The Effect of Corruption, Uncertainty, the Internet, and COVID−19 on Happiness: a Case Study on Iran

One of governments' key responsibilities is to enhance their citizens' and nations' life satisfaction, subjective well−being, or happiness. Recent advancements in the literature have viewed happiness as a collective right for citizens, thereby emphasizing the societal well−being that governments must ensure for every member of society. Furthermore, some argue that happiness is a national issue and that efforts should be made to ensure the happiness of the general public (Lee, 2022).
In addition to critical economic variables such as unemployment and economic growth, other factors such as corruption control, which is one of the institutional indicators and an essential component of good governance, can significantly impact the subjective well−being of society. Corruption has a psychological effect on happiness. People may feel shame when they feel that their actions cannot go through a legal and accountable system, for example, if someone receives a bribe (Li & An, 2020). This understanding can foster empathy and a deeper comprehension of the issue.
Another significant determinant is economic and political uncertainty, which can impact societal happiness in various manners. Uncertainty exerts a detrimental impact on investment, economic expansion, public confidence, and the aspiration for a more promising future. It can directly and indirectly shape individuals' and society's subjective well−being or happiness.
The impact of Internet access on happiness in Iran is a complex issue that warrants further investigation. Internet access can enhance happiness in a country like Iran by improving life satisfaction. However, it is essential to note that it may also lead to frustration and disappointment, especially during harsh economic and non−economic sanctions, when people compare their living standards with those of other nations, negatively impacting their subjective well−being.
This research contributes to the existing literature on happiness in Iran by examining the effect of corruption as an essential indicator of institutional quality, economic and political uncertainty, access to the Internet, the COVID−19 crisis, and sanctions on subjective well−being in Iran. In addition, we include other control variables such as economic growth and unemployment.

Method

To investigate the main determinants of happiness in Iran, we propose estimating the following model:
,                                          (1)
Equation (1) demonstrates that happiness (HAP)[1] is influenced by several factors, including LGDPP (logarithm of real gross domestic product per capita),[2] WUI (world uncertainty for Iran),[3] COR (corruption),[4] NET (internet access),[5] UN (unemployment rate),[6] SAN (US sanctions against Iran),[7] and COVID19 (the COVID−19 crisis).[8]
The model is estimated using the autoregressive distributed lag method (ARDL). Pesaran and Shin (1995) show that if the cointegration vector is obtained using the ARDL method, the least squares estimator is less biased and more efficient in small samples, provided the lags are specified correctly. Using the ARDL method also has the advantage of obtaining a consistent estimation of coefficients in the long−run model when our variables are integrated of order zero or one (i.e., I(0) or I(1)).[9] This method also allows us to specify the speed at which the deviation from equilibrium is corrected in each period. We consider the following  ARDL(p,q₁,q₂,...,q_k) model:[10]
,  (2)
 
where  and s are parameters,  represents the random disturbance term. By using information criteria such as Akaike Information Criterion (AIC), Schwarz Bayesian Criterion (SBC), and Hannan−Quinn Criterion (HQC), it is necessary to select appropriate lags (p and q) for the variables to estimate the short−run model. After choosing the appropriate ARDL model, the coefficients of the long−term model can be calculated. If there is a long−run equilibrium relationship among the variables, the following Error Correction Model (ECM) can be estimated:
A(L)ΔHAP_t =B(L)ΔX_t + (1−Π) ECT_t−1+G 'Z_t + e_t.                                                 (3)
In which, A(L)= 1−a₁L −a₂L² −.... −a_pL^p 
and B(L)= 1 − b_k1L − b_k2L² − .... − b_kj  (k=1,...,5), Π =(a₁ +a₂+ .... +a_p)
Where L is the polynomial lag operator, G is the vector of parameters, and e_t is the disturbance term. In this equation, X=( ), and Δ represents the first−order difference of the variable, ECT_t−1 is the error correction term, and Z_t is the vector of deterministic variables such as constant value and exogenous variables with fixed lags like COVID−19 and SAN. The coefficient of the error correction term captures the speed of adjustment toward long−term equilibrium. The model was estimated for quarterly data from 2005Q1 to 2022Q4, and the bound test was used to check the cointegration among the variables.

Results

Before estimating the model, we performed various unit root tests. The results rejected the null hypothesis of unit roots for the second difference of the variables, indicating the variables are integrated of order zero or one. Since we have a combination of I(0) and I(1) variables, we conducted the bound test for cointegration. The results confirm the presence of a long−run equilibrium relationship among variables. Moreover, we used the Akaike Information Criterion to choose the optimum lags for the short−run model.[11]
Furthermore, the Breusch−Godfrey serial correlation LM and the heteroskedasticity Harvey tests did not reject the null hypotheses of no serial correlation and homoskedasticity. The detailed results of these tests are presented in Table 1.
 
Table 1. The serial correlation and heteroskedasticity tests




(A)                 Breusch−Godfrey Serial Correlation LM Test− Null hypothesis: No serial correlation




F−statistic


1.787593


Prob. F(3,34)


0.1681




Obs R−squared


8.719348


Prob. Chi−Square(3)


0.0333




(B)                Heteroskedasticity Test: Harvey− Null hypothesis: Homoskedasticity




F−statistic


1.221176


Prob. F(26,37)


0.2838




Obs R−squared


29.55665


Prob. Chi−Square(26)


0.2864




Scaled explained SS


20.99953


Prob. Chi−Square(26)


0.7420




Source: Authors' calculation
 
We have estimated three models: (1) a short−run model, (2) a long−run model, and (3) an error correction model (ECM). The estimation of the long−run model reported in Table 2 indicates that less corruption and higher access to the internet increase happiness. Moreover, world uncertainty and higher unemployment reduce subjective well−being. Our finding shows that economic growth is anti−happiness, a phenomenon potentially stemming from the unequal income distribution within the country. The short−run estimation indicates that sanctions and COVID−19 decrease happiness. Our error correction model shows that about 89 percent of errors towards equilibrium are corrected in each period.[12] Our results underscore the role of fighting corruption, reducing uncertainty, and improving income equality in increasing subjective well−being in Iran.
 
Table 2. The estimation of the long−run model. Happiness is the dependent variable




Prob


t−Stat


Standard Error


Coefficeint


Variable




0.0000


−6.456561


0.733549


−4.736207


LGDPP




0.0223


−2.384912


0.023537


−0.056133


UN




0.0000


−4.721460


0.493086


−2.328085


WUI




0.0006


3.744307


0.003161


0.011836


NET




0.0000


5.985792


0.128301


0.767983


COR




0.0000


7.228763


6.870018


49.66173


C




Source: Authors' estimation
 
We examined the structural stability of the coefficients using two tests: the cumulative sum (CUSUM) and the cumulative sum of squares (CUSUMSQ). The first test checks for a systematic change in the estimated coefficients, while the second test investigates sudden and random departures from the constancy of the parameters. Figures 1 and 2 show the results of these tests, respectively. The regression equation is specified correctly if the CUSUM and CUSUMSQ lines do not cross the straight lines drawn at a 5% significance level. Our findings indicate that the model does not exhibit any structural instability.
 
Figure 1. Stability test: CUSUM (cumulative sum)
 
 
Source: Authors' calculation
Note: The figure exhibits a pair of straight lines drawn at a 5% significance level.
CUSUM does not cross the straight lines
 
Figure 2. Stability test:  CUSUMSQ (cumulative sum of squares)
 
Source: Authors' calculation
Note: The figure exhibits a pair of straight lines drawn at a 5% significance level.
CUSUMSQ does not cross the straight lines

Discussion and Conclusions

The estimation results show that reducing corruption increases happiness in Iran. This result is similar to the findings of Tayet al. (2014), Flavin (2019), Li and An (2020), Yan and Wen (2020), and Behera et al. (2024). Corruption can increase business transaction costs, destroy political trust, and reduce subjective well−being.  Another finding indicates that the increase in world economic and political uncertainty for Iran negatively impacts happiness. This adverse effect is likely due to the psychological impact, which diminishes hope for a better future, and the negative implications on investment, employment, and economic conditions. Access to the Internet has been observed to contribute to increased well−being in countries such as Iran, which face severe economic and non−economic sanctions. This access not only enhances the comfort of individuals but also provides them with a valuable source of information.
In addition, the result shows that economic growth in Iran is anti−happiness, possibly due to the unequal distribution of growth among Iranians. Easterlin (1974) proposed the uncertainty about the relationship between income and happiness. According to some empirical evidence, an increase in income under certain conditions may not increase happiness. Frey and Stutzer (2002) consider this relationship to be complex. Yan and Wen (2020) also emphasize that corruption and increasing the income gap are crucial factors that reduce happiness.
Furthermore, the rise in unemployment leads to a decline in happiness in Iran. This finding is similar to the results of Clark and Oswald (1994), Blanchflower (2007), Agan et al. (2009), Abounoori and Asgarizadeh (2013), and Sameem and Buryi (2019). The effect of unemployment on subjective well−being is important, so Li and An (2020) show that reducing unemployment can even partially compensate for the reduction of happiness caused by corruption. This underscores the urgency of addressing unemployment to improve well−being in Iran.
The analysis of the short−term model indicates that the Covid−19 pandemic and sanctions have led to a decrease in happiness. It is imperative to not only consider the impact of variables such as employment and economic growth, but also to underscore the significance of combating corruption, enhancing society's access to the Internet, and reducing economic and political uncertainty to boost subjective well−being in Iran. These findings carry significant implications for policymakers endeavoring to enhance happiness.
Key words: Happiness, Corruption, World uncertainty, Internet, Sanctions, COVID−19, Iran.
Classification JEL: C22, E24, I31.
References

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[1]. Data source: The World Happiness Report (2024). (https://worldhappiness.report).
[2] . Data source: https://databank.worldbank.org/source/world-development-indicators
[3]. Data source: https://worlduncertaintyindex.com/data/
[4]. It captures the corruption within the political system. Source: ICRG published by the Political Risk Services (PRS) Group.
[5] . Data source: https://databank.worldbank.org/source/world-development-indicators
[6] . Data source: https://databank.worldbank.org/source/world-development-indicators
[7] . Data source: Syropoulos et al. (2022).
[8] . Data source: World Health Organization (WHO).
[9]. We must ensure that no time series in our model is integrated of order two or higher.  
[10] .For details see Pesaran and Pesaran (1997)
[11].  We refer the readers to the main manuscript for the results of unit root tests, diagnostic tests, the bound test for cointegration, and model stability tests.
[12].  We refer readers to the main manuscript for the estimation results of the short-run and error correction models. These results can also be obtained by contacting the corresponding author.