Submitted:
30 January 2025
Posted:
31 January 2025
You are already at the latest version
Abstract
The current study addresses political party trust and Environmental, Social, and Governance (ESG) principle application in Italian regions. Trust in political parties is a principal driving force in governance performance, compliance with policies, and citizen trust in institution-made choices. With political and economic diversity in its regions, Italian regions present a case study for testing whether political institution trust increased creates increased ESG use and whether ESG policies have an impact in shaping political trust in reciprocity. Empirical evidence confirms that in high political trust regions, ESG programs have a high opportunity for effective implementation, particularly in social welfare and conservation of environment. In contrast, political institution trust weakness is accompanied with poor ESG pledges, an expression of inefficient governance and reduced accountability in companies. ESG policies actually have an impact on political trust—effective and transparent ESG actions establish institution trust, but shallow and political ESG actions produce mistrust. The observations have a function of projecting the contribution towards balancing institution trust with sustainability through governance quality. Policymakers can contribute towards leveraging political stability in driving ESG integration in a manner that keeps such programs effective and credible for long-term development in regions and for democratic legitimacy.
Keywords:
1. Introduction
2. Literature Review
2.1. ESG Performance and Corporate Governance
2.2. ESG, Politics, and Regulatory Frameworks
2.3. ESG Investing, Financial Markets, and Risk Management
2.4. ESG and Stakeholder Perception
3. Data
4. Econometric Models
4.1. Trust in Political Parties and E-Environment Component
4.2. Trust in Political Parties and the S-Social Component
4.3. Trust in political parties and G-Governance Component
5. Political Implications
6. Conclusions
References
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| Macrotheme | Authors | Methodologies | Geographical Area | Results |
|---|---|---|---|---|
| ESG Performance and Corporate Governance | Zhu, B., & Wang, Y. (2024); Passas, I., Ragazou, K., Zafeiriou, E., Garefalakis, A., & Zopounidis, C. (2022); Mooneeapen, O., Abhayawansa, S., & Mamode Khan, N. (2022); Sun, Y., & Zhu, L. (2024); Alam, A. W. (2022); Harjoto, M. A., & Wang, Y. (2024); Sun, Y., Zhu, L., & Hu, D. (2024); Wang, J. (2023); Liu, H., Wei, S., & Zhang, J. (2023); Kulova, I., & Nikolova-Alexieva, V. (2023); Fuadah, L. L., Mukhtaruddin, M., Andriana, I., & Arisman, A. (2022); Martha, H., & Khomsiyah, K. (2023); Xie, Y. (2024); Luo, Z., Li, Y., Nguyen, L. T., Jo, I., & Zhao, J. (2024); Cornell, B., & Shapiro, A. C. (2021). | Empirical analysis, statistical modeling, case studies, firm-level ESG performance assessment, econometric regression models. | Global, with specific studies on China, EU, and the US. | Strong ESG performance is positively correlated with corporate financial stability, stakeholder trust, and long-term growth, but varies by industry and region. Regulatory support enhances ESG adoption. |
| ESG, Politics, and Regulatory Frameworks | Blomqvist, A., & Stradi, F. (2024); Ollikainen, E. (2022); Costantiello, A., & Leogrande, A. (2023); Norman, A. L. (2024); Guedes, R., Neves, M. E., & Vieira, E. (2024); Zhang, X., Zhang, J., & Feng, Y. (2023); Pardy, B. (2023); He, X. (2024); Kubisch, M. R. (2023); Huang, X., & Yang, L. (2024); Kim, H., Macey, J., & Underhill, K. (2023); Attig, N., El Ghoul, S., & Hossain, A. T. (2024); Rajgopal, S., Srivastava, A., & Zhao, R. (2024). | Policy analysis, legal and regulatory review, econometric models, qualitative comparative analysis, case study evaluation. | Primarily US, EU, and China, with some global perspectives. | ESG policies and regulations play a key role in corporate ESG adoption; political divides and regulatory uncertainty create inconsistencies in implementation and enforcement. |
| ESG Investing, Financial Markets, and Risk Management | Steen, P., Rhodes, A., Carslaw, E., & Cornaglia, M. (2023); Ooi, V., & See, A. W. L. (2024); D'Souza, S. (2023); Chiaramonte, L., Dreassi, A., Girardone, C., & Piserà, S. (2022). | Quantitative financial modeling, risk assessment frameworks, market trend analysis, investment portfolio evaluation. | Europe, US, and Global Markets. | ESG investing shows mixed financial results—some studies find outperformance, while others highlight market inefficiencies and greenwashing risks. Political factors influence investor behavior. |
| ESG and Stakeholder Perception | Tripopsakul, S., & Puriwat, W. (2022); Hasan, M. B., Verma, R., Sharma, D., & Moghalles, S. A. (2024); Cash, D. (2024). | Consumer surveys, sentiment analysis, qualitative thematic analysis, brand perception studies. | Asia (Thailand, India), US, and Global Trends. | ESG positively impacts brand trust and consumer loyalty; regulatory frameworks and transparent ESG reporting strengthen consumer confidence and engagement. |
| Variables | Acronym | Definition | |
|---|---|---|---|
| Trust in the parties | TP | The political party trust level, averaged out, is a significant political gauge of political powers' level of confidence in citizens. Measured between 0 and 10, with 10 signifying full trust and 0 signifying full lack of trust, it is calculated through transparency, effective policies, and a lack of corrupt actions. In the past years, most countries have seen a fall in trust level, an indication of heightened political institution disbelief. It is calculated in relation to socioeconomic background, political orientation, and political party performance in a country in a relatively short span of years. | |
| E-Environment | Historical green density | HGD | The urban environment's surface area (in m²) of significant public-interest historic green spaces and urban parks (according to Legislative Decree 42/2004) in provincial capital municipalities is an important urban environment quality measurement. It is an expression of the density of such green spaces in relation to 100 m² of urbanized urban and city center and inhabited lands according to 2011 Population Census statistics. It is an expression of a city's dedication to protecting cultural landscapes and providing access to urban citizens' green spaces. It is an expression of urbanity, environment, and heritage conservation, and a high value reflects increased urban livability, environment, and heritage conservation, and citizens' welfare and ecologic harmony. |
| Index of duration of hot periods | IDHP | A sound forecaster of extreme temperature events is a period six days and longer when temperature is above the 90th percentile base period (1981–2010). It can monitor severity and frequency of heatwaves, and such events have important implications for infrastructure, public health, and environment. Climate change tends to cause such events to occur with heightened frequency, and it lessens agricultural yields, increases demand for coolers, and increases danger of disease through the heat. Public health preparation, urban planning, and planning for adaption to climate necessitates tracking such events. | |
| Consecutive days without rain | CDWR | The days in a year with a high temperature over 90th base period (1981-2010) for six days and more in succession is a strong marker for extreme temperature events. It can both monitor intensity and frequency of heatwave, and both have strong implications for public health, environment, and infrastructure. Onset of an increased trend in such events is most frequently accompanied with, and is accompanied with, augmented danger of disease through heat, lowered agricultural productivity, and augmented demand for cool, amongst many others. Monitoring such events is important for urban planning, public health preparedness, and planning for adaptations in terms of climate change. | |
| Availability of urban greenery | AUG | The metropolitan urban green spaces per capita in metropolitan and province level municipalities act as a significant urban and environment marker and urban living and an indicator of citizens' access to urban parks, urban gardens, and urban green spaces and its availability promotes well-being, urban air, and urban biodiversity. Greater values represent a larger access to urban leisure spaces, enhancing citizens' physical and mental well-being and urban cool islands and filtering urban pollution. Green spaces distribution can reveal urban planning inequalities. Green spaces must be monitored and extended for city development and improvement in citizens' living standards in a sustainable manner. | |
| Soil waterproofing by artificial covering | SWAC | The proportion of impervious soil in terms of overall land cover is an important urbanization and environment impact indicator. Roads, buildings, and paving restricts infiltration in the ground, increasing flood danger, lowering groundwater recharging, and creating urban heat islands. High proportion signifies high land sealing, most probably a consequence of urban expansion and habitat loss at a high pace. Monitoring of the marker aids in evaluation of urban and natural land cover balance in a city, and subsequently, in planning for urban development in a sustainable path. Mitigation of soil sealing via green infrastructure and permeable coverings is important for urban environment resilience. | |
| Internal material consumption | IMC | Domestic matter consumption is a measure of annual matter use, excluding water and air, consumed and emitted to the environment, or stored in new anthropogenic stocks, including matter embodied in emissions and in waste flows, and matter stored in infrastructure, in durable goods, and in other types of long-lasting goods and assets. Higher domestic consumption is an expression of increased extraction of matter and potentially increased environment burden, and efficient use of matter helps in enhancing sustainability. Quantification helps in comparing efficiency in use of matter, generating matter in form of waste, and transition towards a circular economy, and promotes sustainable development and reduced environment burden. | |
| Unauthorized building | UB | The ratio of illegal constructions out of 100 approved constructions in terms of urban planning compliance and effectiveness in law enforcement is a key performance indicator. Any high value will represent widespread unauthorized development, and in its impact, can lead to environment degradation, inefficient use of lands, and peril in terms of non-conformity with structures, and illegal constructions in such a case can go unchallenged in areas with poor regulating controls and high housing demand, and can lead to unregulated urban expansion. Monitoring such an indicator will allow for evaluation of urban policies' effectiveness and necessity for stricter enforcement actions. Mitigation of unauthorized construction helps in urban development in a sustainable manner and encourages safer living environments. | |
| Protected areas | PA | The proportion of safeguarded natural terrestrial spaces in the official register of safeguarded spaces (EUAP) or in Natura 2000 network is a strong conservation and safeguard gauge of environment and biodiversity. It signifies a high conservation level towards an ecosystem, protecting wildlife habitats, and a high level of ecological integrity when a high proportion is attained. It helps in protecting endangered species, curbing climate change, and improving lands' use in a sustainable manner when such spaces are effectively preserved and increased proportionately. Monitoring such an indicator enables one to evaluate conservation policies and inform strategies for increased expansion of such spaces for increased environment sustainability and adaptability towards humanity. | |
| S-Social | Transition to university | TU | The specific cohort rate, a proportion of new high school students entering university for the first time in the same academic year in which secondary school graduation takes place, is an important post-secondary access indicator. It reflects students' immediate transition to university and can vary with such factors as financial situation, academic aspirations, and availability of educational opportunity. It excludes technical institutes, upper level artistic, musical, and coreutic schools, language mediator schools, and universities abroad. Trend analysis in post-secondary attendance and success in university access policies can be measured through following this rate. |
| Young people neither in employment nor in education (NEET) | NEET | The 15-29 years not in work and in training and education (NEET) proportion is an important labour and educational disengagement youth marker. High proportion of NEET reflects school-to-work transition, financial vulnerability, and social exclusion problem. Economic performance, access to training and education, opportunity at work, and social policies shape its dimensions. Monitoring it enables one to evaluate improvement in youth work and training access programs and evaluate effectiveness in social and educational programs. Decline in its proportion is important for social integration, long-term workforce development, and overall economy development. | |
| Use of libraries | UL | The three years and over adults' proportion visiting a library at least one time in the 12 months preceding survey date is a key information access and cultural activity marker. It is a reading behavior, educational activity, and community use of libraries marker. A rise in proportion reflects growing use of public assets for studying, researching, and enjoyment, and a fall in proportion can represent access barriers and preference shifts towards electronic information. Trends in such a marker can inform an analysis of reading and its role in a life of learning and social cohesion, and inform cultural development policies. | |
| Transformations from unstable to stable jobs | TUSJ | The ratio of insecure workers at t0 (including cooperating and part-time workers) moving into secure jobs (full-time jobs) one year afterward is an important labour marker and job security indicator. High ratio reflects a healthy labour market with career opportunity, but a low value reflects a constraint in accessing long-term jobs. Economic performance, labour policies, and demand in a specific sector drive such a move. Keeping track of such an indicator enables one to evaluate trends in job security and efficient policies in securing jobs, and gain a deeper insight in workforce development and economy resilience. | |
| Economic situation of the family | ESF | The proportion of respondents reporting that financial position worsened a lot, or worsened, in comparison with the previous year is a significant financial welfare and financial security gauge. High proportionate values represent increased financial difficulty, and loss of jobs, living expenses rise, and financial recession can cause them. It is a measure of financial security feelings of householders and can affect consumption and social cohesion. Monitoring such an indicator can monitor economic trends, inform social policies, and identify groups at risk, and contribute to actions taken in an attempt to counteract financial inequality and living standards improvement. | |
| Social participation | SP | The level of civic and community activity level is an expression of a proportion of persons 14 years and older who have participated in one social activity during a 12-month period. Included in activity considered is attendance at an activity sponsored by a church, synagogue, or similar group, attendance at a group cultural, recreative, or other group meeting, and activity in an environment, a peace, or a civil rights group. Included, in addition, is attendance at a union, a professional, or a trade association, a political party, party volunteer work, or payment for a fee for a group of sports. Monitoring of this indicator in terms of social activity and community activity level is important. | |
| Civic and political participation | CPP | The level of citizens 14 years and older with at least one activity in political and civic activity mirrors social and political activity in society. Included in considered activity is discussing politics at least one week a week, staying updated about Italian political life at least one week a week, taking part in web consultation or voting about social and political life (e.g., urban planning, signatures for a petition) in the last three months, and providing feedback about social and political life through websites and social networks in the same three-month period. | |
| G-Governance | Trust in the Italian Parliament | TIP | The average trust score in the Italian Parliament, measured on a scale from 0 to 10, reflects the level of confidence that people aged 14 and over have in this institution. A higher score indicates greater trust in the Parliament’s ability to represent citizens and make effective decisions, while a lower score suggests skepticism or dissatisfaction. This indicator is influenced by political stability, government performance, transparency, and public perception of corruption. Monitoring trust in Parliament helps assess democratic engagement and institutional credibility, providing insights into citizens’ attitudes toward governance and the effectiveness of political leadership over time. |
| Trust in the judicial system | TJS | The average trust score in the judicial system, measured on a scale from 0 to 10, reflects the level of confidence that people aged 14 and over have in the fairness, efficiency, and integrity of the legal system. A higher score indicates strong public trust in the judiciary’s ability to uphold justice, protect rights, and enforce laws, while a lower score suggests concerns about inefficiency, corruption, or bias. This indicator is influenced by factors such as trial duration, transparency, high-profile cases, and perceptions of judicial independence. Monitoring trust in the judiciary helps assess institutional credibility and democratic stability. | |
| Trust in the police and firefighters | TPF | The trust level in firefighters and in the police, rated between 0 and 10, gauges citizens' confidence in these groups' effectiveness, integrity, and dependability, for citizens 14 years and older. High trust is a high rating, indicative of strong confidence in them to deliver security, maintain order, and act appropriately in an emergency, and low trust a low rating, indicative of concern over efficiency, professionalism, and abuse of powers. Responses, perceived fairness, community relations, and press reporting have an impact on trust in them. Monitoring such an indicator gauges citizens' relations with security organs and informs about citizens' feelings about security and about trust in institutions. | |
| Mobility of Italian graduates (25-39 years) | MIG | The level of migration for Italians between 25 and 39 years with a tertiary qualification is calculated as a proportion between net migration (the difference between registrations and deregistrations for a residence change) and total citizens with a tertiary qualification (including university, AFAM, and doctorate qualifications). For national totals, values apply only to flows towards and from abroad, but for territorial subdivisions, inner migration between regions is included. It is an indicator useful for comparing high qualification citizens' mobility, and for tracking trends in brain drain and in a region's attraction for qualified professionals. | |
| Regular internet users | RIU | The proportion of individuals 11 years and older who have ever gone onto the Internet at least one week in the three months preceding a survey is a significant access and use marker of technology. High proportion indicates high use and high Internet penetration, and a low value can indicate restricted access, technological divide, and poor technological competencies. High-speed access availability, affluence level, and individual technological competencies affect such a marker. Trends in Internet use can assess technological policies' effectiveness, technology use in work and studies, and social and educational impact of the Internet. | |
| Availability of at least one computer and internet connection in the family | ACIF | The level of a household with an Internet subscription and one or more computers (desktops, laptops, notebooks, tablets, but not smartphones, gaming machines, and e-book readers, but not PDAs with phone capabilities) is an important sign of technological infrastructure and access to technology tools. Greater proportion signifies increased access and use of work, school, and communications tools via the web, and a low value can represent financial constraints and technology gaps. Incomes, infrastructure availability, and computer competency contribute to this variable. Trends in this variable can gauge improvements in closing technology gaps in access and in providing access to technology for all. | |
| Nurses and midwives | NM | The 1,000 citizens to nurses and midwives proportion is an access and medical system capacity marker for medical professionals access, medical care access, care for patients, and materno and infant care. Greater proportion signifies increased medical care access, increased care for patients, and increased care for materno and infant care. Lower proportion, on the other hand, can mean impending shortages, overworked medical professionals, and less capacity for proper care. Government medical care policies, medical school investments, and population trends affect proportion. Keeping an eye on proportion enables one to gauge efficiency in medical care and whether workforce and asset distribution can and must extend. |
| 1-step dynamic panel, using 320 observations | Fixed-effects, using 360 observations | Random-effects (GLS), using 360 observations | Pooled OLS, using 360 observations | |||||||||
| Coeff. | Std. Error | z | Coeff. | Std. Error | t-ratio | Coeff. | Std. Error | z | Coeff. | Std. Error | t-ratio | |
| Costant | -0.25*** | 0.09 | -2.70 | -0.11*** | 0.14 | -0.83 | 0.15*** | 0.05 | 2.87 | |||
| HGD | 0.24*** | 0.06 | 4.03 | 0.15*** | 0.03 | 4.68 | 0.14*** | 0.03 | 4.60 | 0.06** | 0.02 | 2.53 |
| IDHP | 0.02*** | 0.001 | 11.59 | 0.04*** | 0.002 | 13.60 | 0.04*** | 0.002 | 13.90 | 0.04*** | 0.003 | 14.4 |
| CDWR | 0.02*** | 0.002 | 8.73 | 0.03*** | 0.003 | 9.91 | 0.03*** | 0.003 | 10.57 | 0.03*** | 0.003 | 10.3 |
| AUG | 0.003*** | 0.0003 | 11.75 | 0.004*** | 0.0005 | 7.57 | 0.004*** | 0.0005 | 7.96 | 0.003*** | 0.0004 | 8.67 |
| SWAC | 0.027*** | 0.008 | 3.38 | 0.10*** | 0.013 | 7.99 | 0.11*** | 0.013 | 8.18 | 0.12*** | 0.01 | 8.33 |
| IMC | -0.008*** | 0.002 | -4.17 | -0.009*** | 0.002 | -3.14 | -0.008*** | 0.002 | -2.91 | -0.007** | 0.003 | -2.34 |
| UB | 0.02*** | 0.008 | 2.43 | 0.028*** | 0.005 | 5.27 | 0.02*** | 0.004 | 4.60 | 0.005*** | 0.001 | 2.81 |
| PA | -0.02*** | 0.002 | -10.56 | -0.017*** | 0.003 | -5.38 | -0.01*** | 0.003 | -5.21 | -0.01*** | 0.003 | -3.15 |
| TP(-1) | 0.37*** | 0.02 | 16.73 | |||||||||
| Statistics | Sum squared resid | 70.30 | Mean dependent var | 1.67 | Mean dependent var | 1.67 | Mean dependent var | 1.67 | ||||
| S.E. of regression | 0.33 | Sum squared resid | 87.52 | Sum squared resid | 139.90 | Sum squared resid | 115.87 | |||||
| LSDV R-squared | 0.87 | Log-likelihood | -340.68 | R-squared | 0.83 | |||||||
| LSDV F(27, 332) | 84.82 | Schwarz criterion | 734.35 | F(8, 351) | 217.88 | |||||||
| Log-likelihood | -256.26 | rho | 0.40 | Log-likelihood | -306.77 | |||||||
| Schwarz criterion | 677.33 | S.D. dependent var | 1.38 | Schwarz criterion | 666.52 | |||||||
| rho | 0.40 | S.E. of regression | 0.63 | rho | 0.48 | |||||||
| S.D. dependent var | 1.38 | Akaike criterion | 699.37 | S.D. dependent var | 1.38 | |||||||
| S.E. of regression | 0.51 | Hannan-Quinn | 713.28 | S.E. of regression | 0.57 | |||||||
| Within R-squared | 0.87 | Durbin-Watson | 1.10 | Adjusted R-squared | 0.82 | |||||||
| P-value(F) | 1.0e-131 | P-value(F) | 4.0e-131 | |||||||||
| Akaike criterion | 568.52 | Akaike criterion | 631.55 | |||||||||
| Hannan-Quinn | 611.79 | Hannan-Quinn | 645.45 | |||||||||
| Durbin-Watson | 1.10 | Durbin-Watson | 0.98 | |||||||||
| Tests | Number of instruments = 59 Test for AR(1) errors: z = -3.88129 [0.0001] Test for AR(2) errors: z = 2.79294 [0.0052] Sargan over-identification test: Chi-square(50) = 129.863 [0.0000] Wald (joint) test: Chi-square(9) = 1279.84 [0.0000 |
Joint test on named regressors - Test statistic: F(8, 332) = 284.477 with p-value = P(F(8, 332) > 284.477) = 1.34384e-143 Test for differing group intercepts - Null hypothesis: The groups have a common intercept Test statistic: F(19, 332) = 5.66064 with p-value = P(F(19, 332) > 5.66064) = 3.4907e-12 |
'Between' variance = 0.250123 'Within' variance = 0.243122 theta used for quasi-demeaning = 0.773651 Joint test on named regressors - Asymptotic test statistic: Chi-square(8) = 2305.7 with p-value = 0 Breusch-Pagan test - Null hypothesis: Variance of the unit-specific error = 0 Asymptotic test statistic: Chi-square(1) = 47.7844 with p-value = 4.75752e-12 Hausman test - Null hypothesis: GLS estimates are consistent Asymptotic test statistic: Chi-square(8) = 13.2903 with p-value = 0.102244 |
|||||||||
| WLS, using 362 observations | 1-step dynamic panel, using 322 observations | Random-effects (GLS), using 362 observations | Fixed-effects, using 362 observations | |||||||||
| Coefficient | Std. Error | t-ratio | Coefficient | Std. Error | z | Coefficient | Std. Error | Z | Coefficient | Std. Error | t-ratio | |
| Constant | 0.01** | 0.015 | 0.95 | 0.021 | 0.018 | 1.127 | ||||||
| TU | -0.001*** | 0.0006 | -2.31 | -0.002*** | 0.0008 | -2.76 | 0.023 | 0.04 | 0.48 | -0.001** | 0.0007 | -2.42 |
| NEET | 0.029*** | 0.001 | 16.05 | 0.02*** | 0.002 | 8.94 | -0.001** | 0.0007 | -2.3 | 0.02*** | 0.001 | 14.40 |
| UL | 0.01*** | 0.002 | 6.35 | 0.02*** | 0.004 | 5.14 | 0.02*** | 0.001 | 14.72 | 0.01*** | 0.002 | 6.16 |
| TUSJ | 0.009*** | 0.001 | 5.35 | 0.009*** | 0.001 | 5.14 | 0.017*** | 0.002 | 6.08 | 0.009*** | 0.002 | 4.30 |
| ESF | 0.007*** | 0.001 | 6.81 | 0.009*** | 0.0009 | 10.12 | 0.009*** | 0.002 | 4.25 | 0.008*** | 0.001 | 6.94 |
| 00SP | -0.008*** | 0.001 | -5.75 | -0.007*** | 0.001 | -4.91 | 0.008*** | 0.001 | 6.95 | -0.008*** | 0.001 | -4.86 |
| CPP | 0.036*** | 0.0004 | 74.30 | 0.037*** | 0.001 | 34.46 | -0.008*** | 0.001 | -4.90 | 0.03*** | 0.0006 | 61.27 |
| TP(-1) | -0.008 | 0.021 | -0.3891 | 0.03*** | 0.0006 | 61.44 | ||||||
| Statistics | Sum squared resid | 341.79 | Sum squared resid | 16.03 | Mean dependent var |
1.629 | Mean dependent var | 1.62 | ||||
| R-squared | 0.98 | S.E. of regression | 0.15 | Sum squared resid |
28.90 | Sum squared resid | 16.45 | |||||
| F(7, 354) | 2702.769 | Log-likelihood |
-56.13 | LSDV R-squared | 0.97 | |||||||
| Log-likelihood | -503.26 | Schwarz criterion |
159.41 | LSDV F(26, 335) | 496.40 | |||||||
| Schwarz criterion | 1053.65 | Rho |
0.46 | Log-likelihood | 45.78 | |||||||
| Sum squared resid | 341.79 |
S.D. dependent var |
1.34 | Schwarz criterion | 67.49 | |||||||
| R-squared | 0.98 | S.E. of regression |
0.28 | rho | 0.46 | |||||||
| F(7, 354) | 2702.76 | Akaike criterion |
128.27 | S.D. dependent var | 1.34 | |||||||
| Log-likelihood | -503.26 | Hannan-Quinn |
140.65 | S.E. of regression | 0.22 | |||||||
| Schwarz criterion | 1053.65 | Durbin-Watson |
1.01 | Within R-squared | 0.97 | |||||||
| P-value(F) | 3.9e-250 | |||||||||||
| Akaike criterion | -37.57 | |||||||||||
| Hannan-Quinn | 4.19 | |||||||||||
| Durbin-Watson | 1.01 | |||||||||||
| Tests | Number of instruments = 54 Test for AR(1) errors: z = -2.96158 [0.0031] Test for AR(2) errors: z = -0.303456 [0.7615] Sargan over-identification test: Chi-square(46) = 92.4704 [0.0001] Wald (joint) test: Chi-square(8) = 3886.81 [0.0000] |
'Between' variance = 0.036629 'Within' variance = 0.0454632 mean theta = 0.746654 Joint test on named regressors - Asymptotic test statistic: Chi-square(7) = 12909.5 with p-value = 0 Breusch-Pagan test - Null hypothesis: Variance of the unit-specific error = 0 Asymptotic test statistic: Chi-square(1) = 395.462 with p-value = 5.35545e-88 Hausman test - Null hypothesis: GLS estimates are consistent Asymptotic test statistic: Chi-square(7) = 15.8565 with p-value = 0.0264606 |
Joint test on named regressors - Test statistic: F(7, 335) = 1832.12 with p-value = P(F(7, 335) > 1832.12) = 9.82853e-263 Test for differing group intercepts - Null hypothesis: The groups have a common intercept Test statistic: F(19, 335) = 12.6261 with p-value = P(F(19, 335) > 12.6261) = 2.81378e-29 |
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| Fixed-effects, using 380 observations | 1-step dynamic panel, using 340 observations | Pooled OLS, using 380 observations | Random-effects (GLS), using 380 observations | |||||||||
| Coefficient | Std. Error | t-ratio | Coefficient | Std. Error | z | Coefficient | Std. Error | t-ratio | ||||
| Cost | -0.003 | 0.02 | -0.15 | -0.03 | 0.02 | -1.33 | -0.005 | 0.02 | -0.23 | |||
| TIP | 0.6*** | 0.02 | 24.59 | 0.65*** | 0.03 | 20.09 | 0.67 | 0.02 | 23.58 | 0.69 | 0.02 | 25.05 |
| TJS | 0.04** | 0.02 | 2.248 | 0.08*** | 0.02 | 3.133 | 0.05 | 0.02 | 2.63 | 0.04 | 0.02 | 2.343 |
| TPF | -0.05*** | 0.003 | -13.03 | -0.06*** | 0.01 | -5.836 | -0.05 | 0.004 | -11.19 | -0.05 | 0.003 | -13.12 |
| MIG | 0.002*** | 0.0007 | 3.36 | 0.002*** | 0.001 | 2.755 | 0.002 | 0.0008 | 3.11 | 0.002 | 0.0007 | 3.42 |
| RIU | 0.01*** | 0.001 | 8.17 | 0.01*** | 0.002 | 5.453 | 0.01 | 0.001 | 7.97 | 0.014 | 0.001 | 8.35 |
| ACIF | -0.01*** | 0.001 | -7.20 | -0.01*** | 0.003 | -3.604 | -0.01 | 0.001 | -6.43 | −0.01 | 0.001 | -7.30 |
| NM | 0.01*** | 0.002 | 3.78 | 0.01*** | 0.003 | 3.483 | 0.01 | 0.003 | 3.49 | 0.01 | 0.002 | 3.83 |
| TP(-1) | 0.04*** | 0.03 | 1.61 | |||||||||
| Statistics | Mean dependent var | 1.70 | Sum squared resid | 3.98 | Mean dependent var | 1.70 | Mean dependent var | 1.70 | ||||
| Sum squared resid |
3.42 | S.E. of regression | 0.07 | Sum squared resid | 5.23 | Sum squared resid | 5.26 | |||||
| LSDV R-squared |
0.99 | R-squared | 0.99 | Log-likelihood | 273.97 | |||||||
| LSDV F(26, 353) |
2762.02 | F(7, 372) | 7057.47 | Schwarz criterion | -500.43 | |||||||
| Log-likelihood |
355.51 | Log-likelihood | 274.97 | rho | 0.40 | |||||||
| Schwarz criterion |
-550.64 | Schwarz criterion | -502.43 | S.D. dependent var | 1.35 | |||||||
| rho | 0.40 | rho | 0.62 | S.E. of regression | 0.11 | |||||||
| S.D. dependent var | 1.35 | S.D. dependent var | 1.35 | Akaike criterion | -531.95 | |||||||
| S.E. of regression | 0.09 | S.E. of regression | 0.11 | Hannan-Quinn | -519.44 | |||||||
| Within R-squared | 0.99 | Adjusted R-squared | 0.99 | Durbin-Watson | 1.14 | |||||||
| P-value(F) | 0.00 | P-value(F) | 0.00 | |||||||||
| Akaike criterion | -657.02 | Akaike criterion | -533.95 | |||||||||
| Hannan-Quinn | -614.81 | Hannan-Quinn | -521.44 | |||||||||
| Durbin-Watson | 1.14 | |||||||||||
| Tests | Number of instruments = 62 Test for AR(1) errors: z = -3.49487 [0.0005] Test for AR(2) errors: z = -0.682047 [0.4952] Sargan over-identification test: Chi-square(54) = 73.2119 [0.0419] Wald (joint) test: Chi-square(8) = 29322.9 [0.0000] |
'Between' variance = 0.00510382 'Within' variance = 0.00901372 theta used for quasi-demeaning = 0.708374 Joint test on named regressors - Asymptotic test statistic: Chi-square(7) = 71927.2 with p-value = 0 Breusch-Pagan test - Null hypothesis: Variance of the unit-specific error = 0 Asymptotic test statistic: Chi-square(1) = 315.086 with p-value = 1.70369e-70 Hausman test - Null hypothesis: GLS estimates are consistent Asymptotic test statistic: Chi-square(7) = 5.55496 with p-value = 0.592563 |
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