Institutional Quality and Generalized Trust: A Nonrecursive Causal Model

Abstract

This paper investigates the association between institutional quality and generalized trust. Despite the importance of the topic, little quantitative empirical evidence exists to support either unidirectional or bidirectional causality for the reason that cross-sectional studies rarely model the reciprocal relationship between institutional quality and generalized trust. Using data from the World Values Survey, World Bank, and other data sources in an identified nonrecursive structural equation model, results show that generalized trust and institutional quality form a positive reciprocal relationship, where the connection is stronger from generalized trust to institutional quality. The conclusion discusses implications for theory and policy in this area.

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Fig. 1

Notes

  1. 1.

    See Uslaner (2008) for a similar argument. In this article, Uslaner shows that current levels of trust in the US can, to a large extent, be traced back to the 1930s and 1940s. This suggests that at least a part of the trust observed is prior to contemporary government. Moreover, considering that trust tends to be fairly stable across time (e.g., Bjørnskov 2007), then the trust-institutions relationship identified in prior research is, at most, bidirectional and, at least, unidirectional from trust to political-institutions.

  2. 2.

    The dataset has individual-level information for 66 countries (when considering missing data and the variables I use).

  3. 3.

    I use multiple imputation techniques found in Stata 10.1 to maintain statistical power and a sizable country level sample. I imputed data for the legal property rights (Belarus, Bosnia, Moldova, and Saudi Arabia) and income inequality (Bosnia, Malta, Netherlands, Saudi Arabia, Serbia, and Slovakia) measures. Note that none of the imputed variables have greater than 10 percent missing cases. Also note that I created 1,000 complete data sets with the missing values filled in with different imputations. The values for the missing data were the mean of the 1,000 values across these data sets. Unlike traditional multiple imputation techniques, I did not take into account uncertainty as represented by the variation across the multiple imputations for each missing value since EQS does not permit such a procedure. I used this procedure instead of the maximum likelihood procedure found in EQS to reduce model complexity.

  4. 4.

    I ignore particularized trust in the present analysis since the bulk of research in this area is primarily concerned with investigating the relationship between political institutions and generalized trust. For recent research exploring the determinants of particularized trust see Freitag and Traunmüller (2009), Glanville and Paxton (2007), Gleave et al. (2011), and Radnitz et al. (2009).

  5. 5.

    I also focus on elements of government dealing with fairness and effectiveness since the results of alternative investigatory and confirmatory factor analyses suggest that measures of government should be treated as three separate dimensions: fairness and effectiveness, power-sharing capacity, and universality. This indicates that including, for instance, (a) the Polity IV measure of democracy and the Freedom House measure of political rights (i.e., power-sharing capacity), (b) the World Bank measure of public health expenditures and income inequality (i.e., universality), and (c) the legal property rights and rule of law measures (i.e., fairness and effectiveness) into one dimension is unwarranted. In fact, these indicators should be used only for their respective dimensions. Results available upon request.

  6. 6.

    It is often more desirable to use tetrachoric or polychoric correlation matrix estimation techniques instead of maximum-likelihood with dichotomous scaled data (Nunnally and Bernstein 1994). This is especially the case for CFAs and only the case for SEMs if the categorical measures are endogenous. Since the categorical indicators in the present article are exogenous, I conducted an alternative CFA with a polychoric correlation matrix estimation procedure. The alternative results parallel those presented here. As a result, I present the maximum-likelihood CFA estimates; results available upon request.

  7. 7.

    Although small samples are common in the SEM literature (see MacCallum and Austin 2000), there is little consensus on recommended sample sizes. Kline (2005) notes that “…with less than 100 cases, almost any type of SEM analysis may be untenable unless a very simple model is evaluated” (p. 15). In other words, technical problems, such as non-convergence, and issues of statistical power are more likely to occur with small samples. Note, however, that convergence and maximum likelihood solutions were not an issue in any Table 2 model; all coefficients in our final model were statistically significant (see Table 3); and all evaluated models were simple. This suggests that sample size likely did not bias the present findings.

  8. 8.

    This is a routine practice in the SEM literature to assume zero measurement error for single indicator factors, especially when there are no prior estimates of measurement error in the literature to abstract and assume a reasonable non-zero measurement error. I did, however, analyze the models with varying levels of assumed measurement error estimates, from 0.01 to 0.3, for both generalized trust and monarchy. As expected, measurement error in generalized trust produced underestimation of the β coefficient. Also, as expected, measurement error in monarchy above 0.05 produced weak instrument effects, resulting in either biased estimates, lack of bidirectional or even unidirectional significance between generalized trust and institutional quality, or maximum likelihood convergence issues. These results suggest that some minor unobserved measurement error in the generalized trust and monarchy indicators will not bias the results presented below. For instance, an assumed measurement error of 0.02 for both indicators yielded results similar to those found in Fig. 1.

  9. 9.

    For model 1, Table 2, I also controlled for gross domestic product, which did not significantly influence generalized trust, but it did, however, affect other relationships in the model. It was highly correlated with both the information technologies and institutional quality dimensions (r > 0.77), which resulted in discriminant validity issues and difficulties in converging on a solution associated with the small sample size (n = 64). As a result, I left gross domestic product out of the analysis since both information technologies and institutional quality capture a large portion of its variance. Results are available upon request.

  10. 10.

    I analyzed a number of alternative models to further test the sensitivity of the results. First, some of the variables, specifically legal property rights and income inequality, had less than 10% missing cases. In the original analyses, I used multiple imputation techniques found in Stata 10.1 to overcome this issue. To see if the imputed data may have biased the results, I re-analyzed the models in Table 2 using listwise deletion with the missing cases, which yielded an N of 58. The results indicated that none of the key path coefficients deviated from those presented in Tables 2 and 3. Second, I also explored model-based imputation methods available in EQS 6.1 (Bentler 2003). This method replaces a missing score with an imputed value drawn from a full information maximum-likelihood predictive distribution. Although models 2 through 5 had difficulty converging on a solution, the model in Fig. 1 converged, which paralleled the significant positive feedback effect found therein. Third, and finally, I investigated the pairwise deletion option found EQS 6.1, which did not substantively alter the results presented here.

  11. 11.

    To explore whether 2SLS or generalized method of moments (GMM) is more appropriate for the following instrument validity tests, I used the ivhettest found with ivreg2 in Stata 10.1. Both tests failed to reject the null hypothesis that the disturbance terms are homoskedastic (information technologies instrument, p = 0.11; monarchy instrument, p = 0.88). This indicates that the use of classic 2SLS is efficient and robust.

  12. 12.

    I use the following syntax in Stat 10.1 for ivreg2 and ivregress, respectively: ivreg2 generalized_trust monarchy (institutional_quality = information_technologies) ivregress 2sls generalized_trust monarchy (institutional_quality = information_technologies).

  13. 13.

    I use the following syntax in Stat 10.1 for ivreg2 and ivregress, respectively: ivreg2 institutional_quality information_technologies (generalized_trust = monarchy) ivregress 2sls institutional_quality information_technologies (generalized_trust = monarchy).

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Acknowledgments

I thank Maria Grigoryeva, Jerald Herting, Edgar Kiser, and Margaret Levi for helpful conversations. I also gratefully acknowledge Alex Michalos and the anonymous reviewers for valuable suggestions and comments. Any errors, as always, are my own.

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Correspondence to Blaine G. Robbins.

Appendix

Appendix

Sensitivity Analysis I: Instrument Validity

I conducted a number of tests in Stata 10.1 using the ivregress and ivreg2 commands to determine the relevance of the instrumental variables (Baum et al. 2007).Footnote 11 Instruments must first and foremost be correlated with Y and uncorrelated with ε in the following equation:

$$ Y = \beta_{1} X_{1} + \beta_{2} W_{1} + \varepsilon $$

where Y is the dependent variable of interest, X 1 is the troublesome causal variable, and W 1 is a vector of non-troublesome covariates. If, however, possible instruments are uncorrelated with Y and/or correlated with ε then these instruments are invalid and should be discarded. I find that information technologies are significantly correlated with generalized trust (r = 0.38, p < 0.05) and uncorrelated with the error term (r = 0.03, p > 0.05) in the equation below:

$$ {\text{Generalized Trust}} = \beta_{1} *{\text{Institutional Quality}} + \beta_{2} *{\text{Monarchy}} + \varepsilon $$

This suggests that the information technologies dimension is a promising instrument for institutional quality (Murray 2006). Furthermore, the first-stage regression analysis reveals that the partial R 2 for institutional quality, where information technologies is the instrumental variable, has a relatively high value of 0.59.Footnote 12 A test of underidentification (i.e., Anderson LM test) reveals that p = 0.000, suggesting that I can reject the null hypothesis that the equation is underidentified. That is, the model is identified. Moreover, the Cragg-Donald F statistic [(1, 61) = 88.00] is well above the typical single endogenous regressor cut-off value of 10.0 (Staiger and Stock 1997; Stock and Yogo 2005), which shows that I can reject the null hypothesis that information technologies are weak. Thus, information technologies do not suffer from a weak-instrument problem. In regards to over-identification, no formal tests, such as Sargan’s or Hansen J statistic, are necessary since the equation is exactly identified (i.e., the number of instruments does not exceed the number of endogenous variables). Finally, checking the Fig. 1 Lagrange Multiplier (LM) in EQS 6.1 revealed that the overall model chi-square would not significantly decrease if a fixed-to-zero path from information technologies to generalized trust was freely estimated. This provides more evidence for information technologies as an instrument and that the model in Fig. 1 is properly specified. (Table 4).

Table 4 Results of confirmatory factor analysis for measures

In regards to monarchy, I find that it is significantly correlated with institutional quality (r = 0.49, p < 0.05) and uncorrelated with the error term (r = 0.12, p > 0.05) in the equation below, suggesting that it is a promising instrument:

$$ {\text{Institutional Quality}} = \beta_{1} *{\text{Generalized Trust }} + \beta_{2} *{\text{Information Technologies }} + \varepsilon $$

The first-stage regression analysis also reveals that the partial R 2 for generalized trust, where monarchy is the instrumental variable, has a relatively low value of 0.16.Footnote 13 Although this suggests that monarchy may be a weak instrument, the Cragg-Donald F statistic [(1, 61) = 11.11] is above the typical single endogenous regressor 10.0 cut-off value, indicating that monarchy is not a weak instrument. The Anderson LM test for underidentification shows that I can reject the null hypothesis that the equation is underidentified (p = 0.0017). Once again, no tests are necessary to determine over-identification since the equation is exactly identified. Finally, the Fig. 1 Lagrange Multiplier (LM) paralleled those for the information technologies instrument: the overall model chi-square would not significantly decrease if a fixed-to-zero path from monarchy to institutional quality was freely estimated. All of this provides firm diagnostic evidence, beyond the theoretical and historical reasons outlined in the paper, for monarchy as an instrument.

Sensitivity Analysis II: Alternative Instruments

I also examined a number of alternative IVs for generalized trust. Recent studies by Bergh and Bjørnskov (2011), Bjørnskov (2010) and Tabellini (2007) propose such IVs. Drawing on Kashima and Kashima (1998), Tabellini argues that countries where respect for individual rights is weak also have languages that permit dropping of the personal pronoun, or “pro-dropping.” This suggests that a greater emphasis on collective identity and common rights will occur in countries where “pro-dropping” is forbidden, which should increase generalized trust. The second instrument, used by Bjørnskov, is the average temperature in the coldest month of the year. The theoretical and historical argument here is that a country with harsh winters creates a greater demand for individuals and small groups to depend on strangers outside of their particularized social network for survival. Those in need during cold winters would likely receive help from strangers, while those in countries with milder winters could exclusively rely on their immediate family and friends for survival. The result is a historical development of generalized trust in those societies with colder winters.

I subject these alternative instruments to the same diagnostic test outlined above. “Pro-dropping” is coded as a binary variable, where “1” equals a license to pro-drop in the country’s official language, while “0” equals otherwise. The temperature instrument is a continuous variable that measures a country’s average temperature (Celsius) in the coldest month of the year. When using these alternative IVs, the key results paralleled those presented in Fig. 1, yet failed many of the diagnostic tests. The alternative IVs suffered from (a) underidentification (only temperature, p = 0.70); (b) weak instrument problems where the Cragg-Donald F statistic was well below the typical cut-off value of 10.0 (pro-drop, F = 6.9; temperature, F = 0.14; both, F = 3.38); (c) weak partial-R 2 (pro-drop, R 2 = 0.10; temperature, R 2 = 0.002; both, R 2 = 0.10); and (d) significantly correlated disturbance terms in the SEM (only if pro-drop was in the equation). In addition, pro-drop was significantly correlated with the error term (ε) in the following equation:

$$ {\text{Institutional Quality}} = \beta_{1} *{\text{Generalized Trust}} + \beta_{2} *{\text{Information Technologies }} + \varepsilon $$

I also examined diagnostics with the alternative instruments coupled with monarchy. The best combination of instruments was monarchy and pro-drop. Although the instruments produced identification (p = 0.001) and a moderate partial-R 2 of 0.22, the overall model fit was worse than model 1 in Table 2, the Cragg-Donald F statistic was below the cut-off value of 10.0 (F = 8.23), the Sargan test statistic suggested overidentification (p = 0.02), and the generalized trust and institutional quality disturbance terms were significantly correlated. As a result, these instruments were excluded from the analysis in favor of monarchy.

In the end, monarchy was chosen because of theoretical and historical reasons, which were also supported empirically: all alternative instruments failed other tests and explained less model variation in comparison to monarchy (i.e., monarchy yielded the lowest model IFI, CFI, SRMR and RMSEA) but generated similar results to those found in Fig. 1 with respect to the feedback loop between institutional quality and generalized trust. Results for all of the above analyses, as always, are available upon request.

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Robbins, B.G. Institutional Quality and Generalized Trust: A Nonrecursive Causal Model. Soc Indic Res 107, 235–258 (2012). https://doi.org/10.1007/s11205-011-9838-1

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Keywords

  • Institutional quality
  • Generalized trust
  • Endogeneity bias
  • Feedback effect
  • Structural equation model