Happiness and Innovation Around the World

Abstract

This study investigates the impact of happiness on corporate innovation around the world. Using novel data from the World Happiness Report, we document that happiness is positively associated with firm innovation. The effect is more pronounced for R&D intensive firms. Further, we find that happiness promotes innovation through channels of collaboration, productivity, and risk tolerance. Our results are robust to including a range of controls and to using an instrumental variable approach and quasi-natural experiment. Overall, our results suggest that happiness can foster corporate innovation around the world.

Keywords

happiness innovation Life Ladder sustainable development risk tolerance G02 G31 I31 D60

How Google’s strategy for happy employees boosts its bottom line … “Companies like Google have invested more in employee support and employee satisfaction … For Google, it rose by 37%; … Under scientifically controlled conditions, making workers happier really pays off” … higher employee happiness levels [were] associated with a 12% rise in productivity.

—Forbes (September 17th, 2018)¹

Introduction

Innovation is crucial for sustainable growth and economic development (e.g., Kogan et al., 2017).² Previous literature has identified several country-specific factors that influence innovation, including creditor rights, legal institutions, financial development, stock market liberalization, and social capital (e.g., Acharya et al., 2014; Ghoul et al., 2017; Hsu et al., 2014; Kolk, 2016; Moshirian et al., 2021; Xie et al., 2022). Other research explores the relation between happiness and corporate behaviors, including theories on positive organizational behaviors (e.g., Luthans, 2002a; 2002b), social exchange (Lawler, 2001), and positive emotions (Fredrickson, 2001). However, relatively little is known about how country-level happiness (i.e., affective states) shapes innovation.³ In this paper, we fill this gap by investigating the impact of happiness on innovation.

Happiness is a state of emotional well-being experienced by a person who is living what they perceive to be a good life (De Neve & Ward, 2017; Diener et al., 1999; Frey & Stutzer, 2002).⁴ While happiness is the commonly used colloquial term, it includes a broad and multidimensional concept. This dimension includes people’s ability, capacity, choices, and purpose in life. It is also typically correlated with well-being decisions. The World Happiness Report defines happiness as an assessment of overall life satisfaction.⁵ In their definition, “happiness” is a life satisfaction; a dimension of subjective well-being that assesses peoples’ views of their lives as the best possible. It is related to how people experience and evaluate their lives as a whole. Previous research documents that when people are happier, they are more resilient to failure, more productive at work, and more pleasant to be around (e.g., Ifcher & Zarghamee, 2011; Oswald et al., 2015; Bellet et al., 2024).⁶

While prior studies indicate a positive association between happiness and innovation, recent studies present a nuanced view, leaving the overall relation ambiguous. One stream of literature shows that happiness (broadly employee well-being or satisfaction) increases the likelihood of innovation. Among them, Chen et al. (2016b) find that firms with employee-friendly workplaces achieve greater innovation success. Similarly, Chen et al. (2016a) and Mao and Weathers (2019) show that firms with better employee treatment produce more and higher-quality patents. However, several recent studies suggest that happiness may decrease productivity and innovation. For example, Ko and Choi (2019) show that overtime work is positively related to the firm’s productivity but has an inverted U-shaped relation with innovation. Park and Rahmani (2021) show that employee satisfaction with their work–life balance negatively affects innovation. Collectively, these studies imply that happiness could reduce the likelihood of innovation.

We examine the relation between happiness and innovation. Intuitively, people in positive affective states (i.e., happier states) are more likely to be optimistic and creative. Previous research shows that positive expectations enhance creativity (Isen et al., 1987) and that positive affective states (e.g., happiness) increase attention (Rowe et al., 2007). Survey evidence from Harvard Business Review Analytic Services (2020) supports this view, showing that 87% of executives believe happiness offers a competitive advantage, and 79% believe unhappiness hurts productivity. Happiness can also play out at the country level by interacting with better public goods and higher trust. Xie et al. (2022) show that country-level trust facilitates innovation by acting as an informal contracting mechanism. Chuluun and Graham (2016) show that local happiness induces firm-level investment and R&D. Thus, we conjecture that happiness promotes innovation by enhancing collaboration, productivity, and risk tolerance.

We measure happiness using the Life Ladder (also known as the Cantril Ladder) score, a measure of how people view their lives as a whole, from the World Happiness Report by the United Nations Sustainable Development Solutions Network.⁷ This report summarizes a country-level aggregate view of affective states on life from respondents in more than 150 countries. It shows that happiness varies substantially from year to year, ranging from 3.849 to 7.658 on average across countries.⁸

We first examine the relation between happiness and innovation. Our baseline results show a positive relation between happiness and innovation during 2005–2024. In terms of economic significance, a one-standard-deviation increase in happiness leads to an increase of about 3.48% in innovation. A potential concern is overrepresentation of U.S. firms, which comprise approximately 20% of our sample, but our results remain unchanged after excluding these firms. We also examine a different set of fixed effects to account for time-varying characteristics across firms, industries, and countries and find results consistent with our baseline findings. Next, we examine the heterogeneous effect of happiness on innovation in response to R&D characteristics. Following previous literature (e.g., Acharya & Subramanian, 2009), we find that the positive effect of happiness on innovation is more pronounced for firms with nonzero R&D disclosure, firms with a high patent count, and firms in high-tech industries.

Next, we identify the causal effect of happiness on innovation. This identification is important in our study because the relation between happiness and innovation can be bidirectional. We employ three approaches to address this issue. First, we implement instrumental variable (IV) analyses using natural disasters as the instrument. The coefficients of instrumented happiness are consistent with our baseline results. Second, we implement a quasi-natural experiment using the Fukushima earthquake as a negative and exogenous shock to local happiness and find it has a negative impact on innovation. Third, to alleviate the issue of omitted variables, we augment our regression models by controlling for a wide array of country-level variables across financial development, polarization, and market openness.⁹ The positive relation between happiness and innovation remains unchanged after controlling for unobservable country characteristics. Overall, our findings remain robust across all identification tests.

Last, we investigate potential channels through which happiness facilitates innovation, including collaboration, productivity, and risk tolerance. Previous studies suggest that innovation relies on collaboration among innovators. For example, informal social capital can encourage collaboration by allowing innovators to share resources and expertise (Dovey, 2009; Lerner, 2009; Xie et al., 2022). We find that the effect of happiness is more pronounced in countries with higher collaboration, using high political stability, high regulatory quality, and low corruption as proxies. Productivity also can be a channel for the positive effect on innovation (e.g., Bellet et al., 2024; Oswald et al., 2015). We find that happiness increases the number of patents per employee, indicating that happiness fosters engagement in innovation activities. Finally, happiness promotes innovation via risk tolerance (e.g., Acharya & Subramanian, 2009; Acharya et al., 2014; Manso, 2011). Using legal enforcement as a proxy for risk tolerance, we similarly find that the effect of happiness is greater when risk tolerance is higher.

This paper contributes to several strands of literature. First, it adds to the literature on happiness, and more broadly, on employee well-being and satisfaction. This research documents that happier people are more productive, focused, and creative (e.g., Bellet et al., 2024; Ifcher & Zarghamee, 2011; Oswald et al., 2015; Rowe et al., 2007). Empirical studies similarly show that happier people tend to be more optimistic and that firms with happier employees tend to have higher abnormal returns and increased firm investment (Chuluun & Graham, 2016; Edmans, 2011; Kaplanski et al., 2015). Studies also find that group emotion influences workforce dynamics (Barsade, 2002; Brief & Weiss, 2002; Forgas & George, 2001). This study adds to this literature by providing evidence that country-level happiness enhances firms’ innovative activities.

Closely related to our study, Chen et al. (2016a) and Mao & Weathers (2019) show that firms with better employee treatment produce more and higher-quality patents. For example, firms with employee-friendly workplaces demonstrate more innovation success (Chen et al., 2016b). Using right-to-work (Nguyen & Qiu, 2022) and medical access (Thompson, 2024) as exogenous shocks, research has shown a positive link between employee well-being and corporate innovation. Ko and Choi (2019) document that overtime work is negatively related to employee satisfaction but positively related to firm productivity and innovation, and Park and Rahmani (2021) show that employee satisfaction with work–life balance negatively affects innovation. Adding to this literature, our study assesses country-level happiness (i.e., affective state) and its relation to firm innovation.

Although we reach similar conclusions as papers showing a positive effect of employee well-being on innovation, our approach differs from these studies in several ways. First, we demonstrate that country-level affective states have a more direct effect on firm-level innovation. Prior literature has adopted different measures for estimating employee well-being or satisfaction. In this paper, our measure of happiness is a country-level aggregate of people’s affective states derived from World Happiness Report surveys conducted in more than 150 countries and involving approximately 3,000 respondents in each country. We also explore several different channels through which happiness fosters innovation: collaboration, productivity, and risk tolerance. Our study further strengthens the causal inference using an IV approach and quasi-natural experiment.

This paper also contributes to the literature on finance and innovation in cross-country settings. Previous studies explore how country-specific characteristics such as bankruptcy regimes, legislation, policy, financial development, religiosity, and social capital affect innovation output (e.g., Acharya et al., 2014; Acharya & Subramanian, 2009; Bénabou et al., 2015; Bhattacharya et al., 2017; Brown et al., 2013; Ghoul et al., 2017; Hsu et al., 2014; Kolk, 2016; Luong et al., 2017; Moshirian et al., 2021; Xie et al., 2022). Closely related to our study, Xie et al. (2022) show that trust promotes innovation by encouraging collaboration and enhancing failure tolerance. We add to this line of research by showing that happiness exhibits a positive effect on innovation through the channels of collaboration, productivity, and risk tolerance.

Background and Empirical Implications

Happiness and Innovation: A Nuanced Impact

Happiness is a complex state of joy, contentment, and satisfaction, encompassing positive feelings. It is a sense of life being good and meaningful. While happiness includes positive emotions, it also involves overall life satisfaction and the ability to embrace a range of emotions (for the detailed discussion, see, e.g., Bellet et al., 2024). For this reason, studies in psychology and behavioral economics are careful to distinguish the terms like “employee well-being” or “employee satisfaction.” While the term “happiness” is loosely referring all types of subjective well-being, “employee well-being” is a concept of employee happiness, encompassing a comprehensive state of physical, mental, and emotional health, influenced by work and personal factors. “Employee satisfaction” is more likely a focused assessment of an employee’s contentment with their work-related aspects or specific roles such as compensation or responsibilities (e.g., Bellet et al., 2024).¹⁰

When people are happier, they are more resilient to failure, more productive at work, and more pleasant to be around (e.g., De Neve & Ward, 2017; Diener et al., 1999; Frey & Stutzer, 2002; Ifcher & Zarghamee, 2011). Happiness can also play out at the country level, where it interacts with better public goods, religion, trust, and higher levels of democracy. For example, Bénabou et al. (2015) show that greater religiosity is associated with less favorable views of innovation across countries. Xie et al. (2022) show that trust facilitates innovation by acting as an informal contracting mechanism. In an empirical study in finance, Kaplanski et al. (2015) show that happy people are more optimistic and expect higher stock returns. In addition, Edmans (2011) shows that firms included in the “100 Best Companies to Work For” list tend to have higher future abnormal stock returns. In psychological literature, many studies show that happiness affects behavioral decisions (e.g., Clark et al., 2008; Frey & Stutzer, 2002; Iaffaldano & Muchinsky, 1985; Kahneman & Krueger, 2006; Kenny, 1999; Krause, 2013). These studies indicate that the behaviors of happy people generally differ from that of less happy people. Many studies document how affective states are an important driver of human behaviors, which eventually have an impact on economic outcomes (e.g., Capra, 2004; Card & Dahl, 2011; Chuluun & Graham, 2016; Dahl & DellaVigna, 2009; Ifcher & Zarghamee, 2011; Kirchsteiger et al., 2006; Loewenstein, 2000).

Interestingly, while prior studies indicate a positive association between happiness and innovation, some recent studies present a nuanced view, leaving the overall relationship ambiguous. One stream of literature shows that higher levels of employee well-being or satisfaction increase the likelihood of firm innovation. As discussed, Oswald et al. (2015) and Rowe et al. (2007) find positive correlations between individual happiness and productivity and attention. More closely related to innovation, Chen et al. (2016b) find that firms with employee-friendly workplaces achieve greater innovation success. Chen et al. (2016a) and Mao & Weathers (2019) show that firms with better employee treatment produce more and higher-quality patents. Nguyen and Qiu (2022) document that employee well-being increases corporate innovation using right-to-work as an exogenous shock. Thompson (2024) also shows that employee well-being enhances corporate innovation using medical access as an exogenous shock.

Another stream finds that happiness may decrease productivity and innovation. For example, Ko and Choi (2019) report that overtime work is negatively related to employee satisfaction but positively related to firm productivity, and it has an inverted, U-shaped relation with innovation. This result implies a positive effect of overtime on innovation, at least up to a certain level. Similarly, Park and Rahmani (2021) report that higher employee satisfaction with career opportunities enhances firm-level innovation, but higher employee satisfaction with work–life balance negatively affects innovation. Further, employee satisfaction with their compensation and benefits can significantly reduce the scientific and economic value of the innovative output. Overall, the above studies seem to suggest that happiness could reduce the likelihood of innovation.

Possible Channels

Prompted by previous studies and discussions, we conjecture that happiness can increase the likelihood and efficiency of innovation through several channels. First, we consider collaboration as a channel through which happiness affects innovation. Previous studies suggest that informal social capital can encourage collaboration by allowing innovators to share resources and expertise with each other (Lerner, 2009). According to Dovey (2009), Khanna and Mathews (2016), and Xie et al. (2022), innovations can depend on collaboration among innovators and successful collaboration hinges on trust between people.

Second, we expect that happiness can positively affect innovation by improving productivity. Previous research shows that happiness not only relates to individual productivity (Oswald et al., 2015) but also increases the scope of attention (Rowe et al., 2007). Bellet et al. (2024) provide evidence from a natural experiment that happiness increases the performance of firms. In a similar vein, Bénabou and Tirole (2003) theorize that intrinsic motivation drives the relation between optimism and productivity. Based on the discussion, we conjecture that productivity can be a channel through which happiness affects innovation.

Third, we expect that risk tolerance is a channel through which happiness can affect innovation. Owing to various unpredictable conditions, innovation involves a high probability of failure. Manso (2011) shows that optimal conditions for facilitating innovation include high tolerance for failure and consideration of long-term rewards. Acharya et al. (2014) similarly emphasize that debtor-friendly bankruptcy regimes and strong legal protection for employees promote innovation by alleviating concerns about the adverse impact of failure and encouraging risk-taking. Overall, we expect that happiness fosters innovation through the channel of risk tolerance.

Data and Sample

Descriptive Statistics

We obtain firm-level data from the 2025 Spring version of the Compustat Global & North America database (the period matching the World Happiness Report), which covers international firm-level financial data, and global patent data from the European Patent Office’s World Patent Statistical Database (PATSTAT). We rely on joint availability of innovation measures, financial variables, and the happiness index. Firms in our sample file at least one patent during our sample period, 2005–2024, which matches the availability of happiness index data from the World Happiness Report. We exclude countries with fewer than 50 firm-year observations.

Table 1 presents the descriptive statistics. Panel A (B) reports the sample composition by country (year). In Panel A, we further report the total numbers of firm-year observations and firms, Life Ladder scores, and standard deviations of happiness. Our final sample includes 232,333 firm-year observations for 18,364 firms in 66 countries during 2005–2024. The number of firm-years differs because not all firms in each country are observed throughout our sample period. Denmark reports the highest average level of happiness (7.658) and Zimbabwe the lowest (3.849).

Table 1.

Sample Composition

Panel A. Sample composition by economy
Economy	No. of firm-years	No. of firms	Life Ladder Score	△Life Ladder Score	GDP growth
Argentina	339	22	6.307	0.011	1.770
Australia	6,067	517	7.232	−0.025	2.662
Austria	566	43	7.174	−0.019	1.339
Bahrain	53	4	6.083	0.013	2.987
Bangladesh	242	19	4.541	−0.053	6.257
Belgium	842	67	6.964	−0.020	1.655
Brazil	2,118	152	6.571	0.003	2.065
Bulgaria	131	11	4.927	0.129	2.401
Canada	7,155	590	7.292	−0.038	1.775
Chile	644	38	6.371	0.022	3.007
China	40,385	3,271	5.358	0.041	6.882
Colombia	273	19	6.169	−0.011	3.330
Croatia	227	18	5.688	0.011	1.508
Cyprus	69	7	6.056	−0.009	2.761
Czech Republic	83	7	6.665	0.023	2.272
Denmark	978	73	7.658	−0.027	1.497
Egypt	156	13	4.308	−0.082	4.137
Finland	1,190	94	7.636	0.009	0.970
France	4,417	340	6.651	−0.026	1.045
Germany	5,327	398	6.815	0.015	1.045
Greece	665	45	5.595	−0.031	−0.894
Hong Kong	1,089	78	5.452	0.002	1.961
Hungary	51	4	5.472	0.035	1.737
India	12,123	818	4.362	−0.038	6.267
Indonesia	698	43	5.293	0.034	4.848
Ireland	722	58	7.024	−0.011	4.932
Israel	2,350	204	7.244	0.002	3.821
Italy	2,217	187	6.306	−0.011	0.169
Japan	39,499	2,609	6.032	−0.020	0.496
Jordan	121	11	4.934	−0.133	2.911
Kenya	127	8	4.423	0.014	4.679
Kuwait	163	11	6.347	−0.017	1.027
Latvia	59	4	5.545	0.097	2.096
Lithuania	61	4	5.977	0.042	2.738
Luxembourg	284	30	7.105	0.004	1.913
Malaysia	2,592	164	5.844	−0.004	4.435
Mauritius	98	11	5.867	0.042	2.681
Mexico	648	40	6.702	0.014	1.482
Morocco	55	4	4.976	0.035	2.877
Netherlands	1,114	102	7.435	−0.010	1.802
New Zealand	727	59	7.248	−0.021	2.428
Nigeria	239	18	4.960	−0.006	3.604
Norway	727	79	7.448	−0.015	1.654
Pakistan	826	55	5.083	−0.021	3.675
Peru	303	19	5.636	0.062	4.074
Philippines	859	52	5.420	0.086	5.101
Poland	1,797	153	6.072	0.064	3.588
Portugal	208	16	5.590	0.053	1.221
Romania	240	23	5.964	0.110	2.572
Russia	336	29	5.621	0.035	1.903
Saudi Arabia	411	29	6.499	−0.016	3.644
Singapore	3,571	269	6.518	0.011	4.656
Slovenia	122	10	6.177	0.062	1.295
South Africa	1,762	119	4.935	−0.006	1.756
South Korea	15,596	1,447	5.929	0.018	2.808
Spain	1,296	92	6.487	−0.032	1.159
Sri Lanka	677	39	4.263	−0.018	3.701
Sweden	3,791	323	7.366	0.003	1.720
Switzerland	1,574	139	7.398	−0.042	1.640
Thailand	2,380	152	6.092	0.024	2.582
Türkiye	947	64	5.195	0.020	5.042
United Arab Emirates	180	13	6.791	0.005	3.039
United Kingdom	8,677	710	6.889	−0.011	1.299
United States	48,579	4,273	7.038	−0.015	1.984
Vietnam	402	37	5.534	0.065	6.064
Zimbabwe	108	7	3.849	−0.012	4.606
Total/Mean	232,333	18,364	6.067	0.006	2.730

Panel B. Sample composition by year
Year	Freq.	Percent
2005	4,268	1.84
2006	5,796	2.49
2007	10,274	4.42
2008	10,828	4.66
2009	10,638	4.58
2010	11,563	4.98
2011	11,806	5.08
2012	12,233	5.27
2013	12,358	5.32
2014	12,897	5.55
2015	12,990	5.59
2016	13,153	5.66
2017	13,380	5.76
2018	13,302	5.73
2019	13,272	5.71
2020	13,298	5.72
2021	13,493	5.81
2022	13,377	5.76
2023	13,071	5.63
2024	10,336	4.45
Total	232,333	100

Notes. This table reports the sample composition used in the estimation. Panel A presents the sample composition by country. Panel B shows the sample composition by year.

Table 2 reports the descriptive statistics and correlations for the main variables. We winsorize all continuous variables at the 1st and 99th percentiles to reduce the impact of outliers. Panel A presents the summary statistics. In our sample, the average number of patents is 14.726 with a standard deviation of 54.076. The average number of weighted citations is 76.256 with a standard deviation of 326.751. We find that patent measures are skewed, which can cause bias when using the raw data. Thus, we use the natural logarithm of patent measures plus 1 to minimize issues from extreme values. We also use the natural logarithm of IPC-technology-class-weighted citations plus 1 to avoid truncation issues, especially in recent years. The average Life Ladder (happiness) score is 6.230, with a standard deviation of 0.888 and range of 2.694 to 8.019 across countries. In our regression specification, we include firm size (Ln(TA)), Tobin’s Q (Tobin’s Q), operating cash flow (OCF), sales growth (Sales Growth), leverage (Leverage), research and development (R&D), capital expenditure (Capex), Herfindahl-Hirschman index (HHI), and GDP growth (GDP Growth). See Appendix Table A1 for variable definitions. Table 2, Panel B shows the correlations of the variables based on the same sample.

Table 2.

Summary Statistics

Panel A. Descriptive statistics
Variable	N	Mean	Median	S.D.	Min	Max
No. of patents	232,333	14.726	1.000	54.076	0.000	419
No. of citations	232,333	76.256	0.000	326.751	0.000	2598
PATENT_t+1	232,333	1.083	0.693	1.453	0.000	6.040
CITEPAT_t+1	232,333	0.927	0.000	1.472	0.000	6.077
Patents per employee	173,315	0.134	0.001	1.395	0.000	190
Citations per employee	173,315	0.170	0.000	2.415	0.000	539
Ln(TA)	232,333	6.199	6.087	2.119	1.005	11.727
Tobin’s Q	232,333	1.923	1.315	1.792	0.491	11.779
OCF	232,333	0.045	0.065	0.169	−0.888	0.401
Sales growth	232,333	0.120	0.059	0.417	−0.751	2.770
Leverage	232,333	0.239	0.192	0.233	0.000	1.265
R&D	232,333	0.035	0.003	0.085	0.000	0.553
Capex	232,333	0.049	0.031	0.059	0.000	0.341
HHI	232,333	0.409	0.317	0.306	0.025	1.000
HHI²	232,333	0.261	0.100	0.329	0.001	1.000
Life Ladder	232,333	6.230	6.178	0.888	2.694	8.019
LGDP	232,333	28.967	29.172	1.404	22.208	31.005
GDP growth	232,333	2.944	2.584	3.398	−17.669	24.616
DistFukushima	15,442	375.216	237.106	210.572	134.5543	1047.759
Ring 1 (≤150 km)	15,442	0.015	0.000	0.120	0.000	1.000

Panel B. Pearson’s correlation
		PATENT _t+1	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)	(10)	(11)	(12)
	PATENT _t+1	1
(1)	CITEPAT _t+1	0.918	1
(2)	Life Ladder	0.001	0.049	1
(3)	Ln(TA)	0.308	0.290	0.035	1
(4)	Tobin’s Q	0.027	0.043	0.032	−0.211	1
(5)	OCF	0.067	0.055	−0.123	0.327	−0.168	1
(6)	Sales Growth	−0.002	0.005	0.007	−0.048	0.143	−0.050	1
(7)	Leverage	−0.038	−0.031	0.024	0.157	0.004	−0.110	0.105	1
(8)	R&D	0.090	0.115	0.236	−0.248	0.352	−0.491	0.135	−0.028	1
(9)	Capex	0.032	0.028	−0.117	0.047	0.058	0.140	0.172	0.180	−0.068	1
(10)	HHI	−0.056	−0.048	0.190	0.026	−0.080	0.075	−0.036	0.054	−0.111	−0.022	1
(11)	HHI ²	−0.054	−0.048	0.165	0.032	−0.062	0.071	−0.029	0.052	−0.099	−0.011	0.969	1
(12)	LGDP	0.148	0.157	0.066	0.155	0.136	−0.070	0.000	−0.011	0.152	−0.042	−0.461	−0.434	1
(13)	GDP Growth	0.003	−0.013	−0.442	−0.026	0.125	0.021	0.124	0.012	−0.076	0.164	−0.166	−0.129	0.041

Notes. This table reports the descriptive statistics and correlations of the main variables that are used in the estimation. Panel A presents the basic summary statistics of variables based on a sample with no missing firm- or country-level characteristics. Panel B shows the correlation of the variables (correlation coefficients significant at the 1% level are marked as bold). All variables are defined in the Appendix.

Measuring Happiness

We measure happiness based on data from the World Happiness Report, which surveys roughly 450,000 respondents total from more than 150 countries. The survey asks, “[P]lease imagine a ladder, with steps numbered from 0 at the bottom to 10 at the top. The top of the ladder represents the best possible life for you and the bottom of the ladder represents the worst possible life for you. On which step of the ladder would you say you personally feel you stand at this time?” Respondents rate their current lives from 0 (worst possible life) to 10 (best possible life). The World Happiness Report also assesses social support, freedom to make life choices, generosity, and perception of corruption in society. All variables from the report are reported annually.

Figure 1 illustrates Life Ladder score data from 2024, which ranges from 2.179 to 8.018. Scores are relatively higher in Canada, Australia, and Northern Europe, compared with China, South Asia, Eastern Europe, and Africa, indicating large variations in self-reported happiness across countries.

Figure 1.

World Map of Happiness, 2024. Notes. This figure illustrates happiness according to Life Ladder score data in 2024. Darker (lighter) shades imply higher (lower) levels of happiness

Figure 2 presents the time trend of Life Ladder scores and GDP growth for selected countries and shows that they are not perfectly correlated. In some countries, GDP growth varies in the direction opposite the change in the Life Ladder score. The results suggest our measure of happiness (i.e., Life Ladder score) captures something other than GDP growth, alleviating concerns that the score could be a proxy for a country’s financial or other development level.

Figure 2.

Time Variation of Life Ladder Score and GDP Growth for Selected Countries. Notes. This figure presents the trends in Life Ladder scores and GDP growth for selected countries. The solid red line represents the Life Ladder scores. The blue dotted line indicates GDP growth

Innovation Measures

We measure innovation output using two proxies: patent quantity (PAT_i,t), the total number of patents filed by firm i in year t, and patent quality (CIT_i,t), the total number of forward citations that patent application i receives from its publication date to 2025. Previous international studies use patents to measure innovation output (e.g., Acharya et al., 2014; Acharya & Subramanian, 2009; Hsu et al., 2014; Xie et al., 2022). We retrieve data on patents from PATSTAT (2025 Spring version), which contains rich information on global patents and allows us to track firm-level innovation activities. PATSTAT classifies all applications into patent families, each assigned a family identifier that can correspond to several patent applications. We limit our sample to patent applications with unique family identifiers to avoid overcounting of the same patent filed in multiple countries or years. In our sample, each patent count thus represents a unique innovation. Following prior studies, we use IPC-technology-class-weighted citations to avoid truncation issues, especially in recent years. We also focus on patent quality as higher-quality patents should be cited more frequently by other patents, thus capturing a firm’s innovation (Aghion et al., 2013; Kogan et al., 2017; Xie et al., 2022).

Empirical Results

Effect of Happiness on Innovation

In this section, we present empirical results on how happiness could affect firm-level innovation. We first show the baseline regression results. To test our hypothesis, we analyze the effect of happiness on firms’ innovation by estimating the following regression model:

{I n n o v}_{i c t + 1} = α_{i} + β {L i f e l a d d e r}_{c t} + γ X_{c t} + δ {Y i}_{i c t} + ε_{i c t}

(1)

For $I n n o v$ , we use the logged number of patents, PATENT, in year t+1 of firm i, and the logged number of patent citations, CITEPAT. PATENT quantifies firms’ innovation activity, and CITEPAT accounts for patent quality. For all patent measures, we use measures in t+1. ${L i f e l a d d e r}_{c t}$ is the level of happiness in year t for country c. Following previous literature (e.g., Acharya & Subramanian, 2009), we use firm-level control variables ( $Y_{i c t}$ ) including firm size (Ln(TA)), firm value (Tobin’s Q), operating cash flow (OCF), sales growth (Sales Growth), leverage (Leverage), R&D expense (R&D), and capital expenditure (Capex). We also use country-level control variables ( $X_{c t}$ ), including GDP and growth rate of GDP. We expect the coefficient $β$ to be significantly positive, which would indicate that happiness promotes innovation.

Table 3, Panel A presents the baseline regression results. In columns (1) and (3), we conduct univariate regressions without adding any control variables; we add control variables in columns (2) and (4). In all regressions, we control for time, industry, and firm fixed effects. Standard errors are clustered by country and year (Peterson, 2009). The results in Table 3 provide strong support for our empirical implications. All coefficients of Life Ladder are significantly positive at the 5% level in columns (1) and (2) and at the 1% level in columns (3) and (4). The coefficient of happiness is 0.056 (t-stat = 2.28) in column (2), indicating that a higher level of happiness induces firms’ innovation. In terms of economic magnitude, the impact of happiness is sizable. A one-standard-deviation increase in happiness leads to an increase of about 3.48% (=e^{0.056×0.888/1.453} − 1) in corporate innovation.¹¹

Table 3.

Baseline Regression

Panel A. Firm-level analyses
	(1)	(2)	(3)	(4)
	PATENT _t+1	PATENT _t+1	CITEPAT _t+1	CITEPAT _t+1
Life Ladder	0.139***	0.056**	0.196***	0.102***
Life Ladder	(4.26)	(2.28)	(5.19)	(3.13)
Ln(TA)		0.103***		0.076***
Ln(TA)		(9.43)		(6.13)
Tobin’s Q		0.007		0.007
Tobin’s Q		(1.31)		(1.31)
OCF		0.028		0.037*
OCF		(1.48)		(1.80)
Sales Growth		−0.007		0.003
Sales Growth		(−1.53)		(0.54)
Leverage		−0.108***		−0.115***
Leverage		(−7.66)		(−7.88)
R&D		0.262***		0.420***
R&D		(4.62)		(7.42)
Capex		0.311***		0.247***
Capex		(5.28)		(4.14)
HHI		−0.132		−0.285**
HHI		(−1.32)		(−2.13)
HHI ²		0.119		0.212**
HHI ²		(1.61)		(2.17)
LGDP		0.305***		0.339***
LGDP		(5.63)		(5.94)
GDP Growth		−0.006		−0.011***
GDP Growth		(−1.55)		(−2.64)
Firm FE	Yes	Yes	Yes	Yes
Industry-year FE	Yes	Yes	Yes	Yes
N	232,333	232,333	232,333	232,333
Adj. R²	0.723	0.727	0.691	0.694

Panel B. Aggregate country-level analyses
	(1)	(2)	(3)	(4)
	TOT_PATENT _t+1	TOT_ CITEPAT _t+1	MEAN_PATENT _t+1	MEAN_ CITEPAT _t+1
Life Ladder	0.183***	0.317***	0.135**	0.264***
Life Ladder	(2.74)	(3.67)	(2.15)	(3.29)
MeanLn(TA)	−0.080	−0.162	0.126	0.052
MeanLn(TA)	(−0.78)	(−1.21)	(1.42)	(0.48)
MeanTobin’s Q	−0.128**	−0.085	−0.089*	−0.087
MeanTobin’s Q	(−2.27)	(−0.87)	(−1.96)	(−0.90)
MeanOCF	0.209	0.734	−0.601	−0.107
MeanOCF	(0.19)	(0.56)	(-0.65)	(-0.09)
MeanSales Growth	0.082	0.064	0.098	0.088
MeanSales Growth	(0.38)	(0.22)	(0.51)	(0.34)
MeanLeverage	1.228**	1.147	0.223	0.191
MeanLeverage	(2.23)	(1.34)	(0.47)	(0.25)
MeanR&D	0.819	1.201	−0.421	−0.189
MeanR&D	(0.28)	(0.34)	(−0.15)	(−0.06)
MeanCapex	−3.681**	−2.723	−1.911	−0.762
MeanCapex	(−1.97)	(−1.18)	(−1.10)	(−0.34)
MeanHHI	0.293	−10.219**	3.487	−6.869*
MeanHHI	(0.09)	(-2.34)	(1.25)	(-1.77)
MeanHHI ²	−1.373	6.071*	−2.831	4.541
MeanHHI ²	(-0.57)	(1.85)	(-1.33)	(1.55)
LGDP	0.194	−0.005	−0.112	−0.298
LGDP	(1.19)	(−0.03)	(−0.78)	(−1.57)
GDP Growth	0.014	−0.002	0.011	−0.001
GDP Growth	(1.62)	(−0.16)	(1.46)	(−0.12)
Country FE	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes
N	1,074	1,011	1,074	1,011
Adj. R²	0.9453	0.9405	0.8591	0.9025

Notes. This table reports the baseline test examining the sample-wide effect of happiness on corporate innovation. Panel A presents firm-level estimation results. Firm-level and country-level controls are progressively introduced into the baseline model to control for firm- and country-specific characteristics. Firm and industry-year fixed effects are included in the regressions. Panel B presents aggregate country-level estimation results. Dependent variables are aggregated as either the sum (columns (1) and (2)) or average (columns (3) and (4)) at the country-year level. Firm-level control variables are calculated as the country-year average. Country and year fixed effects are included in the regressions. All variables are defined in the Appendix. Standard errors are robust to heterogeneity and are clustered by country and year. Robust t-values are in parentheses: ***p < 0.01, **p < 0.05, *p < 0.1.

In equation (1), the independent variable, Life Ladder, is measured at the country level, and the dependent variable, PATENT, is measured at the firm level. To address this hierarchical structure, we employ a bottom-up approach by aggregating firm-level innovation measures to the country level, creating sums and averages of innovation indicators for each country-year. Table 3 Panel B presents the results. Columns (1) and (2) show the estimation results for the sums of total patent counts and citations, and columns (3) and (4) display the estimation results for average patent counts and citations across firms. All coefficients of Life Ladder are significantly positive at the 1% level in columns (1) and (2) and at the 5% level in columns (3) and (4). The coefficient of happiness is 0.135 (t-stat = 2.15) in column (3), indicating a positive association between happiness and average firm innovation. The effect is again sizable: a one-standard-deviation increase in happiness leads to an increase of about 9.96% (=e^{0.135×1.004/1.427} − 1) in the average level of corporate innovation.

Cross-Sectional Heterogeneity: R&D Characteristics

Following Acharya and Subramanian (2009) and Chuluun and Graham (2016), we examine the heterogeneous effects of happiness on innovation in response to R&D. The logic also follows Levine et al. (2017): if happiness promotes innovation by improving people’s well-being, then this should be particularly pronounced for firms that with higher innovation intensity. We use three proxies for innovation intensity: R&D Disclosure, Patent Stock, and High Tech. R&D Disclosure is a dummy variable that equals 1 if a firm discloses nonzero R&D expenses, and 0 otherwise. About 41% of Compustat firms do not disclose their R&D. Nondisclosure does not necessarily mean no innovation, but disclosing R&D is a clear signal of positive R&D activities. Patent Stock is a continuous indicator calculated as the sum of historical patent counts using a 15% discount rate. The higher the patent stock, the higher the accumulation of successful experiences in R&D. High Tech is a dummy variable that equals 1 if a firm is in a SIC 3-digit industry coded as 283, 357, 366, 367, 382, 384, or 737, and 0 otherwise. According to Brown et al. (2009), these high-tech industries are innovation-intensive.

Table 4 shows the results. In columns (1) and (2), we test R&D Disclosure. In columns (3) and (4), we test Patent Stock. In columns (5) and (6), we test High Tech. We find that the coefficients of interaction terms in columns (1)–(4) and (6) are significantly positive, indicating that happiness induces more innovation for firms in industries that are more R&D intensive. The results align with those of Chuluun and Graham (2016), who report that higher local happiness levels induce more firm-level investment in R&D. Overall, Table 4 provides evidence that the positive impact of happiness is much stronger for firms that are highly R&D intensive.

Table 4.

Cross-Sectional Variation: Innovation Intensity

	(1)	(2)	(3)	(4)	(5)	(6)
	PATENT _t+1	CITEPAT _t+1	PATENT _t+1	CITEPAT _t+1	PATENT _t+1	CITEPAT _t+1
Life Ladder × R&D Disclosure	0.047*	0.064**
Life Ladder × R&D Disclosure	(1.67)	(2.37)
R&D Disclosure	−0.305	−0.371**
R&D Disclosure	(−1.61)	(−2.09)
Life Ladder × Patent Stock			0.663**	0.775***
Life Ladder × Patent Stock			(2.49)	(2.62)
Patent Stock			−1.821	−2.118
Patent Stock			(−1.04)	(−1.12)
Life Ladder × High Tech					0.022	0.093***
Life Ladder × High Tech					(0.94)	(3.03)
Life Ladder	0.037*	0.071**	0.049**	0.094***	0.051**	0.082***
Life Ladder	(1.71)	(2.55)	(2.07)	(3.03)	(2.18)	(2.85)
Controls	Yes	Yes	Yes	Yes	Yes	Yes
Firm FE	Yes	Yes	Yes	Yes	Yes	Yes
Industry-year FE	Yes	Yes	Yes	Yes	Yes	Yes
N	232,333	232,333	232,333	232,333	232,333	232,333
Adj. R²	0.727	0.694	0.728	0.697	0.727	0.694

Notes. This table reports results examining the effect of happiness on corporate innovation, focusing on innovation intensity. R&D Disclosure is a dummy variable indicating a firm disclosed nonzero R&D expenses, and 0 otherwise. Patent Stock is a continuous indicator calculated as the sum of historical patent counts using a 15% discount rate. High Tech is a dummy variable that equals 1 if a firm is in the SIC 3-digit industry code 283, 357, 366, 367, 382, 384, or 737, and 0 otherwise. The control variables are the same as those in the baseline model. Firm and industry-year fixed effects are included in the regressions. All variables are defined in the Appendix. Standard errors are robust to heterogeneity and clustered by country and year. Robust t-values are in parentheses: ***p < 0.01, **p < 0.05, *p < 0.1.

Tests on Identification

Instrumental Variable Approach

The causal relation between happiness and innovation can be bidirectional. The two directions of causality are not mutually exclusive, and they can occur simultaneously. For example, firms’ active innovation can enhance positive outlooks, leading to more overall happiness. To mitigate this concern, we implement an IV approach.

An individual’s well-being is highly associated with the natural environment. For example, as Berlemann (2016) shows, increases in natural disasters can have a systematic negative effect on individual well-being, especially in countries with developing economies. Innovation tends to involve long-term planning and development, however, meaning that acute natural events (e.g., hurricanes, floods) may be less likely to directly affect innovation-related activities vis-à-vis happiness. We thus predict that the natural environment will affect innovation via more lasting effects on subjective well-being. In other words, climate change—insofar as its effects on happiness—poses a more substantial threat to corporate innovation over the long term, rather than the short term.

We construct Natural Disasters as our IV, calculated as the annual total number of natural disasters (e.g., wildfires, landslides, mass movement, volcanic activity, storms, floods, extreme temperatures, earthquakes, and droughts) in a country, based on the Global Natural Disaster report. Table 5 reports the results. Column (1) presents the first-stage estimation results, showing that Natural Disasters is negatively correlated with the level of happiness, consistent with our prediction. The coefficient for Natural Disaster is −0.029 (t-stat = −2.69), indicating that the IV is negatively associated with the level of happiness. The identification F-test passes the critical value of the appropriate instrument. Column (2) presents the second-stage regression results, showing a coefficient of 0.405 (t-stat = 1.82). The coefficients of instrumented happiness are consistent with our baseline results, which are significantly positive. Overall, the results show that the relation between happiness and innovation remains significantly positive under the IV specification.¹²

Table 5.

Instrumental Variable Analysis

	(1)	(2)
Dep. Var. = PATENT_t+1	1st stage	IV + Disaster
Life Ladder		0.405*
Life Ladder		(1.82)
Natural Disaster	−0.029***
Natural Disaster	(−2.69)
Ln(TA)	0.034***	0.090***
Ln(TA)	(4.63)	(6.31)
Tobin’s Q	−0.008*	0.008
Tobin’s Q	(-1.93)	(1.36)
OCF	0.002	0.018
OCF	(0.13)	(0.92)
Sales Growth	−0.003	−0.007
Sales Growth	(−0.54)	(−1.17)
Leverage	−0.052***	−0.085***
Leverage	(−3.12)	(−3.81)
R&D	0.493***	0.095
R&D	(5.70)	(0.79)
Capex	0.098	0.286***
Capex	(1.32)	(4.09)
HHI	−0.319**	−0.059
HHI	(-2.30)	(-0.45)
HHI2	0.229**	0.069
HHI2	(2.45)	(0.71)
LGDP	0.283**	0.225***
LGDP	(2.45)	(2.77)
GDP Growth	−0.030***	0.006
GDP Growth	(−2.67)	(0.74)
N	217,941	217,941
Adj. R²		−0.098
Firm, Ind-Year FE	Yes	Yes
1st stage F-stat	7.21
Partial R²	0.0128

Notes. This table reports the baseline test examining the sample-wide effect of happiness on corporate innovation using an instrument variables approach. Natural Disaster is the annual total number of natural disasters (e.g., wildfires, landslides, mass movements, volcanic activity, storms, floods, extreme temperatures, earthquakes, and droughts) in a country, taken from the Global Natural Disaster report. Firm and industry-year dummies are included in the regressions. All variables are defined in the Appendix. Standard errors are robust to heterogeneity and clustered by country and year. Robust t-values are in parentheses: ***p < 0.01, **p < 0.05, *p < 0.1.

Quasi-Natural Experiment: Fukushima Earthquake

We use a quasi-natural experiment to mitigate any remaining endogeneity concerns. Exploiting the 2011 Fukushima earthquake as an exogenous adverse shock to happiness levels, we use panel data for companies in Japan before and after the event to analyze its effects on firm innovation. According to Rehdanz et al. (2015), people living in tsunami-affected locations or in areas close to the Fukushima Dai-ichi power plant experienced decreased levels of well-being after the disaster. Closely following the quasi-experimental approach of their study, we merge Geographical Information Systems data with patent data, obtaining firm addresses from Compustat Global.

Table 6 reports the results. We first examine how firm-level innovation changed in response to the Fukushima disaster. In columns (1) and (2), we report the regression results. We include prefecture fixed effects in the regressions.¹³ Standard errors are robust to heterogeneity and clustered by firm and year. Fukushima Earthquake is an indicator variable that equals 1 if the observation year is the disaster year, and 0 otherwise. In column (1), the coefficient of PATENT on Fukushima Earthquake is −0.226 (t-stat = −1.86). In column (2), the coefficient of CITEPAT on Fukushima Earthquake is −0.261 (t-stat = −2.19). The results indicate that the impact of the Fukushima disaster is significantly negative for patenting activities, implying that firms innovate less when the level of happiness decreases. We further test the disaster as a treatment event in a difference-in-differences framework. Ring1 is a dummy variable equal to 1 if distance from Fukushima Daiichi (“Number One”) nuclear power plant is ≤150 km, and 0 otherwise. The results are reported in columns (3) and (4), which show that the interaction term Fukushima Earthquake × Ring1 (≤150 km) is statistically significant for both PATENT and CITEPAT. In column (3), the coefficient of the interaction term is −0.173 (t-stat = −3.24). In column (4), the coefficient of the interaction term is −0.144 (t-stat = −2.50). These results suggest that firms decrease innovation activities when they are adversely affected by a natural disaster. Overall, the results in Table 6 suggest that the Fukushima earthquake is a negative and exogenous adverse shock to the level of happiness, with a resultant decrease in firm innovation activities.

Table 6.

Quasi-Natural Experiment

	(1)	(2)	(3)	(4)
	PATENT _t+1	CITEPAT _t+1	PATENT _t+1	CITEPAT _t+1
Fukushima Earthquake	−0.226*	−0.261*	−0.223*	−0.259*
Fukushima Earthquake	(−1.86)	(−2.19)	(−1.84)	(−2.17)
Fukushima Earthquake × Ring 1 (≤150 km)			−0.173***	−0.144**
Fukushima Earthquake × Ring 1 (≤150 km)			(−3.24)	(−2.50)
Controls	Yes	Yes	Yes	Yes
Observations	15,442	15,442	15,442	15,442
Adjusted R²	0.366	0.382	0.366	0.382
Prefecture FE	Yes	Yes	Yes	Yes
Cluster by firm & year	Yes	Yes	Yes	Yes

Notes. This table reports the effect of happiness on corporate innovation using a quasi-natural experiment based on the Japan Fukushima nuclear disaster. The control variables are the same as those in the baseline model. Prefecture (state) fixed effects are included in the regressions. All variables are defined in the Appendix. Standard errors are robust to heterogeneity and are clustered by firm and year. Robust t-values are in parentheses: ***p < 0.01, **p < 0.05, *p < 0.1.

Potential Channels

In this section, we discuss the potential economic channels (collaboration, productivity, and risk tolerance) through which the level of happiness facilitates corporate innovation.¹⁴

Collaboration Channel

Happiness can increase the likelihood and efficiency of innovation by increasing collaboration. We presume collaboration is more likely to occur among happier people. According to Dovey (2009) and Xie et al. (2022), innovation depends on collaboration among innovators, and successful collaboration hinges on trust between people. Previous studies suggest that an effective legal system and informal social capital also encourage collaboration by allowing innovators to share resources and expertise (Lerner, 2009). Thus, we expect that collaboration is a channel through which the level of happiness facilitates innovation.

Table 7 presents the results of the regression. We use Worldwide Governance Indicators (political stability, regulatory quality, control of corruption) as proxies for collaboration, positing that good governance reduces collaboration costs and fosters voluntary cooperation (Kaufmann et al., 2010). This approach is consistent with evidence linking regulatory quality and anti-corruption measures to improved innovation outcomes (Blanco & Goel, 2023; Nie et al., 2024). In particular, a country is considered to have a high collaboration level if any of these three measures exceed their respective sample medians. We expect and find positive and significant coefficients on the interaction terms between happiness and collaboration across all six columns.¹⁵ The results indicate a more pronounced positive relation between happiness and innovation in countries with high political stability, high regulatory quality, and less corruption, thereby encouraging more collaboration and, in turn, more innovation.

Table 7.

Collaboration Channel

	(1)	(2)	(3)	(4)	(5)	(6)
	PATENT _t+1	CITEPAT _t+1	PATENT _t+1	CITEPAT _t+1	PATENT _t+1	CITEPAT _t+1
Life Ladder × High Political Stability and Absence of Violence	0.105***	0.133***
	(3.19)	(4.37)
High Political Stability and Absence of Violence	−0.673***	−0.806***
High Political Stability and Absence of Violence	(−3.07)	(−3.95)
Life Ladder × High Regulatory Quality			0.098*	0.153***
Life Ladder × High Regulatory Quality			(1.95)	(2.80)
High Regulatory Quality			−0.689**	−0.986***
High Regulatory Quality			(−2.31)	(−2.96)
Life Ladder × High Control of Corruption					0.106**	0.168***
Life Ladder × High Control of Corruption					(2.13)	(3.15)
High Control of Corruption					−0.652**	−1.019***
High Control of Corruption					(−2.21)	(−3.17)
Life Ladder	0.005	0.033	0.012	0.035	0.005	0.027
Life Ladder	(0.22)	(0.99)	(0.42)	(1.18)	(0.18)	(0.90)
Controls	Yes	Yes	Yes	Yes	Yes	Yes
Firm FE	Yes	Yes	Yes	Yes	Yes	Yes
Industry-year FE	Yes	Yes	Yes	Yes	Yes	Yes
N	221,697	221,697	221,697	221,697	221,697	221,697
Adj. R²	0.752	0.720	0.752	0.719	0.752	0.719

Notes. This table reports the test that examines the effect of happiness on corporate innovation by examining the collaboration channel. All three partitioning indices are taken from World Governance Indicators. The control variables are the same as those included in the baseline model. Firm and industry-year fixed effects are included in the regressions from column (1) to (4). All variables are defined in the Appendix. Standard errors are robust to heterogeneity and are clustered by country and year. Robust t-values are in parentheses: ***p < 0.01, **p < 0.05, *p < 0.1.

Productivity Channel

Next, we investigate whether happiness affects innovation by increasing productivity. Happiness can positively affect innovative activities by improving individual productivity (Oswald et al., 2015) and the scope of attention (Rowe et al., 2007). Ifcher and Zarghamee (2011) show that a positive mood makes people more patient in their financial choices—they become less inclined to choose an immediate smaller sum over a larger later sum—and also makes them more willing to think about the future. The theoretical work of Bénabou and Tirole (2003) also suggests that intrinsic motivation serves as the primary channel driving the relation between optimism and productivity. Based on the previous literature, we expect happiness to play an important role in enhancing productivity and innovation output. To examine this conjecture, we use two separate proxies: Patents per employee and Citations per employee. Bhattacharya et al. (2017) use the number of patent inventors who have filed at least one patent in a country-industry-year as a proxy for incentive to innovate. Similarly, we conjecture that more inventors filing patent applications reflects greater productivity.

Table 8 presents the regression results. In column (1), we test patents per employee. In column (2), we test citations per employee. We find that both coefficients of Life Ladder are significantly positive, implying that happiness increases the number of patents per employee and supporting our interpretation that happiness fosters productivity and thus engagement in innovation.

Table 8.

Productivity Channel

	(1)	(2)
	Patents per Employee	Citations per Employee
Life Ladder	0.032***	0.084***
Life Ladder	(3.02)	(3.41)
Ln(TA)	−0.084***	−0.158***
Ln(TA)	(−5.86)	(−3.91)
Tobin’s Q	0.012***	0.053***
Tobin’s Q	(2.74)	(2.86)
OCF	−0.154***	−0.169
OCF	(−2.83)	(−1.16)
Sales Growth	−0.022	−0.018
Sales Growth	(−1.30)	(−0.64)
Leverage	0.078*	0.258**
Leverage	(1.79)	(2.19)
R&D	0.265	0.392
R&D	(1.53)	(0.93)
Capex	−0.055	−0.348**
Capex	(−0.79)	(−2.24)
HHI	0.067	0.005
HHI	(0.81)	(0.04)
HHI ²	−0.079	−0.048
HHI ²	(−1.18)	(−0.49)
LGDP	0.012	−0.059*
LGDP	(0.64)	(−1.80)
GDP Growth	−0.001	−0.005***
GDP Growth	(−0.80)	(−2.62)
Firm FE	Yes	Yes
Industry-year FE	Yes	Yes
N	172,979	172,979
Adj. R²	0.475	0.315

Notes. This table reports results for the test examining the effect of happiness on corporate innovation by examining the productivity channel. Patents per employee (Citations per employee) is the total number of patent counts (citations) scaled by the total number of employees. The control variables are the same as those in the baseline model. Firm and industry-year fixed effects are included in the regressions from column (1) to (4). All variables are defined in the Appendix. Standard errors are robust to heterogeneity and are clustered by country and year. Robust t-values are in parentheses: ***p < 0.01, **p < 0.05, *p < 0.1.

Risk-Tolerance Channel

Next, we investigate whether happiness affects innovation by increasing risk tolerance. As mentioned, innovation involves a high probability of failure. Manso (2011) shows that incentives that facilitate innovation thus should exhibit substantial tolerance for failure. Acharya and Subramanian (2009) also mention that “when bankruptcy code is creditor friendly, excessive liquidations cause levered firms to shun innovation, whereas by promoting continuation upon failure, a debtor-friendly code induces greater innovation” (p. 1). Debtor-friendly bankruptcy regimes and strong legal protection for employees alleviate firms’ and employees’ concerns about the adverse impact of innovation failure and hence encourage their risk-taking and innovation efforts (Acharya et al., 2014). Based on this discussion, we conjecture that risk-tolerance is an important channel through which happiness fosters corporate innovation.

Following Kim et al. (2025), we use three proxies for a risk-tolerant environment—Rule of Law, Repudiation of Contracts, and Judicial Efficiency—which reflect a legal system’s capacity to resolve disputes predictably and promptly. As they argue, efficient ruling, particularly on intellectual property, lowers the perceived risks and potential costs of innovation, encouraging firms’ greater ex-ante investment in novel projects. Table 9 presents the results for the risk-tolerance channel using data from World Governance Indicators and La Porta et al. (1998). We find that the coefficients on interaction terms are significantly, at least marginally, positive in columns (1), (2), (4), (5), and (6). Overall, the results suggest that higher levels of happiness, together with risk-tolerant institutional environments, promote firm innovation.

Table 9.

Risk-Tolerance Channel

	(1)	(2)	(3)	(4)	(5)	(6)
	PATENT _t+1	CITEPAT _t+1	PATENT _t+1	CITEPAT _t+1	PATENT _t+1	CITEPAT _t+1
Life Ladder × Rule of Law	0.087	0.165***
Life Ladder × Rule of Law	(1.61)	(2.97)
Rule of Law	−0.538	−0.991***
Rule of Law	(−1.63)	(−2.90)
Life Ladder × Repudiation of Contracts			0.014	0.055***
Life Ladder × Repudiation of Contracts			(0.83)	(3.23)
Life Ladder × Judicial Efficiency					0.024*	0.051***
Life Ladder × Judicial Efficiency					(1.65)	(3.15)
Life Ladder	0.063	−0.095	−0.041	−0.323***	−0.123	−0.298***
Life Ladder	(1.09)	(−1.37)	(−0.34)	(−2.75)	(−1.14)	(−2.70)
Controls	Yes	Yes	Yes	Yes	Yes	Yes
Firm FE	Yes	Yes	Yes	Yes	Yes	Yes
Industry-year FE	Yes	Yes	Yes	Yes	Yes	Yes
N	107,542	107,542	107,542	107,542	107,542	107,542
Adj. R²	0.817	0.681	0.817	0.680	0.817	0.680

Notes. This table reports results of the test examining the effect of happiness on corporate innovation by examining the risk-tolerance channel. The control variables are the same as those in the baseline model. Firm and industry-year fixed effects are included in the regressions from column (1) to (4). All variables are defined in the Appendix. Standard errors are robust to heterogeneity and are clustered by country and year. Robust t-values are in parentheses: ***p < 0.01, **p < 0.05, *p < 0.1.

Robustness Tests

We further check potential robustness issues in this section. First, we test whether our results remain unchanged after excluding the U.S. sample. In our sample, the number of non-U.S. firm-year observations is 183,737. We also further exclude small countries with less than 100 firm-year observations. Table 10, Panel A reports the estimated results. We find that all coefficients of happiness are significantly positive, indicating that firms in happier countries tend to be more innovative, regardless of the inclusion of very big or small economies.

Table 10.

Robustness Tests

Panel A. Exclusion of selected countries
	(1)	(2)	(3)	(4)
	Exclude USA		Exclude countries with <100 Obs.
	PATENT _t+1	CITEPAT _t+1	PATENT _t+1	CITEPAT _t+1
Life Ladder	0.045*	0.046*	0.055**	0.101***
Life Ladder	(1.66)	(1.88)	(2.24)	(3.09)
Controls	Yes	Yes	Yes	Yes
Firm FE	Yes	Yes	Yes	Yes
Industry-year FE	Yes	Yes	Yes	Yes
N	183,737	183,737	231,902	231,902
Adj. R²	0.712	0.672	0.727	0.694

Panel B. Alternative fixed effects
	(1)	(2)	(3)	(4)	(5)	(6)
	PATENT _t+1	CITEPAT _t+1	PATENT _t+1	CITEPAT _t+1	PATENT _t+1	CITEPAT _t+1
Life Ladder	0.040*	0.083***	0.039*	0.080**	0.041	0.082**
Life Ladder	(1.83)	(2.58)	(1.75)	(2.40)	(1.39)	(2.46)
Controls	Yes	Yes	Yes	Yes	Yes	Yes
Country, Ind-Year FE	Yes	Yes
Country-Ind, Ind-Year FE			Yes	Yes
Firm and year FE					Yes	Yes
N	232,333	232,333	232,333	232,333	232,487	232,487
Adj. R²	0.311	0.296	0.355	0.338	0.718	0.685

Panel C. Alternative measures of innovation
	(1)	(2)	(3)	(4)
	R&D/AT _t+1	Log(R&D) _t+1	Generality _t+1	Originality _t+1
Life Ladder	0.004***	0.059**	0.056**	0.048**
	(6.00)	(2.00)	(2.27)	(2.18)
Controls	Yes	Yes	Yes	Yes
Firm FE	Yes	Yes	Yes	Yes
Industry-year FE	Yes	Yes	Yes	Yes
N	205,301	122,572	232,333	232,333
Adj. R²	0.865	0.954	0.701	0.776

Panel D. Change analyses
	(1)	(2)	(3)	(4)
	△PATENT _t+1	△PATENT _t+1	△CITEPAT _t+1	△CITEPAT _t+1
△Life Ladder	2.371**	2.700**	14.366**	12.586*
△Life Ladder	(1.98)	(2.14)	(2.30)	(1.91)
△Ln(TA)		1.644**		14.227***
△Ln(TA)		(2.22)		(4.36)
△Tobin’s Q		−0.075		0.757
△Tobin’s Q		(-0.60)		(1.12)
△OCF		0.681		4.290
△OCF		(0.55)		(0.65)
△Sales Growth		0.032		1.364
△Sales Growth		(0.14)		(1.41)
△Leverage		−1.967		−6.164
△Leverage		(-1.25)		(-1.12)
△R&D		5.383		13.939
△R&D		(1.18)		(0.68)
△Capex		0.847		9.179
△Capex		(0.32)		(0.62)
△HHI		−4.440		26.812
△HHI		(-0.62)		(0.65)
△HHI ²		3.475		−18.171
△HHI ²		(0.64)		(-0.54)
△GDP		0.017		0.033
△GDP		(1.61)		(0.68)
△GDP Growth		−0.351**		−0.552
△GDP Growth		(-2.12)		(-0.64)
Firm FE	Yes	Yes	Yes	Yes
Industry-year FE	Yes	Yes	Yes	Yes
N	222,158	205,301	222,158	205,301
Adj. R²	−0.017	−0.013	0.049	0.055

Panel E. Controlling for unobservable confounding conditions
	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
	Financial development		Polarization		Market openness		All confounding factors
	PATENT _t+1	CITEPAT _t+1	PATENT _t+1	CITEPAT _t+1	PATENT _t+1	CITEPAT _t+1	PATENT _t+1	CITEPAT _t+1
Life Ladder	0.086***	0.136***	0.056**	0.097***	0.043*	0.053**	0.061**	0.090***
Life Ladder	(3.36)	(4.07)	(2.17)	(2.76)	(1.90)	(2.40)	(2.23)	(3.48)
Credit/GDP	−0.213***	−0.156*					−0.132*	−0.167**
Credit/GDP	(-3.09)	(-1.85)					(-1.92)	(-2.47)
Mcap/GDP	−0.006	−0.057					0.017	0.000
Mcap/GDP	(-0.15)	(-1.10)					(0.55)	(0.01)
Inflation Rate	0.002	0.003					−0.002	−0.002
Inflation Rate	(0.67)	(0.87)					(-0.40)	(-0.49)
Employment Rate	−0.007	−0.018***					0.001	−0.016***
Employment Rate	(-1.23)	(-3.38)					(0.14)	(-2.86)
Polarization: Government Responsibility			0.157	0.272*			0.169	0.241
Polarization: Government Responsibility			(0.88)	(1.76)			(0.97)	(1.63)
Polarization: Income Inequality			−0.050	−0.113**			−0.128**	−0.114**
Polarization: Income Inequality			(-0.86)	(-1.98)			(-2.10)	(-2.17)
Polarization: Business Ownership			0.062	−0.075			0.137	−0.014
Polarization: Business Ownership			(0.39)	(-0.55)			(0.78)	(-0.10)
Capital Account Openness					−0.047***	0.005	−0.039**	0.021
Capital Account Openness					(-3.15)	(0.32)	(-2.14)	(1.20)
Trade Openness					0.085	0.030	0.017	−0.062
Trade Openness					(1.56)	(0.57)	(0.25)	(-0.97)
Controls	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Firm & industry-year FE	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
N	206,717	206,717	217,361	217,361	208,251	208,251	178,681	178,681
Adj. R²	0.732	0.704	0.722	0.690	0.760	0.744	0.756	0.742

Notes. This table reports results of robustness tests examining the sample-wide effect of happiness on corporate innovation. Panel A excludes the United States or small countries. Panel B shows the estimation with alternative fixed effects. Panel C reports the test examining the effect of happiness on R&D spending and alternative innovation quality measures. Panel D presents the estimation results with change analyses. Panel E examines the effect of happiness on corporate innovation by controlling for extra country-level characteristics. Firm and industry-year fixed effects are included in the regressions (unless specified otherwise). All variables are defined in the Appendix. Standard errors are robust to heterogeneity and are clustered by country and year. Robust t-values are in parentheses: ***p < 0.01, **p < 0.05, *p < 0.1.

Second, we add a different set of fixed effects to control for unobserved and time-varying heterogeneity across firms and countries. Table 10, Panel B reports the results. We find that the coefficients of happiness in columns (1) to (5) are significantly positive, suggesting that the effect of happiness on firm innovation is robust to the inclusion of various fixed effects.

Third, we use an alternative measure of innovation. Despite the wide acceptance and usage of patent activities as innovation measures, they could be subject to limitations (e.g., Acharya & Subramanian, 2009; Hsu et al., 2014; Moshirian et al., 2021). For example, Chang et al. (2018) note that firms in many countries, especially those in emerging markets, do not file patent applications with the U.S. patent office, and this proportion varies across countries over time. In addition, firms may keep some inventions secret for strategic purposes as not all firm-level innovation can meet the patenting criteria. For this reason, we use firms’ R&D expenditure as an alternative measure of innovative activities. Table 10, Panel C shows the results. The dependent variables are firm-level R&D expenditure scaled by total assets in column (1), the natural logarithm of R&D expenses in column (2), the generality index in column (3), and the originality index in column (4). The latter two variables are constructed following Hall et al. (2001) to capture the broad potential impact beyond its original scope, as well as fresh perspectives on patents. As in the baseline specifications, we use firm-level control variables including Ln(TA), Tobin’s Q, OCF, Sales Growth, Leverage, R&D, and Capex. We include country-level control variables, including GDP level and growth. We also control for time, industry, and firm fixed effects. The coefficients of happiness are significant at the 5% level across all four columns, indicating that happiness is associated with an increase in firm-level R&D expenditure and innovation quality. Overall, the results in Table 10, Panel C are consistent with our primary conjecture that happiness fosters innovation.

Fourth, to see how changes in Life Ladder scores are associated with changes in innovation output, we conduct a change analysis. The results, presented in Panel D, Table 10, show that the coefficients on △Life Ladder in columns (1) to (4) are statistically significant and positive, suggesting that increases in happiness scores are positively associated with increases in both innovation quantity and quality indicators.

Finally, innovation could be influenced by unobserved factors that affect both the level of happiness and innovation outcomes in countries where firms are located. To address this issue, we consider three categories of country characteristics: financial development, polarization, and market openness. To measure financial development, we use Credit/GDP, Mcap/GDP, Inflation Rate, and Employment Rate (Hou et al., 2025; Romer, 1990; Solow, 1957). To measure polarization, we use Income Inequality, Business Ownership, and Government Responsibility (Gulen & Ion, 2016; Julio & Yook, 2012). To measure market openness, we use Capital Account Openness and Trade Openness (Moshirian et al., 2021). See Appendix Table A1 for variable definitions.

Table 10, Panel E reports the results by adding control variables. In columns (1) and (2), we add variables for financial development. In columns (3) and (4), we add variables for polarization. In columns (5) and (6), we add variables for market openness. In columns (7) and (8), we add all variables. Across all columns, the coefficients of happiness are significantly positive. The results indicate that the positive relation between happiness and innovation remains unchanged after controlling for unobservable confounding country characteristics.

Overall, the results in Table 10 suggest that our finding that happiness enhances firm-level innovation is robust to alternative sample selection, alternative fixed effects, alternative measures of innovation, change analyses, and controlling for unobserved omitted correlated variables.

Conclusion

This paper investigates the effect of happiness on innovation. We conjecture that happiness can increase innovation by enhancing collaboration, productivity, and risk tolerance. Using the Life Ladder score from the World Happiness Report, we find that happiness positively affects innovation, and this effect is more pronounced for firms with high innovation intensity. Our findings are robust to including a range of controls and to using an IV analysis. Further, we find that happiness promotes innovation through channels of collaboration, productivity, and risk tolerance.

Our findings have several implications. First, our evidence shows why governments and firm managers should integrate individual well-being into policies aiming to promote sustainable growth. Our results suggest that countries can enhance their innovation outputs by fostering happiness among workers. We also provide suggestive evidence that happier workers are more productive. Thus, as firms invest more in management practices and services to create and maintain a competitive edge, they should consider the well-being of their employees. High-quality workers that contribute to firm performance tend to stay where they are happiest, which in turn promotes innovation over the long run.

We acknowledge certain limitations of our study. A country-level measure of happiness may not fully capture individual-level variation in happiness or job satisfaction. There is likely to be significant cross-sectional variation in individual happiness within the same country and across firms, income levels, or geographical locations. This variation is lost when we use a country-level measure. However, it is difficult to obtain individual happiness measures, particularly in an international setting. Our aggregate, country-level analyses potentially help to mitigate the hierarchical structure issue. Future research could explore micro-level evidence to complement our findings.

Footnotes

Acknowledgements

We thank Stephannie Larocque (associate editor), two anonymous reviewers, Warren Bailey, Scott Baker, Thomas Chemmanur, Andrew Ellul, Allen Huang, Shane Johnson, Ilker Kaya, Do Won Kwak, Dong Wook Lee, Patrick McColgan, Ponpoje Porapakkarm, Alois Stutzer and conference participants at the 2017 Asiatic Research Institute (ARI) at Korea University, the Brain Korea 21 Plus Economic Research Group, the 2018 Midwest Economic Association meeting, 2018 Financial Management Association Europe, 2021 Asia Pacific Association of Derivatives for their helpful comments. Fangfang Hou acknowledges financial support from the National Natural Science Foundation of China (Grant No. 72302198). All remaining errors are our own.

ORCID iDs

Yuna Heo

Fangfang Hou

Seongkyu “Gilbert” Park

Ethical Considerations

The data used in this study are publicly available identified in the text and no human participants and animals are involved in this study.

Consent to Participate

The data used in this study are publicly available identified in the text. No human participants and animals are involved in this study, so the authors do not need to seek for informed consent from any human participants.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Fangfang Hou acknowledges financial support from the National Natural Science Foundation of China (Grant No. 72302198).

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Notes

Author Biographies

Dr. Yuna Heo is an Assistant Professor of Finance at the University of Basel. She is also a faculty member of the Swiss Finance Institute. She is strengthening the research groups “Money and Finance” and “Energy and Environment” at the Faculty of Business and Economics. Her research interests are in the areas of banking, corporate finance, financial institutions, and climate finance.

Dr. Fangfang Hou is an Associate Professor of Accounting at Xiamen University. Her main research interests are corporate investment and innovation, corporate governance, and information disclosure. Actively involved in the academic community, she serves on the school union and organizes the School of Management’s biweekly seminar series. Her work has been recognized with multiple Best Paper Awards at international conferences such as the International Conference on Accounting and Finance in Emerging Markets.

Dr. Gilbert (Seongkyu) Park is an Associate Professor of Finance and the Assistant Dean for Undergraduate Programs at Willamette University’s Atkinson Graduate School of Management. His research focuses on a wide area of financial economics, including market microstructure, behavioral finance/economics, and international financial markets. He also serves as a board member for the O'Neill Student Investment Fund, an executive board member at the Korea-America Finance Association, and writes columns for The Asia Business Daily.

Appendix

Table A1.

Variable Definitions

Variable	Definition	Main source
No. of Patents	Yearly total number of patent applications of a firm: ${PAT}_{i, t} = \sum_{j \in J (t)} p_{j, t},$ where PAT_i,t is total number of patents filed by firm i in year t. J(t) is the set of all patent applications the firm filed with distinct family ID in year t	PATSTAT 2025 Spring
No. of Citations	Yearly total number of patent citations a firm receives after first publication date of application for all patent applications the firm filed with distinct family ID in year t. We use IPC-technology-class-weighted citations to avoid truncation issues. ${CIT}_{i, t} = \sum_{t = p_{i}}^{2025} \sum_{j \in J (t)} C_{j, i},$ where CIT_i,t is the total number of forward citations received by patent application i from its publication date to 2025. C_j,i is a dummy variable that equals 1 if patent application j cites patent application i, and 0 otherwise. J(t) is the set of all patents applications published in year t	PATSTAT 2025 Spring
PATENT	Natural logarithm of 1 plus total number of patents filed by each firm in each year.	PATSTAT 2025 Spring
CITEPAT	Natural logarithm of 1 plus total number of citations made to each firm’s patents in each year.	PATSTAT 2025 Spring
Life Ladder	Measures answers to Cantril Ladder question, “Please imagine a ladder, with steps numbered from 0 at the bottom to 10 at the top. The top of the ladder represents the best possible life for you and the bottom of the ladder represents the worst possible life for you. On which step of the ladder would you say you personally feel you stand at this time?”	World Happiness Report
Ln(TA)	Natural logarithm of total assets.	Compustat Global & NA
Tobin’s Q	Sum of market value and book value of debt (long-term debt and debt in current liabilities) divided by total assets.	Compustat Global & NA
OCF	Cash flows from operations in year t scaled by lagged total assets.	Compustat Global & NA
Sales Growth	Sales growth, defined as sales growth from t − 1 to t.	Compustat Global & NA
Leverage	Book leverage, calculated as total debt divided by beginning year total assets.	Compustat Global & NA
R&D	R&D spending scaled by lagged total assets.	Compustat Global & NA
Capex	Capital expenditure scaled by lagged total assets.	Compustat Global & NA
HHI	Herfindahl-Hirschman Index, calculated as summed value of squared market value within three-digit SIC industry, which measures industry-level product market competition.	Compustat Global & NA
HHI²	Squared value of Herfindahl-Hirschman Index.	Compustat Global & NA
LGDP	Natural logarithm of annual gross domestic product (in current US dollars).	World Development Indicators
GDP Growth	Growth rate in annual GDP (%).	World Development Indicators
Generality	Indicator assessing how widely applicable or adaptable an innovation is across different contexts, industries, or user groups, calculated as 1 minus Herfindahl-Hirschman Index of technology category distribution of all patents that cite it, following Hall et al. (2001). High generality indicates broad potential impact beyond its original scope.	PATSTAT 2025 Spring
Originality	Indicator assessing novelty of an idea, product, or solution, compared to alternatives, measured as 1 minus Herfindahl-Hirschman index of technology category distribution of all patents it cites, following Hall et al. (2001). High originality indicates significant departure from prior work, introducing fresh perspectives or approaches.	PATSTAT 2025 Spring
R&D Disclosure	Dummy variable indicating that firm disclosed nonzero R&D expenses, and 0 otherwise.	Compustat Global & NA
Patent Stock	Continuous indicator calculated as sum of historical granted patent counts using a 15% discount rate.	PATSTAT 2025 Spring
High Tech	Dummy variable that equals 1 if firm is in SIC 3-digit industry 283, 357, 366, 367, 382, 384, or 737, and 0 otherwise.	Compustat Global & NA
Natural Disaster	Total number of natural disaster incidents in a country, including wildfire, landslide, mass movement, volcanic activity, storm, flood, extreme temperature, earthquake, and drought.	Global Natural Disaster
DistFukushima	Distance from Fukushima I (km).	Compustat Global
Ring 1 (≤150 km)	Dummy variable that equals 1 if distance from Fukushima I ≤150 km, and 0 otherwise.	Compustat Global
Credit/GDP	Private credit from banks and financial institutions to GDP (%), which captures the degree of credit market development.	World Development Indicators
Mcap/GDP	Stock market capitalization to GDP (%), which captures the degree of stock market development.	World Development Indicators
Inflation Rate	Indicator of annual inflation rate, calculated as percentage change in CPI (a fixed or updated basket of consumer goods/services) relative to 2015 U.S. dollar prices.	World Development Indicators
Employment Rate	Employment-to-population ratio for people aged 15+ (%) (Model ILO-estimated).	World Development Indicators
Polarization:Income Inequality	Averaged index of people’s response to “Incomes should be made more equal” or “We need larger income differences as incentives.” Higher scores indicate more need for income inequality.	World Values Survey
Polarization: Business Ownership	Averaged index of people’s response to “Private ownership of business should be increased” or “Government ownership of business should be increased.” Higher scores indicate more need for government ownership of business.	World Values Survey
Polarization:Government Responsibility	Averaged index of people’s response to “People or the government should take more responsibility to provide for themselves.” Higher scores indicate more agreement that government should take responsibility.	World Values Survey
Financial Openness	Capital account liberalization index capturing degree of free movement of capital in and out of a country.	Chinn and Ito (2008)
Trade Openness	Sum of imports and exports scaled by GDP capturing degree of openness of a country to foreign trade.	World Development Indicators
Political Stability and Absence of Violence	Indicator assessing public subjective perception of likelihood of political instability and politically motivated violence, including terrorism.	World Governance Indicators
Regulatory Quality	Indicator measuring ability of government to formulate and implement policies and regulations that enable and promote private-sector development.	World Governance Indicators
Control of Corruption	Indicator measuring perceptions of corruption, defined as exercise of public power for private gain.	World Governance Indicators
Rule of Law	Indicator assessing extent to which agents have confidence in and abide by rules of society, including quality of contract enforcement, property rights, and judiciary.	World Governance Indicators
Repudiation of Contracts	Index capturing “risk of a modification in a contract taking the form of a repudiation, postponement, or scaling down due to budget cutbacks, indigenization pressure, a change in government, or a change in government economic and social priorities” (La Porta et al., 1998, p. 1125). Index ranges from 0 to 10, with higher scores indicating lower risk.	La Porta et al. (1998)
Judicial Efficiency	Index capturing the “efficiency and integrity of the legal environment as it affects business, particularly foreign firms” (La Porta et al., 1998, p. 1124). Values range from 0 to 10, with lower scores indicating lower efficiency levels.	La Porta et al. (1998)

Table A2.

Funding Channel: External Finance Dependence

	(1)	(2)	(3)	(4)	(5)	(6)
	PATENT _t+1	CITEPAT _t+1	PATENT _t+1	CITEPAT _t+1	PATENT _t+1	CITEPAT _t+1
Life Ladder × Firm-level Extfin	−0.007	−0.010
Life Ladder × Firm-level Extfin	(-0.88)	(-1.11)
Firm-level Extfin	0.046	0.054
Firm-level Extfin	(0.89)	(0.97)
Life Ladder × Financial Constraints (WW)			−0.016	−0.092
Life Ladder × Financial Constraints (WW)			(-0.12)	(-0.54)
Financial Constraints (WW)			0.046	0.537
Financial Constraints (WW)			(0.05)	(0.48)
Life Ladder × Industry-level Extfin					−0.026***	−0.001
Life Ladder × Industry-level Extfin					(-3.04)	(-0.10)
Life Ladder	0.056**	0.102***	0.051	0.074*	0.064***	0.102***
Life Ladder	(2.30)	(3.12)	(1.29)	(1.68)	(2.63)	(3.18)
Controls	Yes	Yes	Yes	Yes	Yes	Yes
Firm FE	Yes	Yes	Yes	Yes	Yes	Yes
Industry-year FE	Yes	Yes	Yes	Yes	Yes	Yes
N	229,969	229,969	232,333	232,333	227,056	227,056
Adj. R²	0.727	0.695	0.727	0.694	0.727	0.695

Notes. This table reports the test that examines the effect of happiness on corporate innovation by examining the role of external finance dependence. The control variables are the same as those included in the baseline model. Firm and industry-year fixed effects are included in the regressions from column (1) to (4). All variables are defined in the Appendix. Standard errors are robust to heterogeneity and are clustered by country and year. Industry-level external finance dependence is constructed following Rajan and Zingales (1998). Robust t-values are in parentheses: ***p < 0.01, **p < 0.05, *p < 0.1.

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