A tale of government spending efficiency and trust in the state

Afonso, António; Jalles, João Tovar; Venâncio, Ana Article — Published Version A tale of government spending efficiency and trust in the state Public Choice Provided in Cooperation with: Springer Nature Suggested Citation: Afonso, António; Jalles, João Tovar; Venâncio, Ana (2024) : A tale of government spending efficiency and trust in the state, Public Choice, ISSN 1573-7101, Springer US, New York, NY, Vol. 200, Iss. 1, pp. 89-118, https://doi.org/10.1007/s11127-024-01144-6 This Version is available at: https://hdl.handle.net/10419/315406 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. http://creativecommons.org/licenses/by/4.0/ Vol.:(0123456789) Public Choice (2024) 200:89–118 https://doi.org/10.1007/s11127-024-01144-6 1 3 A tale ofgovernment spending efficiency andtrust inthestate AntónioAfonso1,2,3 · JoãoTovarJalles4,5,6,7· AnaVenâncio8,9 Received: 31 October 2022 / Accepted: 16 January 2024 / Published online: 18 May 2024 © The Author(s) 2024 Abstract This paper empirically links the efficiency and performance assessment of the general government, proxied by efficiency scores, to the trust in government. Government spending efficiency scores are first computed via data envelopment analysis (DEA). Then, relying on panel data and instrumental variable approaches, we estimate the effect of public sector efficiency on citizens trust on national governments. The sample covers 36 OECD countries between 2007 and 2019. We find that the more efficient countries in terms of government spending are Australia, Chile, Ireland, New Zealand, South Korea, Switzerland. Secondly, our main finding is that better public sector spending efficiency is positively associated with citizens’ higher trust in governments. In general, political economy variables and the existence of fiscal rules do not seem to significantly affect our measure of trust. The results hold using alternative proxies for public sector efficiency, alternative measures for trust, specifications with different control variables and different empirical approaches (instrumental variables). Keywords Government spending efficiency· DEA· Panel data analysis· Confidence effects· Ideology· Fiscal rules JEL Classification C14· C23· E44· G15· H11· H50 1 Introduction In a context of scarcer budgetary funds, special attention is given to the more efficient use of public resources, with better government spending performance and efficiency being preferred by policymakers and taxpayers (see, Afonso etal., 2021a, 2021b). At the same time, a more efficient use of public resources and consequently better government performance, is also (positively) internalized by financial markets (see Afonso etal., 2022). We conjecture that such general efficiency-enhancing policy and approach to government´s assets (physical and human) can generate a higher degree of confidence and trust in the state. Trust in the government has been identified as one of the most important foundations upon which the legitimacy and sustainability of political systems are built (Fukuyama, Extended author information available on the last page of the article 90 Public Choice (2024) 200:89–118 1 3 1995). The trust citizens place in their government reflects their confidence in the government’s actions. It is a function of the congruence between citizens’ preferences—their interpretation of what is right and fair and what is unfair—and the perceived actual functioning of government (Bouckaert & Walle, 2003). Public trust helps governments manage and administer a country on a daily basis in a way that reinforces the democratic institutions.1 However, trust in the government has decreased not only in the US but also in several European countries (Intawan & Nicholson, 2018; Pérez-Morote, etal., 2020). Hence, the key question in this paper is whether we can empirically provide strong evidence on the relationship between government´s trust and public sector efficiency. The relevance of public sector efficiency has been addressed by a growing literature. Several authors have identified substantial public spending efficiency differences between countries and scope for spending savings. Most public spending efficiency related studies report that there is room for improvement in terms of government spending efficiency, and this typically implies that more public services could be provided with the same public resources, or conversely, the same level of public resources might be provided with fewer public resources. For OECD and EU countries see, notably the evidence reported by Gupta and Verhoeven (2001), Afonso et al. (2005), Adam et al. (2011), Dutu and Sicari (2016), Afonso and Kazemi (2017), Antonelli and de Bonis (2019), and Afonso etal. (2023). Regarding Emerging Markets see, for instance, Afonso etal. (2010), Herrera and Ouedrago (2018), and for Latin American and Caribbean countries see Afonso etal. (2013). To explain these cross-country efficiency differences, studies have examined, in a two-step analysis, the so-called discretionary factors such as: population size, education, income level, quality of the institutions (property right security and corruption) and quality of the country’s governance level, size of the government, political orientation, voter participation, and civil service competence (Afonso etal., 2005; Hauner and Kyobe, 2008; Antonelli & de Bonis, 2019). Regarding the literature on the level of trust that citizens place in their governments, we can infer that this will depend on the credibility of the government’s commitment to the quality of public policies in relation to the amount of spending. For instance, Alesina and Warcziarg (2000) argue that a more pronounced polarisation of voter preferences in advanced economies and the low quality of government policy, which favour particular groups and less the median voter, both reduce trust. Moreover, unproductive government spending reduces public trust in the State, which might become more damaging for large and ineffective governments (Garen & Clark, 2015). Besley etal. (2010) mentioned that governments somehow associated with rent-seeking and lobbying activities contributed to a lower level of public trust. Hence, one can observe unproductive public spending and lower trust of voters in the government. This may be consistent with the decrease of citizens´ trust in government over the years (Intawan & Nicholson, 2018). On the other hand, Pérez-Morote etal. (2020) mentioned that economic events, corruption, or the disclosure of classified information tended to decrease the trust in government. On the same vein, Belabed and Hake (2018) reported that corruption and weak rule of law undermined trust in European governments. In addition, Foster and Frieden (2017) found via survey 1 The rule of law and independent judiciary are especially relevant since they appropriate functioning is a fundamental driver of trust in government (Blind, 2007; Johnston, Krahn and Harrison, 2006; Knack and Zak, 2003). Furthermore, as well-functioning government institutions matter for business investment decisions, trust in them is a necessary component to propel economic growth (Dasgupta, 2009; Algan and Cahu, 2010). 91 Public Choice (2024) 200:89–118 1 3 responses that economic factors at individual and national levels contributed to the trust in the State over the years. Finally, Rodrigues (2021), for a panel of developed and developing countries, reports adverse effects of inefficient public spending on public trust. Moreover, one might consider the assumption that often politicians know what the ‘right’ policy is, but have private incentives to do something else. Still, it is less clear that politicians do indeed know about the “right” policies. Indeed, for instance, ideological views or lack of information can firmly support different convictions of what is “right” to do. For instance, there might be some interaction between knowledge held and gathered by policy makers and incentive problems that can skew some decisions. In addition, people’s trust in the government is probably also related to their trust in each other. Probably, one can consider that overall economic and societal prosperity is linked and depends on cooperation between individuals and large groups, which is only feasible if trust is indeed present, notably in institutions such as government. Hence, hightrust societies with high moral beliefs, particularly cultural beliefs, can result in better government performance than low-trust societies. This relation can then be perceived as a cultural question as well (see, notably Rose, 2011, 2018). In this study, we first compute composite indicators of government public sector performance. Secondly, we calculate so-called input efficiency scores for the period 2006–2019. Third, we empirically assess the relevance of these efficiency scores on proxies of trust in the government in a panel setting of 36 OECD countries. It naturally follows that the idea of efficiency is also linked to some measure of fiscal prudence embedded in spending rationalization and optimization efforts. We find that the more efficient countries in terms of government spending, in our baseline specification (Model 0), are Australia (2009–2011; 2013; 2019), Chile (2007–2016; 2019); Ireland (2015; 2019), New Zealand (2018), South Korea (2006–2018), and Switzerland (2006–2009; 2014–2016; 2019). Moreover, better spending efficiency is positively associated with citizens’ higher trust in the governments. This result holds using alternative proxies for public sector efficiency, alternative measures for trust, specifications with different control variables and different empirical approaches (instrumental variables). In general, political economy variables and the existence of fiscal rules do not seem to significantly affect our measure of trust. The remainder of the paper is organized as follows. Section2 discusses and constructs the indicators and scores of public sector efficiency. Section3 conducts the empirical panel analysis of trust and efficiency. The last section concludes. 2 Public sector efficiency anddata envelopment analysis To compute the public sector efficiency scores, we use data envelopment analysis (DEA),2 which compares each observation with an optimal outcome. For each country i, we consider the following function: (1) Yi =f ( X i) ,i=1, … , 36 2 DEA is a non-parametric frontier methodology, which draws from Farrell’s (1957) seminal work and that was further developed by Charnes etal. (1978). Coelli etal. (2002) and Thanassoulis (2001) offer introductions to DEA. 92 Public Choice (2024) 200:89–118 1 3 where Y is the composite output measure (Public Sector Performance, PSP) and X is the composite input measure (Public Expenditure, PE), namely government spending-to-GDP ratio. We compute the yearly efficiency scores for 36 OECD member countries3 between 2006 and 2019. The output composite indicator for Public Sector Performance (PSP), as suggested by Afonso etal., (2005, 2022), includes two main components: opportunity and the traditional Musgravian indicators. The opportunity indicators evaluate the performance of the government in administration, education, health and infrastructure sectors. The Musgravian indicators includes three sub-indicators: distribution, stability and economic performance. Table1 summarizes the variables used to construct the PSP indicators. PSP is the average between the opportunity and Musgravian indicators. Accordingly, the opportunity and Musgravian indicators result from the average of the measures included in each sub-indicator. To ensure a convenient benchmark, each sub-indicator measure is first normalized by dividing the value of a specific country by the average of that measure for all the countries in the sample. Table 1 Total public sector performance (PSP) indicator Source: authors’ elaboration Sub index Variable Opportunity indicators Administration Corruption Red tape Judicial independence Property rights Shadow economy Education Secondary school enrolment Quality of educational system PISA scores Health Infant survival rate Life expectancy CVD, cancer, diabetes or CRD survival rate Public infrastructure Infrastructure quality Standard musgravian indicators Distribution Gini index Stabilization Coefficient of variation of growth Standard deviation of inflation Economic performance GDP per capita GDP growth Unemployment 3 The 36 OECD member countries are: Australia, Austria, Belgium, Canada, Chile, Colombia, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Israel, Italy, Japan, Korea, Latvia, Lithuania, Luxembourg, the Netherlands, New Zealand, Norway, Poland, Portugal, Slovakia, Slovenia, Spain, Sweden, Switzerland, Turkey, the United Kingdom, and the United States. We were not able to compute the efficiency scores for Mexico and Costa Rica, due to data unavailability. 93 Public Choice (2024) 200:89–118 1 3 Our input measure, Public Expenditure (PE), is lagged 1 year and expressed as a percentage of GDP in several sectors. More specifically, we consider government consumption, expenditure on education, expenditure on health, public investment, transfers and subsidies and total expenditure. Each area of government expenditure is equally weighted to compute the public expenditure input. Tables7 and 8 in Appendix A provide additional information on the sources and variable construction. Further explanation on the variable’s construction is provided in Afonso etal. (2022). We adopt an input orientated approach, to measure the proportional increase in inputs while holding output constant and assume variable-returns to scale (VRS), to account for the fact that countries might not operate at the optimal scale. The efficiency scores are computed through the following linear programming problem4: where yi is a vector of outputs, xi is a vector of inputs, 𝜆 is a vector of constants, I1′ is a vector of ones, X is the input matrix and Y is the output matrix. The efficiency scores, 𝜃 , range from 0 to 1, such that countries performing in the frontier score 1. More specifically, if θ < 1, the country is inside the production frontier (i.e., it is inefficient), and if θ = 1, the country is at the frontier (i.e., it is efficient).We performed DEA for different models: baseline model (Model 0) includes only one input (PE as percentage of GDP) and one output (PSP); Model 1 uses two inputs, governments’ normalized spending on opportunity and on “Musgravian” indicators and one output, total PSP scores; and Model 2 assumes one input, governments´ normalized total spending (PE) and two outputs, the opportunity PSP and the “Musgravian” PSP scores. Detailed results are illustrated on Tables9, 10 and 11 of Appendix B. Table2 provides a summary of the DEA results for the period 2009–2019 using inputoriented models. The purpose of an input-oriented assessment is to assess by how much input quantities can be proportionally reduced without changing the output quantities produced. Alternatively, and by computing output-oriented measures, one can assess how much output quantities can be proportionally increased without changing the input quantities used. Analyzing our results for the input efficiency scores, we find that the average scores of our baseline model ranged between 0.58 to 0.68, For Model 1, the average scores ranged between 0.63 to 0.71, which means that with the same level of outputs, inputs could decrease between 29 and 37%. Model 2’s input efficiency scores averaged between 0.61 and 0.69. Overall, the countries located in the production possibility frontier, hence the more efficient ones in terms of government spending for Model 0 are: Australia (2009–2011; 2013; 2019), Chile (2007–2016; 2019); Ireland (2015; 2019), New Zealand (2018), South Korea (2006–2018), and Switzerland (2006–2009; 2014–2016; 2019). (2) min 𝜃,𝜆 𝜃 s .t.−yi+Y𝜆≥ 0 𝜃 xi−X𝜆≥0 I 1�𝜆=1 𝜆≥0 4 This is the equivalent envelopment form (see Charnes etal., 1978), using the duality property of the multiplier form of the original model. 94 Public Choice (2024) 200:89–118 1 3 Table 2 Summary of DEA input efficiency scores 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 Model 0 Efficient 2 3 3 4 3 3 2 3 3 4 3 1 2 3 Name CHE; KOR CHE; CHL; KOR CHE; CHL; KOR AUS; CHE; CHL; KOR AUS; CHL; KOR AUS; CHL; KOR CHL; KOR AUS; CHL; KOR CHE; CHL; KOR CHE; CHL; IRL; KOR CHE; CHL; KOR KOR KOR; NZL AUS; CHL; IRL Average 0.61 0.60 0.59 0.61 0.58 0.58 0.58 0.58 0.63 0.63 0.65 0.65 0.68 0.66 Median 0.57 0.56 0.55 0.56 0.53 0.53 0.53 0.54 0.59 0.60 0.62 0.63 0.64 0.64 Min 0.44 0.43 0.41 0.44 0.41 0.40 0.39 0.39 0.42 0.42 0.46 0.45 0.48 0.47 Max 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 Stdev 0.15 0.15 0.15 0.16 0.16 0.16 0.16 0.16 0.15 0.16 0.15 0.14 0.15 0.14 Model 1 Efficient 2 3 3 4 3 3 3 3 4 4 4 2 4 3 Name CHE; KOR CHE; CHL; KOR CHE; CHL; KOR AUS; CHE; CHL; KOR AUS; CHL; KOR AUS; CHL; KOR AUS; CHL; KOR AUS; CHL; KOR CHE; CHL; KOR; USA CHE; CHL; IRL; KOR CHE; CHL; IRL; KOR CHL; KOR CHL; IRL; KOR; NZL AUS; CHL; IRL Average 0.65 0.64 0.63 0.67 0.66 0.65 0.65 0.65 0.70 0.71 0.71 0.69 0.71 0.71 Median 0.63 0.60 0.58 0.62 0.60 0.61 0.62 0.64 0.67 0.70 0.70 0.69 0.69 0.69 Min 0.50 0.48 0.47 0.53 0.51 0.49 0.48 0.48 0.50 0.52 0.52 0.46 0.48 0.48 Max 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 Stdev 0.14 0.14 0.13 0.14 0.14 0.14 0.14 0.14 0.14 0.13 0.13 0.13 0.14 0.13 95 Public Choice (2024) 200:89–118 1 3 Table 2 (continued) 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 Model 2 Efficient 3 3 3 4 4 4 3 4 3 4 4 2 4 5 Name CHE; ESP; KOR CHE; CHL; KOR CHE; CHL; KOR AUS; CHE; CHL; KOR AUS; CHE; CHL; KOR AUS; CHE; CHL; KOR AUS; CHE; CHL AUS; CHE; CHL; KOR CHE; CHL; KOR CHE; CHL; IRL; KOR CHE; CHL; IRL; KOR CHE; KOR CHE; IRL; KOR; NZL AUS; CHE; CHL; DNK; IRL Average 0.66 0.63 0.63 0.64 0.61 0.62 0.63 0.64 0.66 0.67 0.68 0.69 0.69 0.69 Median 0.64 0.60 0.59 0.59 0.56 0.57 0.59 0.60 0.64 0.67 0.68 0.67 0.65 0.64 Min 0.46 0.44 0.47 0.48 0.47 0.46 0.46 0.44 0.46 0.50 0.51 0.50 0.48 0.48 Max 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 Stdev 0.15 0.15 0.14 0.15 0.16 0.16 0.15 0.16 0.15 0.15 0.14 0.14 0.15 0.16 Summary of the DEA results for the periods 2006–2019 using input-oriented models. Model 0 uses one input, government’ normalized total spending and one output, the total PSP. Model 1 uses two inputs, governments’ normalized spending on opportunity and on “Musgravian” indicators and one output, total PSP. Model 2 assumes one input, government’ normalized total spending and two outputs, the opportunity PSP and the “Musgravian” PSP scores. The results obtained from the three models are illustrated on Tables9, 10 and 11 of Appendix B 96 Public Choice (2024) 200:89–118 1 3 3 Trust andpublic sector efficiency To estimate the impact of public sector efficiency ( PSEi,t) on trust ( T i,t ) , we run the following reduced-form panel regression for the period between 2007 and 2020: where 𝛼i are country-fixed effects included to capture unobserved heterogeneity across countries, and time-unvarying factors such as geographical variables which may affect the degree of trust; 𝛿t are time effects to control for global shocks (such as commodity prices or the world´s business cycle); 𝜀i,t is an i.i.d. error term satisfying usual assumptions of zero mean and constant variance. .Our dependent variable is trust in government ( Tit) measured by the share of people who report having confidence in the national government. This indicator was retrieved from the OECD Stats (OECD, 2022) and it reflects the percentage of all survey respondents answering “yes” to the survey question: “In this country, do you have confidence in … national government?”.5 The main independent variable is the 1 year-lag input efficiency scores (PSEi , t− 1 ) , as computed in the previous section. We also include a vector of other determinants of trust in government, (Xit−1) , lagged 1 year to reduce potential reverse causality concerns.6 This vector includes the following variables: the logarithm of population and the age dependency ratio (as percentage of working-age population) included to control for the size of the social benefits, both variables retrieved from World Bank´s World Development Indicators; the debt-to-GDP ratio to control for the size of government retrieved from the IMF´s World Economic Outlook; a dummy variable equaling one for single-party majority government to control for political cohesion, and dummy variable for the right government to control for the political ideology, both retrieved from the Database of Political Institutions (Cruz etal., 2021) and Comparative Political Dataset, respectively.7 According to related literature, left-wing governments prefer larger governments, which might be subjected to more elite capture, consequently less efficient (Blais etal., 1993; Cusack, 1997; Hick and Swank, 1992; Jensen, 2011).8 (3) T i,t=𝛼i+𝛿t+𝛽PSEi,t−1+𝛾X � i , t− 1 +𝜀i,t 5 Data on trust is not available for all the years for the folowin countries: Australia, Austria, Belgium, Czech Republic, Estonia, Finland, Greece, Hungary, Iceland, Ireland, Latvia, Luxembourg, the Netherlands, New Zealand, Norway, Portugal, Slovakia, Slovenia, Switzerland and Turkey. 6 Similar results are obtained using contemporaneous regressors instead (not shown, but available from the authors upon request). 7 Summary statistics of these variables are provided in the appendix. Note that the ideology variable available in the Database of Political Institutions is often incorrect. For this reason the Comparative Political Data set was used which more accurately displays the nature of the ideological streams in power across countries and over time. 8 This understanding of the issue has been put down by Gary Becker´s – 1992 Nobel Laureate in Economics – Business Week columns under titles such as “To root out corruption, boot out big governments” or “If you want to cut corruption, cut government”. According to Becker “the source of official corruptiuon is the same everywhere: large governments with the power to dispense many goodies to different groups” (…) Therefore, smaller government is “the only surefire way to reduced corrption”. 103 Public Choice (2024) 200:89–118 1 3 Table 7 DEA output components Sub index Variable Source Series Opportunity Indicators Administration Corruption Transparency International’s Corruption Perceptions Index (CPI) (20062019) Corruption on a scale from 10 (Perceived to have low levels of corruption) to 0 (highly corrupt), 2006–2011; Corruption on a scale from 100 (Perceived to have low levels of corruption) to 0 (highly corrupt), 2012–2019 Red Tape World Economic Forum: The Global Competitiveness Report (2006–2017) Burden of government regulation on a scale from 7 (not burdensome at all) to 1 (extremely burdensome) World Economic Forum: Global Competitiveness Index 4.0 (2018–2019) Judicial Independence World Economic Forum: The Global Competitiveness Report (2006–2017) Judicial independence on a scale from 7 (entirely independent) to 1 (heavily influenced) World Economic Forum: Global Competitiveness Index 4.0 (2018–2019) Property Rights World Economic Forum: The Global Competitiveness Report (2006–2017) Property rights on a scale from 7 (very strong) to 1 (very weak) World Economic Forum: Global Competitiveness Index 4.0 (2018–2019) Property rights on a scale from 100 (very strong) to 0 (very weak) Shadow Economy Medina and Schneider (2019) (2006–2017) Shadow economy measured as percentage of official GDP. Reciprocal value 1/x For the missing years, we assumed that the scores were the same as in the previous years 104 Public Choice (2024) 200:89–118 1 3 Table 7 (continued) Sub index Variable Source Series Education Secondary School Enrolment World Bank, World Development Indicators (2006–2019) Ratio of total enrolment in secondary education Quality of Educational System World Economic Forum: The Global competitiveness Report (2006–2017) Quality of educational system on a scale from 7 (very well) to 1 (not well at all). For the missing years, we assumed that the scores were the same as in the previous years PISA scores PISA Report (2006, 2009, 2012, 2015, 2018)aSimple average of mathematics, reading and science scores for the years 2018, 2015, 2012, 2009. For the missing years, we assumed that the scores were the same as in the previous years Health Infant Survival Rate World Bank, World Development Indicators (2006–2019) Infant survival rate = (1000-IMR)/1000. IMR is the infant mortality rate measured per 1000 lives birth in a given year Life Expectancy World Bank, World Development Indicators (2006–2019) Life expectancy at birth, measured in years CVD, cancer, diabetes or CRD Survival Rate World Health Organization, Global Health Observatory Data Repository (2000,-2019) CVD, cancer and diabetes survival rate = 100-M. M is the mortality rate between the ages 30 and 70. For the missing years, we assumed that the scores were the same as in the previous years 105 Public Choice (2024) 200:89–118 1 3 Table 7 (continued) Sub index Variable Source Series Public Infrastructure Infrastructure Quality World Economic Forum: The Global competitiveness Report (2006–2017) Infrastructure quality on a scale from 7 (extensive and efficient) to 1 (extremely underdeveloped) World Economic Forum: Global Competitiveness Index 4.0 (2018–2019) Quality of road infrastructure from 7 (extensive and efficient) to 1 (extremely underdeveloped) Efficiency of train services from 7 (extensive and efficient) to 1 (extremely underdeveloped) Efficiency of air transport services from 7 (extensive and efficient) to 1 (extremely underdeveloped) Efficiency of seaport services from 7 (extensive and efficient) to 1 (extremely underdeveloped) Reliability of water supply from 7 (extensive and efficient) to 1 (extremely underdeveloped) Standard Musgravian indicators Distribution Gini Index Eurostat (2006–2019) Gini index on a scale from 1(perfect inequality) to 0 (perfect equality). Transformed to 1-Gini OECD (2006–2019) For the missing years, we assumed that the scores were the same as in the previous years World Bank, World Bank, Development Research Group (2006–2019) b Stabilization Coefficient of variation of growth IMF World Economic Outlook (WEO database) (2006–2019) Coefficient of variation = standard deviation/ mean of GDP growth based on 5year data. GDP constant prices (percent change). Reciprocal value 1/x Standard deviation of inflation IMF World Economic Outlook (WEO database) (2006–2019) Standard deviation of inflation based on 5-year consumer prices (percent change) data. Reciprocal value 1/x 106 Public Choice (2024) 200:89–118 1 3 Table 7 (continued) Sub index Variable Source Series Economic performance GDP per capita IMF World Economic Outlook (WEO database) (2006–2019) GDP per capita based on PPP, current international dollar GDP growth IMF World Economic Outlook (WEO database) (2006–2019) GDP constant prices (percent change) Unemployment IMF World Economic Outlook (WEO database) (2006–2019) Unemployment rate, as a percentage of total labor force. Reciprocal value 1/x a For Costa Rica, we were only able to collect data for the years 2018, 2015 and 2012 b For Colombia we were collected data from World Bank 107 Public Choice (2024) 200:89–118 1 3 Table 8 Input components a From IMF World Economic Outlook (WEO database), we retrieved data for Belgium for the period between 2001 to 2007, France for the period between 2000 and 2014, Greece for the period between 2006 and 2015, South Korea for the period between 2001 and 2009 and 2012 and 2015, for Turkey for the period between 2012 and 2014, and for the USA for the period 2010 and 2012. For the missing years, we assumed that the scores were the same as in the previous years b We were not able to collect data on the following countries: Canada, Mexico, New Zealand, and Turkey. For the missing years, we assumed that the scores were the same as in the previous years c We were not able to collect data on the following countries: Australia, Canada, Chile, Colombia, Costa Rica, Mexico, New Zealand, Israel and South Korea. For the missing years, we assumed that the scores were the same as in the previous years d From IMF World Economic Outlook (WEO database), we retrieved data for New Zealand for the period 2005 and 2012. For Turkey, we retrieve data from European Commission, AMECO database. For Turkey, we were only able to get data for the period between 2009 and 2015. We were not able to collect data for Canada. For the missing years, we assumed that the scores were the same as in the previous years e From IMF World Economic Outlook (WEO database), we retrieved data for Canada for the period between 2000 and 2017, for New Zealand for the period 2009 and 2017 and for Turkey for the period 2004 and 2017. We were not able to collect data for Mexico. For the missing years, we assumed that the scores were the same as in the previous years Sub Index Variable Source Series Opportunity indicators Administration Government Consumption IMF World Economic Outlook (WEO database) (2005–2018) General government final consumption expenditure (% of GDP) at current prices Education Education Expenditure UNESCO Institute for Statistics (2005–2018)aExpenditure on education (% of GDP) Health Health Expenditure OECD database (2005–2018)bExpenditure on health compulsory (% of GDP) Public infrastructure Public Investment European Commission, AMECO (2005–2018)cGeneral government gross fixed capital formation (% of GDP) at current prices Standard Musgravian indicators Distribution Social Protection Expenditure OECD database (2005–2018)dAggregation of the social transfers (% of GDP) Stabilization/economic performance Government Total Expenditure OECD database (2005–2018)eTotal expenditure (% of GDP) 108 Public Choice (2024) 200:89–118 1 3 Table 9 Input-oriented DEA VRS efficiency scores model 0 Bold indicates the efficient countries 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 AUS 0.74 0.66 0.66 1.00 1.00 1.00 1.00 1.00 0.79 0.69 0.69 0.68 0.71 1.00 AUT 0.56 0.50 0.47 0.49 0.47 0.46 0.46 0.46 0.50 0.50 0.52 0.51 0.54 0.53 BEL 0.47 0.49 0.48 0.49 0.48 0.47 0.46 0.45 0.48 0.49 0.52 0.52 0.54 0.53 CAN 0.71 0.60 0.71 0.61 0.58 0.56 0.56 0.57 0.75 0.64 0.64 0.63 0.65 0.64 CHE 1.00 1.00 1.00 1.00 0.79 0.82 0.75 0.78 1.00 1.00 1.00 0.78 0.81 0.81 CHL 0.98 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 0.98 0.99 1.00 COL 0.77 0.76 0.77 0.82 0.81 0.79 0.80 0.83 0.83 0.81 0.85 0.86 0.88 0.75 CZE 0.52 0.53 0.55 0.57 0.54 0.54 0.54 0.54 0.57 0.61 0.60 0.64 0.69 0.65 DEU 0.50 0.50 0.52 0.55 0.51 0.52 0.53 0.52 0.60 0.59 0.60 0.58 0.60 0.58 DNK 0.48 0.43 0.41 0.44 0.41 0.40 0.39 0.39 0.44 0.44 0.47 0.46 0.49 0.64 ESP 0.76 0.65 0.55 0.54 0.52 0.51 0.51 0.53 0.56 0.59 0.63 0.65 0.67 0.65 EST 0.66 0.63 0.59 0.55 0.53 0.58 0.59 0.56 0.61 0.63 0.64 0.64 0.64 0.64 FIN 0.49 0.48 0.46 0.49 0.44 0.44 0.43 0.40 0.42 0.42 0.46 0.45 0.48 0.47 FRA 0.53 0.43 0.41 0.44 0.42 0.42 0.41 0.41 0.44 0.45 0.47 0.46 0.48 0.47 GBR 0.65 0.59 0.54 0.57 0.53 0.52 0.52 0.52 0.63 0.60 0.62 0.62 0.64 0.63 GRC 0.50 0.48 0.48 0.47 0.47 0.48 0.47 0.46 0.51 0.52 0.55 0.55 0.56 0.57 HUN 0.52 0.44 0.47 0.51 0.53 0.53 0.53 0.52 0.53 0.54 0.56 0.63 0.63 0.60 IRL 0.67 0.60 0.53 0.50 0.45 0.47 0.53 0.55 0.68 1.00 0.91 0.91 1.00 1.00 ISL 0.57 0.58 0.48 0.51 0.52 0.54 0.52 0.55 0.59 0.62 0.65 0.59 0.63 0.60 ISR 0.54 0.55 0.58 0.62 0.64 0.65 0.67 0.71 0.70 0.68 0.71 0.70 0.69 0.68 ITA 0.48 0.48 0.49 0.50 0.49 0.50 0.50 0.49 0.50 0.53 0.55 0.56 0.58 0.57 JPN 0.61 0.76 0.60 0.63 0.60 0.57 0.56 0.55 0.59 0.61 0.64 0.64 0.66 0.65 KOR 1.00 1.00 1.00 1.00 1.00 1.00 0.99 1.00 1.00 1.00 1.00 1.00 1.00 0.96 LTU 0.63 0.61 0.58 0.56 0.53 0.53 0.57 0.62 0.67 0.70 0.74 0.76 0.79 0.75 LUX 0.64 0.68 0.57 0.60 0.55 0.55 0.54 0.53 0.66 0.61 0.63 0.61 0.62 0.62 LVA 0.64 0.63 0.62 0.60 0.56 0.57 0.60 0.58 0.61 0.66 0.69 0.72 0.72 0.68 NLD 0.51 0.56 0.65 0.74 0.52 0.51 0.49 0.49 0.54 0.55 0.58 0.58 0.61 0.61 NOR 0.51 0.52 0.50 0.55 0.49 0.50 0.50 0.48 0.58 0.51 0.48 0.45 0.48 0.47 NZL 0.59 0.55 0.55 0.62 0.58 0.53 0.50 0.55 0.69 0.66 0.68 0.68 1.00 0.70 POL 0.50 0.50 0.52 0.55 0.55 0.63 0.59 0.54 0.56 0.58 0.62 0.63 0.64 0.61 PRT 0.47 0.49 0.51 0.51 0.48 0.47 0.49 0.50 0.51 0.55 0.59 0.62 0.64 0.63 SVK 0.55 0.57 0.62 0.61 0.56 0.56 0.57 0.57 0.57 0.57 0.56 0.62 0.65 0.62 SVN 0.46 0.47 0.50 0.51 0.48 0.46 0.47 0.44 0.46 0.50 0.54 0.58 0.61 0.60 SWE 0.44 0.43 0.43 0.47 0.47 0.47 0.45 0.43 0.48 0.50 0.51 0.49 0.50 0.50 TUR 0.66 0.68 0.70 0.71 0.68 0.71 0.74 0.69 0.72 0.75 0.76 0.74 0.76 0.72 USA 0.67 0.63 0.60 0.61 0.59 0.58 0.59 0.61 0.81 0.72 0.72 0.70 0.73 0.72 Count23343323343123 Average 0.61 0.60 0.59 0.61 0.58 0.58 0.58 0.58 0.63 0.63 0.65 0.65 0.68 0.66 Median 0.57 0.56 0.55 0.56 0.53 0.53 0.53 0.54 0.59 0.60 0.62 0.63 0.64 0.64 Min 0.44 0.43 0.41 0.44 0.41 0.40 0.39 0.39 0.42 0.42 0.46 0.45 0.48 0.47 Max 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 Stdev 0.15 0.15 0.15 0.16 0.16 0.16 0.16 0.16 0.15 0.16 0.15 0.14 0.15 0.14 109 Public Choice (2024) 200:89–118 1 3 Table 10 Input-oriented DEA VRS efficiency scores model 1 Bold indicates the efficient countries 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 AUS 0.83 0.70 0.69 1.00 1.00 1.00 1.00 1.00 0.89 0.70 0.69 0.71 0.74 1.00 AUT 0.60 0.57 0.56 0.59 0.58 0.57 0.58 0.58 0.61 0.62 0.62 0.58 0.59 0.60 BEL 0.54 0.58 0.56 0.60 0.60 0.59 0.56 0.55 0.58 0.59 0.61 0.59 0.59 0.60 CAN 0.74 0.62 0.77 0.67 0.66 0.62 0.62 0.63 0.79 0.71 0.69 0.65 0.65 0.65 CHE 1.00 1.00 1.00 1.00 0.95 0.98 0.90 0.90 1.00 1.00 1.00 0.88 0.88 0.91 CHL 0.98 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 COL 0.79 0.77 0.77 0.86 0.85 0.80 0.82 0.85 0.83 0.85 0.88 0.87 0.88 0.77 CZE 0.53 0.54 0.56 0.60 0.57 0.58 0.58 0.58 0.64 0.66 0.60 0.64 0.69 0.66 DEU 0.63 0.64 0.66 0.71 0.68 0.66 0.67 0.67 0.72 0.73 0.73 0.69 0.69 0.69 DNK 0.52 0.50 0.47 0.53 0.51 0.49 0.48 0.48 0.50 0.52 0.53 0.50 0.52 0.72 ESP 0.82 0.65 0.57 0.61 0.59 0.59 0.61 0.66 0.73 0.77 0.76 0.77 0.78 0.78 EST 0.71 0.67 0.62 0.56 0.57 0.62 0.63 0.56 0.62 0.66 0.66 0.64 0.64 0.64 FIN 0.53 0.55 0.54 0.59 0.56 0.55 0.53 0.49 0.51 0.53 0.56 0.52 0.54 0.55 FRA 0.55 0.51 0.49 0.55 0.54 0.53 0.52 0.52 0.54 0.57 0.58 0.55 0.56 0.57 GBR 0.66 0.65 0.62 0.66 0.64 0.61 0.61 0.61 0.68 0.68 0.69 0.66 0.66 0.67 GRC 0.54 0.53 0.55 0.56 0.57 0.62 0.64 0.65 0.66 0.70 0.70 0.66 0.63 0.70 HUN 0.56 0.48 0.55 0.66 0.68 0.67 0.66 0.65 0.64 0.62 0.59 0.70 0.64 0.61 IRL 0.68 0.63 0.56 0.55 0.58 0.53 0.61 0.64 0.73 1.00 1.00 0.92 1.00 1.00 ISL 0.69 0.70 0.49 0.57 0.53 0.56 0.52 0.56 0.61 0.65 0.65 0.59 0.66 0.67 ISR 0.54 0.56 0.58 0.64 0.69 0.68 0.69 0.73 0.70 0.72 0.73 0.70 0.69 0.68 ITA 0.56 0.54 0.58 0.62 0.62 0.63 0.64 0.64 0.67 0.72 0.72 0.70 0.71 0.73 JPN 0.64 0.78 0.67 0.74 0.71 0.69 0.66 0.64 0.67 0.69 0.71 0.69 0.70 0.71 KOR 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 0.96 LTU 0.64 0.63 0.59 0.60 0.61 0.58 0.58 0.65 0.71 0.77 0.77 0.77 0.79 0.79 LUX 0.64 0.73 0.67 0.73 0.69 0.66 0.68 0.64 0.73 0.74 0.75 0.70 0.71 0.73 LVA 0.65 0.67 0.67 0.62 0.64 0.65 0.63 0.59 0.63 0.71 0.72 0.75 0.72 0.68 NLD 0.57 0.58 0.67 0.74 0.59 0.57 0.55 0.55 0.59 0.62 0.63 0.60 0.62 0.64 NOR 0.52 0.55 0.52 0.60 0.54 0.55 0.55 0.53 0.59 0.54 0.52 0.46 0.48 0.48 NZL 0.65 0.55 0.57 0.62 0.60 0.54 0.53 0.56 0.77 0.68 0.72 0.72 1.00 0.77 POL 0.59 0.56 0.58 0.62 0.64 0.70 0.63 0.61 0.66 0.67 0.70 0.72 0.70 0.67 PRT 0.50 0.53 0.55 0.59 0.57 0.52 0.59 0.64 0.67 0.72 0.73 0.76 0.73 0.75 SVK 0.60 0.58 0.68 0.70 0.66 0.65 0.64 0.66 0.67 0.66 0.57 0.65 0.67 0.67 SVN 0.51 0.51 0.55 0.58 0.56 0.55 0.55 0.55 0.54 0.58 0.60 0.65 0.67 0.65 SWE 0.50 0.51 0.50 0.55 0.55 0.55 0.54 0.50 0.53 0.55 0.57 0.52 0.52 0.52 TUR 0.69 0.69 0.73 0.80 0.82 0.83 0.86 0.73 0.75 0.81 0.81 0.74 0.76 0.72 USA 0.82 0.72 0.70 0.66 0.60 0.60 0.63 0.65 1.00 0.80 0.78 0.77 0.78 0.81 Count23343333444243 Average 0.65 0.64 0.63 0.67 0.66 0.65 0.65 0.65 0.70 0.71 0.71 0.69 0.71 0.71 Median 0.63 0.60 0.58 0.62 0.60 0.61 0.62 0.64 0.67 0.70 0.70 0.69 0.69 0.69 Min 0.50 0.48 0.47 0.53 0.51 0.49 0.48 0.48 0.50 0.52 0.52 0.46 0.48 0.48 Max 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 Stdev 0.14 0.14 0.13 0.14 0.14 0.14 0.14 0.14 0.14 0.13 0.13 0.13 0.14 0.13 110 Public Choice (2024) 200:89–118 1 3 Table 11 Input-oriented DEA VRS efficiency scores model 2 Bold indicates the efficient countries 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 AUS 0.79 0.74 0.72 1.00 1.00 1.00 1.00 1.00 0.81 0.76 0.74 0.74 0.71 1.00 AUT 0.59 0.57 0.57 0.56 0.55 0.54 0.55 0.56 0.57 0.57 0.57 0.58 0.54 0.53 BEL 0.56 0.53 0.54 0.53 0.51 0.51 0.50 0.50 0.52 0.54 0.57 0.56 0.54 0.53 CAN 0.71 0.67 0.73 0.68 0.66 0.65 0.66 0.68 0.76 0.72 0.70 0.70 0.65 0.64 CHE 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 CHL 0.98 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 0.98 0.99 1.00 COL 0.77 0.76 0.77 0.83 0.82 0.81 0.83 0.89 0.90 0.82 0.85 0.86 0.88 0.75 CZE 0.52 0.53 0.56 0.57 0.54 0.54 0.54 0.54 0.58 0.61 0.60 0.64 0.69 0.65 DEU 0.63 0.61 0.62 0.61 0.59 0.59 0.62 0.64 0.65 0.67 0.66 0.66 0.60 0.58 DNK 0.56 0.53 0.52 0.51 0.48 0.48 0.46 0.46 0.49 0.51 0.52 0.53 0.49 1.00 ESP 1.00 0.77 0.56 0.54 0.52 0.51 0.52 0.56 0.58 0.62 0.65 0.66 0.67 0.65 EST 0.71 0.63 0.61 0.56 0.54 0.59 0.61 0.59 0.64 0.67 0.67 0.68 0.64 0.64 FIN 0.62 0.60 0.60 0.58 0.54 0.55 0.55 0.54 0.53 0.52 0.55 0.55 0.56 0.57 FRA 0.55 0.48 0.48 0.49 0.48 0.47 0.47 0.47 0.48 0.50 0.51 0.50 0.48 0.48 GBR 0.66 0.62 0.59 0.60 0.57 0.57 0.59 0.61 0.64 0.67 0.68 0.69 0.64 0.63 GRC 0.50 0.49 0.48 0.48 0.47 0.48 0.47 0.46 0.51 0.52 0.55 0.55 0.56 0.57 HUN 0.70 0.44 0.47 0.52 0.53 0.53 0.53 0.52 0.53 0.54 0.56 0.63 0.63 0.60 IRL 0.68 0.60 0.56 0.52 0.47 0.50 0.60 0.65 0.70 1.00 1.00 0.99 1.00 1.00 ISL 0.71 0.64 0.57 0.59 0.63 0.64 0.62 0.66 0.67 0.70 0.72 0.67 0.64 0.60 ISR 0.57 0.56 0.58 0.62 0.64 0.65 0.67 0.72 0.72 0.69 0.73 0.72 0.69 0.68 ITA 0.48 0.48 0.49 0.50 0.49 0.50 0.50 0.49 0.50 0.53 0.55 0.56 0.58 0.57 JPN 0.75 0.83 0.70 0.70 0.67 0.66 0.64 0.65 0.68 0.70 0.71 0.72 0.67 0.67 KOR 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 0.96 LTU 0.63 0.62 0.58 0.57 0.53 0.53 0.57 0.62 0.68 0.70 0.74 0.76 0.79 0.75 LUX 0.67 0.73 0.66 0.66 0.62 0.62 0.64 0.64 0.67 0.69 0.69 0.69 0.63 0.62 LVA 0.64 0.64 0.62 0.61 0.56 0.57 0.60 0.58 0.61 0.66 0.69 0.72 0.72 0.68 NLD 0.68 0.62 0.69 0.74 0.60 0.60 0.61 0.63 0.65 0.68 0.68 0.70 0.70 0.69 NOR 0.60 0.57 0.56 0.59 0.53 0.54 0.56 0.56 0.58 0.56 0.52 0.51 0.48 0.48 NZL 0.65 0.58 0.60 0.67 0.64 0.59 0.59 0.66 0.72 0.74 0.75 0.77 1.00 0.70 POL 0.50 0.50 0.53 0.56 0.56 0.79 0.62 0.54 0.56 0.58 0.62 0.63 0.64 0.62 PRT 0.48 0.50 0.52 0.52 0.48 0.47 0.50 0.53 0.56 0.59 0.62 0.65 0.64 0.64 SVK 0.55 0.57 0.68 0.62 0.57 0.56 0.57 0.57 0.57 0.57 0.56 0.62 0.65 0.63 SVN 0.46 0.47 0.51 0.51 0.48 0.46 0.47 0.44 0.46 0.50 0.54 0.58 0.61 0.60 SWE 0.51 0.51 0.52 0.54 0.55 0.55 0.54 0.52 0.52 0.55 0.57 0.56 0.53 0.51 TUR 0.66 0.68 0.70 0.71 0.68 0.72 0.74 0.71 0.74 0.76 0.76 0.74 0.76 0.72 USA 0.80 0.72 0.71 0.68 0.65 0.65 0.67 0.71 0.83 0.80 0.81 0.82 0.81 0.76 Count33344434344245 Average 0.66 0.63 0.63 0.64 0.61 0.62 0.63 0.64 0.66 0.67 0.68 0.69 0.69 0.69 Median 0.64 0.60 0.59 0.59 0.56 0.57 0.59 0.60 0.64 0.67 0.68 0.67 0.65 0.64 Min 0.46 0.44 0.47 0.48 0.47 0.46 0.46 0.44 0.46 0.50 0.51 0.50 0.48 0.48 Max 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 Stdev 0.15 0.15 0.14 0.15 0.16 0.16 0.15 0.16 0.15 0.15 0.14 0.14 0.15 0.16 111 Public Choice (2024) 200:89–118 1 3 Appendix C See Tables12, 13, 14, 15, 16 and 17. Table 12 Summary statistics Variable Obs Mean Std. dev Dependent variable Trust 464 0.42 0.16 Independent variables PSE_0 (t − 1) 464 0.62 0.15 PSE_1 (t − 1) 464 0.68 0.14 PSE_2 (t − 1) 464 0.65 0.15 ln(Population) (t − 1) 464 16.44 1.46 Age dependency ratio (t − 1) 464 50.70 5.60 Debt-to-GDP ratio (t − 1) 464 65.54 44.00 Right (t − 1) 464 0.52 0.50 Majority 464 0.14 0.35 Deficit rule (t − 1) 301 0.65 0.48 Debt rule (t − 1) 301 0.61 0.49 Structural balance rule (t − 1) 301 0.48 0.50 Expenditure rule (t − 1) 301 0.49 0.50 Instrumental Variable Governance efficiency (t − 1) 464 1.26 0.55 Table 13 Unconditional regression on alternative output efficiency scores Clustered standard errors in parenthesis. *, **, *** denote statistical significance at the 10, 5 and 1 percent levels, respectively. Country and time fixed effects included but omitted for reasons of parsimony Specification (1) (2) (3) Dependent variable Trust Trust Trust PSE_0 (t − 1) 0.243*** (0.065) PSE_1 (t − 1) 0.234*** (0.068) PSE_2 (t − 1) 0.320* (0.180) Constant 0.232*** 0.237*** 0.152 (0.058) (0.062) (0.156) Country effects Yes Yes Yes Time effects Yes Yes Yes Observations 463 463 464 R-squared 0.195 0.190 0.157 112 Public Choice (2024) 200:89–118 1 3 Table 14 Conditional regression on alternative output efficiency scores Clustered standard errors in parenthesis. *, **, *** denote statistical significance at the 10, 5 and 1 percent levels, respectively. Country and time fixed effects included but omitted for reasons of parsimony Specification (1) (2) (3) Dependent variable Trust Trust Trust PSE_0 (t − 1) 0.158** (0.074) PSE_1 (t − 1) 0.156* (0.078) PSE_2 (t − 1) 0.212 (0.171) Ln(Population) (t − 1) − 0.612** − 0.621** − 0.661*** (0.246) (0.245) (0.240) Age dependency ratio (t − 1) 0.008* 0.008* 0.008* (0.004) (0.004) (0.005) Debt-to-GDP ratio (t − 1) − 0.002*** − 0.002*** − 0.002*** (0.001) (0.001) (0.001) Right (t − 1) 0.012 0.012 0.012 (0.013) (0.013) (0.014) Majority (t − 1) 0.004 0.004 0.004 (0.020) (0.020) (0.020) Constant 10.027** 10.170** 10.780** (4.142) (4.128) (4.036) Country effects Yes Yes Yes Time effects Yes Yes Yes Observations 463 463 464 R-squared 0.297 0.297 0.286