Construction of the data and the variables for analysis

To analyze the effect of trading peers on firms’ trading decisions two datasets are com- piled. The first dataset will examine export at the country level, but not yet the product level. This will allow us to assess the importance of country level spillovers irrespec- tive of the product dimension. The other dataset will examine export behavior at the country-product level allowing for the examination of the specificity of spillover effects. This approach allows comparing spillovers at the product and country dimensions. This section explains the construction of the country-product dataset, the country level data is compiled in a similar fashion with the exception that the production dimension is omitted.

CEU

eTD

Collection

Dimensions

The main export dataset is where the unit of observation is a firm-product-country cell. The three dimensions are determined as follows. For the firm the longitudinal dimension is given by the range set by the firm’s minimum and maximum year observed in the balance sheet data. However, only those firms are kept which have exported at least once in their time present in the sample. That is, firms that never trade are excluded. The reason for this is purely technical. For firms where trading behavior cannot be observed, one cannot form choice sets of neither for products nor countries. For firms where trade information is available, the product and country dimensions take into account all the possibilities that can be described by the firms export behavior. A simple example will make this more clear. Assume that a firm exports two products,A and B. Product A is sold in countries a and b, while product B is sold to countries a and c. Then the choice set of the firm for a single year would look like this:

product A A B B

country a b a c

These choices are available for the firm for all years whenever it is observed. The construction process results in an unbalanced panel at the country-product-firm level. Notice that the country and product choices are restricted to those revealed by the firms, for two reasons. Once, what set of countries show demand for a particular product. This is known by the firm but not by the researcher. Second, the researcher does not know the exact product lines of individual firms. In this respect we can keep our focus on the timing of the entry.

Spillover variables

Variable peersir is defined as the number of firms in the same location r as firm i

engaged in the same export activity as the one firm i will enter. To allow for variation by product and country and capture the effect of agglomeration of exporters. Hence, four mutually exclusive variables are defined as the number of other firms in the same

region who18

• export good other than g to country other than k • export good g to a country other than k

• export good other than g to country k • export good g to country k

18_{The definition of spillovers are different from the ones used in the analysis of Koenig et al. (2010). They consider}

”all countries” rather than countries other than k. This modification allows us to incorporate all spillover variable in a single regressions and test their difference within one model.

CEU

eTD

Collection

The first variable in the list variable counts all other firms whose trade relation has no

common country or product element with Ti(r)gkt. The last variable in the list counts

all firms in the same locality who have the exact trade relationship firm i is considering establishing.

This definition allows the capturing of several aspects of the agglomeration economies. For example, depending on th e g and k dimension, it captures the range available varieties in a location that are sufficiently good for international markets and also the variety of trade connections with other countries. It proxies the density of productive, internationally active firms. It can proxy the availability of managers with exporting experience in general and specific country in a given product line. With that, it sums up the successful local strategies and market channels and the size of the pool of lo-

cal information. In section 3.5.4, an alternative definition of the peersir variable is

investigated.19

Table 3.3: Distribution of peers across firms by country in 2000

Dependent variable is: 0 1

Number of cells firms cells firms

with other country peer

0 14289 4229 1889 958

1-10 1721 948 517 384

11-20 1516 635 479 264

21-50 2833 886 942 468

over 50 12258 2823 3368 1464

with same country peer

0 19293 5135 3310 1565 1-10 7406 3246 2186 1367 11-20 1339 963 341 305 21-50 2369 1271 654 482 over 50 2210 1384 704 569 Cell is country-firm

The effect of the number of peers is the focus of the present paper and requires defining proximity. I define firms to be geographically close if their headquarters that most likely call on trade decisions are located in the same municipality (NUTS 5).

The following tables describe the distribution of spillover variables over the choice-sets defined in Section 3.4.3. Table 3.3 collects statistics for the number of peers by trading partner countries. The first two columns of the table describes distribution of choice

cells and the number of firms when the dependent variable is zero.20 _{The last columns}

show the analogous statistics for instances when the dependent variable is zero. The upper panel describes the spillover variable for other country trade, the lower panel for same country trade. The distribution of peers is bimodal in both firms and the number of cells. Most firms do not have any peers at all and up-to a point, the share of firms with high number of peers decreases. At the same time a significant share of firms have

19_{I will include the value of trade to give weight to local information or export strategies.}

20_{It is important to note that the distribution of the number of firms is not additive. As a firm can export to more}

CEU

eTD

Collection

also many peers. This second modus in the peer distribution is caused by the large share of firms located in the capital, Budapest. In this extreme case, that more than 500 firms are found, who start to export in 2000 have more than 50 peers that export to the same country.

To look at the more specific peer relations, Table 3.4 collects statistics on the product and country level distribution of peers. The Table is constructed analogously to 3.3, but instead of two, describes four spillover variables. First the different product peers, then the same product peers. In this table one can also observe the bimodal nature of firm number and cell distribution. Again, the reason for this is the capital, which holds significant share of all firms. At the same time, it also hold relatively more multi- country and multi-product firms that results in high numbers in the 21-50 or above 50 peer categories. As the definition of peers gets more narrow and more specific, the fewer and fewer number of firms and cell have higher and higher number of peers. However, in the most specific case, about a hundred different firms is detected who start exporting in 2000 and has between 21 to 50 peers, who have already traded the same good with the same country.

Table 3.4: Distribution of exporting firms by country and product, 2000

Dependent variable is: 0 1

Number of cells firms cells firms

with same country, different product peers

0 77186 7854 15070 3567

1-10 1995 934 751 408

11-20 1324 627 416 235

21-50 1682 803 586 353

over 50 12297 2070 3353 1098

with other country, different product peers

0 76942 7847 15001 3558

1-10 640 294 180 123

11-20 444 208 224 110

21-50 1211 513 409 236

over 50 15247 2503 4362 1356

with different country, same products peers

0 78356 7926 15411 3648

1-10 7840 2439 2432 1188

11-20 2770 982 760 400

21-50 4133 1139 1200 501

over 50 1385 495 373 200

with same country, same product peers

0 77609 7898 15659 3617 1-10 15607 3228 4121 1586 11-20 929 586 280 216 21-50 339 256 116 96 Cell is country-product-firm

3.5

Results

This section estimates spillovers first at the country level, then at the country and product level. For easier display of the results, the spillover variables are rescaled to

CEU

eTD

Collection

express figures in 1000 firms.