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