each document is characterized by a vector of proportions, representing the share of the document associated with each topics. Using STM, we identify a set of 15 topics<sup>15</sup> discussed within the _gram sabhas_ , and explore how these topics vary with the identifiable characteristics of speakers and villages — including the gender of the speaker, the position of the speaker, and the reservation status of the village council president (female and/or Scheduled Caste). Te generated topics are presented in Table 2, which lists the highest probability words in each topic, as well as the FREX words, which are both frequent and exclusive, thereby identifying the words that distinguish topics.<sup>16</sup> Figure 1 presents the distribution of these topics across the full corpus.

Table 2: Top Word Stems by Topic

|Topic|Top Word Stems|
|---|---|
|Water<br>|Highest Prob: water, road, tank, street, get, facil, arrang<br>FREX: road,water,fx,tank, pipe,street,drink|
|Benefciary & Voter Lists|Highest Prob: get, give, given, card, name, person, list<br>FREX:give, get,card,name, given, poverti,receiv|
|Employment & Wages|Highest Prob: ask, peopl, work, one, told, talk, know<br>FREX: talk,told,ask,whatev,know,one,mistak|
|Service Failures|Highest Prob: come, tell, want, say, money, done, commot<br>FREX: say,tell,commot,money,want,come,bus|
|Greetings and Tanks|Highest Prob: presid, take, meet, panchayat, request, ofc, member<br>FREX: request,thank,hospit, particip, presid,conduct,meet|
|Ration Shop|Highest Prob: day, need, time, proper, shop, ration, petit<br>FREX: day,need,time, proper, petit,ration,shop|
|Housing and Land Titles|Highest Prob: hous, place, construct, month, pata, make, everi<br>FREX:pata,said,construct,hous, gave,remain,make|
|Allocation of Funds|Highest Prob: rupe, scheme, govern, panchayat, fund, amount, provid<br>FREX: rupe,amount,allot, govern,fund,thai,scheme|
|Toilet Construction|Highest Prob: build, toilet, land, built, govern, pay, use<br>FREX: build,built,toilet, pay,land,hall,maintain|
|Education|Highest Prob: school, villag, children, women, panchayat, complex, pass<br>FREX: school,children,complex,sanitari, pass,educ,villag|
|Intro to PVP|Highest Prob: group, loan, plf, regard, bank, vprc, inform<br>FREX: loan, plf,bank,vprc,regard,certif,appoint|
|SHGs|Highest Prob: women, group, peopl, panchayat, help, list, self<br>FREX: self,award, poor,status,help,women,survey|
|Environmental Protection|Highest Prob: scheme, hous, work, employ, subject, canal, select<br>FREX: canal, gandhi,subject,employ, guarante, propos,set|
|Announcements, Resolutions, and Voter’s Pledge|Highest Prob: sabha, gram, approv, panchayat, inform, place, report<br>FREX: sabha, gram,approv,audit,read,report, pledg|
|Maintenance of Public Goods|Highest Prob: panchayat, expens, discuss, use, regard, plastic, mainten<br>FREX: plastic, mainten, expens, releas, avoid, discuss, instal|

# A key challenge in the _text as data_ literature, particularly with unsupervised methods, lies

15Since this method assumes a fixed, user-specified number of topics, we first assess the relative performance of models under a range of values ( _K ∈_ 5 _,_ 50), and choose _K_ = 15 for the preferred specification. Tis specification performs relatively well on a number of empirical tests (residuals fit, held-out likelihood, semantic coherence, and exclusivity of topics), and yields topic clusters consistent with our substantive understanding of village assembly discussions. For robustness, we also show full results for _K_ =20 and _K_ =30 models in Appendix B.

16See Roberts et al. (2016) for a fuller explanation of FREX.

19