Table 3: Agglomeration effects for college graduates

|||Log|wage||
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Log work city size|0.0515<br>(0.0105)<sup>_∗∗∗_</sup>|||0.0214<br>(0.0112)<sup>_∗_</sup>|
|Log college city size||0.0796<br>(0.0051)<sup>_∗∗∗_</sup>||0.0714<br>(0.0077)<sup>_∗∗∗_</sup>|
|Log high school city size|||0.0399<br>(0.0067)<sup>_∗∗∗_</sup>|-0.0069<br>(0.0047)|
|Female|-0.1006<br>(0.0053)<sup>_∗∗∗_</sup>|-0.0967<br>(0.0049)<sup>_∗∗∗_</sup>|-0.1021<br>(0.0049)<sup>_∗∗∗_</sup>|-0.0966<br>(0.0048)<sup>_∗∗∗_</sup>|
|Ethnic origin|-0.1104<br>(0.0281)<sup>_∗∗∗_</sup>|-0.0787<br>(0.0258)<sup>_∗∗∗_</sup>|-0.0998<br>(0.0371)<sup>_∗∗∗_</sup>|-0.0763<br>(0.0258)<sup>_∗∗∗_</sup>|
|Technological school|-0.0842<br>(0.0228)<sup>_∗∗∗_</sup>|-0.1022<br>(0.0227)<sup>_∗∗∗_</sup>|-0.0839<br>(0.0222)<sup>_∗∗∗_</sup>|-0.1026<br>(0.0217)<sup>_∗∗∗_</sup>|
|t&tinstitute|-0.2235<br>(0.0317)<sup>_∗∗∗_</sup>|-0.2118<br>(0.0441)<sup>_∗∗∗_</sup>|-0.2231<br>(0.0339)<sup>_∗∗∗_</sup>|-0.2132<br>(0.0439)<sup>_∗∗∗_</sup>|
|Experience|0.1128<br>(0.0047)<sup>_∗∗∗_</sup>|0.1106<br>(0.0060)<sup>_∗∗∗_</sup>|0.1145<br>(0.0055)<sup>_∗∗∗_</sup>|0.1097<br>(0.0059)<sup>_∗∗∗_</sup>|
|Age indicators|Yes|Yes|Yes|Yes|
|Year indicators|Yes|Yes|Yes|Yes|
|2-digit sector indicators|Yes|Yes|Yes|Yes|
|Observations|190,817|190,817|190,817|190,817|
|_R_<sup>2</sup>|0.2183|0.2313|0.2128|0.2336|

_Notes_ : All specifications include a constant. Coefficients are reported with robust standard errors in parenthesis, which are clustered by urban area of work.<sup>_∗∗∗_</sup> ,<sup>_∗∗_</sup> , and<sup>_∗_</sup> indicate significance at the 1, 5, and 10 percent levels. t&t stands for technical and technological.

of 0.054 (Duranton, 2016).<sup>7</sup> In columns (2) and (3), we instead examine how wages vary based on the cities where individuals went to college or high school, respectively. In column (2) we obtain a higher elasticity of 0.08 for college city size, while in column (3) we obtain a lower elasticity of 0.04 for high school city size.

In column (4) we control for work city, college city, and high school city sizes. We find that the elasticity with respect to work city size shrinks from 0.052 to 0.021 and becomes only marginally significant, while the elasticity with respect to college city size remains relatively high at 0.071 and the elasticity with respect to high school city size becomes almost zero. Thus, agglomeration effects seem to arise from college city size rather than work city size.<sup>8</sup>

> 7It is worth noting that for many workers we observe wage data more than one year (usually two or three years). In all estimations we restrict the analysis to the last observed wage (often the second or third year after entering the labor market), to avoid capturing an internship spell that takes place immediately upon graduating from college. Given the reduced number of annual observations per worker and the sizeable number of workers with only one wage observation, we cannot include worker fixed effects in our estimations.

> 8We are aware that when we include simultaneously the three location variables, the coefficients on city sizes are estimated exclusively on the basis of migrants. It is not our purpose to provide causal estimates for each of these city sizes. At this point, we want to underscore the relatively larger effect that college city size has on wages. To unfold potential drivers for this large magnitude, we will later analyze location trajectories of migrants, with special emphasis on those who move for work to bigger and smaller cities.

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