昨天阅读2个小时,累积阅读50个小时。
his brings us to the question at hand: does the nature of a country’s political regime influence the accuracy of its reported statistics? It is easy to think of ways in which corrupt governments can falsify data. First, to make it seem that a country is growing, leaders may simply direct those computing the numbers to inflate them. They may even use draconian methods to enforce such instructions, as was the case when the Soviet government executed the officials responsible for the 1937 census; or when the Greek government tried and convicted—for making statements that undermined the ‘national interest’—the chief statistician (a former IMF official) engaged in 2010 to clean up the country’s data collection and reporting process.
But, the problem is a broader one that arises from the incentives to misrepresent wherever statistical independence and proper oversight are lacking. For example, even in the absence of outright punishment, data producers and reporters in autocratic regimes (especially those involved in industrial production) may seek to curry favor with the government by inflating their reports.
To examine the extent of misreporting, Martínez turns to information on night-time light. In theory, governments could simply put up lights to make their country look prosperous, but the example of North Korea suggests that this is not a common practice. Absent such purposeful distortions, we can look to see whether the relationship between night-time light and GDP is the same across political regimes.
To do this, we need three types of data: GDP, night-time illumination, and a quantitative representation of the nature of a country’s political regime. While the first is readily available, the second is extremely difficult to compute. Fortunately, HSW have posted their data here. As for the third, since the 1970s, Freedom House has constructed the annual Freedom World Index (FWI) for a broad cross-section of countries. Using the FWI, Martínez divides countries into three categories: “Free”, “Partially Free” and “Not Free”. He then comes to the following stark conclusion: over the period from 1992 to 2008, night-time light grew on average by five percent per year in all three groups (see Table 1 on page 39). But, comparing the “Free” and the “Not Free”, average annual GDP growth differed by nearly a full percentage point (3.57% versus 4.46%), resulting in a very large 26 percentage-point gap over the full 16-year period.
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