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.

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