Actual value and historical data chart for India Political Stability And Absence Of Violence Terrorism Percentile Rank

The average for 2023 based on 193 countries was -0.07 points. The highest value was in Liechtenstein: 1.61 points and the lowest value was in Syria: -2.75 points. The indicator is available from 1996 to 2023. Below is a chart for all countries where data are available.

India IN: Political Stability and Absence of Violence/Terrorism: Estimate data was reported at -0.826 NA in 2017. This records an increase from the previous number of -0.954 NA for 2016. India IN: Political Stability and Absence of Violence/Terrorism: Estimate data is updated yearly, averaging -1.152 NA from Dec 1996 (Median) to 2017, with 19 observations. The data reached an all-time high of -0.826 NA in 2017 and a record low of -1.509 NA in 2003. India IN: Political Stability and Absence of Violence/Terrorism: Estimate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s India – Table IN.World Bank.WGI: Country Governance Indicators. Political Stability and Absence of Violence/Terrorism measures perceptions of the likelihood of political instability and/or politically-motivated violence, including terrorism. Estimate gives the country's score on the aggregate indicator, in units of a standard normal distribution, i.e. ranging from approximately -2.5 to 2.5.

Actual value and historical data chart for India Political Stability And Absence Of Violence Terrorism Percentile Rank Upper Bound Of 90percent Confidence Interval

Yearly (annual) dataset of the India Political stability index, including historical data, latest releases, and long-term trends from 1996-12-31 to 2023-12-31. Available for free download in CSV format.

The British Crown Dependency of Jersey was ranked as the most politically stable country worldwide in 2023, ahead of the Cayman Islands and Liechtenstein. The Caribbean Islands are known for their favorable conditions for large international companies and wealthy individuals, with no income and fortune tax. Lowest stability in Syria On the other end of the scale, Syria had the lowest political stability. The Middle Eastern-country suffered from a civil war between 2012 and 2024, with the Syrian government battling a range of military groups, including the terrorist organization Islamic State. Fragile State Index Another way of measuring political stability is the Fragile States Index, compiled annually by the Fund for Peace. In 2024, Somalia was ranked as the most fragile state ahead of Sudan. The index measures state fragility on a range of economic, social, and political indicators.

According to Economic Freedom ranking in 2024, India's score for government integrity stood at ****, a significant decrease from the previous year. This put the country in the repressed range, while the overall score stood at **** in the same year.

CODE.PY OUTPUT

Institutional Benchmarking and Regression Results

To contextualize institutional performance, we first compare India and Pakistan against the global mean of common-law economies on three governance indicators: Protecting Minority Investors (PMI), Enforcing Contracts (EC), and a composite index of the Legal-Political Environment. The global averages for common-law countries are:

• PMI: 18.6

• EC: 37.9

• Legal-Political Environment: 7.44 (on a 0–10 scale)

Relative to these benchmarks:

• India outperforms the PMI average by 5.6 ranks, but underperforms EC by 125.1 ranks and legal-political environment by 2.74 points.

• Pakistan underperforms on all three: +9.4 ranks in PMI (worse), +118.1 ranks in EC, and –4.24 points on legal-political environment.

These gaps suggest that formal investor protections (PMI) are stronger in India, but contract enforcement and broader institutional trust lag significantly in both countries.

Regression Analysis: Institutional Predictors of Governance Outcomes

We ran robust Ordinary Least Squares (OLS) regressions with Control of Corruption (cc), Rule of Law (rl), and Political Stability (pv) (from the Worldwide Governance Indicators) as predictors of country performance in both PMI and EC.

✅ Protecting Minority Investors (PMI)

• Model Fit: R² = 0.364; F(3, 182) = 46.91; p < 0.001

• Significant predictors:

  • Rule of Law (β = –62.74, p < 0.001): Strong negative relationship, consistent with countries with weaker rule of law having higher PMI ranks (i.e., worse protections).

  • Political Stability (β = +22.28, p < 0.001): Higher stability is associated with better (lower) PMI rank.

  • Control of Corruption (β = +15.91, p = 0.078): Marginally significant.

✅ Enforcing Contracts (EC)

• Model Fit: R² = 0.389; F(3, 182) = 52.32; p < 0.001

• Significant predictors:

  • Rule of Law (β = –47.55, p < 0.001): Again, weak rule of law predicts poor performance.

  • Political Stability (β = +11.27, p = 0.029): More stable environments enforce contracts more efficiently.

  • Control of Corruption was not significant (p = 0.61).

These results underscore the salience of Rule of Law and Political Stability in explaining variation in corporate governance effectiveness across countries.

Peer-Adjusted Z-Scores: Rule of Law and Judicial Independence

To further refine the comparison, we standardized India and Pakistan’s scores relative to global peers using z-scores:

• India: Judicial Independence z = +1.39, Rule of Law z = +1.28

• Pakistan: Judicial Independence z = +0.25, Rule of Law z = –0.95

This highlights India’s relative institutional strength in legal capacity, while Pakistan falls below global norms, particularly on rule of law.

CODE2.PY OUTPUT

Summary Statistics and Cross-National Comparison

We begin by comparing key governance indicators for India and Pakistan over the 1996–2020 period using data from the V-Dem dataset. Table X reports descriptive statistics for six core institutional quality variables:

• v2x_rule: Rule of Law

• v2x_jucon: Judicial Constraints on the Executive

• v2xlg_legcon: Legislative Constraints

• v2x_freexp: Freedom of Expression

• v2x_polyarchy: Electoral Democracy Index

• v2x_corr: Control of Corruption

Key Observations from the 23-year panel:

• Rule of Law (v2x_rule): India displays a high mean score of 0.579 (SD = 0.029), while Pakistan lags significantly behind at 0.237 (SD = 0.023).

• Judicial Constraints (v2x_jucon): India again leads with a mean of 0.814, compared to 0.537 for Pakistan.

• Control of Corruption (v2x_corr): Interestingly, Pakistan scores higher (0.868) than India (0.566), suggesting a potential data artifact or performative anti-corruption signaling.

These descriptive statistics show a consistent pattern of stronger rule-of-law institutions in India. However, India’s governance edge does not hold across all indicators—especially corruption control, which exhibits counterintuitive results.

T-Test Results: India vs. Pakistan

We formally test whether the differences in means between India and Pakistan are statistically significant using two-sample t-tests:

The highly significant differences in nearly all variables confirm that India and Pakistan follow distinct institutional trajectories—though India’s relative weakness in corruption control invites further scrutiny under the CMF framework.

Structural Breaks in India’s Democratic Governance

Using the ruptures package and a rolling t-test approach, we detect structural breakpoints in India’s democratic trajectory:

• Based on v2x_polyarchy, break years are identified at 2011, 2016, and 2021.

• The rolling t-test method suggests more granular shifts starting as early as 2001, with notable accelerations around 2011–2019.

These breakpoints align with major political and constitutional developments in India and support the CMF argument that formal continuity in legal benchmarks may obscure deeper institutional volatility.

CODE3.PY OUTPUT

Summary Statistics and Institutional Quality Comparison: India vs. Pakistan

We begin by reporting descriptive statistics for two core institutional variables—Control of Corruption (v2x_corr) and Judicial Constraints on the Executive (v2x_jucon)—drawn from the V-Dem dataset for the years 1996–2020:

• Control of Corruption (v2x_corr):

  • India: Mean = 0.566, SD = 0.027, indicating relatively consistent performance with moderate corruption control.

  • Pakistan: Mean = 0.868, SD = 0.051, suggesting surprisingly strong corruption scores, but with greater variability. This may reflect methodological distortions or performative anti-corruption institutions that lack substantive checks—a key focus of our Critical Macro-Finance (CMF) interpretation.

• Judicial Constraints (v2x_jucon):

  • India: Mean = 0.814, SD = 0.013, indicating strong and stable judicial oversight over executive actions.

  • Pakistan: Mean = 0.537, SD = 0.140, reflecting weaker, more volatile institutional constraints.

T-Test Results: Are India and Pakistan Statistically Different?

Two-sample t-tests confirm that these differences are highly statistically significant:

These results validate the hypothesis that India and Pakistan exhibit substantially divergent institutional trajectories—though not always in expected directions. India shows stronger judicial oversight, while Pakistan appears to outperform in corruption metrics, warranting

In India, ** percent of respondents rank regulatory compliance as the most important risk related to open-source AI tools. Intellectual property issues and cybersecurity follow with ** and ** percent, respectively. Lower-ranked concerns include political stability and environmental impact with ***** and ** percent, respectively.

This dataset provides information about regimes in 10 Indian States (Karnataka, Kerala, Goa, Madhya Pradesh, Gujarat, Haryana, Bihar, West Bengal, Meghalaya, and Nagaland) for the period of 1990-2017. The dataset contains variables that are proxy of 'power' and 'stability' of state-level regimes for successive rounds of elections to legislative assemblies covering 1990-2017 period. These data were collected from the Election Commission of India and State Election Commissions available through their websites. We also used free internet searches for specific information including Wikipedia pages of state assembly elections as well as political leaders. The following points will help better understand the dataset and its strengths and limitations:There are two sheets in the associated MS Excel file. The first one, 'Regime variables' provides data about five variables chronologically for the successive election rounds (covering the 1990-2017 period) for 10 Indian states. These variables include: (1) names of chief ministers including indicating president rule in these states; (2) tenure of these chief ministers including duration of president rule if any; (3) names of political party/parties forming governments in these states; (4) type of governments: a single party majority government, a single party majority government that keeps alliances with other parties, and a coalition (multi-party) government; (5) number of seats (constituencies) won by various political parties in a given election round. The second sheet, 'Abbreviations-Political Parties' provide expansion of short forms used for some of the political parties in the dataset in the first sheet.We curated this dataset as part of the broader project wherein we were interested in assessing the power and stability of state regimes. We used the type of government and the winning part/parties share of total seats as proxy measures for the power of the regime. We calculated the regime switch (how frequently the regime change in successive election rounds) and the average tenure of chief ministers as proxy measures for the stability of the regime. It was not easy to find all the data we needed from one source. We could not locate certain data e.g. data on exact composition of political coalitions especially when frequent changes took place in such coalitions post elections in a few states such as Meghalaya and Goa. So, there are likely to be errors and omissions in data. We tried our best to capture data from authoritative sources as much as possible given the limited time and resources we had.This dataset was produced as part of the broader research project that explored the political economy of tobacco, titled “Deciphering an epidemic of epic proportion: the role of state and tobacco industry in tobacco control in post-liberalised India (1990-2017)”. We thank the DBT/Wellcome Trust India Alliance for funding this project through the Intermediate (Clinical and Public Health) Fellowship awarded to Upendra Bhojani (IA/CPHI/17/1/503346).

Immigration Service Market Outlook

The global immigration service market size was valued at approximately USD 25 billion in 2023 and is projected to reach USD 40 billion by 2032, growing at a CAGR of 5.5% during the forecast period. The growth of the immigration services market is largely fueled by increasing globalization and the consequent rise in cross-border movements, driven by both personal ambitions and corporate strategies.

One of the primary growth factors for the immigration service market is the increasing demand for skilled labor in various developed economies. Countries like the United States, Canada, Australia, and many European nations are witnessing a significant skill gap in their labor markets, prompting them to ease immigration policies and provide more opportunities for skilled workers. This has led to an increase in demand for immigration services such as visa applications, work permits, and residency services. Additionally, the aging population in many developed nations adds to the urgency of attracting younger, skilled immigrants to maintain economic stability.

Another significant growth driver is the rise of multinational corporations (MNCs) and their need to deploy human resources globally. With businesses expanding their operations across borders, there is a growing requirement for corporate immigration services to manage work permits, intra-company transfers, and compliance with local immigration laws. This trend is particularly noticeable in sectors like IT, healthcare, engineering, and finance, where specialized skills are in high demand, and talent mobility is crucial for business operations. The increasing ease of doing business internationally has also encouraged smaller enterprises to explore global markets, further boosting the demand for immigration services.

The socio-political environment also plays a crucial role in shaping the immigration service market. Political stability and favorable immigration policies in certain regions make them attractive destinations for immigrants. For instance, countries that are known for their inclusive policies and transparent immigration processes tend to attract more immigrants. Moreover, the evolving geopolitical landscape, including scenarios like Brexit or shifts in U.S. immigration policies, significantly impacts the flow of immigrants and the demand for various immigration services. These changes necessitate the continuous adaptation and evolution of immigration services to meet new regulatory requirements and client needs.

When it comes to regional analysis, North America and Europe remain dominant players in the immigration service market due to their attractive job markets and robust economies. However, the Asia Pacific region is emerging as a significant player, driven by rapid economic development and increasing opportunities in countries like China, India, Japan, and Australia. Latin America and the Middle East & Africa are also showing potential growth, albeit at a slower pace, due to improving economic conditions and political reforms in certain countries. Each region presents unique opportunities and challenges, requiring tailored strategies and services to meet the specific needs of immigrants and corporations.

In recent years, the advent of Online Visa Service has revolutionized the immigration landscape, providing a more streamlined and accessible approach to visa applications. These digital platforms allow applicants to submit their visa requests and track their progress from the comfort of their homes, eliminating the need for physical visits to embassies or consulates. The convenience offered by online services is particularly beneficial for individuals in remote locations or those with busy schedules. Moreover, online visa services often incorporate advanced technologies such as artificial intelligence and machine learning to enhance the accuracy and efficiency of the application process. This digital transformation is not only improving user experience but also reducing processing times and minimizing errors, making it a preferred choice for tech-savvy applicants and service providers alike.

Service Type Analysis

Visa services form a crucial segment of the immigration service market, addressing the fundamental need for legal entry and stay in a foreign country. This segment is highly diversified, covering various types of visas such as student visas, tourist visas, business visas, and permanent residency visas, among others. The demand for visa servi

As of 2024, Mumbai had a gross domestic product of *** billion U.S. dollars, the highest among other major cities in India. It was followed by Delhi with a GDP of around *** billion U.S. dollars. India’s megacities also boast the highest GDP among other cities in the country. What drives the GDP of India’s megacities? Mumbai is the financial capital of the country, and its GDP growth is primarily fueled by the financial services sector, port-based trade, and the Hindi film industry or Bollywood. Delhi in addition to being the political hub hosts a significant services sector. The satellite cities of Noida and Gurugram amplify the city's economic status. The southern cities of Bengaluru and Chennai have emerged as IT and manufacturing hubs respectively. Hyderabad is a significant player in the pharma and IT industries. Lastly, the western city of Ahmedabad, in addition to its strategic location and ports, is powered by the textile, chemicals, and machinery sectors. Does GDP equal to quality of life? Cities propelling economic growth and generating a major share of GDP is a global phenomenon, as in the case of Tokyo, Shanghai, New York, and others. However, the GDP, which measures the market value of all final goods and services produced in a region, does not always translate to a rise in quality of life. Five of India’s megacities featured in the Global Livability Index, with low ranks among global peers. The Index was based on indicators such as healthcare, political stability, environment and culture, infrastructure, and others.

In financial year 2023, expense on environmental protection accounted for nearly **** percent of the total government expenditure in India. In comparison to the last few fiscal years, more funds were earmarked for environmental stability that year.