more transient nature of the HGF phase relative to FDI. This contributes to the generalizability of the FDI spillover methodology to other problems.

The paper is organized as follows. Section 2 describes the data used, the construction of the main variables of interest and the empirical strategy. Section 3 presents our results. Finally, Section 4 summarizes the evidence presented.

# 2 Data and Empirical Strategy

## 2.1 Data

The primary data set is firm level corporate income tax statements during the period 1998‐2014 from the Hungarian National Tax Authority (NAV). The data set has almost universal coverage as it covers all firms that require double‐entry bookkeeping.<sup>2</sup> The sample includes more than 95 percent of employment and value added of the business sector and about 55 percent of the full economy in terms of GDP. The data set includes information on a wide range of matters such as ownership, financial information, employment, and industry at the NACE 4‐digit code level and the location of their headquarters. The Centre for Economic and Regional Studies, Hungarian Academy of Science (CERS‐HAS) extensively cleaned and harmonized the data.

Given the scope of our analysis, we restrict the data in several ways. First, we exclude non‐profit organizations. Second, we drop firms that operate either in agriculture or in the non‐market service sectors of the economy.<sup>3</sup> Third, we drop all firm‐year observations with fewer than 5 employees.<sup>4</sup> Finally, we drop all firm‐year observations with missing, negative or zero employment, turnover, material cost, capital or value added. As we will discuss in detail in section 2.3, our regression will be run for 3‐year intervals, which further reduces the number of observations. We also restrict the sample to surviving firms, given our primary interest in TFP change. Finally, we exclude firms which operate in the capital, Budapest.<sup>5</sup> Our final regression sample consists of more than 140,000 firm‐year observations.<sup>6</sup> The data include the most important balance sheet items. Nominal variables were deflated by the appropriate deflators from Eurostat. We rely on two proxies of productivity. First, labor productivity is value added over employment. Second, we estimate total factor productivity (TFP) using the semi‐ 2 Double‐entry bookkeeping system is an accounting system where every entry has a corresponding entry in a different account. For example, a cash purchase of equipment will appear as a credit on the cash account and a debit on the equipment/capital account. Only sole traders and very small firms are not required to use double‐ entry bookkeeping in Hungary.

3 Non‐market services sectors are sectors 53, 58, 75, 84 to 94, and 96 to 99, based on NACE rev 2. 4 We do this to avoid overestimating the incidence of HGFs. Besides, these micro‐sized firms are likely to include individual entrepreneurs who operate a formal firm to reduce their tax burden (Semjén et al., 2009), and many small firms are likely to underreport their sales, earnings and wages paid (Tonin, 2011).

5 We do this for two reasons. First, many larger firms register their headquarters in Budapest even though most of their activities are conducted elsewhere. Second, Budapest, where 20% is a very large geographic entities compared to other counties or microregions, represents an outlier. Our results are robust to including Budapest in the sample.

6 The full sample includes a total of 1.7 million observations for our regression years (1998, 2001, 2004, 2007, 2009, 2011); 320,000 firms have at least 5 employees and 260,000 firms are observable for at least 3 years. From these, we have all the variables to estimate TFP for 210,000, from which we exclude 70,000 because they operate in Budapest.

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