# Early Flood Warning Using Satellite-Derived Convective System and
Precipitation Data - A Retrospective Case Study of Central Vietnam

###### Abstract

This paper addresses the challenges of an early flood warning caused by
complex convective systems (CSs), by using Low-Earth Orbit and Geostationary
satellite data. We focus on a sequence of extreme events that took place in
central Vietnam during October 2020, with a specific emphasis on the events
leading up to the floods, i.e., those occurring before October 10th, 2020. In
this critical phase, several hydrometeorological indicators could be
identified thanks to an increasingly advanced and dense observation network
composed of Earth Observation satellites, in particular those enabling the
characterization and monitoring of a CS, in terms of low-temperature clouds
and heavy rainfall. Himawari-8 images, both individually and in time-series,
allow identifying and tracking convective clouds. This is complemented by the
observation of heavy/violent rainfall through GPM IMERG data, as well as the
detection of strong winds using radiometers and scatterometers. Collectively,
these datasets, along with the estimated intensity and duration of the event
from each source, form a comprehensive dataset detailing the intricate
behaviors of CSs. All of these factors are significant contributors to the
magnitude of flooding and the short-term dynamics anticipated in the studied
region.

Index Terms—  Convective systems, Floods, Sentinel-1, Himawari-8, GPM IMERG,
WindSat, ASCAT, SMAP.

##  1 Introduction

Severe convective systems (CSs) are regularly observed in sub-tropical and
tropical regions, namely the Gulf of Guinea, the Gulf of Mexico, as well as
the whole Southeast Asia, Australia, Indian subcontinent, etc. They are
typically associated with extreme weather events such as intense
thunderstorms, heavy rainfall, and strong surface wind gusts. The development
of a CS often brings heavy precipitation over a small area in a short period,
leading to flash floods. These events pose significant hazards due to their
rapid onset, not allowing sufficient time for warnings and evacuations.
However, accurately nowcasting and forecasting CSs remains a major challenge
for meteorologists due to their sudden ignition and fast development. During
the last four decades, considerable progress has been made in addressing this
challenge, thanks to the arrival of many advanced GEOstationary (GEO)
satellites, including Meteosat, GOES, Himawari, and Gaofen, covering
Europe/Africa, Americas, and Asia-Pacific, respectively. Nevertheless, a more
comprehensive understanding of the intricate relationships between deep
convective clouds and extreme weather events is essential to enhance the
prediction of natural disasters such as destructive floods caused by heavy
rainfall.

Mesoscale CS tracking and identification are mainly based on the analyses of
brightness temperature and precipitation. [1, 2, 3] indicated the strong
relationship between deep convective clouds and strong surface winds via
intense downdrafts, based on the collocation of GEO (Meteosat) and Low-Earth
Orbit (LEO) Sentinel-1 and Aeolus satellite imagery. Furthermore, [2, 3]
showed that the high-resolution (HR) surface wind patterns, derived from
Sentinel-1 (S1) images, moved (horizontally) in the same direction as the deep
convective clouds detected by Meteosat. In particular, [3] discussed the
relationship between strong surface winds, heavy rainfall, and deep convection
through the collocation of S1 SAR, WindSat scatterometer, and Meteosat data.
They concluded that that intense surface winds were followed by heavy rainfall
associated with deep convection approximately 30 minutes later. Regarding the
relationship between deep convective clouds, heavy rainfall, and floods, [4]
utilized radar data to characterize convective storms and connected them to
the flood events that occurred on the northwestern Mediterranean coast. The
results showed that the area was mostly affected by shallow but efficient
convection, associated with high rainfall rates, which tend to exceed
infiltration capacities and/or saturate catchments quickly and thus lead to
fast run-off and flooding. [5] investigated the contribution of mesoscale CSs
to the selected flood cases in southern West Africa based on the analysis of
rainfall rates derived from brightness temperature obtained from the NOAA
Climate Data Record of Gridded Satellite Data. It indicated that floods in
this area had between 31% and 60% of contribution from mesoscale CSs.

This paper tackles the challenges of issuing an early flood warning based on
the analyses of multi-source LEO (SAR, scatterometers, and radiometers) and
GEO satellite data. The different sensors provide complementary observations
regarding hydrometeorological factors contributing to a flood event. This
study explores the correlation between deep convective clouds identified by
GEO satellites, patterns of strong surface winds, and heavy rainfall observed
by LEO satellites, all of which are associated with severe flooding. The
findings are validated through the mapping of flood extents using SAR
technology. A specific case study of the flood events from Oct.
$3^{\mathrm{rd}}$ to $10^{\mathrm{th}}$, 2020 that occurred in the central
region of Vietnam is carried out in this paper. As reported by the Red Cross
[6], this area experienced prolonged, heavy rains that caused severe and
widespread flooding and landslides in eight provinces between Oct.
$6^{\mathrm{th}}$ and $14^{\mathrm{th}}$, 2020. This was due to the
combination of numerous weather systems, including Inter Tropical Convergence
Zones (ICZ) combining with cold air, tropical storms, and typhoons.

##  2 Methodology

###  2.1 Data selection and preparation

Fig. 1 illustrates the region of interest (ROI), i.e.,
$14^{\circ}-20^{\circ}$N, $103^{\circ}-110^{\circ}$E, including eight
provinces of Vietnam, where flood events were observed starting from Oct.
$6^{\mathrm{th}}$, 2020 [7]. It also consists of the coastal areas from which
CSs are formed and developed before arriving on land and producing strong
surface winds and heavy rainfall. The ROI include the provinces Nghe An (NA),
Ha Tinh (HT), Quang Binh (QB), Quang Tri (QT), Thua Thien Hue (TT), Quang Nam
(QN1) and Quang Ngai (QN2), as well as the city of Da Nang (DN).

Fig. 1: Region of interest (ROI), including eight provinces and ocean areas of
Vietnam. Table 1: Satellite Images Used for Early Flood Warning - Case Study
of the Central Region of Vietnam, Oct. 2020.

Satellite | Type/Mode | Variable | Availability
---|---|---|---
Sentinel-1 | LEO/ | Wind, | Oct. 03, 05, 06
C-SAR | Flood extent | & 10
WindSat | LEO/ | Wind/ | Oct. 07 & 08
Radiometer | Precipitation |
ASCAT- | LEO/ | Wind | Oct. 03-10
A/B/C | Scatterometer |
SMAP | LEO/ | Wind | Oct. 04, 06, 07
Radiometer | & 09
Himawari-8 | GEO | Brightness | Oct. 03-10,
temperature | every 10 min
GPM IMERG | Multi-satellite | Precipitation | Oct. 03-10,
every 30 min

The satellite data, utilized to investigate the relationship between deep
convective clouds, strong surface winds, heavy rainfall, and flood events for
the period of Oct. 3${}^{\textnormal{rd}}$ to 10${}^{\textnormal{th}}$, 2020,
are shown in Table 1. To detect deep convective clouds that can produce strong
surface winds and heavy rainfall, we rely on the GEO Himawari-8 (H8) images
provided every 10 minutes. We use S1 SAR images (available on four different
days) to retrieve the HR surface wind speed and map flood extent. Fig. 2
presents the flooded areas observed along the coast of the QB and QT
provinces, Vietnam, on Oct. 10${}^{\textnormal{th}}$, 2020. They are mapped
using the S1 image acquired on the Oct. 10${}^{\textnormal{th}}$ with respect
to a reference non-flood S1 image acquired on Aug. 11${}^{\textnormal{th}}$,
2020. Recent research works [8, 9] on flood forecasting relied on S1-derived
flood extent map as validation and assimilation data. The radiometers WindSat
and SMAP also provide ocean surface wind speed and direction for 2 to 4 days,
at a coarser resolution. Similarly, using scatterometer data (ASCAT-A/B/C), we
can extract the wind speed over the ocean surfaces. WindSat data can also
offer precipitation data in addition to half-hourly GPM IMERG precipitation
accumulations [10].

Fig. 2: S1-derived flooded areas observed along the coast of QB and QT
provinces, Vietnam on Oct. 10${}^{\textnormal{th}}$, 2020.

###  2.2 CS Identification from LEO and GEO Images

####  2.2.1 Deep convective clouds

H8 images observe deep convective clouds that typically exhibit a low
brightness temperature between 200 and 220 K. They correspond to convective
wind patterns observed on LEO images, namely S1, WindSat, ASCAT-A/B/C, and
SMAP. The time-series of H8 images are also taken into account to detect cloud
movements (i.e., general direction and velocity) during extreme weather, and
track those with low temperature . This enables a broad assessment of the area
affected by the investigated CS, particularly in the presence of concurrent
heavy rainfall.

####  2.2.2 Surface convective wind patterns

Surface convective wind patterns can be derived from the images acquired by
SARs, scatterometers, and radiometers. The wind speed observed over sea
surfaces, potentially associated with the coldest convective clouds, can be
estimated using S1 images. Such wind speed can be up to 25 m/s. It has been
shown that the intense downdrafts associated with deep convective clouds can
induce surface wind gusts when they hit the surface of the sea [1, 2, 3]. In
other words, vertical winds assert the active level of a CS, as well as being
an element that induces heavier rainfall through downdrafts. The strong wind
gusts significantly impact sea surface roughness, leading to observable high-
intensity radar backscattering or areas with increased Normalized Radar Cross
Section (NRCS) in the images captured by SARs and scatterometers. Using
Geophysical Model Functions, e.g. CMOD for C-band, LMOD for L-band, and XMOD
for X-band, we can retrieve surface wind speed from NRCS, as well as wind
direction, and other radar parameters.

Regarding the radiometer data, surface wind speed is retrieved from the sea
surface brightness temperature measured by SMAP and WindSat. To this end, [8]
took into account the differences between measured sea surface brightness
temperatures and those of a flat ocean surface and matched these differences
to the Radiative Transfer Model (RTM). Once the differences in brightness
temperature and other parameters are determined, one can invert the RTM to
estimate surface wind speed.

####  2.2.3 Rainfall

In addition to strong surface winds, deep convective clouds have the potential
to generate substantial rainfall, a crucial factor contributing significantly
to the initiation of flash flood events, as discussed in [5]. In this study,
we utilized the precipitation accumulation data provided by the GPM IMERG [9]
and by WindSat (over the ocean surface). As demonstrated in [3], convective
rainfall may occur subsequent to the onset of strong surface winds, aligning
with the intensity of the deep convective clouds.

###  2.3 Data Fusion

Fig. 3: Data and variables of interest.

The combination of precipitation and brightness temperature has been leveraged
to identify, locate, and track mesoscale CSs. Fig. 3 summarizes this fusion
framework between the input data. Using H8 and GPM IMERG, in addition to S1 HR
and WindSat/ASCAT/SMAP low-resolution (LR) identification of surface wind
direction and speed, a series of typical operational questions can be answered
based on decision-level data fusion taking into account anecdotal evidence
from each dataset. Except brightness temperature, every variable of interest
can be estimated or measured from at least two satellites. This enables
enhanced spatial coverage and temporal sampling, particularly for areas with
restricted coverage or infrequent revisits. Surface wind speed estimates are
provided in LR by WindSat/ASCAT/SMAP and in HR by S1 data. They are to be
analyzed in conjunction with the location of low-temperature clouds and
presence of intense precipitation, constituting deep convections.
Precipitation measurements can also be either from GPM IMERG or from WindSat,
offering complementary rainfall observations over ocean surface regions.

##  3 Early Flood Warning Decision System

This section unveils the outcomes of satellite data analysis and integration,
focusing on the identification and surveillance of intense CSs and the
corresponding extreme weather events responsible for the floods witnessed in
the central region of Vietnam in October 2020. (Fig. 2). The identification of
the relationship between deep convective clouds and extreme weather events is
facilitated in recent years, thanks to GEO H8 data acquired every 10 minutes,
and continuous observations every 30 minutes from GPM IMERG Early Run.

Fig. 4 shows the collocation of H8, S1, and GPM IMERG images acquired on the
Oct 5th, 2020, i.e., before the reported flood events [7]. They show,
respectively, (a) cloud brightness temperature, (b) strong surface wind
patterns, and (c) heavy rainfall occurring in the region. The deepest
(coldest) convective clouds depicted by a brightness temperature of around 200
K (Fig. 4(a)), align with strong surface wind zones ranging from 18 to 25 m/s
along the coast (Fig. 4(b)), coupled with escalating rainfall accumulation
(Fig. 4(c)). Such a combination can be found in convection lines, typically
squalls [1, 2, 3] over the coastal areas. This substantiates the presence of a
CS that developed along the coast (effectively an Intertropical Convergence
Zone or ICZ), attributed to the S1-derived HR wind patterns and the frequent
temporal sampling provided by H8 and GPM IMERG data. Next, considering the
observed westward movement of these low-temperature clouds (Fig. 4(d)),
jointly with the coarse surface wind direction provided by WindSat, it can be
inferred that the area comprising four regions (namely TT, DN, QN1, and QN2)
was poised to experience substantial rainfall, potentially leading to floods
in the following days, with DN identified as the most vulnerable area. Using
GPM IMERG data, the heavy precipitation persisted in these areas (partly due
to the incoming tropical storm Linfa) until Oct. 9${}^{\textnormal{th}}$,
2020, when it became less severe in DN, QN1 and QN2.

Fig. 5 depicts the integration of H8, WindSat surface wind speed, and
rainfall, along with GPM IMERG images obtained on October
7${}^{\textnormal{th}}$, 2020. This timeframe falls after the occurrence of
the initial ICZ and before the onset of the series of typhoons/tropical
storms, which commenced on October 11${}^{\textnormal{th}}$, 2020, as
documented in [6, 7]. They depict, respectively, observed deep convective
clouds, strong surface winds, and intense precipitation. On the H8 image (Fig.
5(a)), as shown earlier in Fig. 4(a), we can identify the deep convective
clouds based on its low brightness temperature of approximately 200 K. It
should be noted that like GPM IMERG data, WindSat can provide precipitation
estimates over sea surfaces, but at a larger spatial resolution. Similarly,
the WindSat-derived surface wind maps are coarser than S1 ones; however, their
larger swath allow to observe a wider offshore wind area as well as identify
future incoming events.

At the same time and location, we observe strong surface wind patterns
(12.5–20 m/s) and heavy rainfall rate (8–13 mm/h) on the WindSat images (Fig.
5(b)-5(c)). GPM IMERG also confirms the intense precipitation present over the
whole coastal region. Continuous observations in these areas, with H8, GPM and
WindSat/ASCAT/SMAP between Oct. 7${}^{\textnormal{th}}$ and Oct.
10${}^{\textnormal{th}}$ suggests that these areas are likely to experience
prolonged periods of intense winds and heavy rainfall. The matching of these
observed variables demonstrates the strong association between deep convective
clouds and extreme weather events. This further supports the widespread
utilization of GEO images in combination with wind and rainfall data for the
early detection of flood events in regions or seasons where severe CSs occur
regularly.

(a) Himawari-8, 22:40 UTC

(b) S1, 22:36:04 UTC

(c) GPM, 22:30-23:00 UTC

(d) Extent of clouds

Fig. 4: Combination of satellite data for observing a severe CS and its
associated factors, on Oct. 5${}^{\textnormal{th}}$, 2020, including (a) deep
convective clouds detected by H8 band 10 (central wavelength: 7.35 $\mu$m),
(b) strong surface wind pattern observed by S1 (c) heavy rainfall extracted
from GPM IMERG, and (d) Extent of clouds under 220 K between 22:00-23:50 UTC
showing a westward direction.

In addition, concerning wind speed data over the studied ROI, a summary of
satellite observations leading up to Oct 10${}^{\textnormal{th}}$, 2020
combining S1, WindSat, ASCAT-A/B/C and SMAP data has been analyzed. In order
to harmonize the wind speed estimated from SARs and scatterometers, they are
categorized into “none”, “weak”, “moderate”, and “severe”, respectively
corresponding to less than 5 m/s, 5-10 m/s, 10-15 m/s, and greater than 15
m/s. From this collection, it can be noted that on the Oct.
6${}^{\textnormal{th}}$, 2020, there is an indication of wind getting stronger
(starting from Oct. 5${}^{\textnormal{th}}$ PM and subsequently reaching
severe levels in the afternoon of October 6${}^{\textnormal{th}}$. Over the
next three days, severe winds continue to be prevalent across the entire ROI,
In conjunction with the presence of observed low-temperature clouds and heavy
rainfall resulting from the initial Intertropical Convergence Zone (ICZ) and
tropical storm Linfa, it is evident that factors contributing to flooding have
accumulated, even though official warnings had not been issued at that time.
As a matter of fact, the areas impacted by heavy rainfall associated with many
observed severe CSs correspond to the detected flooded areas (Fig. 2).

(a) Himawari-8, 23:10 UTC

(b) WindSat, 23:12 UTC

(c) WindSat, 23:12 UTC

(d) GPM, 23:00-23:30 UTC

Fig. 5: Combination of various satellite data for observing a severe CS and
its associated factors, on Oct. 7${}^{\textnormal{th}}$, 2020: (a) deep
convective clouds detected by H8 band 10 (central wavelength: 7.35 $\mu$m),
(b) strong surface wind pattern and (c) heavy rainfall observed by WindSat,
and (d) heavy rainfall extracted from GPM IMERG data.

##  4 Conclusions

This paper presents a preliminary concept of an early flood warning system
that is based on the use of multiple satellite data, namely Low-Earth Orbit
(LEO) imagery data and Geostationary (GEO) imagery data. We examine a series
of extreme events that transpired in central Vietnam in October 2020, with a
specific focus on the period leading up to it (before Oct. $10^{\mathrm{th}}$,
2020). Within these events, various hydrometeorological indicators could be
identified in advance, particularly those capable of tracking and monitoring
extreme events, such as low-temperature clouds and prolonged heavy rainfall in
specific areas. If these pertinent indicators had been provided in a timely
manner, they could have been valuable for forecasting the geographical extent
and severity of the ensuing extreme flood events Therefore, in this research
work, we investigated the collocation of different LEO/GEO datasets in order
to determine the causality between intense rainfall, low-temperature clouds,
strong wind speed, and the potential for causing substantial flooding in the
same location. This work demonstrates the feasibility of issuing a warning
between several hours and one day in advance due to the presence of high
rainfall rates and low-temperature clouds covering large coastal regions. In
addition, utilizing H8 time-series of brightness temperature images, it
becomes viable to forecast the trajectory and velocity of these convective
low-temperature clouds moving from the sea towards the land. This allows us to
identify the regions susceptible to significant rainfall. Lastly, using
different low-resolution scatterometers in combination with the HR wind speed
estimation from S1 images, local wind patterns and CS effects could be
derived, showing the severity of wind in the studied areas.

##  5 Acknowledgment

This work is supported by the Luxembourg National Research Fund (FNR) in the
framework of the CORE project C20/SR114703579.

## References

  * [1] T. V. La and C. Messager, “Convective system dynamics viewed in 3d over the oceans,” _Geophysical Research Letters_ , vol. 48, no. 5, p. e2021GL092397, 2021.
  * [2] ——, “Convective system observations by leo and geo satellites in combination,” _IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing_ , vol. 14, pp. 11 814–11 823, 2021.
  * [3] ——, “Different observations of sea surface wind pattern under deep convection by sentinel-1 sars, scatterometers, and radiometers in collocation,” _IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing_ , vol. 15, pp. 3686–3696, 2022.
  * [4] A. del Moral, M. del Carmen Llasat, and T. Rigo, “Connecting flash flood events with radar-derived convective storm characteristics on the northwestern mediterranean coast: knowing the present for better future scenarios adaptation,” _Atmospheric Research_ , vol. 238, p. 104863, 2020\.
  * [5] W. A. Atiah, L. K. Amekudzi, and S. K. Danuor, “Mesoscale convective systems and contributions to flood cases in southern west africa (swa): A systematic review,” _Weather and Climate Extremes_ , p. 100551, 2023.
  * [6] International Federation of Red Cross And Red Crescent Societies (IFRC), “Vietnam: Floods - Final Report, Operation n° MDRVN020 ,” https://reliefweb.int/report/viet-nam/vietnam-floods-final-report-operation-n-mdrvn020, accessed: 2024-XX-XX.
  * [7] Viet Nam Disaster Management Authority (VNDMA), “Flash update no. 4: Viet Nam floods, landslides and storms (as of 28 October 2020),” https://phongchongthientai.mard.gov.vn/en/Pages/flash-update-no-4-viet-nam-floods-landslides-and-storms-as-of-28-october-2020-.aspx, accessed: 2024-XX-XX.
  * [8] T. H. Nguyen, S. Ricci, C. Fatras, A. Piacentini, A. Delmotte, E. Lavergne, and P. Kettig, “Improvement of Flood Extent Representation With Remote Sensing Data and Data Assimilation,” _IEEE Transactions on Geoscience and Remote Sensing_ , vol. 60, pp. 1–22, 2022.
  * [9] T. H. Nguyen, S. Ricci, C. Fatras, A. Piacentini, E. Simon, R. Rodriguez Suquet, and S. Pena Luque, “Gaussian Anamorphosis for Ensemble Kalman Filter Analysis of SAR-Derived Wet Surface Ratio Observations,” _IEEE Transactions on Geoscience and Remote Sensing_ , vol. 62, no. 1-21, 2023\.
  * [10] T. Meissner, L. Ricciardulli, and F. J. Wentz, “Capability of the smap mission to measure ocean surface winds in storms,” _Bulletin of the American Meteorological Society_ , vol. 98, no. 8, pp. 1660–1677, 2017.


* Bhattacharya (2021) Bhattacharya, Jyotirmoy, “Indian Urban Workers’ Labour Market Transitions,” 2021.  Working Paper, arXiv:2110.05482.
  * Black et al. (2020) Black, Sandra E., Jeffrey T. Denning, and J. Rothstein, “Winners and Losers? The Effect of Gaining and Losing Access to Selective Colleges on Education and Labor Market Outcomes,” 2020.  R&R, _American Economic Journal: Applied Economics_.
  * Bloom et al. (2012) Bloom, Nicholas, Rafaella Sadun, and John Van Reenan, “The Organization of Firms Across Countries,” The Quarterly Journal of Economics, 2012, 127 (4), 1663–1705.
  * Bohren et al. (2022) Bohren, Aislinn J., Peter Hull, and Alex Imas, “Systemic Discrimination: Theory and Measurement,” NBER Working Paper, 2022.
  * Brown et al. (2011) Brown, A., C. Merkl, and D. J. Snower, “Job Market Signalling,” Labour Economics, 2011, 18 (2), 168–179.
  * BusinessLine (2018) BusinessLine, “An Inclusive India Inc.,” 2018.  Business Line, January 13, 2018.
  * Cahuc et al. (2014) Cahuc, P., S. Carcillo, and T. Le Barbanchon, “Do Hiring Credits Work in Recessions? Evidence from France,” 2014.  IZA Discussion Paper 8330.
  * Chakravartty and Subramanian (2021) Chakravartty, Paula and Ajantha Subramanian, “Why is Caste Inequality Still Legal in America?,” 2021.  _The New York Times_ , May 25, 2021.
  * Clark (2015) Clark, Gregory, The Son Also Rises: Surnames and the History of Social Mobility, The Princeton Economic History of the Western World, 2015.
  * Correa et al. (2019) Correa, Juan A., Pablo Gutiérrez, Miguel Lorca, Raúl Morales, and Francisco Parro, “The Persistent Effect of Socioeconomic Status on Education and Labor Market Outcomes: Evidence from Chile’s Administrative Records,” Applied Economic Analysis, 2019, 27 (79), 63–90.
  * Datta (2017) Datta, Surja, A History of the Indian University System: Emerging from the Shadows of the Past, Palgrave Macmillan, 2017.
  * Dayanandan et al. (2019) Dayanandan, Ajit., Han Donker, and Ravi Saxena, “The Role of Caste for Board Membership, CEO, and Interlocking,” Pacific-Basin Finance Journal, 2019, 54, 29–41.
  * Deshpande (2011) Deshpande, Ashwini, The Grammar of Caste: Economic Discrimination in Contemporary India, Oxford: Oxford University Press, 2011.
  * Deshpande and Newman (2007) and Katherine Newman, “Where the Path Leads: The Role of Caste in Post-University Employment Expectations,” Economic and Political Weekly, 2007, 42 (41), 4133–4140.
  * Economic Survey (2021-22) Economic Survey, “Economic Survey,” 2021-22.  Ministry of Finance, Government of India.
  * Eschleman et al. (2014) Eschleman, Kevin J., Jamie Madsen, Gene Alarcon, and Alex Barelka, “Benefiting from Creative Activity: The Positive Relationships Between Creative Activity, Recovery Experiences, and Performance-Related Outcomes,” Journal of Occupational and Organizational Psychology, 2014, 87 (3), 579–598.
  * Fernandez (2018) Fernandez, Marilyn, The New Frontier: Merit vs. Caste in the Indian IT Sector, Oxford: Oxford University Press, 2018.
  * Fisher v. University of Texas (2016) Fisher v. University of Texas, “Fisher v. University of Texas,” 2016\.  579 U.S. —.
  * Freeman et al. (2011) Freeman, Jonathan B., Andrew M. Penner, Aliya Saperstein, Matthias Scheutz, and Nalini Ambady, “Looking the Part: Social Status Cues Shape Race Perception,” PLOS ONE, 2011, 6 (9), 1–10.
  * Frisancho Robles and Krishna (2015) Frisancho Robles, Veronica C. and Kala Krishna, “Affirmative Action in Higher Education in India: Targeting, Catch Up, and Mismatch,” Higher Education, 2015.
  * Gaddis (2017) Gaddis, S. Michael, “How Black Are Lakisha and Jamal? Racial Perceptions from Names Used in Correspondence Audit Studies,” Sociological Science, 2017, 4 (19), 469–489.
  * Gould (2010) Gould, William, Bureaucracy, Community and Influence in India: Society and the State, 1930s - 1960s, Routledge Studies in South Asian History, 2010.
  * Gumperz (1961) Gumperz, John J., “Speech Variation and the Study of Indian Civilization,” American Anthropologist, 1961, 63 (5), 973–988.
  * Hazell et al. (2021) Hazell, Jonathan, Christina Patterson, Heather Sarsons, and Bledi Tsaka, “National Wage Setting,” 2021.  Working Paper.
  * Hoffman et al. (2018) Hoffman, Mitchell, Lisa B. Kahn, and Danielle Li, “Discretion in Hiring,” The Quarterly Journal of Economics, 2018, 133 (2), 765–800.
  * Howell (2010) Howell, J., “Assessing the Impact of Eliminating Affirmative Action in Higher Education,” Journal of Labor Economics, 2010, 28 (1), 113–166.
  * India Today (2015) India Today, “Pre-Placement Offers Trends in Elite Indian Colleges,” 2015.  Accessed July 27, 2022.
  * Jayaraman (2005) Jayaraman, R., “Personal Identity in a Globalized World: Cultural Roots of Hindu Personal Names and Surnames,” The Journal of Popular Culture, 2005, 38 (3), 476–490.
  * Jodhka (2008) Jodhka, Surinder S, “Caste & Corporate Sector,” Indian Journal of Industrial Relations, 2008, 44 (2), 185–193.
  * Jodhka and Naudet (2019) Jodhka, Surinder S. and Jules Naudet, Mapping the Elite: Power, Privilege, and Inequality, Oxford University Press, 2019.
  * Jodhka and Newman (2007) and Katherine Newman, “In the Name of Globalisation: Meritocracy, Productivity and the Hidden Language of Caste,” Economic and Political Weekly, 2007, 42 (41), 4125–4132.
  * Jodkha (2017) Jodkha, Surinder S., Caste in Contemporary India, Routledge India, 2017\.
  * Kapor (2022) Kapor, Adam J., “Distributional Effects of Race-Blind Affirmative Action,” 2022.  R&R, _Econometrica_.
  * Kline et al. (2021) Kline, Patrick M., Evan K. Rose, and Christopher R. Walters, “Systemic Discrimination Among Large U.S. Employers,” 2021.  R&R, _The Quarterly Journal of Economics_.
  * Kothari Committee Report (1976) Kothari Committee Report, “Kothari Committee Report,” 1976.  Report of the Committee on Recruitment Policy and Selection Methods, Union Public Service Commission, 1976.
  * Kothari (2013) Kothari, Rima, “Caste in a Casteless Language? English as a Language of ‘Dalit’ Expression,” Economic and Political Weekly, 2013, 48 (39), 60–68.
  * Lang and Spitzer (2020) Lang, Kevin and Ariella Kahn-Lang Spitzer, “How Discrimination and Bias Shape Outcomes,” Future of Children, 2020, 30 (1), 165–190.
  * Loury (2002) Loury, Glenn C., “The Anatomy of Racial Inequality,” 2002.  Harvard University Press.
  * Madheswaran (2008) Madheswaran, S., “Is Affirmative Action Policy for Private Sector Necessary?,” Indian Journal of Industrial Relations, 2008, 44 (2), 164–184.
  * Mamgain (2019) Mamgain, Rajendra P., Formal Labour Market in Urban India: Job Search, Hiring Practices and Discrimination, SAGE Publications Pvt. Ltd, 2019\.
  * Mamidi (2011) Mamidi, Pavan, “Strategic Manipulation of Caste Identity Signals,” 2011\.  Doctoral Dissertation, Oxford University.
  * Mandal Commission Report (1980) Mandal Commission Report, “Mandal Commission Report,” 1980.  New Delhi: National Commission for Backward Classes, Government of India. November 1, 1980. Retrieved March 26, 2019.
  * McArthur (2021) McArthur, Heather V., “Hiring For The Future: A Playbook For Building A Diverse, Inclusive & Equitable Workforce,” 2021.  _Forbes_ , March 18, 2021.
  * Mishra (2015) Mishra, Neha, “India and Colorism: The Finer Nuances,” Washington University Global Studies Law Review, 2015, 14, 725–750.
  * Mocanu (2022) Mocanu, Tatiana, “Designing Gender Equity: Evidence from Hiring Practices and Committees,” 2022.  Working Paper.
  * National Sample Survey Organisation (2001) National Sample Survey Organisation, “Concepts and Definitions Used in NSS,” 2001.  Ministry of Statistics & Programme Implementation, Government of India.
  * Naukri.com (2019) Naukri.com, “10 Common Interview Questions & Answers for Freshers,” 2019.  Naukri.com, October 9, 2019.
  * Newman and Thorat (2012) Newman, K. and S. Thorat, Blocked by Caste: Economic Discrimination in Modern India, Oxford University Press, 2012.
  * Parameswaran and Cardoza (2015) Parameswaran, R. and K. Cardoza, “Melanin on the Margins: Advertising and the Cultural Politics of Fair/Light/White Beauty in India,” Journalism & Communication Monographs, 2015, 11 (3), 213–274.
  * Press Trust of India (2019) Press Trust of India, “Nearly Seven Lakh Vacant Posts in Central Government Departments: Personnel Ministry,” 2019.  The Economic Times, November 21, 2019.
  * Ransubhe (2018) Ransubhe, Suryanarayan, Sociology of Translation, Atulya Publications, 2018.
  * Rao (2016) Rao, Yogita, “31 Firms, Mostly Startups, Can’t Hire on IIT Campuses This Year,” 2016.  _Times of India_ , August 26, 2016.
  * Report of the Expert Group on Diversity Index (2008) Report of the Expert Group on Diversity Index, “Report of the Expert Group to Propose a Diversity Index and Workout the Modalities of Implementation,” 2008.  Submitted to Ministry of Minority Affairs Government of India.
  * Rivera (2012) Rivera, Lauren A., “Hiring as Cultural Matching: The Case of Elite Professional Service Firms,” American Sociological Review, 2012, 77 (6), 999–1022.
  * Rivera (2015) , Pedigree: How Elite Students Get Elite Jobs, Princeton University Press, 2015.
  * Rose (2022) Rose, Evan K., “A Constructivist’s Perspective on Empirical Discrimination Research,” 2022.  Forthcoming, _Journal of Economic Literature_.
  * Sachar Committee (2006) Sachar Committee, “Social, Economic and Educational Status of the Muslim Community,” 2006.  Prime Minister’s High Level Committee, Cabinet Secretariat, Government of India.
  * Sarkar et al. (2017) Sarkar, Sudipa, Soham Sahoo, and Stephan Klasen, “Employment Transitions of Women in India: A Panel Analysis,” 2017.  IZA Discussion Paper No. 11086.
  * Sarsons (2022) Sarsons, Heather, “Interpreting Signals in the Labor Market: Evidence from Medical Referrals,” 2022.  Working Paper.
  * Sarsons et al. (2022) , Jonas Hjort, and Xuan Li, “Across-Country Wage Compression in Multinationals,” 2022.  R&R, _American Economic Review_.
  * Savage (2015) Savage, Mike, Social Class in the 21st Century, Pelican Books, 2015\.
  * SFFA v. Harvard (2021) SFFA v. Harvard, “SFFA v. Harvard,” 2021.  Docket nos. 20-1199 and 21-707.
  * Singh (2018) Singh, Aniket, The Complete Book Of Internships in India: Intern Abroad This Summer, Notion Press Inc., 2018.
  * Stevens (2009) Stevens, Michael P., Creating a Class: College Admissions and the Education of Elites, Harvard University Press, 2009.
  * Subramanian (2019) Subramanian, Ajantha, The Caste Of Merit: Enginnering Education in India, Harvard University Press, 2019.
  * The State of Inequality in India Report (2022) The State of Inequality in India Report, “The State of Inequality in India Report,” 2022.  Economic Advisory Council to the Prime Minister, Government of India.
  * Train (2003) Train, Kenneth, Discrete Choice Models with Simulation, Cambridge University Press, 2003.
  * Wiltshire (2020) Wiltshire, Caroline R., Uniformity and Variability in the Indian English Accent, Cambridge University Press, 2020.
  * World Bank (2020) World Bank, “World Bank “Ease of Doing Business” Rankings,” 2020.  Accessed June 25, 2022.
  * Yale School of Management (2021) Yale School of Management, “Yale SOM Employment Data,” 2021.  Accessed July 26, 2022.

ONLINE APPENDIX

##  Appendix A Employer Registration Form

  * Step 1:

After the invitation, companies should register basic details using the online
portal.

  * Step 2:

Register basic details on online portal:

A) Company Details:

    * –

Company Name* :

    * –

Password* :

    * –

Confirm Password* :

    * –

Website* :

B) Contact Details:

    * –

Name* :

    * –

Designation* :

    * –

Contact Number* :

    * –

Address* :

  * Step 3:

After registering basic details, companies should enter job details, and
select majors who qualify to apply.

A) Job Details:

    * –

Job Designation* :

    * –

Offer Types* : Domestic $\square$ International $\square$

    * –

Startup* : Yes $\square$ No $\square$

    * –

Job Description* : Job_Details.pdf [details of non-pecuniary amenities here]

    * –

Probable number of slots per job*:

B) Select the Majors you wish to recruit from:

    * –

Bachelor of Technology:
All $\square$ Electrical Eng. $\square$ Aerospace Eng. $\square$ Mechanical
Eng. $\square$
Metallurgical Eng. $\square$ Civil Eng. $\square$ Material Eng. $\square$
Ocean Eng. $\square$ Computer Science $\square$

    * –

Dual Degree:
All $\square$ Electrical Eng. $\square$ Aerospace Eng. $\square$ Mechanical
Eng. $\square$
Metallurgical Eng. $\square$ Civil Eng. $\square$ Material Eng. $\square$
Ocean Eng. $\square$ Computer Science $\square$

    * –

Master of Technology:
All $\square$ Electrical Eng. $\square$ Aerospace Eng. $\square$ Mechanical
Eng. $\square$
Metallurgical Eng. $\square$ Civil Eng. $\square$ Material Eng. $\square$
Ocean Eng. $\square$ Computer Science $\square$

    * –

Master of Science:
All $\square$ Physics $\square$ Chemistry $\square$ Mathematics $\square$

C) Salary Details:

Degree | Gross Annual Pay | Gross Monthly Pay | Additional Comments
---|---|---|---
Bachelor of Technology |  |  |
Dual Degree |  |  |
Master of Technology |  |  |
Master of Science |  |  |

##  Appendix B Modeling Job Applications

The difference in composition of job applications across castes is not
economically significant in my setting. However, I show below that the model
can be extended to incorporate job application behavior. Therefore, the
decision to omit job applications is not a restriction on the generality of my
model of the job placement process.

Choosing Jobs Instead of Job Portfolios. The key trick in modeling job
application behavior is to convert the student’s search from one over
potential _job application portfolios_ to one over _jobs_. The intuition is
simple: for any job a student applied to, the expected marginal benefit from
adding the job to his application vector should exceed the cost of applying to
the job.

Let $A_{i}$ denote the application vector of student $i$. Following the
notation in Howell (2010), define

$A_{i/k}=\begin{cases}\\{m|m\in A_{i},m\neq k\\}\hskip
8.5359pt&\text{if}\hskip 5.69046ptk\in A_{i}\\\ \\{m|m\in
A_{i}\\}\cup\\{k\\}\hskip 8.5359pt&\text{if}\hskip 5.69046ptk\notin
A_{i}\end{cases}$ (OA.1)

Then, it must be true that

$\displaystyle MV_{i/k}>0\hskip 8.5359pt\forall k\in A_{i}$ (OA.2)
$\displaystyle MV_{i/p}<0\hskip 8.5359pt\forall p\notin A_{i}$ (OA.3)

$MV_{i/k}=V(A_{i})-V(A_{i/k})$ denotes the marginal value from modifying the
application vector according to Equation OA.1 above. To make the computation
tractable, one proceeds by reducing the search space by eliminating dominated
strategies. Following Howell (2010), we categorize strategies into four main
categories: adjacent, non-adjacent, single-swap, and multiple-swap strategies.

Consider an application vector, $A_{i}=\\{\text{Goldman
Sachs},\text{Microsoft},\text{Google}\\}$. Removing “Goldman Sachs” from the
application vector is an _adjacent strategy_. Removing both “Goldman Sachs”
and “Google” from the application vector is a _non-adjacent strategy_.
Replacing “Goldman Sachs” with “Facebook” in the application vector is a
_single-swap strategy_. Replacing “Goldman Sachs” and “Microsoft” with
“Facebook” and “Uber” in the application vector is a _multiple-swap strategy_.

Howell (2010) shows that if a student’s application strategy is preferred to
all adjacent and single-swap strategies, then it will also be preferred to all
non-adjacent and multiple-swap strategies. Hence, to begin with, a student
only needs to examine $J$ application patterns and find the first job to apply
to. Next, he needs to evaluate $J-1$ applications and find the second job to
apply to and so on. At most, he needs to evaluate a total of
$J+(J-1)+\dots+2+1=\frac{J(J+1)}{2}$ applications. The complexity of the
problem is reduced dramatically. When searching over _job portfolios_ , the
complexity of the problem is $\mathcal{O}(2^{J})$, where $J$ is the number of
jobs. However, when searching over _jobs_ , the complexity of the problem is
only $\mathcal{O}(J)$, where $J$ is the number of jobs. The cost of job
applications can then be modeled in a manner similar to Howell (2010).
Finally, a logit kernel smoother is used to obtain closed form solutions
(Train; 2003).

##  Appendix C Calculation of Job Offer Probabilities

In this section, I show how to calculate job offer probabilities, which take
into account the key features of the job placement process.

Let $A^{k}_{i}$ be a vector of indicators that takes the value 1 if student
$i$ applies to a job allotted interview day $k$.OA.1OA.1OA.1Recall, a “job”
means a job designation within a firm. Similarly, let $Z^{k}_{i}$ be a vector
of indicators that takes the value 1 if student $i$ gets accepted from a job
allotted interview day $k$. Taking student applications as given, job $j$
accepts student $i$ on interview day $k$ with probability $\pi^{i}_{j}$, which
depends upon both student and job characteristics.

For a given interview day allotment to firms, define the probability of
interview day $k$ job offers given interview day $k$ job applications,
conditional on being eligible for an interview day $k$ job offer, by

$\displaystyle
f_{k}(Z^{k}_{i}|A^{k}_{i})=\prod_{j=1}^{J}\bigg{(}A^{k}_{ij}\bigg{[}\pi^{i}_{j}Z^{k}_{ij}+(1-\pi^{i}_{j})(1-Z^{k}_{ij})\bigg{]}+(1-A^{k}_{ij})(1-Z^{k}_{ij})\bigg{)}.$
(OA.4)

From Section 7.1, it is reasonable to assume interview day allotments to jobs
as exogenous. However, for the purposes of the illustration of the formula for
job offer probabilities, it will be easier to also assign probabilities to
interview day allotments.

Recall that $Z_{i}$ is the offer vector for student $i$ and $A_{i}$ is the
application vector for student $i$. Let $f(Z_{i}|A_{i})$ denote the
probability of realizing $Z_{i}$ given $A_{i}$. Then, $f(Z_{i}|A_{i})$ is
defined as

$\displaystyle
f(Z_{i}|A_{i})=\begin{cases}\prod_{l=0,1,\dots,K}\widetilde{f}_{l}(\underbrace{(0,0,\dots,0)}_{J}|A_{i})\hskip
5.69046pt\hskip 56.9055pt&\text{if}\hskip 2.84544ptZ_{ij}=0\hskip
2.84544pt\forall j\\\
\sum_{k=1}^{K}\bigg{(}\prod_{l=0,1,\dots,k-1}\widetilde{f}_{l}(\underbrace{(0,0,\dots,0)}_{J}|A_{i})\bigg{)}\widetilde{f}_{k}(Z_{i}|A_{i})\hskip
56.9055pt&\text{else},\end{cases}$ (OA.5)

where $\widetilde{f}_{l}(Z_{i}|A_{i})=\sum_{\\{m:Z_{i}\times
D^{l}_{m}=Z_{i}\\}}\text{Pr}(D^{l}_{m})f_{l}(Z^{l}_{i}|A^{l}_{i})$ is the
probability of realizing the offer vector $Z_{i}$ on interview day $l$,
$D^{l}_{m}$ is a collection of indicator variables denoting a possible
interview day assignment for day $l$ and $m=1,\dots,2^{|\\{1,\dots,J\\}|}$.
For completeness,

  * (1)

Let $\widetilde{f}_{0}(\underbrace{(0,0,\dots,0)}_{J}|A_{i})=1$.

  * (2)

If, for a given $k$, there is no such $m$ such that $Z_{i}\times
D^{k}_{m}=Z^{k}_{i}$, then set
$\sum_{\\{m:Z_{i}\times
D^{k}_{m}=Z^{k}_{i}\\}}\text{Pr}(D^{k}_{m})f_{k}(Z^{k}_{i}|A^{k}_{i})=0$.

The term
$\prod_{l=0,1,\dots,k-1}\widetilde{f}_{l}(\underbrace{(0,0,\dots,0)}_{J}|A_{i})$
is the probability that student $i$ is eligible for a job offer on interview
day $k$.

##  Appendix D Jobs Follow Cutoff Hiring Rules

######  Proposition 1.

Each job $j$ follows a cutoff hiring rule denoted by $\underline{k^{*}_{j}}$
and hires a student $i$ iff $V_{ij}>\underline{k^{*}_{j}}$.

###### Proof.

The proof follows from Kapor (2022). I prove the proposition above by
contradiction. Let Hire$\\{j\\}:\\{1,\dots,I\\}\rightarrow[0,1]$ be a hiring
rule used by job $j$ that satisfies Equation 9. Suppose it is not a cutoff
rule. Then there exist two students $i$ and $i^{\prime}$ such that
$V_{ij}>V_{i^{\prime}j}$ but Hire$\\{j\\}(i)<1$ and
Hire$\\{j\\}(i^{\prime})>0$. Let $P_{ij}$ and $P_{i^{\prime}j}$ denote the
probabilities that students $i$ and $i^{\prime}$ accept offers from job $j$.
Then, for some $\epsilon>0$, it is feasible for job $j$ to increase
Hire$\\{j\\}(i)$ by $\frac{\epsilon}{P_{ij}}$, reduce
Hire$\\{j\\}(i^{\prime})$ by $\frac{\epsilon}{P_{i^{\prime}j}}$, and increase
overall cohort quality. ∎

##  Appendix E Estimation Details and Standard Errors

I describe each of the choice probabilities below, the likelihood function to
be estimated, and the estimation method.

Job Choice by Students. Conditional on $q_{i}\sim N(0,\sigma^{2}_{q})$ and
given the assumption that each element in the vector of job acceptance shocks,
$\epsilon_{i}$, follows independent Type-1 extreme value distributions, the
probability of student $i$ choosing job $j$ at the job choice stage is

$\displaystyle\text{Pr}(C^{*}_{i}=j|X_{ij},w_{j},\text{NP}_{j},q_{i})=\frac{\text{exp}(u_{ij})}{\sum_{k\in\mathcal{O}(Z_{i})}\text{exp}(u_{ik})},$
(OA.6)

where $\mathcal{O}(Z_{i})$ denotes the offer set of student $i$, $X_{ij}$ is
the vector of student and firm characteristics, $w_{j}$ is the (log) salary,
$\text{NP}_{j}$ is the vector of non-pecuniary amenities, and
$u_{ij}=X_{ij}^{\prime}{\beta}+\text{NP}_{j}^{\prime}{\Psi}+w_{j}\tau+q_{i}+q_{i}\times\sum_{m=1}^{M}\gamma_{m}\text{NP}_{jm}$.

Student Choice by Jobs. Conditional on $q_{i}\sim N(0,\sigma^{2}_{q})$ and
given the assumption that the idiosyncratic match specific term $\mu_{ij}$
between student $i$ and job $j$ follows a standard logistic distribution, the
probability of student $i$ getting accepted from job $j$ is

$\displaystyle\pi^{i}_{j}(S_{ij},w_{j},q_{i},\underline{k^{*}_{j}})=\frac{\text{exp}(S_{ij}^{\prime}{\alpha}+\text{Disadv.
Caste}_{i}\times\eta-
w_{j}\phi+q_{i}\delta-\underline{k^{*}_{j}})}{1+\text{exp}(S_{ij}^{\prime}{\alpha}+\text{Disadv.
Caste}_{i}\times\eta-w_{j}\phi+q_{i}\delta-\underline{k^{*}_{j}})},$ (OA.7)

where $S_{ij}$ includes student and job characteristics. Student
characteristics include controls for pre-college skills, within-college
academic performance, previous labor market experience, and other employer-
relevant skills, including indicators for whether the student qualified got
past the application reading, written aptitude test, or group debate stage.
Job characteristics include indicators for the job sector and the entire set
of over 50 non-pecuniary amenities, as applicable. These job characteristics
are usually interacted with student characteristics entering $S_{ij}$. The
term $w_{j}$ denotes the (log) salary offered by job $j$, $q_{i}$ is
econometrician-unobserved student-level attributes, and
$\underline{k^{*}_{j}}\equiv\underline{k^{*}_{j}}(w_{j},\overline{X_{j}})$ is
a job-specific cutoff that is estimated for each job $j$ with the vector
$\overline{X_{j}}$ denoting features of the job besides wage. Let
$f(Z_{i}|A_{i})$ denote the probability of realizing a job offer vector
$Z_{i}$ given an application vector $A_{i}$. The formula for $f(Z_{i}|A_{i})$
is shown in Appendix Section C.

Likelihood. Let $\theta$ denote the parameters to be estimated. The complete
likelihood contribution of student $i$ with endogenous job offers and job
choices, $(Z^{*}_{i},C^{*}_{i})$, is given by

$\displaystyle\mathcal{L}_{i}(Z^{*}_{i},C^{*}_{i}|A_{i},\overline{X_{i}},\theta)=$
$\displaystyle\int_{q}f(Z^{*}_{i}|A_{i},\overline{X_{i}},q,\theta)\times\text{Pr}(C^{*}_{i}=j|Z^{*}_{i},\overline{X_{i}},q,\theta)\hskip
1.42271ptdF(q|\theta),$ (OA.8)

where $A_{i}$ is the application vector for student $i$ and $\overline{X_{i}}$
is the vector of all other exogenous characteristics entering the likelihood
function of student $i$.

Let $\mathcal{L}^{r}_{i}(\theta)$ be the likelihood for individual $i$ in
simulation $r$. Define

$\displaystyle\widehat{\mathcal{L}_{i}}(\theta)=\frac{1}{R}\sum_{r=1}^{R}\mathcal{L}^{r}_{i}(\theta),$
(OA.9)

where $R$ is the total number of simulation draws. The MSL estimator is then
defined by

$\displaystyle\widehat{\theta}_{MSL}=\arg\max_{\theta}\frac{1}{N}\sum_{i=1}^{N}\text{log}\hskip
2.84544pt\widehat{\mathcal{L}_{i}}(\theta)=\arg\max_{\theta}\bigg{(}\frac{1}{N}\sum_{i=1}^{N}\text{log}\hskip
2.84544pt\bigg{[}\frac{1}{R}\sum_{r=1}^{R}\mathcal{L}^{r}_{i}(\theta)\bigg{]}\bigg{)}.$
(OA.10)

If $R$ rises at any rate with $N$, the MSL estimator is consistent (Train;
2003).

I calculate standard errors using the information identity. By the information
identity, the sample hessian, $\widehat{H}$, can be computed by the average
outer product of the gradient of simulated likelihood evaluated at
$\widehat{\theta}_{MSL}$ i.e.

$\displaystyle\widehat{H}=\frac{1}{N}\sum_{i=1}^{N}\nabla_{\theta}\text{log}\hskip
2.84544pt\widehat{\mathcal{L}_{i}}(\widehat{\theta}_{MSL})\nabla_{\theta}\text{log}\hskip
2.84544pt\widehat{\mathcal{L}_{i}}(\widehat{\theta}_{MSL})^{\prime}.$ (OA.11)

Then, $\widehat{H}^{-1}$ is a consistent estimate of the variance of
$\sqrt{N}(\widehat{\theta}_{MSL}-\theta^{*})$, where $\theta^{*}$ is the
vector of true parameter values.

##  Appendix F Modeling Choices and Their Implications (Continued)

In this section, I discuss some other modeling choices and argue that they do
not necessarily limit the scope of my analysis for this population. These
include:

  1. 1.

Firm entry. The cost of doing business in India is substantial. World Bank’s
“Ease of Doing Business” rankings lists India low, alongside Mexico, Colombia,
and Jamaica. Moreover, starting a new business in India takes about 5 times
the duration as it does in the U.S. (World Bank; 2020). Given these factors,
it is unlikely that elite firm entry would meaningfully respond to
compensatory policies, such as hiring subsidies, in the short- to medium-term.

  2. 2.

Student preference measures kept fixed in counterfactuals. It is possible that
compensatory policies for disadvantaged castes may change the willingness of
advantaged caste students to accept elite jobs. While modeling these dynamics
would be interesting, my model does not capture such “full equilibrium”
effects.

  3. 3.

Banning subjective interviews. My analysis does not consider such
counterfactuals. Legally challenging practices such as personal interviews is
likely untenable, as employers typically have free rein to value non-group
characteristics, like candidate background (Lang and Spitzer; 2020). Moreover,
the loss in screening precision due to the removal of subjective screening
practices may outweigh equity gains (Mocanu; 2022).

  4. 4.

Not modeling multiple firm screening stages and job applications. The choice
of modeling firm screening as a one-stage process is guided by the
decomposition of the earnings gap. This choice does not necessarily restrict
my model’s generalizability. The extension of my model of firm screening can
be done in a manner similar to the basic model of labor demand laid out in
Section 7.2.2, with the written test, group debate, and interview stage each
having its own cutoff. Modeling application behavior of elite Indian college
students is not necessarily economically interesting, as streamlined and
centralized application systems effectively make students apply for all
eligible jobs (Section 7.1).

  5. 5.

Not considering wages, performance, hiring by firms of workers from other
universities, and job changing. The job placement process studied in this
paper offers a representative window into how elite college graduates
transition into elite entry-level jobs, firms offer about the same job-
specific wage to students from other universities across all locations in
India, and focusing on just initial placements in elite jobs is important (see
point 1 above and Section 7.1). Moreover, under the plausible assumption that
advantaged castes benefit more from “job changing” (e.g., by procuring other
job offers once the current job starts and using them as leverage), my paper
likely underestimates caste disparities in initial
placements.OA.2OA.2OA.2Participants in the placement processes of elite Indian
colleges are barred from offline job search (Section 6.3).

  6. 6.

Same random effect, $q$, entering both student and firm utility. See the
discussion under “The above specification of the random effect is not
necessarily a limitation” in Section 7.2.2.

  7. 7.

Omitting $q$ from student utility does not affect the main conclusions in the
paper. The main result on the student side—that there are no average caste
differences in preferences over non-pecuniary amenities—holds with or without
the inclusion of a random effect in student utility (Section 9.1). In
addition, the magnitudes of the coefficients on employer utility (crucial for
counterfactuals) do not critically depend upon $q$ entering student utility.

  8. 8.

Equity-efficiency tradeoff. My counterfactual analysis does not directly take
this tradeoff into account. My estimates show that the model-implied subsidy
equivalent to elite firms of the pre-college intervention policy is about 0.6%
of average annual salary, which is only about 10% of the caste penalty.
Therefore, the efficiency gains from pre-college interventions (omitted from
my cost-effectiveness analysis) are likely small. Moreover, leaning toward
equity through interventions in later stages, like hiring subsidies, might not
necessarily sacrifice efficiency. Recent work has shown that disadvantaged
groups are likely to benefit the most from selective education or job
opportunities, whereas displaced advantaged groups are likely to be not much
worse off (Black et al.; 2020). Similar legal arguments have been made
recently in the U.S. in favor of redistributive policies in later stages,
especially in college admissions (Fisher v. University of Texas; 2016).

##  Appendix G Tables and Figures

Table OA.1: Foreign-Based versus India-Based Firms in the Data

Designation | Count |  | Proportion |  |
---|---|---|---|---|---
Foreign-Based Firms | 622 |  | 96.58 |  |
India-Based Firms | 22 |  | 3.42 |  |
Total Firms | 644 |  | 1 |  |

  * •

Notes: Online Appendix Table OA.1 shows the proportion of foreign-based MNCs
versus India-based firms in the data that spans from 2012-2015.

Table OA.2: Proportion of Offices and Entry-Level Employees Outside India for
Foreign-Based MNCs

(1) | (2) | (3) | (4)
---|---|---|---
Firm Name | Headquarters | Offices Outside India (%) | Entry-Level Employees Outside India (%)
Citicorp | New York, USA | 99.62 | 98.62
eBay Inc. | San Jose, USA | 97.67 | 98.22
Rolls Royce | London, UK | 97.61 | 96.36
LinkedIn | Sunnyvale, USA | 94.66 | 96.73
Google | Mountain View, USA | 94.28 | 97.50
Intel | Santa Clara, USA | 97.11 | 96.48
Amazon | Seattle, USA | 97.45 | 96.13
Boston Consulting Group | Boston, USA | 96.27 | 96.23
Cisco | San Jose, USA | 96.59 | 84.91
Schlumberger | Houston, USA | 98.60 | 98.91
NetApp | Sunnyvale, USA | 96.77 | 83.33
Citrix | Fort Lauderdale, USA | 94.11 | 95.85
Ronald Berger | Munich, Germany | 96.15 | 96.83
Applied Materials | Santa Clara, USA | 96.91 | 96.30
Epic | Verona, USA | 96.29 | 98.23
General Electric | Boston, USA | 97.92 | 94.73
Analog Devices Pvt. Ltd. | Norwood, USA | 96.67 | 98.75
ARM Embedded Technologies | Cambridge, UK | 92.59 | 97.20
Microsoft | Redmond, USA | 98.17 | 95.58
VISA | San Francisco, USA | 97.72 | 96.35
Texas Instruments | Dallas, USA | 96.71 | 96.56
Samsung | Suwon-si, South Korea | 98.69 | 96.34
J.P. Morgan & Chase | New York, USA | 95.59 | 97.42
Capital One | McLean, USA | 95.12 | 98.08
ASEA Brown Boveri (ABB) | Zurich, Switzerland | 95.60 | 97.14
Caterpillar | Deerfield, USA | 97.95 | 94.02

  * Notes: Online Appendix Table OA.2 reports the proportions of offices of foreign-based MNCs that are outside of India for a select sample of firms. In addition, the table also reports the shares of firm-specific entry-level employees that are hired from India. Column (1) includes the firm name, column (2) denotes the location of the firm headquarters, column (3) reports the proportions of offices of foreign-based MNCs that are outside of India, and column (4) shows the shares of firm-specific entry-level employees that are hired from India. The average of the proportions reported in column (3) is 96.65%. The average of the proportions reported in column (4) is 95.88%. The numbers reported in columns (3) and (4) are also similar in magnitude across all jobs in the sample, although only a select number of jobs are shown in this table. Some of the above data is available at Craft.com, which is a supplier intelligence platform, whereas other data required a combination of information made available through LinkedIn or personal websites, brochures, and HR departments of firms.

Table OA.3: Distribution of Students by Caste for Each College Degree

Degree | Adv. Caste | Disadv. Caste | Total |  |
---|---|---|---|---|---
Bachelor of Technology | 579 | 710 | 1289 |  |
Dual Degree | 622 | 617 | 1239 |  |
Master of Technology | 616 | 586 | 1202 |  |
Master of Science | 350 | 127 | 477 |  |
$N$ | 2167 | 2040 | 4207 |  |
Fraction | 0.51 | 0.49 | 1 |  |

  * •

Notes: Online Appendix Table OA.3 includes the total number of students
belonging to each caste for each college degree. The college degrees included
are Bachelor of Technology (B.Tech.), Dual Degree (a five-year integrated
bachelor’s and master’s degree), Master of Technology (M.Tech.) and Master of
Science (M.S.). Adv. Caste stands for advantaged caste and Disadv. Caste
stands for disadvantaged caste.

Table OA.4: Differences in Pre-College Skills across Castes

|  | B.Tech. Degree |  |  |  |  |
---|---|---|---|---|---|---|---
|  | Adv. Caste |  |  | Disadv. Caste |  | Difference (S.D.)
Avg. entrance exam score |  |  -0.41 |  |  | -0.37 |  | $\phantom{-}0.78^{***}$
Avg. 10th grade score |  |  -0.07 |  |  | -0.06 |  | $\phantom{-}0.13\phantom{{}^{***}}$
Avg. 12th grade score |  |  -0.04 |  |  | -0.03 |  | $\phantom{-}0.07\phantom{{}^{***}}$
|  | Dual Degree |  |  |  |  |
|  | Adv. Caste |  |  | Disadv. Caste |  | Difference (S.D.)
Avg. entrance exam score |  |  -0.34 |  |  | -0.38 |  | $\phantom{-}0.72^{***}$
Avg. 10th grade score |  |  -0.03 |  |  | -0.03 |  | $\phantom{-}0.06\phantom{{}^{***}}$
Avg. 12th grade score |  | -0.03 |  |  |  -0.03 |  | $-0.06\phantom{{}^{***}}$
|  | M.Tech. Degree |  |  |  |  |
|  | Adv. Caste |  |  | Disadv. Caste |  | Difference (S.D.)
Avg. entrance exam score |  |  -0.26 |  |  | -0.28 |  | $\phantom{-}0.54^{***}$
Avg. 10th grade score |  |  -0.04 |  |  | -0.04 |  | $\phantom{-}0.08\phantom{{}^{***}}$
Avg. 12th grade score |  |  -0.02 |  |  | -0.02 |  | $\phantom{-}0.04\phantom{{}^{***}}$
|  | M.S. Degree |  |  |  |  |
|  | Adv. Caste |  |  | Disadv. Caste |  | Difference (S.D.)
Avg. entrance exam score |  | -0.02 |  |  |  -0.07 |  | $-0.09\phantom{{}^{***}}$
Avg. 10th grade score |  |  --0.001 |  |  |  --0.001 |  | $\phantom{-}0.002\phantom{{}^{**}}$
Avg. 12th grade score |  |  -0.01 |  |  | -0.02 |  | $\phantom{-}0.03\phantom{{}^{***}}$

  * •

Notes: Online Appendix Table OA.4 documents differences in pre-college skills
across castes. Pre-college skills include scores on 10th and 12th grade
national level examinations, and college entrance exam scores. All scores are
pooled and normalized to have zero mean and unit standard deviation. College
entrance exam scores are originally ranks, which have been renormalized so
that higher numbers are better. The difference across castes is reported in
standard deviation units. Adv. Caste stands for advantaged caste and Disadv.
Caste stands for disadvantaged caste. *$p<0.1$; **$p<0.05$; ***$p<0.01$.

Table OA.5: Differences in Average Overall GPA (Not Adjusted for Major) across
Castes

|  | B.Tech. Degree |
---|---|---|---
| Adv. Caste | Disadv. Caste | Difference (S.D.)
Avg. Overall GPA | 0.51 | -0.42 | $0.93^{***}$
|  | Dual Degree |
| Adv. Caste | Disadv. Caste | Difference (S.D.)
Avg. Overall GPA | 0.43 | -0.43 | $0.86^{***}$
|  | M.Tech. Degree |
| Adv. Caste | Disadv. Caste | Difference (S.D.)
Avg. Overall GPA | 0.33 | -0.35 | $0.68^{***}$
|  | M.S. Degree |
| Adv. Caste | Disadv. Caste | Difference (S.D.)
Avg. Overall GPA | 0.05 | -0.13 | $0.18^{**}$

  * •

Notes: Online Appendix Table OA.5 documents differences in average college GPA
(not adjusted for major) across castes. All scores are pooled and normalized
to have zero mean and unit standard deviation. Adv. Caste stands for
advantaged caste and Disadv. Caste stands for disadvantaged caste. *$p<0.1$;
**$p<0.05$; ***$p<0.01$.

Table OA.6: Differences in Previous Labor Market Experience across Castes

| B.Tech. and Dual Degrees |  |
---|---|---|---
| Adv. Caste | Disadv. Caste | Difference
Avg. Internship Duration (Weeks) | 8.00 (0.06) | 7.81 (0.07) | $\phantom{-}0.19^{**}$
Fraction Worked in the IT Sector | 0.22 (0.05) | 0.22 (0.04) | $\phantom{-}0.00\phantom{{}^{**}}$
Fraction Worked in the Consulting Sector | 0.35 (0.05) | 0.37 (0.05) | $-0.02\phantom{{}^{**}}$
Fraction Worked in the Manufacturing Sector | 0.43 (0.05) | 0.41 (0.06) | $\phantom{-}0.02\phantom{{}^{**}}$
Fraction Worked at a Startup | 0.34 (0.05) | 0.30 (0.05) | $\phantom{-}0.04\phantom{{}^{**}}$
Total Internship Pay ($) |  -3042.24 (249.40) | 2877.28 (220.89) | $\phantom{-}164.96\phantom{{}^{**}}$
| M.Tech. and M.S. Degrees |  |
| Adv. Caste | Disadv. Caste | Difference
Avg. Part-Time/Full-Time Employment Duration (Weeks) | 68.48 (4.52) |  -68.93 (6.96) | $-0.45\phantom{{}^{***}}$
Fraction Worked in the IT Sector | 0.36 (0.04) | 0.18 (0.07) | $\phantom{-}0.18^{***}$
Fraction Worked in the Consulting Sector | 0.19 (0.04) | 0.15 (0.06) | $\phantom{-}0.04\phantom{{}^{***}}$
Fraction Worked in the Manufacturing Sector | 0.45 (0.05) | 0.67 (0.08) | $-0.12^{***}$
Total Part-Time/Full-Time Employment Pay ($) | 22523.80 (1458.03) | 19645.89 (1390.32) | $2877.91$

  * •

Notes: Online Appendix Table OA.6 documents differences in previous labor
market experience across castes. Previous labor market experience includes
internship duration (weeks), part-time or full-time employment duration
(weeks), total pay during internships, total pay during part-time or full-time
employment, sectors of employment and employment in startups. Standard errors
are reported in parentheses. All dollar amounts are in purchasing power parity
units. T-tests are conducted for differences in overall means. *$p<0.1$;
**$p<0.05$; ***$p<0.01$.

Table OA.7: Firm Transition Matrix

From/To | Elite Private-Sector |  | Other |  |  |  |
---|---|---|---|---|---|---|---
Elite Private-Sector | 97.88 |  | 2.12 |  |  |  |
Other | 1.21 |  | 98.79 |  |  |  |

  * •

Notes: Online Appendix Table OA.7 shows the probability of transitioning from
elite private-sector jobs to “other” jobs. “Other” jobs include elite public-
sector jobs, other private-sector jobs, unemployment, and so on. The dataset
is constructed from separate samples of the India Human Development Survey
(IHDS) and the Periodic Labor Force Survey (PLFS), which is collected by the
National Sample Survey Office (NSSO). Note that the NSSO defines unemployment
as a situation in which all those who owing to lack of work are not working,
but seek work through employment exchanges, intermediaries, friends or
relatives (National Sample Survey Organisation; 2001). Therefore, being
unemployed in the data collected by the NSSO is closer to being actually out
of work (e.g., it does not include self-employment). On the other hand,
students who are “unemployed” through job the placement process I study could
include those who are self-employed or taking gap years. The sample
construction from the PLFS follows Bhattacharya (2021). The same construction
from the IHDS follows Sarkar et al. (2017).

Table OA.8: Total Number of Firms and Average Salary by Sector

| (1) | (2)
---|---|---
Sector | Total (Fraction) | Avg. Salary ($)
Technology | 335 (0.52) | 67302.64
Consulting | 129 (0.20) | 63544.02
Manufacturing | 180 (0.28) | 43525.25

  * Notes: Online Appendix Table OA.8 shows the distribution of firms by sector and the average salary across all jobs by sector. Column (1) shows the number of firms in each sector with their proportions in parentheses. Column (2) shows the average salary of all jobs in a given sector. All dollar amounts are in purchasing power parity (PPP) terms.

Table OA.9: Non-Pecuniary Amenities

Row Number | Non-Pecuniary Amenity | Additional Details
---|---|---
1. | Variable annual pay? |
2. | Is the variable compensation taxable? |
3. | Restricted stock units? |
4. | Paid leave? | Non-personal, non-educational purposes leave
5. | Sickness or disability leave? |
6. | Signing bonus? |
7. |  Bonus for spending 1 years at the firm? |
8. | Bonus for spending 2 years at the firm? |
9. | Bonus for spending 3 years at the firm? |
10. | Bonus for spending 4 years at the firm? |
11. | Annual bonus? |
12. | Variable bonus? | In addition to fixed bonus
13. | Performance bonus? | Could be project specific
14. | Stakeholder bonus? |
15. | Festival bonus? |
16. | Loyalty bonus? | Might vary by job tenure
17. | ELRP bonus? | Also called deferred compensation
18. | Probation completion bonus? |
19. | Relocation bonus? |
20. | Relocation assistance? | Arranging moving company
21. | Employees’ provident fund (EPF)? | Similar to a 401k benefit
22. | Voluntary provident fund? | Voluntary employee contribution over and above EPF
23. | Medical insurance? |
24. | Dental insurance? |
25. | Eye insurance? |
26. | Life insurance? |
27. | Food allowance? |
28. | Temporary accommodation? |
29. | Stipend during temporary accommodation? |
30. | Travel allowance? | Air, rail and road travel
31. | Leave travel concession (LTC)? | Non-work-related travel

  * •

Notes: Online Appendix Table OA.9 shows the complete list of unique non-
pecuniary amenities offered by each job (job designation within a firm) along
with an added description of the perks, unless self-explanatory.

Table OA.9: Non-Pecuniary Amenities (Continued)

Row Number | Non-Pecuniary Amenity | Additional Details
---|---|---
32. | House rent allowance (HRA)? |
33. | Telephone/mobile phone allowance? |
34. | Conveyance allowance? | Covers travel between work and residence
35. | Night shift allowance? |
36. | Counseling services? |
37. | Option to work from home? |
38. | Paid maternity Leave? |
39. | Sodexo Coupons? | Tax-free vouchers for restaurants, grocery stores, etc.
40. | Flexible working hours? |
41. | Paid day care for kids? |
42. | Happy fridays? |
43. | Gym subsidies? |
44. | Lunch on company campus? |
45. | Child psychology services? |
46. | Personal development classes? | Yoga, cooking, dancing, etc.
47. | Family days? |
48. | Smoking zones? |
49. | Telemedicine? |
50. | Parental day care? |
51. | Financial literacy classes? |
52. | Employee assistance program? |
53. | Subsidized personal leave? | Usually up to 6 months
54. | Subsidized educational leave? |
55. | Subsidized high-school education for kids? |
56. | Subsidized housing? |
57. | Gratuity? | Lump sum payment after 4 years and 8 months of service
58. | Leave encashments? | Unused paid leave reimbursed as part of salary
59. | Option to return after sabbatical? |

  * •

Notes: Online Appendix Table OA.9 shows the complete list of unique non-
pecuniary amenities offered by each job (job designation within a firm) along
with an added description of the perks, unless self-explanatory.

Table OA.10: Earnings Gap

Baseline Log Earnings (USD PPP)) |
---|---
Baseline Specification |
| (1) | (2) | (3) | (4)
Coefficient | Linear | Quadratic | Cubic | Splines
Disadv. Caste |  $-0.113^{***}$ (0.014) |  $-0.105^{***}$ (0.017) |  $-0.104^{***}$ (0.019) |  $-0.104^{***}$ (0.024)
$N$ | 2927 | 2927 | 2927 | 2927
$R^{2}$ | 0.452 | 0.532 | 0.553 | 0.578
Adjusted $R^{2}$ | 0.447 | 0.486 | 0.490 | 0.497
|  Manufacturing Sector
Coefficient | Linear | Quadratic | Cubic | Splines
Disadv. Caste |  $-0.084^{***}$ (0.022) |  $-0.070^{**}$ (0.027) |  $-0.091^{***}$ (0.032) |  $-0.091^{***}$ (0.032)
$N$ | 789 | 789 | 789 | 789
$R^{2}$ | 0.344 | 0.547 | 0.604 | 0.619
Adjusted $R^{2}$ | 0.323 | 0.402 | 0.408 | 0.431
|  Technology Sector
Coefficient | Linear | Quadratic | Cubic | Splines
Disadv. Caste |  $-0.080^{***}$ (0.022) |  $-0.077^{***}$ (0.028) |  $-0.061^{*}$ (0.033) |  $-0.071^{**}$ (0.033)
$N$ | 1435 | 1435 | 1435 | 1435
$R^{2}$ | 0.418 | 0.535 | 0.574 | 0.575
Adjusted $R^{2}$ | 0.406 | 0.438 | 0.443 | 0.446
|  Consulting Sector
Coefficient | Linear | Quadratic | Cubic | Splines
Disadv. Caste |  $-0.119^{***}$ (0.032) |  $-0.104^{**}$ (0.041) |  $-0.087^{*}$ (0.048) |  $-0.109^{**}$ (0.054)
$N$ | 703 | 703 | 703 | 703
$R^{2}$ | 0.494 | 0.636 | 0.688 | 0.689
Adjusted $R^{2}$ | 0.473 | 0.502 | 0.528 | 0.528
|  Client-Facing Jobs
Coefficient | Linear | Quadratic | Cubic | Splines
Disadv. Caste |  $-0.117^{***}$ (0.029) |  $-0.129^{***}$ (0.037) |  $-0.115^{***}$ (0.044) |  $-0.119^{***}$ (0.045)
$N$ | 822 | 822 | 822 | 822
$R^{2}$ | 0.437 | 0.568 | 0.614 | 0.616
Adjusted $R^{2}$ | 0.418 | 0.434 | 0.454 | 0.456
|  Non-Client-Facing Jobs
Coefficient | Linear | Quadratic | Cubic | Splines
Disadv. Caste |  $-0.080^{***}$ (0.016) |  $-0.070^{***}$ (0.020) |  $-0.074^{***}$ (0.022) |  $-0.071^{***}$ (0.022)
$N$ | 2105 | 2105 | 2105 | 2105
$R^{2}$ | 0.499 | 0.581 | 0.609 | 0.610
Adjusted $R^{2}$ | 0.492 | 0.522 | 0.528 | 0.529

  * Notes: Online Appendix Table OA.10 includes estimates from an earnings regression run on the sample of all students who graduated with jobs. The dependent variable is log earnings. I include detailed controls including measures of pre-college skills, within-college academic performance, previous labor market experience, and other employer-relevant skills (Section 4). Each column is a separate regression and includes all the controls mentioned above. In column (1), all controls enter linearly. In column (2), GPA and entrance exam scores enter as quadratic polynomials, while other controls enter linearly. In column (3), GPA and entrance exam scores enter as cubic polynomials, while other controls enter linearly. In column (4), estimates are reported from a fully flexible quadratic polynomial regression with all possible interactions between controls. PPP stands for purchasing power parity. *$p<0.1$; **$p<0.05$; ***$p<0.01$.

Table OA.11: GPA and Entrance Exam Score Comparisons of All Students vs. Those
Without Jobs

| GPA Comparisons
---|---
| B.Tech. Degree
(1) |  | (2) |  | (3) |  | (4)
| Overall |  |  |  | Students without Jobs |
Adv. Caste |  | Disadv. Caste |  | Adv. Caste |  | Disadv. Caste
8.08 |  | 7.00 |  | 7.97 |  | $6.58^{***}$
| Dual Degree
| Overall |  |  |  | Students without Jobs |
Adv. Caste |  | Disadv. Caste |  | Adv. Caste |  | Disadv. Caste
8.05 |  | 7.15 |  | 8.02 |  | $6.86^{**}$
| M.Tech. Degree
| Overall |  |  |  | Students without Jobs |
Adv. Caste |  | Disadv. Caste |  | Adv. Caste |  | Disadv. Caste
8.33 |  | 7.62 |  | $8.00^{***}$ |  | $7.35^{***}$
| M.S. Degree
| Overall |  |  |  | Students Without Jobs |
Adv. Caste |  | Disadv. Caste |  | Adv. Caste |  | Disadv. Caste
8.49 |  | 8.42 |  | 8.46 |  | $8.23^{*}$
| Entrance Exam Score Comparisons
| B.Tech. Degree
| Overall |  |  |  | Students without Jobs |
Adv. Caste |  | Disadv. Caste |  | Adv. Caste |  | Disadv. Caste
-1617.89 |  | -3707.45 |  | $-1879.32^{*}$ |  | $-4315.18^{**}$
| Dual Degree
| Overall |  |  |  | Students without Jobs |
Adv. Caste |  | Disadv. Caste |  | Adv. Caste |  | Disadv. Caste
-2096.60 |  | -4067.13 |  | $-2602.79^{***}$ |  | $-5743.80^{***}$
| M.Tech. Degree
| Overall |  |  |  | Students without Jobs |
Adv. Caste |  | Disadv. Caste |  | Adv. Caste |  | Disadv. Caste
-653.94 |  | -2445.64 |  | $-1052.61^{***}$ |  | $-3310.677^{**}$
| M.S. Degree
| Overall |  |  |  | Students without Jobs |
Adv. Caste |  | Disadv. Caste |  | Adv. Caste |  | Disadv. Caste
-558.94 |  | -1416.09 |  | -642.18 |  | -1411.26

  * •

Notes: Online Appendix Table OA.11 compares the average GPA and entrance exam
scores of all students versus those of students without jobs. T-tests are
conducted for differences in overall means versus means of students without
jobs _within_ each caste. Significance denoted by asterisks are shown in the
third and fourth columns. Adv. Caste stands for advantaged caste and Disadv.
Caste stands for disadvantaged caste. *$p<0.1$; **$p<0.05$; ***$p<0.01$.

Table OA.12: Job-Specific Salary Comparisons between Glassdoor Salaries versus
those in my Sample

(1) | (2) | (3) | (4) | (5)
---|---|---|---|---
Firm Name | Job Designation | Job Location | Headquartered in | $\Delta(\%)$
Citicorp | Analyst | India | New York, USA | 0.07
eBay Inc. | Software Engineer | India | San Jose, USA | 1.29
Indian Register of Shipping | Assistant Surveyor | India | Powai, India | 1.45
Rolls Royce | Engineering Graduate | India | London, UK | 1.46
LinkedIn | Software Engineer | India | Sunnyvale, USA | 1.86
Google | Software Engineer | India | Mountain View, USA | 2.76
Intel | Component Design Engineer | India | Santa Clara, USA | 2.44
Amazon | Area Manager | India | Seattle, USA | 0.47
Boston Consulting Group | Associate | India | Boston, USA | 1.42
Cisco | Software Engineer | India | San Jose, USA | 1.80
Schlumberger | Software Engineer | India | Houston, USA | 3.08
NetApp | Member Technical Staff | India | Sunnyvale, USA | 1.48
Citrix | Software Engineer | India | Fort Lauderdale, USA | 2.66
Ronald Berger | Business Analyst | India | Munich, Germany | 2.80
Applied Materials | Application Engineer | India | Santa Clara, USA | 0.85
Epic | Software Developer | India | Verona, USA | 1.64
General Electric | Edison Engineer | India | Boston, USA | 2.33
Analog Devices Pvt. Ltd. | Software Engineer | India | Norwood, USA | 1.88
ARM Embedded Technologies | Graduate Engineer | India | Cambridge, UK | 4.01
Microsoft | Software Engineer | India | Redmond, USA | 2.31
VISA | Software Engineer | India | San Francisco, USA | 1.74
Texas Instruments | Analog Engineer | India | Dallas, USA | 4.85
Samsung | Software Engineer | India | Suwon-si, South Korea | 4.92
J.P. Morgan & Chase | Associate | India | New York, USA | 1.38
Capital One | Associate | India | McLean, USA | 4.35
ASEA Brown Boveri (ABB) | Software Engineer | India | Zurich, Switzerland | 1.38
Caterpillar | Associate Engineer | India | Deerfield, USA | 2.88

  * Notes: Online Appendix Table OA.12 includes comparisons between job-specific salaries offered on Glassdoor versus my sample. The table reports these comparisons for select firms in the sample. Column (1) includes the firm name, column (2) includes job designation, column (3) includes the job location, column (4) reports the location of the firm headquarters, and column (5) reports the absolute percentage difference between the salaries from Glassdoor (in PPP) versus those in my sample (in PPP), where the denominator is the average salary across all jobs in the sample ($56,767.29 PPP). The average of $\Delta(\%)$ reported in column (5) is 2.21%. This average difference is also similar in magnitude across all jobs in the sample, although only a select number of jobs are shown in Online Appendix Table OA.12. The PPP conversion factor is taken from the OECD website. Job-specific salaries in India are taken from the Glassdoor website and then compared to those offered in my sample.

Table OA.13: Predicting Interview Days with Job Characteristics and “Firm
Identity”

| Dependent Variable: Assigned a Particular Interview Day
---|---
|  Job Characteristics Only
| (1) | (2) | (3)
Coefficient | Logistic | Random Forest | Decision Tree
Accuracy | 0.734 | 0.759 | 0.721
95% CI | [0.690, 0.7745] | [0.716, 0.798] | [0.676, 0.762]
Kappa | 0.304 | 0.366 | 0.356
| Job Characteristics and “Firm Identity”
Coefficient | Logistic | Random Forest | Decision Tree
Accuracy | 0.948 | 0.951 | 0.952
95% CI | [0.923, 0.967] | [0.926, 0.969] | [0.929, 0.971]
Kappa | 0.879 | 0.884 | 0.890

  * Notes: Online Appendix Table OA.13 includes measures of predictive accuracy of interview day assignments given firm characteristics and measures of “firm identity.” “Firm identity” is proxied by previous interview day assignment of the same firm. The dependent variable is the interview day assigned to a firm. Controls include job salaries, job sectors, and job titles. In column (1), an ordered logistic model is estimated. In column (2), a random forest model is estimated. In column (3), a decision tree model is estimated. Accuracy is the total number of correct predictions divided by the total number of observations. The Kappa statistic, which lies between 0 and 1, measures how classification results compare to values assigned by chance. A higher Kappa statistic is better. Full regression results are available on request.

Table OA.14: Model Fit: Job Offer, Job Choice, Unemployment, and Earnings Gap

| Model Fit
---|---
| (1) |  |  | (2) |  |
| Job Offer
|  | Data |  | Model |  |
Consulting |  | 0.25 |  |  0.23 |  |
Technology |  | 0.48 |  |  0.51 |  |
Manufacturing |  | 0.27 |  |  0.26 |  |
| Job Choice
|  | Data |  | Model |  |
Consulting |  | 0.24 |  |  0.22 |  |
Technology |  | 0.49 |  |  0.51 |  |
Manufacturing |  | 0.27 |  |  0.27 |  |
| Unemployed
|  | Data |  | Model |  |
— |  | 0.30 |  |  0.31 |  |
| Earnings Gap
|  | Data |  | Model |  |
— |  | -11.3% |  |  -10.6% |  |

  * •

Notes: Online Appendix Table OA.14 compares the moments in the data to the
corresponding model-simulated moments. Earnings gap reported in the first
column corresponds to the regression specification where all controls enter
linearly. Model-simulated moments are computed by simulating the model 300
times for each observation in the sample and then averaging over the number of
observations and the number of simulation draws.

Table OA.15: Select Job Cutoffs by Pay Category, Job Sector, and Job Title

| Job Cutoffs (Job Utility) |
---|---|---
| Pay Category |
Parameter | Estimate | Std. Error
Top 25% | $-16.300^{***}$ | 0.749
50%-75% | $-16.487^{***}$ | 0.765
25%-50% | $-16.779^{***}$ | 0.762
Bottom 25% | $-17.138^{***}$ | 0.767
| Job Sector |
Parameter | Estimate | Std. Error
Technology | $-17.031^{***}$ | 0.788
Consulting | $-16.165^{***}$ | 0.734
Manufacturing | $-16.274^{***}$ | 0.724
| Job Title |
Parameter | Estimate | Std. Error
Engineer | $-16.643^{***}$ | 0.760
Consultant | $-16.415^{***}$ | 0.751
Manager | $-17.253^{***}$ | 0.782

  * •

Notes: Online Appendix Table OA.15 includes estimates of the job cutoffs by
pay category, job sector, and job title for aggregate firms. An “aggregate”
firm in a given category (e.g., sector) has the hiring cutoff averaged over
all firms in that category. Note that the job cutoff estimates are not
structural parameters, as they are allowed to change under counterfactual
policies. Full estimation tables are available upon request. Average Salary =
$56,767.29 (PPP), $N$ = 4207 (no. of students), $J$ = 644 (no. of jobs). PPP
stands for purchasing power parity. * significant at 10%, ** significant at
5%, *** significant at 1%.

Table OA.16: Differences in Job-Specific Salaries between Indian Locations
versus those in Establishments Located in the “MNC headquarter region or
similar”

(1) | (2) | (3) | (4) | (5)
---|---|---|---|---
Firm Name | Job Designation | Job Location | Headquartered in | $\Delta(\%)$
Citicorp | Analyst | India | New York, USA | 2.12
eBay Inc. | Software Engineer | India | San Jose, USA | 0.45
Rolls Royce | Engineering Graduate | India | London, UK | 2.67
LinkedIn | Software Engineer | India | Sunnyvale, USA | 3.52
Google | Software Engineer | India | Mountain View, USA | 2.34
Intel | Component Design Engineer | India | Santa Clara, USA | 1.74
Amazon | Area Manager | India | Seattle, USA | 2.82
Boston Consulting Group | Associate | India | Boston, USA | 0.66
Cisco | Software Engineer | India | San Jose, USA | 2.91
Schlumberger | Software Engineer | India | Houston, USA | 3.22
NetApp | Member Technical Staff | India | Sunnyvale, USA | 1.32
Citrix | Software Engineer | India | Fort Lauderdale, USA | 3.67
Ronald Berger | Business Analyst | India | Munich, Germany | 2.49
Applied Materials | Application Engineer | India | Santa Clara, USA | 3.56
Epic | Software Developer | India | Verona, USA | 3.47
General Electric | Edison Engineer | India | Boston, USA | 3.04
Analog Devices Pvt. Ltd. | Software Engineer | India | Norwood, USA | 1.76
ARM Embedded Technologies | Graduate Engineer | India | Cambridge, UK | 3.94
Microsoft | Software Engineer | India | Redmond, USA | 2.91
VISA | Software Engineer | India | San Francisco, USA | 2.84
Texas Instruments | Analog Engineer | India | Dallas, USA | 3.11
Samsung | Software Engineer | India | Suwom-si, South Korea | 2.75
J.P. Morgan & Chase | Associate | India | New York, USA | 2.74
Capital One | Associate | India | McLean, USA | 3.67
ASEA Brown Boveri (ABB) | Software Engineer | India | Zurich, Switzerland | 3.61
Caterpillar | Associate Engineer | India | Deerfield, USA | 3.42

  * Notes: Online Appendix Table OA.16 includes select comparisons between real job-specific salaries offered by MNCs at Indian locations versus locations in the “MNC headquarter region or similar” (typically North America and Europe). Column (1) includes the firm name, column (2) includes job designation, column (3) includes the job location, column (4) denotes the location of the firm headquarters, and column (5) reports the absolute percentage difference between real job-specific salaries at Indian locations versus locations in the “MNC headquarter region or similar,” where the denominator is the average salary across all jobs in my sample ($56,767.29 PPP). The average of $\Delta(\%)$ reported in column (5) is 2.72%. This average difference is also similar in magnitude across all jobs in the sample, although only a select number of jobs are shown in Online Appendix Table OA.16. Firm salaries for Indian locations are taken from my sample. Firm salaries at locations in the “MNC headquarter region or similar” (typically North America and Europe) are taken from a combination of Glassdoor and Levels.fyi.

Table OA.17: Negative Correlation between College GPA and Entrance Exam Score

|  Dependent Variable: log GPA
---|---
|  B.Tech. Degree Students
| (1) | (2) | (3)
Coefficient | All | Non-Selective Majors | Selective Majors
Disadv. Caste |  $-0.171^{***}$ (0.010) |  $-0.162^{***}$ (0.011) |  $-0.187^{***}$ (0.020)
Entrance Exam Score |  $-0.025^{***}$ (0.006) |  $-0.008\phantom{{}^{***}}$ (0.007) |  $-0.060^{***}$ (0.010)
$N$ | 1289 | 902 | 387
$R^{2}$ | 0.237 | 0.232 | 0.264
Adjusted $R^{2}$ | 0.230 | 0.225 | 0.249
|  Dual Degree Students
Coefficient | All | Non-Selective Majors | Selective Majors
Disadv. Caste |  $-0.147^{***}$ (0.009) |  $-0.140^{***}$ (0.012) |  $-0.155^{***}$ (0.014)
Entrance Exam Score |  $-0.029^{***}$ (0.006) |  $-0.021^{**\phantom{*}}$ (0.010) |  $-0.036^{***}$ (0.007)
$N$ | 1239 | 780 | 459
$R^{2}$ | 0.221 | 0.190 | 0.276
Adjusted $R^{2}$ | 0.212 | 0.182 | 0.262
|  M.Tech. Degree Students
Coefficient | All | Non-Selective Majors | Selective Majors
Disadv. Caste |  $-0.071^{***}$ (0.007) |  $-0.078^{***}$ (0.010) |  $-0.048^{***}$ (0.013)
Entrance Exam Score |  $-0.033^{***}$ (0.006) |  $-0.042^{***}$ (0.011) |  $-0.022^{***}$ (0.004)
$N$ | 1202 | 840 | 362
$R^{2}$ | 0.245 | 0.271 | 0.206
Adjusted $R^{2}$ | 0.236 | 0.264 | 0.183
|  M.S. Degree Students
Coefficient | All | Non-Selective Majors | Selective Majors
Disadv. Caste |  $-0.011^{*\phantom{**}}$ (0.056) |  $-0.019^{**\phantom{*}}$ (0.008) |  $\phantom{-}0.003^{\phantom{***}}$ (0.011)
Entrance Exam Score |  $-0.004^{***}$ (0.001) |  $-0.004\phantom{{}^{***}}$ (0.010) |  $-0.005^{***}\phantom{}$ (0.001)
$N$ | 477 | 322 | 155
$R^{2}$ | 0.076 | 0.055 | 0.157
Adjusted $R^{2}$ | 0.046 | 0.031 | 0.098

  * Notes: Online Appendix Table OA.17 includes estimates from a regression of grade point averages of B.Tech., Dual, M.Tech., and M.S. degree holders on student characteristics. The dependent variable is log GPA. Controls include college major, entrance exam score (standardized), grades on 10th and 12th grade national level examinations (standardized), and caste. College major includes indicators for each major. College entrance exam scores (ranks) have been renormalized so that higher numbers are better. In column (1), I report results for all students. In column (2), I report results only for students in non-selective majors. In column (3), I report results only for students in selective majors. *$p<0.1$; **$p<0.05$; ***$p<0.01$.

Table OA.18: Earnings Gap under Subsidies versus Pre-College Intervention

| Earnings Gap (%) |
---|---|---
| Perfectly Elastic Labor Demand |
Hiring Subsidy |  | Pre-College Intervention
-5.5% |  | -8.9%
| Perfectly Inelastic Labor Demand |
Hiring Subsidy |  | Pre-College Intervention
-7.6% |  | -9.5%

  * •

Notes: Appendix Table OA.18 shows the earnings gap under hiring subsidies and
the pre-college intervention policy.

Table OA.19: Displacement Effects (Unemployment)

| Subsidies and Pre-College Intervention
---|---
| $\%$ Unemployed |  $\Delta$ Unemployed ($\%$) |
|  Adv. Caste Disadv. Caste Overall |  Adv. Caste Disadv. Caste | Overall
Baseline |  25% 36% 31% | — — | —
|  Perfectly Elastic Labor Demand |  |
Subsidy |  25% 24% 28% |  0-0% -35% | -20%
PCI |  25% 31% 25% |  0-0% -15% | -9%
|  Perfectly Inelastic Labor Demand |  |
Subsidy |  33% 28% 31% |  1+31% -23% | -0%
PCI |  28% 33% 31% | +12% -9% | -0%
| Hiring Quotas
| $\%$ Unemployed |  $\Delta$ Unemployed ($\%$) |
|  Adv. Caste Disadv. Caste Overall |  Adv. Caste Disadv. Caste | Overall
Baseline |  25% 36% 31% | — — | —
Hiring Quotas |  35% 31% 33% | +37% -16% | +7%

  * •

Notes: Online Appendix Table OA.19 shows unemployment by caste under the
baseline, hiring subsidies, PCI and quotas. “PCI” stands for the pre-college
intervention (PCI) policy.

Figure OA.1: This figure shows that students from both disadvantaged
and advantaged castes belong within each entrance exam score or GPA decile.

(a) Manufacturing

(b) Technology

(c) Consulting

(d) Client Facing

(e) Non-Client Facing

Figure OA.2: This figure shows the coefficient $\beta$ corresponding to the
regression in Equation 1 across job sectors and job types. $\beta$ represents
the percentage difference in the average salary at each job search stage
between advantaged and disadvantaged castes. Each dot is the coefficient
$\beta$ from a separate regression. The vertical bars are 95% confidence
intervals. These regressions include controls. This figure shows that the
increase in the earnings gap is even _more_ concentrated for technology,
manufacturing, and non-client-facing jobs. The entire earnings gap among these
jobs occurs after personal interviews.

(a) Advantaged Caste Hires Under Perfectly Elastic Demand

(b) Disadvantaged Caste Hires Under Perfectly Elastic Demand

(c) Advantaged Caste Hires Under Perfectly Inelastic Demand

(d) Disadvantaged Caste Hires Under Perfectly Inelastic Demand

Figure OA.3: This figure shows how the model bounds both the negative and
positive employment effects on advantaged and disadvantaged castes,
respectively, under hiring subsidies and the pre-college intervention policy.
The distribution of advantaged caste “scores” are shown in red. These scores
are to the right of the distribution of disadvantaged caste scores, shown in
blue. Scores can be calculated from Equation 7.2.2. Under both hiring
subsidies and the pre-college intervention policy, the distribution of
disadvantaged caste scores shifts to the right. The top panel represents a
scenario where labor demand is perfectly elastic. In this scenario, there is
no displacement of advantaged castes and the number of disadvantaged caste
hires is at least as large as in the baseline. The bottom panel represents a
scenario where labor demand is perfectly inelastic. In this scenario, the
number of disadvantaged caste hires is at least as large as in the baseline
but not as large as when labor demand is perfectly elastic. On the other hand,
the displacement of advantaged castes is larger than when demand is perfectly
elastic (where it is zero).