig**<br>|**hts and over-re**<br>|**porting**<br>||||
|ln(GDP)|6.168|1.007|2.992|9.364|3642|
|ln(Lights)|1.257|1.086|-2.423|4.055|3642|
|∆ ln(GDP)|0.120|0.030|-0.151|0.315|3642|
|∆ ln(Lights)|0.074|0.211|-0.988|1.218|3642|
|∆ ln(Predicted GDP)|0.020|0.004|0.003|0.042|3642|
|Over-reporting (pp)<br>**Panel B: City party secretar**<br>|0.001<br>**ies’ characteris**<br>|1.377<br>**tics**<br>|-6.238<br>|7.221<br>|3642<br>|
|Female|0.042|0.201|0|1|1041|
|Minority|0.060|0.237|0|1|1041|
|YearsInOffice|3.879|1.771|0.252|10.67|1179|
|Age (assuming office)|50.61|3.762|39|60|1179|
|Native|0.070|0.254|0|1|1179|
|Bachelor|0.293|0.455|0|1|1179|
|Master|0.511|0.500|0|1|1179|
|Ph.D|0.135|0.342|0|1|1179|
|Connected to PPS|0.212|0.409|0|1|1179|
|CYL Exper.|0.287|0.448|0|1|1179|
|Prov Exper.|0.599|0.485|0|1|1179|
|Years in the Party|27.33|4.649|10|37|1179|
|(assuming office)||||||
|Rookie|0.830|0.375|0|1|1179|
|**Panel C: City-level socioeco**|**nomic indices**|||||
|Share of industry (%)|48.30|11.52|15.70|90.97|3642|
|Share of service (%)|35.56|7.864|8.580|69.88|3642|
|ln(Population)|5.816|0.671|2.771|7.122|3642|
|Target growth (%)|12.69|2.823|2.900|35|2895|

_Notes_ : This table shows summary statistics on the key variables used in the empirical analysis. The unit of observation used in the calculation of summary statistics for time-invariant demographic characteristics (i.e., gender and ethnicity) is the individual leader while that for time-varying variables is the city-leader spell. For variables that vary across years within the city-leader spell such as age and number of years as the party member, the summary statistics are calculated based on the year when the leader assumed office in the city.

39
Table 2: Political cycle of GDP manipulation: Baseline results

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|---|---|---|---|---|---|---|---|
|PS YearsInOffice|0.131<sup>***</sup><br>(0.042)|0.116<sup>***</sup><br>(0.039)|0.121<sup>***</sup><br>(0.037)|0.141<sup>***</sup><br>(0.034)|0.096<sup>***</sup><br>(0.029)|0.104<sup>***</sup><br>(0.028)|0.116<sup>***</sup><br>(0.029)|
|PS YearsInOffice<sup>2</sup>|-0.014<sup>**</sup><br>(0.007)|-0.012<sup>**</sup><br>(0.006)|-0.013<sup>**</sup><br>(0.006)|-0.016<sup>***</sup><br>(0.005)|-0.009<sup>**</sup><br>(0.005)|-0.010<sup>**</sup><br>(0.004)|-0.012<sup>***</sup><br>(0.004)|
|Female||-0.024<br>(0.187)|-0.035<br>(0.171)|-0.141<br>(0.133)|-0.051<br>(0.127)|-0.135<br>(0.104)|-0.057<br>(0.133)|
|Minority||-0.021<br>(0.164)|-0.105<br>(0.155)|-0.024<br>(0.134)|-0.017<br>(0.118)|-0.016<br>(0.117)|-0.122<br>(0.143)|
|Age||0.210<br>(0.197)|0.168<br>(0.188)|0.088<br>(0.152)|0.155<br>(0.166)|0.174<br>(0.157)|0.121<br>(0.132)|
|Age<sup>2</sup>||-0.002<br>(0.002)|-0.002<br>(0.002)|-0.001<br>(0.001)|-0.002<br>(0.002)|-0.002<br>(0.001)|-0.001<br>(0.001)|
|Native||-0.006<br>(0.120)|0.005<br>(0.111)|-0.001<br>(0.101)|0.002<br>(0.099)|0.039<br>(0.102)|0.037<br>(0.111)|
|Connected to PPS||-0.260<sup>***</sup><br>(0.085)|-0.171<sup>**</sup><br>(0.079)|-0.142<sup>**</sup><br>(0.071)|-0.139<sup>**</sup><br>(0.069)|-0.089<br>(0.067)|-0.086<br>(0.076)|
|Prov Exper.||0.188<sup>***</sup><br>(0.065)|0.142<sup>**</sup><br>(0.064)|0.075<br>(0.057)|0.089<sup>*</sup><br>(0.048)|0.052<br>(0.048)|0.058<br>(0.048)|
|CYL Exper.||0.052<br>(0.076)|0.043<br>(0.074)|0.023<br>(0.063)|0.051<br>(0.057)|0.043<br>(0.053)|0.017<br>(0.060)|
|Years in the Party||0.015<br>(0.009)|0.017<sup>*</sup><br>(0.009)|0.013<sup>*</sup><br>(0.007)|0.011<br>(0.007)|0.010<br>(0.007)|0.013<sup>*</sup><br>(0.007)|
|Rookie PS||-0.070<br>(0.090)|-0.033<br>(0.090)|0.050<br>(0.083)|0.024<br>(0.068)|0.012<br>(0.068)|-0.070<br>(0.081)|
|City Controls|||<br>Yes|<br>Yes|<br>Yes||<br>Yes|
|Flexible City Controls||||||Yes||
|Provincial Controls||||Yes||||
|City FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|<br>Year FE|Yes|Yes|Yes|Yes||||
|Province-Year FE|||||Yes|Yes|Yes|
|Provincial Trend<br>||||Yes||||
|City Trend|||||||Yes|
|<br>#of Observations|3642|3642|3642|3642|3642|3642|3642|
|#of Cities|281|281|281|281|281|281|281|
|Adj R<sup>2</sup>|0.15|0.15|0.27|0.38|0.60|0.66|0.65|

_Notes_ : The dependent variable is the difference between reported GDP growth and predicted true GDP growth imputed based on the nighttime lights data of prefecture-level cities. City controls include city-level time-varying economic controls, i.e., the share of industrial and tertiary value added in GDP, log population, and growth of nighttime lights. In column (6), we control these city-level economic variables flexibly by allowing for provincespecific coefficients. Leader controls also include a set of dummies indicating educational background, ranging from secondary school to Ph.D. in addition to the ones shown in the table. Provincial controls include the corresponding characteristics of the provincial party secretaries, provincial growth target, and three dummies indicating the timing of the provincial Party Congress as well as one year before and after the Congress. Standard errors clustered at the city level are reported in parentheses.<sup>***</sup> ,<sup>**</sup> and<sup>*</sup> denote significance at 1%, 5% and 10%, respectively.

40
Table 3. The political cycle of GDP manipulation: Robustness tests

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|Mayor YearsInOffice|0.056<br>(0.036)|0.053<br>(0.035)|0.032<br>(0.036)|0.023<br>(0.034)||
|Mayor YearsInOffice<sup>2</sup>|-0.004<br>(0.005)|-0.005<br>(0.005)|-0.002<br>(0.004)|-0.003<br>(0.004)||
|PS YearsInOffice|||0.090<sup>***</sup><br>(0.031)|0.098<sup>***</sup><br>(0.030)||
|PS YearsInOffice<sup>2</sup>|||-0.008<sup>*</sup><br>(0.005)|-0.009<sup>**</sup><br>(0.004)||
|PS YearsInOffice=2|||||0.128<sup>***</sup><br>(0.038)|
|PS YearsInOffice=3|||||0.136<sup>***</sup><br>(0.049)|
|PS YearsInOffice=4|||||0.124<sup>**</sup><br>(0.053)|
|PS YearsInOffice=5|||||0.161<sup>**</sup><br>(0.074)|
|PS YearsInOffice=6|||||0.101<br>(0.085)|
|Mayor YearsInOffice =2|||||0.015<br>(0.040)|
|Mayor YearsInOffice =3|||||-0.039<br>(0.062)|
|Mayor YearsInOffice =4|||||-0.037<br>(0.071)|
|Mayor YearsInOffice =5|||||-0.017<br>(0.085)|
|Mayor YearsInOffice =6|||||-0.019<br>(0.108)|
|City Controls|Yes||Yes|||
|<br>Flexible City Controls||Yes||Yes|Yes|
|# of Observations|3642|3642|3642|3642|3642|
|Adj R<sup>2</sup>|0.59|0.65|0.60|0.66|0.66|

_Notes_ : This table presents the results of several robustness checks that take mayors’ characteristics into account. Column (5) uses year-in-office dummies instead of a continuous measure. All the specifications include leader controls, city FEs, and province-year FEs. Leader controls and city controls are the same as those in Table 2. Standard errors clustered at the city level are reported in parentheses.<sup>***</sup> ,<sup>**</sup> and<sup>*</sup> denote significance at 1%, 5% and 10%, respectively.

41
Table 4. Robustness checks: The role of political connections

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|PS YearsInOffice|<br>0.096<sup>***</sup><br>(0.029)|<br>0.103<sup>***</sup><br>(0.030)|<br>0.098<sup>***</sup><br>(0.029)|<br>0.096<sup>***</sup><br>(0.029)|<br>0.096<sup>***</sup><br>(0.029)|<br>0.105<sup>***</sup><br>(0.030)|
|PS YearsInOffice<sup>2</sup>|-0.009<sup>**</sup><br>(0.005)|-0.009<sup>**</sup><br>(0.005)|-0.009<sup>**</sup><br>(0.005)|-0.009<sup>**</sup><br>(0.005)|-0.009<sup>**</sup><br>(0.005)|-0.009<sup>**</sup><br>(0.005)|
|Connected to PPS|-0.139<sup>**</sup>|-0.031|-0.141<sup>**</sup>|-0.139<sup>**</sup>|-0.137<sup>**</sup>|-0.030|
||(0.069)|(0.082)|(0.069)|(0.069)|(0.069)|(0.082)|
|Prov Exper.|0.089<sup>*</sup><br>(0.048)|0.104<sup>**</sup><br>(0.048)|0.086<sup>*</sup><br>(0.048)|0.089<sup>*</sup><br>(0.048)|0.117<sup>**</sup><br>(0.051)|0.128<sup>**</sup><br>(0.051)|
|CYL Exper.|0.051|0.058|0.050|0.051|0.052|0.057|
||(0.057)|(0.057)|(0.056)|(0.057)|(0.056)|(0.057)|
|Mayor Exper.||0.126<sup>*</sup><br>(0.071)||||0.128<sup>*</sup><br>(0.071)|
|Prov PSC Mem.||-0.188||||-0.186|
|||(0.184)||||(0.186)|
|Vice Governor||-0.154<br>(0.227)||||-0.155<br>(0.224)|
|PPS Hometown|||-0.427<sup>***</sup>|||-0.415<sup>***</sup>|
||||(0.116)|||(0.123)|
|PPS Workplace|||-0.058|||-0.041|
||||(0.141)|||(0.133)|
|Both PS & PPS Female||||0.093||0.114|
|||||(0.278)||(0.289)|
|Hometown Ties with PPS||||0.006<br>(0.132)||-0.024<br>(0.131)|
|Shared Prov. Exper. with PPS|||||-0.098<br>(0.073)|-0.093<br>(0.073)|
|Shared City Exper. with PPS|||||-0.056|-0.050|
||||||(0.145)|(0.144)|
|# of Observations|3642|3642|3642|3642|3642|3642|
|# of Cities|281|281|281|281|281|281|
|Adj R<sup>2</sup>|0.60|0.60|0.60|0.60|0.60|0.60|

_Notes_ : This table presents the political cycle of GDP manipulation with more measures of political connections included. The dependent variable is the difference between reported real GDP growth and predicted real GDP growth imputed based on the nighttime lights data of prefecture-level cities. All the specifications include city socioeconomic controls, leader controls, city FEs, and province-year FEs. Leader controls and city controls are the same as those in Table 2. Standard errors clustered at the city level are reported in parentheses.<sup>***</sup> ,<sup>**</sup> and<sup>*</sup> denote significance at 1%, 5% and 10%, respectively.

42
Table 5: Heterogeneity

||Marketiza|tion Index|Corruptio|n(ETC)|Prior Mayo|r/PS Exp.|1(Growth<br>�|_>_Target)|
|---|---|---|---|---|---|---|---|---|
||(1) High|(2) Low|(3) High|(4) Low|(5) Yes|(6) No|(7) Yes|(8) No|
|PS YearsInOffice|0.031|0.189<sup>∗∗∗</sup>|0.147<sup>∗∗∗</sup>|0.064<sup>∗</sup>|0.142<sup>∗∗∗</sup>|0.068|0.013|0.325<sup>∗∗∗</sup>|
||(0.033)|(0.044)|(0.046)|(0.035)|(0.036)|(0.082)|(0.031)|(0.085)|
|PS YearsInOffice<sup>2</sup>|−0.004|−0.021<sup>∗∗∗</sup>|−0.017<sup>∗∗∗</sup>|−0.005|−0.016<sup>∗∗∗</sup>|−0.001|0.001|−0.045<sup>∗∗∗</sup>|
||(0.005)|(0.007)|(0.006)|(0.006)|(0.005)|(0.008)|(0.004)|(0.013)|
|City Controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Leader Controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|City FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Province-Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|# of Observations|1726|1916|1683|1959|2697|851|1920|859|
|# of Cities|133|148|130|151|272|165|273|242|
|Adj_R_<sup>2</sup>|0.57|0.62|0.64|0.54|0.60|0.68|0.70|0.68|

_Notes_ : This table presents the heterogeneity with respect to the baseline results on the political cycle of GDP manipulation along different dimensions. In columns (1)–(2), the “High” and “Low” groups are divided based on the median of the provincial marketization index in 2000 while in columns (3)–(4), the two groups are divided based on the median of the provincial share of entertainment and travel costs over sales following Cai, Fang, and Xu (2011). City controls include city-level time-varying economic controls (the share of industrial and tertiary value added in GDP, log population, and growth of nighttime lights), and leader controls are the same as the ones displayed in the table of baseline results. Standard errors clustered at the city level are reported in parentheses.<sup>***</sup> ,<sup>**</sup> and<sup>*</sup> denote significance at 1%, 5% and 10%, respectively.

43
Table 6: Neighborhood effects: Geographic vs. political neighbors

||Citi<br>|esinthe sam<br>|e province<br>||Citiesin<br>|otherprovi<br>|nces<br>|
|---|---|---|---|---|---|---|---|
||(1)<br>All|(2)<br>350km|(3)<br>400km|(4)<br>450km|(5)<br>350km|(6)<br>400km|(7)<br>450km|
|Ave. neighbors’ over-reporting|0.635<sup>∗∗∗</sup>|0.255<sup>∗∗∗</sup>|0.400<sup>∗∗∗</sup>|0.521<sup>∗∗∗</sup>|0.061<sup>∗∗</sup>|0.042|0.011|
||(0.016)|(0.072)|(0.091)|(0.105)|(0.030)|(0.042)|(0.040)|
|PS YearsInOffice|0.108<sup>∗∗∗</sup>|0.101<sup>∗∗∗</sup>|0.099<sup>∗∗∗</sup>|0.098<sup>∗∗∗</sup>|0.096<sup>∗∗∗</sup>|0.098<sup>∗∗∗</sup>|0.098<sup>∗∗∗</sup>|
||(0.030)|(0.028)|(0.027)|(0.027)|(0.028)|(0.028)|(0.028)|
|PS YearsInOffice<sup>2</sup>|−0.011<sup>∗∗</sup>|−0.009<sup>∗∗</sup>|−0.009<sup>∗∗</sup>|−0.009<sup>∗∗</sup>|−0.009<sup>∗∗</sup>|−0.009<sup>∗∗</sup>|−0.009<sup>∗∗</sup>|
||(0.005)|(0.004)|(0.004)|(0.004)|(0.004)|(0.004)|(0.004)|
|Lag own over-reporting|0.138<sup>∗∗∗</sup>|0.219<sup>∗∗∗</sup>|0.215<sup>∗∗∗</sup>|0.212<sup>∗∗∗</sup>|0.225<sup>∗∗∗</sup>|0.225<sup>∗∗∗</sup>|0.226<sup>∗∗∗</sup>|
||(0.028)|(0.033)|(0.032)|(0.032)|(0.033)|(0.033)|(0.033)|
|City Controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Leader Controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Provincial Controls|Yes|||||||
|Provincial Trend|Yes|||||||
|City FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Province-Year FE||Yes|Yes|Yes|Yes|Yes|Yes|
|# of Observations|3264|3264|3264|3264|3264|3264|3264|
|# of Cities|272|272|272|272|272|272|272|

_Notes_ : This table presents the results of a dynamic, spatially autoregressive Durbin model estimated by MLE following Yu, De Jong, and Lee (2008). Each column corresponds to a different spatial weighting matrix in terms of geographic distance defined in the header of each column. City controls include city-level time-varying socioeconomic controls (the share of industrial and tertiary value added in GDP, log population, and growth of nighttime lights) of their own and their neighbors, and leader controls are the same as the ones displayed in the table of baseline results. Provincial controls include _YearsInOffice_ , _YearsInOffice_<sup>2</sup> and _Native_ for the provincial party secretaries, provincial growth target, and three dummies indicating the timing of the provincial People’s Congress as well as one year before and after the Congress. Year fixed effects are also controlled in column (1). Standard errors clustered at the city level are reported in parentheses.<sup>***</sup> ,<sup>**</sup> and<sup>*</sup> denote significance at 1%, 5% and 10%, respectively.

44
# Table 7: Neighborhood effects: Comparable neighbors

||Within p<br>|rovince<br>|A<br>|cross provinces<br>|<br>|
|---|---|---|---|---|---|
||(1)<br>Comparable|(2)<br>Non-<br>comparable|(3)<br>Comparable|(4)<br>Comparable<br>W/I 400km|(5)<br>Non-comp.<br>W/I 400km|
|Ave. neighbors’ over-reporting|0.751<sup>∗∗∗</sup>|0.189<sup>∗∗∗</sup>|0.130|0.072<sup>∗</sup>|0.035|
||(0.129)|(0.052)|(0.104)|(0.039)|(0.030)|
|PS YearsInOffice|0.094<sup>∗∗∗</sup>|0.106<sup>∗∗∗</sup>|0.104<sup>∗∗∗</sup>|0.101<sup>∗∗∗</sup>|0.100<sup>∗∗∗</sup>|
||(0.027)|(0.028)|(0.028)|(0.028)|(0.028)|
|PS YearsInOffice<sup>2</sup>|−0.009<sup>∗∗</sup>|−0.010<sup>∗∗</sup>|−0.010<sup>∗∗</sup>|−0.009<sup>∗∗</sup>|−0.009<sup>∗∗</sup>|
||(0.004)|(0.004)|(0.004)|(0.004)|(0.004)|
|Lag own over-reporting|0.211<sup>∗∗∗</sup>|0.220<sup>∗∗∗</sup>|0.223<sup>∗∗∗</sup>|0.223<sup>∗∗∗</sup>|0.223<sup>∗∗∗</sup>|
||(0.032)|(0.032)|(0.033)|(0.033)|(0.033)|
|City Controls|Yes|Yes|Yes|Yes|Yes|
|Leader Controls|Yes|Yes|Yes|Yes|Yes|
|City FE|Yes|Yes|Yes|Yes|Yes|
|Province-Year FE|Yes|Yes|Yes|Yes|Yes|
|# of Observations|3264|3264|3264|3264|3264|
|# of Cities|272|272|272|272|272|

_Notes_ : This table presents the results of a dynamic, spatially autoregressive Durbin model estimated by MLE following Yu, De Jong, and Lee (2008). Each column corresponds to a different spatial weighting matrix in terms of economic distance defined in the header of each column. Two cities are defined as comparable neighbors if the difference in their per capita GDP in 2001 is within one standard deviation across cities. City controls include citylevel time-varying socioeconomic controls (the share of industrial and tertiary value added in GDP, log population, and growth of nighttime lights) of their own and their neighbors, and leader controls are the same as the ones displayed in the table of baseline results. Standard errors clustered at the city level are reported in parentheses.<sup>***</sup> ,<sup>**</sup> and<sup>*</sup> denote significance at 1%, 5% and 10%, respectively.

45
Table 8: Political cycles, neighborhood effect, and the degree of competition

||STD. of_YearsInOffice_<br>|IQR. of_YearsInOffice_<br>|
|---|---|---|
||(1)|(2)|
|Ave. neighbors’ over-reporting|0.636<sup>∗∗∗</sup><br>(0.016)|0.635<sup>∗∗∗</sup><br>(0.016)|
|PS YearsInOffice|0.337<sup>∗∗∗</sup><br>(0.096)|0.300<sup>∗∗∗</sup><br>(0.074)|
|PS YearsInOffice<sup>2</sup>|−0.051<sup>∗∗∗</sup>|−0.048<sup>∗∗∗</sup>|
||(0.014)|(0.012)|
|PS YearsInOffice × DoC|−0.138<sup>∗∗</sup>|−0.086<sup>∗∗∗</sup>|
||(0.056)|(0.032)|
|PS YearsInOffice<sup>2</sup>× DoC|0.025<sup>∗∗∗</sup><br>(0.008)|0.017<sup>∗∗∗</sup><br>(0.006)|
|City Controls|Yes|Yes|
|Leader Controls|Yes|Yes|
|Provincial Controls|Yes|Yes|
|Provincial Trend|Yes|Yes|
|City FE|Yes|Yes|
|# of Observations|3264|3264|
|# of Cities|272|272|

_Notes_ : This table presents the results of a dynamic, spatially autoregressive Durbin model estimated by MLE following Yu, De Jong, and Lee (2008). Neighbors are defined as all other cities in the same province. The degree of competition is measured by the yearly standard deviation of _YearsInOffice within_ the same province in columns (1), and the yearly interquartile range (IQR) within the same province in columns (2). We restrict these two variables to be time-invariant within the term by using their values in the initial year of the term. City controls include city-level time-varying socioeconomic controls (the share of industrial and tertiary value added in GDP, log population, and growth of nighttime lights) of their own and their neighbors, and leader controls are the same as the ones displayed in the table of baseline results. Standard errors clustered at the city level are reported in parentheses.<sup>***</sup> ,<sup>**</sup> and<sup>*</sup> denote significance at 1%, 5% and 10%, respectively.

46
Table 9: Effect of GDP over-reporting on promotion

||<br>|Linear Prob<br>|.<br>|Or<br>|dered Probi<br>|t<br>|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Reported growth|0.003<br>(0.004)|||0.005<br>(0.002)<sup>∗</sup>|||
|Over-reporting|||0.017<br>(0.010)<sup>∗</sup><br>[0.011]<sup>∗</sup>|||0.018<br>(0.006)<sup>∗∗∗</sup>|
|Predicted true growth||0.001<br>(0.006)<br>[0.006]|−0.005<br>(0.007)<br>[0.008]||0.003<br>(0.004)|−0.003<br>(0.004)|
|City Controls|Yes|Yes|Yes|Yes|Yes|Yes|
|Leader Controls|Yes|Yes|Yes|Yes|Yes|Yes|
|City FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Province-Year FE|Yes|Yes|Yes|Yes|Yes|Yes|
|# of Observations|2460|2460|2460|2486|2486|2486|
|# of Cities|279|279|279|279|279|279|
|Adj_R_<sup>2</sup>|0.07|0.08|0.07||||

_Notes_ : This table shows the marginal effects of reported GDP growth, predicted true growth, and estimated over-reporting on promotion using the linear and ordered Probit model. Reported growth, predicted true growth, and estimated over-reporting are averaged annual rates of the city since the PS assumed office until the current year. City controls include log population, the log level of GDP per capita, and log government expenditures, all measured in the year before the PS assumed office. A full set of year-in-office dummies is also included. Parametric and bootstrapped standard errors clustered at the city level are reported in parentheses and brackets, respectively.<sup>***</sup> ,<sup>**</sup> and<sup>*</sup> denote significance at 1%, 5% and 10%, respectively.

47
Table 10: Over-reporting less: Ratchet effect

||**Under-repor**<br>|**ting**: 1(Reporte<br>|d growth<Pred<br>|icted growth)<br>|**Fulfilling**<br>|**target**: 1(Re<br>|ported growth><br>|Target)<br>|
|---|---|---|---|---|---|---|---|---|
||(1)<br>Whole<br>Sample|(2)<br>2×BW|(3)<br>1.5×BW|(4)<br>1×BW|(5)<br>Whole<br>Sample|(6)<br>2×BW|(7)<br>1.5×BW|(8)<br>1×BW|
|**Panel A: All city secretarie**|**s**<br>||||||||
|1(Predicted GDP >Target)|0.185<sup>***</sup>|0.187<sup>***</sup>|0.186<sup>***</sup>|0.177<sup>***</sup>|0.426<sup>***</sup>|0.193<sup>***</sup>|0.122<sup>**</sup>|0.095<sup>*</sup>|
|**(i.e. Solid performer)**|(0.029)|(0.037)|(0.040)|(0.046)|(0.029)|(0.042)|(0.048)|(0.057)|
|# of Observations|2895|2605|2335|1797|2895|2043|1633|1115|
|# of Cities|281|280|279|278|281|279|278|277|
|Adj R<sup>2</sup>|0.15|0.14|0.15|0.17|0.57|0.47|0.42|0.30|
|**Panel B: City secretaries w**<br>|**ith tenure leng**<br>|**th less than 2**<br>|**years (i.e., less**<br>|**likely to influenc**<br>|**e target setting)**<br>||||
|1(Predicted GDP >Target)|0.159<sup>***</sup>|0.157<sup>***</sup>|0.134<sup>**</sup>|0.150<sup>**</sup>|0.458<sup>***</sup>|0.257<sup>***</sup>|0.202<sup>***</sup>|0.103|
||(0.039)|(0.049)|(0.055)|(0.062)|(0.034)|(0.048)|(0.055)|(0.065)|
|#of Observations|1437|1226|1057|800|1437|1133|963|685|
|#of Cities|281|279|275|269|281|278|273|261|
|Adj R<sup>2</sup>|0.18|0.18|0.20|0.21|0.59|0.51|0.48|0.39|
|City Controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Leader Controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Province FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

_Notes_ : This table presents the ratchet effect of GDP manipulation using a regression discontinuity design. The running variable, i.e., the difference between the predicted real GDP growth and the target growth, is interacted with the dummy variable of interest to allow for differential slopes across the cutoff. The optimal bandwidth is chosen according to Calonico, Cattaneo, and Titiunik (2014). City controls include city-level time-varying economic controls (the share of industrial and tertiary value added in GDP, log population, and growth of nighttime lights), and leader controls are the same as the ones displayed in the table of baseline results. Standard errors clustered at the city level are reported in parentheses.<sup>***</sup> ,<sup>**</sup> and<sup>*</sup> denote significance at 1%, 5% and 10%, respectively.

48
Table 11. Under-reporting: Scapegoating effects

||(1)|(2)|
|---|---|---|
|Turnover|0.035<br>(0.023)|0.047<sup>*</sup><br>(0.027)|
|Scapegoatable|0.012|0.004|
||(0.030)|(0.043)|
|Turnover×Scapegoatable|-0.090<sup>**</sup>|-0.096<sup>*</sup>|
||(0.043)|(0.051)|
|Demoted|-0.011|0.006|
||(0.027)|(0.035)|
|Turnover×Demoted|-0.005|-0.032|
||(0.039)|(0.049)|
|Scapegoatable×Demoted|0.013<br>(0.050)|0.024<br>(0.081)|
|Turnover×Scapegoatable×Demoted|0.148<sup>**</sup>|0.192<sup>**</sup>|
||(0.064)|(0.086)|
|City Controls|Yes|Yes|
|Leader Controls|Yes|Yes|
|City FE|Yes|Yes|
|Province-Year FE|Yes|Yes|
|Pre-Post Dummies||Yes|
|# of Observations|3418|3132|
|# of Cities|281|281|
|Adj R<sup>2</sup>|0.48|0.49|

_Notes_ : This table presents the heterogeneous effects of the predecessor’s promotion status on GDP manipulation at the turnover year when the incumbent assumes office around the end of the year. The dependent variable is a dummy variable that equals one if the city’s reported real GDP growth is less than the predicted real GDP growth using the nighttime lights data, i.e., a dummy indicating under-reporting GDP growth. City controls include city-level time-varying economic controls (the share of industrial and tertiary value added in GDP, log population, and growth of nighttime lights), and leader controls are the same as the ones displayed in the table of baseline results. Pre/Post dummies indicate one year pre and post the turnover year respectively. In column 2, we further include the Pre/Post dummies and their interactions with _Demoted_ and _Scapegoatable_ dummies to control for the potential differential effects in these years. Standard errors clustered at the city level are reported in parentheses.<sup>***</sup> ,<sup>**</sup> and<sup>*</sup> denote significance at 1%, 5% and 10%, respectively.

49
<!-- Start of picture text --> Panel A: Cumulative Growth up to Q1 Panel B: Cumulative Growth up to Q2<br>iQ ~<br>-N<br>+<br>-oO aw<br>S<br>8 6 4 2 0 2 4 6 8 6 4 -2 0 2 4 6 8<br>Q1 GDP Growth - Growth Target Q2 GDP Growth - Growth Target<br>p-value=0.113 p-value=0.413<br><!-- End of picture text --> Panel C: Cumulative Growth up to Q3 Panel D: Cumulative Growth up to Q4 <!-- Start of picture text --> ~ ~<br>o2 o<br>N N<br>oO oO<br>6 4 #2 0 2 4 6 8 -4 -2 0 2 4 6 8<br>Q3 GDP Growth - Growth Target Q4 GDP Growth - Growth Target<br>p-value=0.010 p-value=0.002<br>——_ point estimate 95% C.l.<br><!-- End of picture text -->
<!-- Start of picture text --> Ke)<br>N<br>N<br>Ww<br>Ke)<br>°<br>oO<br>-10 -5 0 5 10<br>Predicted GDP Growth — Target Growth<br>p=0.624<br><!-- End of picture text -->
Table A.1: Robustness: Alternative ways of predicting GDP growth based on nighttime lights

||Drop<br>Northwest|Calibrated<br>lights|Altern<br>wh|ative slopes o<br>en predicting<br>growth|f light<br>true||Adding<br>when pred|more contr<br>icting true g|ols<br>rowth||Clark et al.<br>(2020)|
|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|
||||One|Prov-<br>Spec|Region-<br>Period<br>-Spec|Squared<br>Lights|Electricity|Loans|Freight|All three||
|YearsInOffice|0.101<sup>∗∗∗</sup>|0.094<sup>∗∗∗</sup>|0.093<sup>∗∗∗</sup>|0.105<sup>∗∗∗</sup>|0.093<sup>∗∗∗</sup>|0.088<sup>∗∗∗</sup>|0.103<sup>∗∗∗</sup>|0.107<sup>∗∗∗</sup>|0.102<sup>∗∗∗</sup>|0.116<sup>∗∗∗</sup>|0.265<sup>∗∗∗</sup>|
||(0.030)|(0.031)|(0.028)|(0.032)|(0.029)|(0.033)|(0.032)|(0.033)|(0.030)|(0.035)|(0.081)|
|YearsInOffice<sup>2</sup>|−0.010<sup>∗∗</sup>|−0.008<sup>∗</sup>|−0.008<sup>∗</sup>|−0.010<sup>∗∗</sup>|−0.008<sup>∗</sup>|−0.008<sup>∗</sup>|−0.010<sup>∗</sup>|−0.009<sup>∗</sup>|−0.010<sup>∗∗</sup>|−0.010<sup>∗</sup>|−0.022<sup>∗</sup>|
||(0.005)|(0.005)|(0.004)|(0.005)|(0.005)|(0.005)|(0.005)|(0.005)|(0.005)|(0.005)|(0.013)|
|City Controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Leader Controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|City FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Prov-Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|# of Obs.|3399|3642|3642|3642|3642|3642|3639|3642|3640|3637|3636|
|# of Cities|262|281|281|281|281|281|281|281|281|281|281|
|Adj_R_<sup>2</sup>|0.61|0.50|0.58|0.57|0.60|0.53|0.53|0.55|0.56|0.49|0.40|

_Notes_ : This table presents several robustness checks for the political cycle of GDP manipulation. The dependent variable is the difference between reported real GDP growth and predicted real GDP growth imputed based on the nighttime lights data of prefecture-level cities. City controls include city-level time-varying economic controls (the share of industrial and tertiary value added in GDP, log population, and growth of nighttime lights) and leader controls are the same as the ones displayed in the table of baseline results. Standard errors clustered at the city level are reported in parentheses.<sup>***</sup> ,<sup>**</sup> and<sup>*</sup> denote significance at 1%, 5% and 10%, respectively.

52
Table A.2: Robustness: Alternative specifications, estimation techniques, and placebo tests

||Reported gr<br>controlling li|owth while<br>ghts growth|Reported<br>growth<br>|Predicted<br>growth|Lights<br>growth|Targe<br>year_t_|t of<br>+ 1|Ove|r-reporting||
|---|---|---|---|---|---|---|---|---|---|---|
||(1)<br>Region-<br>specific|(2)<br>Province-<br>specific|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)<br>System<br>GMM|
|PS YearsInOffice|0.254<sup>∗∗∗</sup><br>(0.072)|0.252<sup>∗∗∗</sup><br>(0.072)|0.221<sup>∗∗∗</sup><br>(0.070)|0.000<br>(0.000)|0.221<br>(0.390)|0.023<br>(0.058)|−0.036<br>(0.054)|0.101<sup>∗∗∗</sup><br>(0.033)|0.104<sup>∗∗∗</sup><br>(0.029)|0.156<sup>∗∗∗</sup><br>(0.037)|
|PS YearsInOffice<sup>2</sup>|−0.024<sup>∗∗</sup>|−0.023<sup>∗∗</sup>|−0.019<sup>∗</sup>|−0.000|−0.033|−0.011|−0.007|−0.009<sup>∗</sup>|−0.009<sup>∗∗</sup>|−0.015<sup>∗∗∗</sup>|
||(0.011)|(0.011)|(0.011)|(0.000)|(0.057)|(0.008)|(0.007)|(0.005)|(0.004)|(0.005)|
|Reported growth|||||||0.274<sup>∗∗∗</sup><br>(0.035)||||
|Target growth||||||||0.174<sup>∗∗∗</sup><br>(0.022)|||
|L.Over-reporting|||||||||0.141<sup>∗∗∗</sup><br>(0.034)|0.183<sup>∗∗∗</sup><br>(0.036)|
|City Controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Leader Controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|City FE|||Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Province-Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|# of Observations|3642|3642|3642|3642|3642|2887|2887|2890|3361|3361|
|# of Cities|281|281|281|281|281|281|281|281|281|281|
|Adj_R_<sup>2</sup>|0.56|0.56|0.65|0.72|0.78|0.74|0.78|0.65|0.62||

_Notes_ : This table presents several robustness checks for the political cycle of GDP manipulation. The dependent variable is the difference between reported real GDP growth and predicted real GDP growth imputed based on the nighttime lights data of prefecture-level cities. City controls include city-level timevarying economic controls (the share of industrial and tertiary value added in GDP, log population, and growth of nighttime lights (except for columns 4 and 5)) and leader controls are the same as the ones displayed in the table of baseline results. Standard errors clustered at the city level are reported in parentheses. ***, ** and * denote significance at 1%, 5% and 10%, respectively.

53
Table A.3: Robustness: Subsamples, alternative levels of clustering, and measures of variables

||Drop P<br>Total|Ss with<br>Tenure|Deputy-prov<br>Cities Dropped|Hu’s<br>Tenure<br>(2003–12)|Matched by<br>Longest Tenure|Cluster<br>the Lev|ing at<br>el of|
|---|---|---|---|---|---|---|---|
||(1)<br>≤1 Year|(2)<br>≤2 Years|(3)|(4)|(5)|(6)<br>Prov-Year|(7)<br>Province|
|PS YearsInOffice|0.099<sup>∗∗∗</sup>|0.102<sup>∗∗∗</sup>|0.104<sup>∗∗∗</sup>|0.112<sup>∗∗∗</sup>|0.090<sup>∗∗∗</sup>|0.096<sup>∗∗∗</sup>|0.096<sup>∗∗</sup>|
||(0.030)|(0.031)|(0.032)|(0.035)|(0.031)|(0.029)|(0.036)|
|PS YearsInOffice<sup>2</sup>|−0.009<sup>∗∗</sup>|−0.010<sup>∗∗</sup>|−0.010<sup>∗</sup>|−0.012<sup>∗∗</sup>|−0.010<sup>∗∗</sup>|−0.009<sup>∗∗</sup>|−0.009<sup>∗</sup>|
||(0.005)|(0.004)|(0.005)|(0.006)|(0.004)|(0.004)|(0.005)|
|City Controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Leader Controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|City FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Province-Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|# of Observations|3620|3362|3447|2806|3642|3642|3642|
|# of Cities|281|281|266|281|281|281|281|
|Adj_R_<sup>2</sup>|0.60|0.60|0.60|0.63|0.60|0.60|0.60|

_Notes_ : This table presents several robustness checks for the political cycle of GDP manipulation. The dependent variable is the difference between reported real GDP growth and predicted real GDP growth imputed based on the nighttime lights data of prefecture-level cities. City controls include city-level time-varying economic controls (the share of industrial and tertiary value added in GDP, log population, and growth of nighttime lights) and leader controls are the same as the ones displayed in the table of baseline results. Standard errors clustered at the city level (except columns (6) and (7)) are reported in parentheses.<sup>***</sup> ,<sup>**</sup> and<sup>*</sup> denote significance at 1%, 5% and 10%, respectively.

54
# **Appendix B. Measuring promotions, lateral moves, and demotions**

We discuss how we use the CPED data to determine promotions and demotions in detail here. Our database has all city PSs for all prefecture-level administrative units from 2000 up to 2019.

We classify all PSs into nine groups by their ranks when they assumed office and when they left office. Each PS could be ranked at one of the three hierarchical levels when they assume (leave) office: the deputy-provincial level with the membership of provincial Party Standing Committee (PSC), the deputy-provincial level without the membership of provincial PSC,<sup>54</sup> or the prefecture level. We distinguish deputy-provincial level PSs by whether they are members of the provincial PSC or not because similar to the Politburo Standing Committee in the central committee, the provincial PSC is the highest decision-making bureau at the province level. Though most members in the provincial PSC hold the same administrative level with vice governors, vice directors of the provincial Party Congress, and vice-chairmen of the provincial PPCC, they maintain more real power. In total there are nine possible starting-ending level pairs for a city PS.<sup>55</sup> Because no PSs are downgraded from deputy-provincial level to prefecture-level during their tenure in the city secretaryship, we end up with seven groups of city PSs.

For a PS that does not experience a rank change during his tenure, we only need to compare the rank of the PS’s current position and that of his destination job. For a PS who experiences a rank change during the term, we compare the changing ranks within his tenure. Specifically, if his hierarchical level is from either a deputyprovincial level without provincial PSC membership or a prefectural level to a deputy-provincial level with provincial PSC membership, we regard this rank change as a promotion during his tenure. In contrast, if he loses the PSC membership during his term as the city secretary, this is a demotion.<sup>56</sup> Apart from this additional check of determining promotions and demotions for PSs who have a rank change during their term, we reclassify all the 7 PS groups into 3 classes based on their hierarchical levels when they leave office. The three groups are labeled as: class-A consisting of PSs of deputy-provincial level with the membership of the provincial PSC when leaving office; class-B consisting of PSs of the deputy-provincial level without membership of the provincial PSC when leaving office; and class-C consisting of PSs of the prefecture level when leaving office.

Next, we manually identify destination positions for all city party secretaries by exploring the rich information from the CPED data set. We categorize all these new jobs into 45 mutually exclusive and jointly exhaustive groups. These 45 groups are:

- (1) Investigated for corruption or other illegal activities during the PS term, and it is categorized as a demotion for all PSs of class-A, B, and C.

- (2) Suspended or dismissed during the PS term. PSs in this group are usually those who make serious mistakes (from the upper government’s point of view) and take major responsibilities in public affairs. This case is also classified as a demotion for all three classes of PSs.

- (3) Died during the PS term: some city secretaries died of disease or accidents during their term while some PSs committed suicide in fear of investigation. Since public announcements rarely mention the latter case, we practically treat this case as a demotion.

- (4) Full and direct retirement after the PS term. Typically, local leaders do not retire in the full sense immediately after leaving office (Li and Zhou 2005). A city leader who approaches the retirement age will be placed in some honorary position in PC or PPCC (Wang, Zhang, and Zhou 2020). We follow Li and Zhou (2005) in grouping retirement and demotion together, and we label them as a demotion for all three classes of PSs.

> 54 This might be that the city secretary also holds a deputy-provincial level position at the same time other than the provincial PSC membership, for example, the vice governorship, the vice directorship of the provincial People’s Congress (PC) or the vice chairmanship of the provincial People’s Political Consultative Conference (PPCC).

> 55 For PSs whose rank is changed to the deputy-provincial level with provincial PSC membership, we modify the timeline to six months before they leave office to accommodate the fact that provincial Party Congress mostly take place in the middle of the year, around half a year before the annual meeting of the city People’s Congress.

> 56 For example, Mr. Yan Rongzhu, who became the party secretary of Yantai, a coastal prefecture level city in Shandong Province in June 2001, was a prefecture level PS at the time when he took office. In April 2004, he became a member in the Shandong Provincial PSC but still remained his office in Yantai until two and half years later when he returned to the provincial capital and became a fulltime member of the PSC in charge of propaganda affairs. When we measure his career path in his tenure as the party secretary in Yantai, we regard the level change in April 2004 as a promotion since his hierarchical level was increased from prefecture to deputyprovincial while see his transfer from the city party secretary to the full-time provincial PSC member in October 2006 as a lateral move as both positions are of the same level (membership of the provincial PSC).

55
Groups (1) to (4) above are for city secretaries who do not have a destination job. The next two groups consist of PSs whose next positions are chief-provincial level.

- (5) Positions with chief-provincial level rank in the party or the government: these positions mainly include provincial party secretaryship, governorship, or ministership in the central party commission or government. Transferring to these positions is classified as a promotion for all three classes of PSs.

- (6) Positions with chief-provincial level ranks in institutions other than the party committee and the government: these positions mainly include directorship of the provincial PC or chairmanship of the provincial PPCC. Being assigned these positions is categorized as a demotion for class (a) PSs since these new positions generally are endowed with less power than provincial PSC membership, a lateral move for class-B PSs, and a demotion for class-C PSs as it indicates a full level increase in ranks for class-C PSs.

Groups (7) to (12) are positions below the chief-provincial level in central institutions.

- (7) Deputy-provincial positions in the central party committee: these positions mainly include vice ministership in the publicity department, organization department of the central committee, or vice secretaryship in the central commission for discipline inspection, etc. Moving to these positions is regarded as a lateral move for class-A PSs, and a demotion for PSs of both class-B and class-C.

- (8) Deputy-provincial positions in the State Council: these positions consist of vice ministership in ministries of the central government. Arrangements to these positions are classified as a demotion for class-A PSs, a lateral move for class-B PSs, and a demotion for class-C PSs.

- (9) Deputy-provincial positions in central institutions other than the central party committee or the State Council: transferring to these positions is regarded as a demotion for both class-A and class-B PSs, and a lateral move for class-C PSs.

- (10) Chief-prefecture level positions with chief-prefecture level rank in the central party committee. We distinguish chief-prefecture level ranks from chief-prefecture level positions because, in the Chinese political system, there exists a large share of officials who hold a deputy level position but enjoy a chief level rank ( _Gaopei_ in Chinese). This might be due to the importance of the bureau in which the positions are,<sup>57</sup> or due to rank “stickiness” for officials.<sup>58</sup> Moving to these positions is regarded as a demotion for both class-A and class-B PSs, and a lateral move for class-C PSs.

- (11) Chief-prefecture level positions with chief-prefecture level ranks in the State Council: similar to (10), it is viewed as a demotion for both class-A and class-B PSs, and a lateral move for class-C PSs.

- (12) Other positions in central institutions: positions in this group include not only chief-prefecture level positions in central institutions other than the central party committee or the State Council, but also deputy-prefecture level positions with chief-prefecture level ranks in the central party committee or the State Council. Being assigned positions in this group is categorized as a demotion for all three classes of PSs.

Groups (13) to (17) are positions in the provincial party committee.

- (13) Full-time membership in the provincial PSC: this includes PSC membership that is not a city secretaryship. Normally, the standing committee of a provincial party includes the provincial party secretary, the governor, the vice secretary, the top-ranked vice governor, the minister of the publicity department ( _xuanchuan bu_ ), the minister of the organization department ( _zuzhi bu_ ), the minister of the united front work department ( _tongzhan bu_ ), the secretary of the commission for discipline inspection ( _jiwei_ ), the secretary of the commission for political and legal affairs ( _zhengfawei_ ), the secretary-general ( _mishu zhang_ ), the commander of the provincial military ( _sheng junqu_ ) and one or two city secretaries (typically the provincial capital and economically or politically the second most important city). Transferring to these positions is seen as a lateral move for class-A PSs and a demotion for both class-B and class-C PSs.

- (14) Positions that are normally members of the provincial PSC but actually not: under rare circumstances, some positions listed in group (13) are non-provincial PSC membership. Thus, it is viewed as a demotion for classA PSs, a lateral move for class-B PSs, but still a promotion for class-C PSs.

> 57 For example, many deputy directors of the National Development and Reform Commission, a ministry in the State Council, are chief-provincial rank.

> 58 For example, Mr. Liu Shuguang, a former city secretary in Weifang, a central prefecture city in Shandong Province, was appointed as the vice director in the People’s Congress in Zibo, a neighboring prefecture city. Though the normal rank of a city vice director of PC is deputy-prefecture, he still enjoys the chief-prefecture rank in the new position, the same as his rank in Weifang.

56
- (15) Positions for vice secretary generals and vice-ministers of the organization department: although these are deputy-prefecture positions, they are in the center of the provincial committee and hold power in routine works or personnel control. Besides, PSs transferring to these positions always enjoy a chief-prefecture rank. Given the importance of these two positions, we regard these two arrangements as a lateral move for class-C PSs. However, it is obvious a demotion for both class-A and class-B PSs.

- (16) Vice positions in other departments in the provincial party committee: these include vice ministership in the department of publicity or united front work; or vice secretaryship of the commissions for discipline inspection or political and legal affairs. Transferring to these positions is categorized as a demotion for all classes of city secretaries.

- (17) Other positions in the provincial party committee: this is also viewed as a demotion for all classes of PSs.

Groups (18) to (23) consist of positions in the provincial government.

- (18) Vice governorship without membership in the provincial PSC: typically, a provincial government has around 6 vice governors, among which the top-ranked vice governor is also a member in the provincial PSC. Transferring to non-PSC-membership vice governorship positions is regarded as a demotion for class-A PSs, a lateral move for class-B PSs, and a promotion for class-C PSs.

- (19) Assistantship to the governor: these positions are chief-prefecture level positions. Being appointed to these positions is classified as a demotion for class-A and class-B PSs, a lateral move for class-C PSs.

- (20) Directorship in the three important departments in the provincial government: these three positions include the head of the provincial development and reform commission, the head of public safety, and the secretaryship general of the provincial government. Moving to these positions is categorized as a demotion for both class-A and class-B PSs, but a lateral move for class-C PSs.

- (21) Directorship in other provincial departments: Transferring to these positions is seen as a demotion for all classes of PSs.

- (22) Positions for vice secretary generals of the provincial government: also, this is classified as a demotion for all classes of PSs.

- (23) Other positions in the provincial government: again, this is viewed as a demotion for all classes of PSs.

Positions in the provincial PC, PPCC, court, and procuratorate are included in the group (24) to group (28).

- (24) Vice directorship of the provincial PC: these are mainly honorary positions for those who approach retirement. However, these positions still enjoy a deputy-provincial rank. Thus, we categorize it as a lateral move of classC PSs, and a demotion for both class-A and class-B PSs.

- (25) Vice chairmanship of the provincial PPCC: similar to group (24), transferring to these positions is regarded as a lateral move for class-C PSs and a demotion for the other two classes.

- (26) Other positions in the provincial PC or PPCC: this is categorized as a demotion for all classes of PSs.

- (27) Head of the provincial court or the provincial procuratorate: these two positions are deputy-provincial ranks as the vice directorship of PC or vice chairmanship of PPCC. In this sense, we categorize it as a lateral move of class-C PSs, and a demotion for class-A and class-B PSs.

- (28) Other positions in the provincial court or the provincial procuratorate: transferring to these positions is viewed as a demotion.

From (29), we focus on PSs transferring within the city level.

- (29) Secretaryship in deputy-provincial level cities: since deputy-provincial level cities are mostly regional centers and enjoy a higher political status, relocating to these cities as secretaries are regarded as a promotion for most PSs unless their current working city itself is deputy-provincial level. In other words, transferring to these positions is viewed as a promotion for both class-B and class-C PSs. For class-A PSs, it is a lateral move if the current city is deputy-provincial level and a demotion otherwise.

- (30) Mayorship in deputy-provincial level cities with membership in the provincial PSC: mayors are rarely PSC members; however occasionally, some mayors of deputy-provincial level cities enter the standing committee of the provincial party. Transferring to these positions is classified as a lateral move for class-A PSs as long as the current city is not at the deputy-provincial level. For class-B and class-C, it is regarded as a demotion.

57
- (31) Mayorship in deputy-provincial level cities without membership in the provincial PSC: these positions are deputy-provincial level. Thus, it is a demotion for class-A PSs, a lateral move for class-B PSs, and a demotion of class-C PSs.

- (32) Other positions at the party committee or government in deputy-provincial level cities: transferring to these positions is regarded as a demotion for all classes of PSs.

- (33) Secretaryship in provincial capital cities that are not deputy-provincial level: as has been mentioned, secretaries at provincial capital cities are always members of the provincial PSC. Thus, moving to these cities is classified as a promotion for both class-B and class-C PSs. For class-A PSs, it depends on the current city level. If the current city is deputy-provincial level, it is a demotion. If it is a provincial capital without deputy-provincial status, it is a lateral move. Otherwise, it is a demotion.

- (34) Mayorship with provincial PSC membership in provincial capital cities that are not deputy-provincial level: this is regarded as a demotion for class-A PSs, but a demotion for both class-B and class-C PSs.

- (35) Mayorship without provincial PSC membership in provincial capitals that are not deputy-provincial level: since these positions are usually in a prefecture-level rank, it is regarded as a demotion for both class-A and class-B PSs, but a lateral move for class-C PSs, as they move from a non-capital city to provincial capital, though from secretaryship to the mayorship.

- (36) Other positions in the party committee or government in provincial capital cities that are not deputy-provincial level: transferring to these positions is viewed as a demotion for all classes of PSs.

- (37) Secretaryship with provincial PSC membership in prefecture-level non-capital cities: this is regarded as a promotion for both class-B and class-C PSs. For class-A PSs, it is a demotion if the current city is either a provincial capital or a deputy-provincial level; otherwise, it is a lateral move.

- (38) Secretaryship without provincial PSC membership in prefecture-level non-capital cities: this is a demotion for both class-A and class-B PSs. For class-C PSs, it is a lateral move unless the official becomes the PSC member within six months in his new job, which we define as a demotion.<sup>59</sup>

- (39) Mayorship in prefecture-level non-capital cities: this is categorized as a demotion for all classes of PSs.

- (40) Directorship of PC or chairmanship of PPCC in deputy-provincial cities: similar to (24)and (25), these are honorary deputy-provincial level positions. Thus, it is a lateral move for class-C PSs and a demotion for both class-A and class-B PSs.

- (41) Other positions in the city level PC or PPCC: definitely, transferring to these positions is viewed as a demotion for all three classes of PSs.

- (42) Other positions at the city level: again, it is regarded as a demotion.

The remaining three groups include positions in alternative institutions.

- (43) Positions in the military: only a couple of city secretaries were appointed in the military, mainly positions in the production and construction corps in Xinjiang. We check these positions manually.

- (44) Positions in local state-owned corporations: we also manually check these positions and classify them accordingly.

- (45) Positions in universities, associations, and non-governmental organizations: transferring to these positions is viewed as a demotion.

> 59 For example, Mr. Wang Hao, a former PS in Zibo city, became the secretary in Yantai in April 2017. Two months later, he joined the provincial PSC during his term in Yantai. Since it is only two months since he assumed new office, it is unlikely that he became the PSC member due to his work in the new job. Thus, we regard his transfer from Zibo to Yantai as a promotion.

58
# **Appendix C. Statistical framework of Henderson et al. (2012)**

We introduce the statistical framework to relate lights to GDP growth in Henderson, Storeygard, and Weil (2012) (HSW hereafter). We specify how we apply the modified version to our analysis.

Let _y*_ be the growth (or log difference) in true real GDP, _y_ the officially reported version, and _l_ the growth of observed light. HSW assumes the following relationships between reported GDP growth, light growth, and true GDP growth: GDP growth: ∗ ; (A.1) yy ii,tt ii,tt∗ ii,tt yy = yy + εε , (A.2) ll where _i_ and _t_ index region and year, respectively. ii,tt _ε_<sup>_y_</sup> in Eq. (A.1) is the classical measurement error in the official ii,tt ii,tt ll = ββyy + εε 2 2 2 GDP growth data. _β_ is the elasticity of lights to true GDP. The variance of _y_<sup>∗</sup> , _ε_<sup>_y_</sup> and _ε_<sup>_l_</sup> are denoted ∗ , ∗ and , respectively. The error terms of the above two equations are assumed to be uncorrelated, i.e., ) = 0. yy yy ll σσ σσ σσ Then we use lights growth to predict GDP growth with the following regression: YYc (εε<sup>yy</sup> ,εε<sup>ll</sup> , (A.3) where _ψ_ is the elasticity of light growth to the true real GDP growth. In practice, Eq. (A.3) is estimated by using ii,tt ii,tt ii,tt yy = ψψll + μμ levels of lights and GDP, augmented with region and year fixed effects. Here the unit of analysis is the city. We further modify Eq. (A.3) to allow for region-specific slopes of lights so that the relationship between nighttime lights and real GDP may differ for regions at different stages of development. We classify China into eight economic regions as mentioned earlier. We thus run the following regression: , (A.4) where _i_ , _r,_ and _t_ index city, region, and year respectively. To improve precision, the fitted values from the above ii,rr,tt rr ii,rr,tt ii,rr tt ii,rr,tt llYY( G PP ) = αα+ ψψ llYY( NNYYNNhttllYYNNhttYY ) + μμ + δδ + εε regression are combined with the official GDP growth figures to arrive at a composite estimate which will have a lower error than either one separately. That is ∗ , (A.5) where is the light-predicted growth from Eq.(A.4), and λ is the optimal weight that minimizes the variance of ii,tt ii,tt ii,tt yy� = λλyy + (1 −λλ)yy� measurement error in the above estimate relative to the true value of economic growth, i.e., ii,tt yy� ). (A.6) From Eq. (A.6), we solve for the optimal weight yy<sup>2</sup> as: ccY (yy�<sup>∗</sup> −yy<sup>∗</sup> ) = λλ<sup>2</sup> σσ + (1 −λλ)<sup>2</sup> ccY (yy�−yy<sup>∗</sup> 2 2 = 2 <u>y2</u> λλ2<sup>∗</sup> 2 = <u>φ ρ</u> , (A.7) where is the correlation between λλ<sup>∗</sup> _l_ and σσyy<sup>2</sup> _y_ (ββ. In our context, we also allow for region-specific<sup>2</sup> σσyy∗σσll+σσσσ lly∗)+σσll σσyy∗ 1−ρρφ−<sup>22</sup> when calculating the optimal . ρρ ρρ Based on Eqs. (A.1) and (A.2), we can obtain three sample moments from the observed data: + 2 λλ<sup>∗</sup> 2 2 2 2 ∗ , ∗ and ∗ . However, we still need one more equation to solve for the above 2 ccY (yy) = σσyy<sup>2</sup> <u>y</u> unknown parameters. To achieve this, we specify a value for σσyy ccY (ll) = ββ σσyy + σσll YYc (ll, yy) = ββσσyy 2 ~~,~~ i.e., the ratio of signal to the total variance σσ y∗ in measured GDP growth. If we divide the countries into two groups based on the levels of measurement error in φφ= σσyy∗+σσyy2 GDP following HSW, by assuming a specific value for _φ_ for the countries with high quality of GDP measurement (developed countries), e.g., 0.9, we can derive the corresponding value of _φ_ for the other group (developing countries like China), i.e., 0.594 in this case. Substituting 0.594 and the region-specific ρ into Eq. (A.7), we get the optimal weight _λ_ . For our sample, the variation of _λ_ is quite small among the eight regions, ranging from 0.576 to 0.594.

0.594. Under this framework, GDP manipulation naturally follows as the difference between the officially reported growth and the predicted true growth in Eq. (A.5): ). rr ii,rr,tt ii,rr,tt (1 −λλ )(yy −yy� 59