This dataset provides values for COMPOSITE PMI reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

Composite PMI: Headline: sa: China data was reported at 51.800 NA in Mar 2025. This records an increase from the previous number of 51.500 NA for Feb 2025. Composite PMI: Headline: sa: China data is updated monthly, averaging 51.850 NA from Apr 2022 (Median) to Mar 2025, with 36 observations. The data reached an all-time high of 55.600 NA in May 2023 and a record low of 37.200 NA in Apr 2022. Composite PMI: Headline: sa: China data remains active status in CEIC and is reported by S&P Global. The data is categorized under World Trend Plus’s S&P Global Purchasing Managers' Index: Headline – Table CN.OTC: Composite PMI Headline. [COVID-19-IMPACT]

Business Confidence in the United States increased to 49 points in June from 48.50 points in May of 2025. This dataset provides the latest reported value for - United States ISM Purchasing Managers Index (PMI) - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

China Purchasing Managers' Index: Composite Output data was reported at 52.800 % in Nov 2018. This records a decrease from the previous number of 53.100 % for Oct 2018. China Purchasing Managers' Index: Composite Output data is updated monthly, averaging 54.100 % from Jan 2017 (Median) to Nov 2018, with 23 observations. The data reached an all-time high of 55.100 % in Sep 2017 and a record low of 52.800 % in Nov 2018. China Purchasing Managers' Index: Composite Output data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Business and Economic Survey – Table CN.OP: Purchasing Managers' Index. Composite PMI output index is a weighted index compiled by indices of manufacturing’s production and non-manufacturing’s business activity, the weights are the proportion of manufacturing and non-manufacturing sectors as of GDP. 综合PMI产出指数由制造业生产指数与非制造业商务活动指数加权求和而成，权数分别为制造业和非制造业占GDP的比重。

This dataset provides values for MANUFACTURING PMI reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

India's Purchasing Managers Index (PMI) data - current and historical values for composite, manufacturing and services index, in addition to expert analysis.

In March 2025, the Composite Purchasing Managers Index (PMI) in the United Kingdom was 52, compared with 50.5 in the previous month. Following the COVID-19 outbreak, the composite PMI fell from 53 in February 2020, to 13.8 by April, highlighting the dire economic situation brought on by the pandemic. The composite PMI recovered from July onwards, but fell to 49 in November 2020, with a slight recovery in December preceding an even further drop to 41.2 in January 2021.

Business Confidence in China increased to 49.70 points in June from 49.50 points in May of 2025. This dataset provides - China Business Confidence - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Nigeria Non Manufacturing PMI (NMI) data was reported at 58.400 % in Nov 2018. This records an increase from the previous number of 57.000 % for Oct 2018. Nigeria Non Manufacturing PMI (NMI) data is updated monthly, averaging 53.400 % from Jul 2014 (Median) to Nov 2018, with 53 observations. The data reached an all-time high of 62.100 % in Dec 2017 and a record low of 41.000 % in Sep 2016. Nigeria Non Manufacturing PMI (NMI) data remains active status in CEIC and is reported by Central Bank of Nigeria. The data is categorized under Global Database’s Nigeria – Table NG.S001: Purchasing Managers Index. The composite Non Manufacturing PMI is a simple average of the following diffusion indices: business activity, new orders, employment level and inventories.

The Taiwan Procurement Manager Index survey results include Manufacturing Procurement Manager Index (PMI), Non-Manufacturing Manager Index (NMI) composite index, and reference indicators.

Philadelphia Fed Manufacturing Index in the United States increased to 15.90 points in July from -4 points in June of 2025. This dataset provides the latest reported value for - United States Philadelphia Fed Manufacturing Index - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

采购经理指数:综合产出在11-01-2018达52.800%，相较于10-01-2018的53.100%有所下降。采购经理指数:综合产出数据按月更新，01-01-2017至11-01-2018期间平均值为54.100%，共23份观测结果。该数据的历史最高值出现于09-01-2017，达55.100%，而历史最低值则出现于11-01-2018，为52.800%。CEIC提供的采购经理指数:综合产出数据处于定期更新的状态，数据来源于国家统计局，数据归类于中国经济数据库的经济和企业调查 – Table CN.OP : 采购经理指数。

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

Univariate-unadjusted and multivariate-adjusted Cox proportional hazard model analysis for the composite endpoint of all-cause death and ADHF re-hospitalization.