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.

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