where our dependent variable ln w���� denotes the hourly nominal wage of individual _i_ , working in occupation _o_ , in industry _j_ that is in district _d_ .<sup>27</sup> It is important to note that _d_ denotes the district where the job, and not necessarily the worker is located. α� denotes a full set of 186 3-digit industry dummies (with industries as defined in the 2000 Indonesian Standard Industrial Classification, KBLI 2000), and α� a full set of 241 occupation dummies (with occupations as defined in the 1982 Indonesian Classification of Occupations, KJI 1982). Xi denotes a vector of individual worker characteristics that we can control for in our regressions. Specifically, we control for a worker’s age and age<sup>2</sup> , which can (among others) be considered as an (imperfect) measure of his/her overall workforce experience. We do not have information on the latter, but we always include a worker’s experience on his/her _current job_ and its square, which is measured as the number of years he/she has been working on the job, as well as a dummy variable indicating whether he/she ever held a job before his/her current job. Next, we control for a worker’s highest completed level of education,<sup>28</sup> as well as two dummy variables indicating whether he/she completed at least one, respectively two, additional formal training courses for which he/she got a certificate. Finally, we include a dummy variable indicating whether a worker is an “own account worker” or an employee.<sup>29</sup> Xd is a set of dummy variables for the 8 main Indonesian islands (groups)<sup>30</sup> that the district is located on, and ε���� captures all other unobserved nominal wage determinants.

Finally, S�� denotes our main independent variable of interest. In our main specification, we follow De la Roca and Puga (2017) in the specification of this variable. For all districts that belong to a metro area, it is defined as “ _the number of people living within 10 km of the average person in the metropolitan area, m, to which district, d, belongs_ ”, i.e. it takes the same value for all districts belonging to the same metro area. For all districts that do not belong to a metro area, it is defined as _“the number of people living within 10 km of the average person in the district, d”_ . Crucially, this variable varies depending on the algorithm/approach used to define metro areas. We use this variable instead of a simpler density measure, as it is (see De la Roca and Puga, 2017, p.112) better able to deal with the noise introduced by urban boundaries that vary across urban areas in their tightness around built-up areas. We calculate this variable using the same _Landscan_ - _2012_ gridded population data that we used to define metro areas using the AI and cluster algorithms (see Section 3.1 above).

27 SAKERNAS reports monthly income for sampled individuals. Based on this, and a person’s reported total working hours during the last week, we calculate his/her hourly wage as: (monthly income / (365/12))  (7/hours worked last week). SAKERNAS reports both monthly income earned in cash as well as in goods. In all results reported here, we use total monthly income, i.e. total monthly income in cash and in goods combined. All our findings are robust to using only total monthly income in cash (see e.g. Table A8 in Appendix A). This is not that surprising as the share of income earned in cash is 0.981 (1.0) for the average (median) worker. Only for less than 0.75 percent of workers does non-cash income make up more than 50 percent of their total income.

> 28 SAKERNAS indicates a worker’s highest level of completed education in one of 13 different categories of (completed) education. From lowest to highest level of education, these categories are: no schooling, incomplete primary school, primary school, package A, general junior high school, vocational junior high school, package B, general senior high school, vocational senior high school, package C, diploma I/II, diploma III, div/S1, and S2/S3 (university).

> 29 In our main sample, which we define below, the share of “own account workers” is about 30 percent. Our results are robust to only considering employees in our regressions. See e.g. Table A7 in Appendix A.

> 30 These islands (groups) are: Bali, Java, Kalimantan, Maluku, Nusa Tenggara, Papua, Sulawesi, and Sumatera.

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