# What Does the Indian Parliament Discuss?
An Exploratory Analysis of the Question Hour in the Lok Sabha

###### Abstract

The TCPD-IPD dataset is a collection of questions and answers discussed in the
Lower House of the Parliament of India during the Question Hour between 1999
and 2019. Although it is difficult to analyze such a huge collection manually,
modern text analysis tools can provide a powerful means to navigate it. In
this paper, we perform an exploratory analysis of the dataset. In particular,
we present insightful corpus-level statistics and a detailed analysis of three
subsets of the dataset. In the latter analysis, the focus is on understanding
the temporal evolution of topics using a dynamic topic model. We observe that
the parliamentary conversation indeed mirrors the political and socio-economic
tensions of each period.

Keywords: Parliament of India, dynamic topic model, latent Dirichlet
allocation, TCPD-IPD, political data

languageresourceLanguage Resources

What Does the Indian Parliament Discuss?
An Exploratory Analysis of the Question Hour in the Lok Sabha

Suman Adhya, Debarshi Kumar Sanyal
---
Indian Association for the Cultivation of Science
Jadavpur, Kolkata – 700032, India
<EMAIL_ADDRESS><EMAIL_ADDRESS>

Abstract content

##  1\. Introduction

The Parliament of India is the highest legislative body of India. The members
of its Lower House or the Lok Sabha are directly elected by the people while
its Upper House comprises representatives elected by the members of all State
Legislative Assemblies. Although parliamentary proceedings are immensely
useful to a political scientist, they are too large to be manually analyzed.
This motivates the use of algorithmic tools to explore them. In this paper, we
analyze the TCPD-IPD dataset tpcd of around 298K pairs of questions and
answers (QA) in English discussed in the Lok Sabha during the Question Hour –
the first hour of every business day of the Parliament – from 1999 to 2019
spanning four Lok Sabha terms (13th term: 1999-2004, 14th: 2004-09, 15th:
2009-14, 16th: 2014-19). During the Question Hour, any Member of Parliament in
the Lok Sabha (abbreviated: MP) may ask any question to the ministers related
to the administrative activity of the government, and thus, hold it
accountable for its actions [Sanyal, 2016, Tripathi and Kumar, 2021]. Question
time is also an integral part of many other parliamentary democracies like
those of Canada, Australia, and the UK [Martin and Rozenberg, 2014].

Technical specification of the TCPD-IPD dataset appears in [Bhogale, 2019].
But the dataset has not been explored, except in [Sen et al., 2019] where the
authors aim to identify, for a few chosen themes, whether the questions asked
by MPs echo the trend in mass media and social media. Topic modeling has been
used to analyze the parliamentary proceedings of various countries, see, e.g.,
[Greene and Cross, 2017, Gkoumas et al., 2018, Ishima, 2020]. In this paper,
we study TCPD-IPD using the following pipeline. First, a static topic model of
the entire dataset is built and the top topics are identified. Then a subset
of the dataset is selected for further analysis as follows: (a) A dynamic
topic model is built on it; (b) The temporal evolution of topics and words in
a topic are plotted; (c) The top-ranking documents at a given time in each of
these plots are analyzed. We obtain interesting insights from this analysis.
This demonstrates the effectiveness of our technique. Our specific
contributions are:

  1. 1.

We describe important high-level statistical features of the dataset and bias
in the participation of MPs (Section 2.).

  2. 2.

We identify the top topics in the entire dataset (Section 3.).

  3. 3.

We make a more nuanced study of the QA pertaining to three specific ministries
– Finance, Railways, and Health and Family Welfare — by building a dynamic
topic model in each case (Section 4.).

  4. 4.

For each ministry mentioned above, we identify words that showed significant
variation in their probability in a topic over time (Section 4.) and the major
events to which they relate. Thus, word choreography in a dynamic topic model
is used to reconstruct events in political history. We hope the insights and
lessons from the past will help inform future responses to critical national
issues.

##  2\. High-Level Features of TCPD-IPD

We enumerate below some interesting insights we obtained from the dataset.

###  2.1. Ministry-wise data distribution

TCPD-IPD contains questions related to 85 different ministries. Figure 1 shows
the data distribution for the top ten ministries (comprising almost $50\%$ of
the full dataset). Clearly, Finance, Railways and Health and Family Welfare
are the top three ministries.

Figure 1: Ministry-wise data distribution.

###  2.2. Term-wise data distribution

Among a total of 298,292 questions, the number of questions asked in each term
of the Lok Sabha is as follows: 13th: 73,531; 14th: 66,371; 15th: 79,401;
16th: 78,989. We found out that over the span covered in this dataset, Lok
Sabha always met for more than 50 sittings in a year, except in 2004 (48
sittings) and 2008 (46 sittings). This correlates with the lowest number of
questions asked in 2004 (9398 questions) and 2008 (9851 questions). Two of the
three sessions in 2004 and all sessions in 2008 are included in the 14th Lok
Sabha. Although the fewer sittings are highlighted in many news reports
[Anuja, 25 Nov 2020], we did not find mention of its impact on Question Hour,
which we clearly identified above.

###  2.3. Participation of MPs

Unlike the previous Lok Sabha terms, in the 16th the ruling alliance asked
more questions than the opposition. In that term, they had a historic $65\%$
share of the House. Since the numeric strength of the ruling alliance is, by
rule, higher than that of the opposition, we normalize them to get an idea of
participation from each side had the number of representatives from either
side been equal. We believe this will afford a fairer comparison between their
participation. Let $R_{n}$ and $O_{n}$ be the number of members of the ruling
alliance and the opposition, respectively, and $R_{q}$ and $O_{q}$ be the
number of questions asked by the ruling alliance and the opposition,
respectively. Then $R_{pp}=\left(R_{q}\times\frac{O_{n}}{R_{n}+O_{n}}\right)$,
$O_{pp}=\left(O_{q}\times\frac{R_{n}}{R_{n}+O_{n}}\right)$. As seen in Figure
2, in the transformed space, the opposition still asks more questions than the
ruling alliance, matching the expectations of a healthy democracy.

Figure 2: Participation of ruling alliance vis-a-vis that of the opposition.

###  2.4. Gender and caste bias in participation

Over the four Lok Sabha terms covered by the dataset, 91.6% of questions were
asked by men while 8.4% of questions were raised by women. The average gender
ratio of men to women was $8.3\\!\\!:\\!\\!1$ over the same four terms. Thus
the skewed gender ratio correlates with the distribution of questions. It is
noteworthy here that the Women’s Reservation Bill proposing the reservation of
one-third of the seats in the Lok Sabha for women has been pending since 2010
[Marwah, 2019], thus, allowing the bias to continue. As regards the caste
distribution, 80.6% of questions were raised by MPs from the general caste
while the rest come from the reserved categories. Note that 24.03% of Lok
Sabha seats are reserved for the reserved categories while the rest belong to
the general caste. Figure 3 shows the number of questions on gender and caste-
related issues asked in the Parliament; Appendix 6.5. lists the keywords we
used for this analysis.

(a) Gender-related.

(b) Caste-related.

Figure 3: Gender and caste related discussions in each Lok Sabha term.

##  3\. Topic Model for TCPD-IPD

Figure 4: Hot topics discussed in the Indian parliamentary QA sessions.

We used topic modeling to get a thematic view of the TCPD-IPD dataset.
Researchers have observed that Latent Dirichlet Allocation (LDA) ([Blei et
al., 2003]), which employs Gibbs sampling for inference, often produces
superior topics than those from modern variational inference-based topic
models; see, e.g., [Blei et al., 2017, Lisena et al., 2020]. This motivated us
to use LDA instead of neural topic models. We pre-processed the entire TCPD-
IPD corpus, used LDA to extract 50 topics, and manually labeled them (See
Appendix 6.1.-6.3.). We filtered out a few noisy, heterogeneous topics and
among the rest plotted the top ten topics in Figure 4. Clearly, there is a
huge emphasis on growth in the economy and science, at the state and national
levels.

##  4\. Ministry-wise Analysis

We have selected three ministries – Finance, Railways, and Health and Family
Welfare – for further analysis. We set the topic count to 20 for all three
ministries. To model the temporal variation in topics, we had to make a choice
between the classical Dynamic Topic Model ([Blei and Lafferty, 2006]) which is
essentially an adaptation of LDA for sequential data (hereafter, called
LDAseq) and the more modern Dynamic Embedded Topic Model (D-ETM) ([Dieng et
al., 2019]), both developed by David Blei and collaborators. In our
experiments, we found LDAseq produced better topics (see Appendix 6.4.). A
similar observation is reported made in [Dieng et al., 2019].

Using LDAseq, we obtained the temporal evolution of the probabilities of five
topics for each ministry, as shown in Figure 5. In Finance, the peaks in
‘agricultural loan’ in 2008 and 2014 relate to the debt waivers announced at
that time, though farmers’ loans remain a perennially important topic. The
focus on ‘rural development’ slowly reduces while other topics like ‘banking’,
‘economic growth’ (more questions asked when GDP change is unexpectedly high
or low), and ‘pension schemes’ remain more stable. In the Railways, the early
2000s witnessed many new government projects and associated parliamentary QA,
but the focus gradually shifted to ‘infrastructure development’ and ‘passenger
amenity’, where questions veered around the increasing private participation.
The announcement of many projects on rail safety in the last Lok Sabha term is
indicated by an increased presence of the topic ‘railway safety’. The steep
price hike in passenger and freight fares in 2014 sparked intense deliberation
in the Parliament. In the Ministry of Health and Family Welfare, discussions
on women and child care and rural medical infrastructure increased after the
National Health Mission was launched in 2005. The focus on medical research in
the early part of the decade led to the establishment of many premier medical
institutions throughout the country but gradually the interest waned.

The rare words in a topic are often more informative and captured specific
events or issues. So in the following sub-sections, we choose a few
representative topics from each ministry and show the temporal evolution of
the probability of the rare words in the selected topics. In each plot, we
also annotate one of the dominant words with example questions asked during
the Question Hour.

Figure 5: Temporal evolution of topics in 3 subsets of TCPD-IPD using LDAseq.

###  4.1. Finance

(a) Topic: Banking

(b) Topic: Pension Reforms

Figure 6: Selected topics obtained by running LDAseq on the Finance subset of
TCPD-IPD.

We have selected two topics, ‘banking’ and ‘pension reforms’, and plotted the
probability of a few selected tokens in them as a function of time in Figure
6. In the topic ‘banking’ shown in Figure 6(a), we find that the word
‘credit_card’ peaks in 2007. Our analysis shows that most of the questions
around this time are related to the growing credit card frauds in India and
the sudden rise in credit card interest rates by some banks, coinciding with
reports in mass media. Another aspect of credit card-related discussion is
Kisan Credit Card (KCC) – a low-interest credit card for farmers, which was
introduced in 1998 and significantly improved in 2004. The steady rise in
discussion on debit cards correlates with the increasing adoption of debit
cards (that avoided the debt trap of credit cards) in India. With
demonetization and increased government emphasis on end-to-end digital – as
opposed to cash – transactions, terms like ‘atm’, ‘digital_transaction’, and
‘cyber_security’ gain prominence while the popularity of more traditional
mediums like ‘cheque’ reduces.

Figure 6(b) shows the topic of pension reforms. In late 2003, the Government
of India notified that it was abolishing the then-existing government-funded
pension system for all its new employees and that they would come under the
National Pension System (NPS) to be administered through the new Interim
Pension Fund Regulatory and Development Authority (PFRDA). NPS enabled
subscribers to make planned savings for post-retirement income. NPS was
extended to all Indian citizens in 2009. Being a monumental change, NPS
provoked a number of questions that peaked around 2011; MPs wanted to know the
details of the scheme, including its performance, implementation challenges,
extension to unorganized sectors, and even its security. The government
introduced the Voluntary Retirement Scheme (VRS) in nationalized banks in the
early 2000s to reduce the financial load on the public exchequer. Thousands of
employees across various organizations accepted VRS in 2000-2001. Given the
high unemployment rate in the country, VRS generated a lot of panic among
people and pointed questions in the Parliament on the government’s future
plans for its workforce. The other visible terms ‘apy’, ‘dbt’, and ‘jan_dhan’
refer to recent financial inclusion programs.

###  4.2. Railways

Here, we highlight only one topic, namely, ‘infrastructure development’ which
is displayed in Figure 7.

Figure 7: Topic Infrastructure Development in Railways.

Observe the peak of the term ‘public_private’ around 2010 when the Railways
ministry introduced a new model for a public-private partnership to modernize
the Indian Railways. Indeed, there have always been questions and panic in the
Lok Sabha on the public-private models as privatization of the economy could
increase fares, job loss, and casualization of labor [Makhija, 2006, Reddy,
2019]. A related term ‘joint_venture’ was a part of many discussions. While
early uses of it (in the 2000s) focused on joint ventures of Indian Railways
with other public sector companies, the recent focus (since 2015) has been on
the increasing role of private players. Indeed similar exchanges occurred
between the MPs and the Civil Aviation Ministry on the privatization of
airlines. Terms like ‘rail_vikas’ and ‘irsdc’ refer to companies owned by
Indian Railways and entrusted with the maintenance of Railways. The rise in
freight volumes led to the ideation of Dedicated Freight Corridors (DFC) in
2005 and generated a number of questions (‘dedicated_freight’) related to
their cost, progress, and expansion. Discussions on the introduction of bullet
trains have been present for a long time but they gathered momentum when a
vision document was tabled by the government in December, 2009 and the
construction of the Mumbai-Ahmedabad high-speed rail corridor started in 2017.

###  4.3. Health and Family Welfare

Figure 8: Topic Communicable Diseases in Health and Family Welfare.

Here, we have chosen the topic ‘Communicable Diseases’. Figure 8 shows the
evolution of word probabilities in this topic. Clearly, we find an increasing
focus on the word ‘virus’ because India has been repeatedly hit by the Swine
influenza virus (such as H1N1), including in 2009 when swine-flu turned into a
pandemic and in 2014-15 [Kshatriya et al., 2018]. MPs in the Lok Sabha
enquired about the number of cases, test administration, government
interventions, vaccination drives, the role of WHO, and the effort to develop
indigenous vaccines. Another major disease in India has been malaria but a
response in Lok Sabha informs us that malarial death reduced steadily over the
years, and that is attested to by the steady decline in its focus in the
Parliament. India recorded thousands of deaths due to Acute Encephalitis
Syndrome (AES) in 2008-14 [Ghosh and Basu, 2016] and Japanese Encephalitis in
2005-11 [Adhya et al., 2013]. During these unfortunate occurrences, the Lok
Sabha witnessed intense discussions on diseases.

##  5\. Conclusion and Future Work

We identified the salient features of TCPD-IPD and then illustrated the
temporal evolution of topics in three important subsets of the data. In the
future, we will attempt to automatically detect topical change points,
annotate them with trigger events (e.g., VRS announcement), auto-summarize the
top documents containing a specific topic or word at a given time, and
motivate investigative reporting or research on the impact of the most
sensitive topics discussed in the Parliament (e.g., the effect of VRS on mid-
age employees). We hope our study will help construct more probing
parliamentary questions and formulate better national policies.

##  6\. Appendix

###  6.1. Data preprocessing for topic modeling

We have removed the punctuation from the dataset, then lowercased and
lemmatized the words. We have also removed the stopwords and filtered out the
remaining words having document frequency lower than $0.001$ and higher than
$0.95$. We have only kept the words that have at least 3 characters. Finally,
we removed the documents with less than 3 words in them. After preprocessing,
we created bigrams to better capture word co-occurrence statistics.

###  6.2. Configuration for topic models

We used the following hyperparameters to run LDA, LDAseq, and D-ETM.

  1. 1.

LDA [Blei et al., 2003]: We use the Gensim implementation of the LDA model. To
enable reproducibility, we use a fixed random seed, i.e., set the random_state
$=2021$. We set passes to $20$ and use the default values for the remaining
hyperparameters.

  2. 2.

LDAseq [Blei and Lafferty, 2006]: We use the Gensim implementation. We set the
random_state value as $2021$ and passes as $20$. For the rest of the
hyperparameters, we use the defaults.

  3. 3.

D-ETM [Dieng et al., 2019]: We use the original implementation111
https://github.com/adjidieng/DETM with batch_size $=64$ and epochs $=100$, and
keep the default values for the rest of the hyperparameters.

###  6.3. Topics in TCPD-IPD

We used LDA to extract $50$ topics from the entire TCPD-IPD dataset as it
achieved the highest coherence score (see Figure 9) when topic count was
varied from $25$ to $200$ in steps of $25$.

Figure 9: NPMI values for the different number of topics in LDA over the full
TCPD-IPD dataset.

The topics with their manual labels are shown in Table 1. Note that we could
not run LDAseq because it was taking too much time. It did not terminate even
after running for 72 hours. We could not run D-ETM as it experienced the
posterior collapse, which could not be resolved even after changing the
hyperparameters.

Top-5 words in topic Manual label country, foreign, agreement, international,
sign Foreign affairs standard, quality, safety, use, pollution Pollution
urban, city, housing, delhi, construction Urban development security, state,
police, home, affair Law & order road, highway, national, construction, state
Road construction bank, loan, rbi, credit, account Banking committee, state,
review, recommendation, report Advisory body state, andhra_pradesh,
uttar_pradesh, maharashtra, tamil_nadu States fund, crore, release, year,
state Budget gas, oil, milk, production, natural_gas Petroleum air, airport,
defence, civil_aviation, airline Civil aviation post, employee, central,
office, officer Central Employee export, import, product, trade, textile
Market overview project, complete, work, cost, sanction Project case, court,
high_court, person, disability Judicial system solar, renewable_energy,
energy, system, power Renewable energy act, state, provision, section, rule
Act & regulations service, information, provide, telecom, network Information
technology payment, tax, pay, revenue, amount Tax power, plant, capacity,
supply, state Electricity plant sport, fertilizer, ltd, limit, corporation
Fertilizer plant madam, action, complaint, case, report Grievance water,
river, state, resource, drinking_water Water resources education, school,
university, student, human_resource HR in Education scheme, state,
development, provide, implement State development coal, mine, production,
mineral, mining Coal mining rural, district, area, village, functional Rural
issues delhi, mumbai, city, gujarat, chennai Metropolitan areas health, state,
family_welfare, drug, medical Health services answer, lok_sabha, reply,
statement, lay Parliament farmer, agriculture, crop, agricultural, production
Agriculture china, bangladesh, island, nepal, disaster Natural disaster
increase, year, rate, country, reduce Country growth steel, port,
connectivity, capacity, major Ports shipping tribal, schedule, minority,
scholarship, tribe Tribal affairs food, price, consumer, state, foodgrain Food
price sector, private, policy, public, investment Private sector woman, child,
employment, worker, labour Woman & child labour ngo, bihar, society,
organisation, organization NGO tourism, culture, site, tourist, development
Tourism industry, development, infrastructure, unit, scheme Industry company,
issue, guideline, application, insurance Insurance research, technology,
centre, training, national Research & Technology railway, train, station,
passenger, rail Railway land, forest, area, environment_forest, state Wildlife
conservation state, proposal, set, chhattisgarh, propose Miscellaneous
vehicle, procurement, website, award, contract Miscellaneous year, wise,
state, last_three, number Miscellaneous due, pleased, loss, reason, affect
Miscellaneous thereto, reaction, chaudhary, manoj, true Miscellaneous

Table 1:  Topics in the entire TCPD-IPD dataset.

###  6.4. Topics in subsets of TCPD-IPD

Dataset Coherence Diversity Topic Quality LDAseq D-ETM LDAseq D-ETM LDAseq
D-ETM Finance 0.088 0.078 0.652 0.496 0.057 0.039 Railways 0.129 0.094 0.686
0.558 0.088 0.052 Health 0.103 0.096 0.617 0.571 0.064 0.055

Table 2: Topic quality analysis.

We have carried out a pilot study on dynamic topic modeling with data subsets
for the three ministries (Finance, Railways, and Health and Family Welfare).
We set the topic count to 20. We have divided each data subset into year-wise
slices, partitioned each slice into train:validation:test as 8:1:1, and run
LDAseq and D-ETM on them. Then we calculated the coherence (NPMI), diversity,
and topic quality ($=NPMI\times Diversity$) for each model by averaging them
over the time slices. Table 2 shows that LDAseq performs best and hence we
choose it to extract topics from the ministry-specific datasets. The topics
that appear in Figure 5 in the main text have been manually labeled by looking
at their top five words. Table 3 shows these topic compositions.

Year Top-5 words in topic Manual label Ministry of Finance 1999 rate, growth,
cent, increase, year Economical growth 2009 rate, cent, growth, increase, year
2019 growth, cent, rate, economy, sector 1999 bank, rbi, reserve, issue,
guideline Banking 2009 bank, rbi, issue, guideline, reserve 2019 bank, rbi,
fraud, issue, transaction 1999 project, state, world, development, bank Rural
development 2009 project, state, development, infrastructure, rural 2019
project, development, state, infrastructure, fund 1999 bank, loan, credit,
nabard, state Agricultural loan 2009 bank, loan, credit, farmer, year 2019
loan, bank, farmer, credit, scheme 1999 scheme, employee, pension, interest,
deposit Pension reforms 2009 scheme, pension, fund, deposit, interest 2019
scheme, account, pension, state, pradhan_mantri Ministry of Railways 1999
work, complete, progress, project, line Railway project 2009 work, complete,
section, line, gauge_conversion 2019 work, section, complete, line,
gauge_conversion 1999 freight, traffic, passenger, good, ticket Ticket price
2009 ticket, passenger, freight, increase, scheme 2019 passenger, ticket,
fare, freight, train 1999 system, safety, track, committee, report Railway
safety 2009 system, track, safety, committee, signal 2019 system, track,
safety, train, committee 1999 project, corporation, rail, development, company
Infrastructure development 2009 project, development, corridor, rail, identify
2019 development, project, corridor, rail, high_speed 1999 station, facility,
provide, platform, provision Passenger amenity 2009 station, facility,
provide, platform, work 2019 station, provide, facility, platform, scheme
Ministry of Health and Family Welfare 2000 research, institute, study,
council, develop Medical research 2009 research, study, clinical_trial,
institute, council 2019 research, medical, study, clinical_trial, council 2000
child, population, programme, national, reproductive Woman & child healthcare
2009 child, programme, national, care, woman 2019 child, care, woman,
national, provide 2000 disease, malaria, control, case, death Communicable
diseases 2009 disease, control, case, report, malaria 2019 patient, treatment,
provide, free, scheme 2000 centre, care, area, service, rural Rural Medical
Infrastructure 2009 patient, treatment, provide, free, hospital 2019 national,
public, healthcare, provide, include 2000 patient, treatment, provide,
hospital, free Financial aid 2009 project, crore, fund, expenditure, cost 2019
project, fund, crore, cost, completion

Table 3:  Temporal topics for each ministry in the TCPD-IPD dataset.

###  6.5. Keyword extraction for gender and caste-related discussions

Word Top 20 neighbors gender gender, gender_equality, gender_disparity, women,
woman, gender_sensitivity, gender_sensitization, gender_gap, gender_parity,
girl, gender_sensitive, female_literacy, sex, male_female, disparity,
child_sex, child, girl_child, literacy_rate, sex_selective caste caste, tribe,
scs, obcs, obc, schedule, caste_tribe, scheduled_tribe, dalit, social_justice,
scs_sts, scheduled_caste, vijay_sampla, empowerment_napoleon, belong,
subbulakshmi_jagadeesan, atrocity, minority, pal_gurjar, empowerment_smt

Table 4: Top 20 neighbors (using Word2Vec) for each keyword.

To generate keywords for the analysis of gender and caste-related discussions,
we have applied SkipGram, which is one of the models used in the neural
network-based word2vec algorithm to generate word embeddings [Mikolov et al.,
2013]. We have run SkipGram on the entire TCPD-IPD dataset and taken the top
twenty neighbors (based on the cosine similarity of the generated word
vectors) of each of the keywords ‘gender’ and ‘caste’. The words are shown in
Table 4. Then, we counted the documents that contain those words. We have
ignored the words shown in italics in the table as they introduced many
irrelevant documents into the count.

###  6.6. Explanation of certain terms

Words in the main text that are difficult to understand outside the Indian
context are explained below.

  1. 1.

‘npci’: National Payments Corporation of India, created by the Reserve Bank of
India under the Ministry of Finance, to enable digital payments and settlement
systems in India.

  2. 2.

‘nabard’: National Bank for Agriculture and Rural Development, operating under
the Ministry of Finance. It regulates the institutions that supply financial
help to rural society.

  3. 3.

Kisan Credit Card (KCC): Farmers’ Credit Card.

  4. 4.

‘apy’: Atal Pension Yojana. ‘Yojana’ means scheme.

  5. 5.

‘dbt’: Direct Benefit Transfer.

  6. 6.

‘jan_dhan’: Pradhan Mantri Jan Dhan Yojana. Translates to ‘Prime Minister’s
People’s Wealth Scheme’.

  7. 7.

‘rail_vikas’: Railway Vikas Nigam Limited is owned by the Ministry of Railways
involved in building rail infrastructure.

  8. 8.

‘irsdc’: Indian Railway Station Development Corporation.

  9. 9.

‘scs’, ‘obc’, ‘obcs’, ‘scs_sts’, ‘scheduled_tribe’, ‘scheduled_caste’: These
words denote historically disadvantaged communities in India. Scheduled
Castes, Scheduled Tribes, and Other Backward Classes are abbreviated as SC,
ST, and OBC, respectively.

##  7\. Bibliographical References

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##  8\. Language Resource References

## References

  * Trivedi Centre for Political Data, 2019 Trivedi Centre for Political Data, Ashoka University.  (2019).  TPCD-IPD: TCPD Indian Parliament Dataset (Question Hour) 1.0.