South Asian Review of Business and Administrative Studies Vol. 5, No. 1, June 2023

Volume and Issues Obtainable at Center for Business Research and Consulting,

IBMAS, The Islamia University of Bahawalpur Pakistan

South Asian Review of Business and Administrative Studies
ISSN: 2710-5318; ISSN (E): 2710-5164

Volume 5, No.1, June 2023

Journal homepage: https://journals.iub.edu.pk/index.php/sabas

Efficacy of Opt-in vs. Opt-out Default Nudges to Encourage Socially
Responsible Investing: The Moderating Role of Financial Literacy

Ms. Khadija Ashfaq, PhD scholar, International Islamic University Islamabad, Pakistan
Dr. Abdul Raheman, Professor and Dean, International Islamic University Islamabad, Pakistan

ARTICLE DETAILS ABSTRACT

History

Revised format:

May 2023

Available Online:

June 2023

Keywords
Behavioral finance,
default nudges,
financial literacy,
socially responsible
investment

This study intends to investigate the impact of opt-in and opt-out default
nudges on Pakistani investors' decisions to make socially responsible
investments, with the moderating influence of investors' financial literacy.
A commercial online panel is used to gather data as part of an experiment
with an incentive-based online survey. A total of N = 518 individuals is
randomly assigned to two treatment groups—opt-in and opt-out—and
one control group. The empirical findings of this study show that, although
being less effective than the opt-out nudge effect, the opt-in nudge effect
nevertheless has a considerable impact on SRI decisions. The study's
results also show that financial literacy moderator has partially significant
impact on the efficacy of default nudges. In order to improve investment
instruments that might encourage SRI investment in society, SRI
policymakers can benefit from this study.

Corresponding author’s email address: khadija.phdfin62@iiu.edu.pk

DOI: https://doi.org/10.52461/sabas.v4i2.1831

Introduction

In 2015, the UN adopted the sustainable development goals (SDGs) as a universal call to stimulate

action in areas of critical importance. Pakistan is one of the first countries to adopt and commit to

the United Nations 2030 Agenda for Sustainable Development. In order to implement the UN

SGDs, socially responsible investment (SRI) can be used as a measure to promote responsible

finance. SRI has grown recently (Nilsson et al., 2014; Falcone et al., 2018), businesses now have

a chance to strengthen their sustainable business models through CSR initiatives. A socially

responsible investment (SRI) is a type of investment that fosters both meaningful societal

development and solid financial returns. SRI refers to the process of incorporating environmental,

social, and governance (ESG) considerations into investment decisions (Sandberg & Nilsson,

2011). In the emerging and frontier economies, SRI is not popular and is still in its initial phase.

They have to join this global momentum, even at the most basic level of implementation, or else

they will be left behind. An effective and inexpensive strategy to encourage acceptance of SRI

seems to be required.

Choice architecture interventions have gained popularity among public policymakers over the past

ten years as a way to encourage pro-social behaviour in either individuals or society as a whole.

https://journals.iub.edu.pk/index.php/sabas
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https://doi.org/10.52461/sabas.v4i2.1

South Asian Review of Business and Administrative Studies Vol. 5, No. 1, June 2023

These interventions are referred to as "nudges," which are defined as little adjustments to the

choice architecture that modify behaviour in a predictable way without prohibiting or monetarily

restricting any of the alternatives (Thaler & Sunstein, 2008). The term "nudging" refers to a broad

range of strategies that all have the goal of making the desirable action the default choice. Early

nudge research found positive behavioural effects and other advantages of nudges as a policy

instrument, including simplicity of adoption and cost-effectiveness (Benartzi et al., 2017).

Jachimowicz et al. (2019) argued that default is the most simple and effective nudge that can be

used to influence behavior. In terms of behaviour change, default nudges are among the most

successful (Hummel & Maedche, 2019), which makes them particularly interesting in regard to

how effectively autonomy is honoured. In contrast to the opt-in default, which is sometimes

referred to as an express consent policy and calls for individuals to explicitly state their choices,

the opt-out default holds that everyone is willing to accept the preselected option unless they

expressly opt-out of doing so (Etheredge, 2021). Usually, frequencies are much greater in an opt-

out system than in an opt-in system. Defaults are typically regarded as the most typical example

of nudging (e.g. Thaler & Sunstein, 2008) and have most consistently been classified as Type 1

(Hansen & Jespersen, 2013) or non-educative (Sunstein, 2016) nudges.

Several behavioural areas, such as sustainable behaviour (Pichert & Katsikopoulos, 2008; Vetter

& Kutzner, 2016) and financial behaviour, provide examples of default effects (Madrian & Shea,

2001). Johnson and Goldstein (2003) presented a striking disparity in the percentage of citizens

registered as organ donors as the most illuminating difference between having an opt-in and an

opt-out system. Countries that followed an opt-in approach had consent rates between 4.25% and

27.5%, but those that followed an opt-out system saw consent rates between 85.9% and 99.98%.

The question of whether this variation in consent rates actually translates into greater donation

rates has been contested, but the variation powerfully demonstrates the influence defaults may

have on behaviour.

Furthermore, there are increasing issues and criticism concerning the legitimacy of opt-out default

nudges. Even nudges that retain a pretence of decision freedom, according to Hausman and Welch

(2010), may reduce a person's autonomy. According to Smith et al. (2013), default nudging can

violate people's autonomy and their capacity to make informed decisions, even when the results

are positive. According to a survey, default nudges were rated less positively and as greater threats

to autonomy (Jung and Mellers, 2016). So, in this study, we compare the effectiveness of more

manipulative and less autonomous opt-out default with less manipulative and more autonomous

opt-in default. This comparison is drawn to check whether it is possible to nudge an individual

without compromising on autonomy. To examine these potential trade-offs and the implications

for empirical validation of default nudges designed to improve responsible investment, we

conducted an online experiment comparing the effectiveness of opt-in and opt-out default nudges

on share of individual who choose SRI as an investment decision in a hypothetical investment

scenario.

Agnew and Szykman (2005) found that the effectiveness of information architecture vary with

financial literacy of the individual. They further elaborated that individuals with low levels of

knowledge are more likely than those with high levels of knowledge to choose the default

allocation. Carpenter et al. (2021) demonstrated that choice architecture seems to differentially

assist those who have a lot at stake, poor family income, high cognitive capacity, and low financial

literacy to avoid making the worst choice. Few studies are available in the literature on how to

identify the role of financial literacy in the effectiveness of nudges. So, we further investigate the

moderating effects of financial literacy on the efficacy of opt-out and opt-in default nudges in order

to understand the impact of financial literacy in this study.

South Asian Review of Business and Administrative Studies Vol. 5, No. 1, June 2023

Literature Review

Thaler and Sunstein (2008) define "nudge" as a component of the choice architecture that

influences people’s decisions predictably without changing the incentives or constraints they face.

People frequently lack the time, desire, or resources necessary to think intentionally, clearly, and

logically. As a result, rather than being the outcome of logical and rational processes, most acts

are the result of habits, heuristic processes, unconscious associations, or automatic and learnt

responses (Hofmann et al., 2009; Kahneman, 2011; Smith & DeCoster, 2000). Then, nudges take

advantage of flaws that affect automatic unconscious processes and passive decision-making, such

as the fact that people do not fully consider their options, they tend to follow the path of "least

resistance," they lack clear preferences and complete information, and choices will inevitably be

influenced by defaults, framing and anchoring, to capitalize on the unconscious interaction

between a person and the environment, and thus of the so-called System 1 way of thinking

(Sunstein & Thaler, 2003; Thaler & Sunstein, 2008).

Camilleri et al. (2019) tested two interventions to improve retirement savings investment decisions

by doing an experiment. They studied the efficacy of a "nudge" by maneuvering the default option

and the efficacy of a "signpost" by influencing the display of a pictograph, briefing the expected

return of each option. Their findings suggested that both smart defaults and better risk information

by using pictographs can be used to positively influence behavior. Gajewski et al. (2021) examined

the effect of nudges on the behaviour of investors in favour of socially responsible investment

(SRI) by setting up two online experiments with 713 US retail investors. By using three nudges,

i.e., SRI as the default investment, an SRI explanation message, and negative priming ethical

values by revealing shocking images, they found that the SRI default option was the most efficient

nudge to alter investors' behaviour towards SRI. The remaining two nudges marginally increased

the SRI investment, but in isolation they appeared non-significant.

For instance, in the area of economics, a customer may be accidentally persuaded to take the

default option (Brown and Krishna, 2004). Research demonstrates that increasing defaults caused

consumers to save more for retirement and buy more insurance (Johnson et al., 1993; Madrian and

Shea, 2001). Defaults frequently impact the approval of policies in the health sector, including

those pertaining to organ donation and transplantation (Johnson and Goldstein, 2003; Abadie and

Gay, 2006; Ahmad et al., 2019). The opt-out approach has also been proposed as a successful

intervention to change employee behaviour in the workplace, including stand-up working

(Venema et al., 2018), enrolling in pension plans (Thaler and Sunstein, 2008; Robertson-Rose,

2021), and energy efficiency (Brown et al., 2013; Egebark and Ekström, 2016). In addition, default

nudges are simple and inexpensive to apply (Thaler and Sunstein, 2008), making them appropriate

for fostering policy support. The majority of research examined how opt-in and opt-out default

options affected the health, energy, and savings and retirement areas. There have been few research

on the impact of default options on socially responsible investing, and none have evaluated the

efficacy of opt-in and opt-out defaults.

Bassen et al. (2019) provided empirical evidence for the effectiveness of climate labelling as a

potential nudge for climate-friendly investing. Madrian and Shea (2001) discovered that automatic

enrollment (opt-out default) had a significant impact on the saving behaviour of employees in large

US corporations. Johnson and Goldstein (2003) showed that changing the default increases the

rate of organ donation in Europe from 15% (opt-in default) to 98% (opt-out default). In addition,

defaults can have a significant impact on behaviour, with a wide range in effect sizes, according

to a recent meta-analysis of default effects (Jachimowicz et al., 2019). This meta-analysis revealed

that when a certain choice is established as the opt-out default as opposed to an opt-in scenario,

the chance of selecting it is.68 SDs greater. Nevertheless, the authors also found considerable

South Asian Review of Business and Administrative Studies Vol. 5, No. 1, June 2023

impact size heterogeneity, which might imply moderating. The features of the study showed that

default effects are smaller for judgements involving sustainable behaviour (as opposed to decisions

not pertaining to sustainable behavior) and bigger in consumer domains (than in non-consumer

domains).

As an often mentioned example of nudges, default nudges have been employed more and more to

affect a variety of societal concerns (Nicolao et al., 2018; Zhao et al., 2022). The opt-out default

believes that all people are prepared to accept the preselected choice unless they clearly "opt-out"

of doing so, whereas the opt-in default is also known as a "express consent" policy and requires

people to manifestly declare their preferences (Etheredge, 2021). Aysola et al. (2016) compared

opt-in vs. opt-out defaults in the health sector. “Opt-in Default Nudge”, are those nudges in which

participant are asked to opt-in the default SRI fund (Steffel et al, 2016). The explicit consent is

required from a person in opt-in default system (Meszaros et al., 2020). It is simply the facilitation

of behavior. This type of nudge is used in another study by Meske et al. (2020), where they

compare opt-in checkbox nudge and force choice nudge in the form of a text box. On the basis of

literature, the following relationship is expected:

H1a: The opt-in nudge has a significant positive impact on SRI decision of investors.

Meszaros et al. (2020) define out-of-the-box default as a system in which the consent of a person

is automatically assumed. It falls under the category of manipulation of behavior. Johnson and

Goldstein (2003) compare the effectiveness of opt-out default over opt-in default. The “opt-out

default nudge”, are nudges in which the default SRI fund is pre-selected for participants and they

are asked to fill out a short form if they want to opt out of the default option (Gajewski et al.,

2021). Default nudges are used to reduce the complexity of decision-making. The following

relationship is anticipated based on the literature:

H1b: The opt-out nudge has a significant positive impact on SRI decision of investors.

According to Agnew and Szykman's (2005) research, an individual's financial literacy affects how

effective information architecture is. They went on to explain that people with less information

are more inclined to select the default allocation than people with greater knowledge are. Anderson

and Robinson (2018) examined how financial literacy moderated investors’ reactions to nudges.

According to their findings, financially literate investors and those who believed they were not

financially literate were less reactive to nudges. Investors who mistakenly believed that they were

financially literate were more responsive to nudges. Carpenter et al. (2021) showed that people

with a lot on the line, low family income, high cognitive ability, and little financial literacy appear

to receive differential assistance from choice architecture to avoid making the worst decision.

Considering the relevant literature, the following two relationships are predicted:

H2a: The financial literacy moderates the relationship between opt-in nudge and SRI decision

of investors.

H2b: The financial literacy moderates the relationship between opt-out nudge and SRI decision

of investors.

Methodology

We use a sample size of N = 518, a representative sample of potential private investors from

Pakistan, who are recruited through a commercial online panel and pay compensation for it. This

approach of using a commercial online panel has been used in many studies, like Ingendahl et al.,

(2020) and Hainmueller & Hiscox (2010). This sample size is obtained after excluding very

inattentive responses and those who are uncertain about their responses. Following Levin et al.

(2020), randomization is done through Qualtrics software in this study. Participants in a

commercial online panel are randomly assigned to a control group and two experimental groups.

South Asian Review of Business and Administrative Studies Vol. 5, No. 1, June 2023

Yet, the exclusions were disproportionate, leading to three distinct group sizes: 179 (control

group), 176 (opt-in), and 163 (opt-out). Here, a sufficient sample size of the control group and

each experimental group is employed to reliably detect treatment effects (Gajewski et al., 2021;

Momsen & Stoerk, 2014).

In this study, the online survey experiment approach is used to collect data from a sample of

potential private investors in Pakistan. Firstly, the participants have received general instructions

about the importance of giving serious responses in order to ensure research quality. Participants

are asked to complete the hypothetical investment task of allocating PKR 1000,000 in any one

investment option provided by the investment bank, as described by Gajewski et al. (2021). These

investment options are equity funds, SRI funds, asset allocation funds, and bond funds. These

options are equally efficient in terms of risk/return. The risk-return profile of SRI funds is the same

as that of the Equity Fund. In opt-in test group, participant are asked to opt-in the SRI fund before

presenting all choices. In opt-out test group, SRI fund is pre-selected for participants and they are

asked to fill out a short form if they want to opt out of the default option. In control group, there is

no default option.

Qualitative data is collected by using an online questionnaire in the survey experiment approach.

This study implements the online survey experiment through Qualtrics, which is a secure web-

based survey platform. The experiment is performed remotely through an incentivized web-based

questionnaire. It takes almost 10 minutes to complete the questionnaire. After the initial

instructions, the first question is a hypothetical investment task. After this task, participants are

required to answer questions about demographics, financial literacy, risk tolerance, and social

behavior. The participants are asked to answer the closed-ended questions on the given scale in

the questionnaire. The data is qualitative and comes from interventional nudges. It is quantified by

assigning dummies to each nudge.

The variations in features and other factors are statistically controlled when individuals are

randomly allocated to several treatment groups and a control group (Momsen and Stoerk, 2014).

Using a Kruskal-Wallis test, the randomization effect is examined. This non-parametric statistical

test is used to determine whether or not there is a statistically significant difference between all

experimental groups. Only the qualitative response regression model or probability model is used

in this investigation since SRI choice is a categorical variable. In the experiment, choosing an SRI

fund as an investment choice is rated as 1, while choosing any other investment option is rated as

0. As the SRI decision (dependent variable) is a binary variable, the linear probability model

(LPM) and the logit model are used to estimate the model.

This study moves on to conduct moderation analysis to see how financial literacy affects the

relationship between SRI (a dependent variable) and default nudges (an independent variable).

Moderator is introduced as an interaction term in both LPM and logit models. To measure financial

literacy, we use the Big 3 scale of Lusardi and Mitchell (2011). In this scale, three questions about

inflation, interest, and risk are asked, and a financial literacy score is calculated. Demographics,

risk tolerance level and social behavior of investors are used as control variable in this study. Age,

gender, education, marital status, employment, income and investment experience are taken as

demographics in this study. For investment experience, score is calculated by asking them about

their investment history. To measure risk tolerance, a modified scale from Kapteyn and Teppa

(2011) is used, which was also employed in the study by Apostolakis et al. (2016). Social behavior

is the type of behaviour that leads to people being drawn to charitable causes. To measure this

behavior, we ask several questions to be answered on a 5-point Likert scale (Polonsky et al., 2002).

South Asian Review of Business and Administrative Studies Vol. 5, No. 1, June 2023

Results:

Table 1 shows the descriptive statistics on the composition of both treatment groups and the control

group alongside the three dimensions of covariates i.e. demographics, risk tolerance level, and

social behavior, and a moderator i.e. financial literacy. Demographics include age, gender,

education, marital status, employment, income, and investment experience.

Table 1: Descriptive Statistics

The participants are randomly allocated to all three experimental groups. So, it is anticipated that

due to randomization, the difference among the all three groups is insignificant. When

randomization worked effectively, it means there is no need of control variables. The Kruskal-

Wallis rank test is used to test whether randomization is effective. The p-values of kruskal-wallis

test shows that randomization worked, excepting the odd of education and financial literacy, the

differences among all groups are insignificant.

Next, we test whether any of the treatment or nudging groups differ substantially from the control

group in terms of the likelihood of selecting an SRI fund. Table 2 reports the empirical findings of

both LPM and logit models. Here, the dummies are assigned to each experimental treatment group,

with the control group omitted. Omitted categories are used as reference categories in this model.

Variables Attributes Opt-in Opt-out Control
Kruskal-Wallis test

P-values

n [%] n [%] n [%]

Demographics

Age 18-24 77 [44.3] 60 [36.8] 65 [36.3] 0.534

25-34 56 [31.8] 58 [35.6] 70 [39.1]

35-49 36 [20.5] 41 [25.2] 40 [22.4]

≥ 50 7 [4.0] 4 [2.5] 4 [2.2]

Gender Male 112 [63.6] 110 [67.5] 119 [66.5] 0.815

Female 64 [36.4] 53 [32.5] 60 [33.5]

Education Ph.D. or equivalent 15 [8.5] 15 [9.2] 22 [12.3] 0.008***

Master's degree 41 [23.3] 45 [27.6] 59 [33]

Bachelor's degree 57 [32.4] 49 [30.1] 59 [33]

Intermediate 61 [34.7] 51 [31.3] 39 [21.8]

High School or less 2 [1.1] 3 [1.8] 0 [0]

Martial Status Married 76 [43.2] 78 [47.9] 95 [53.1] 0.257

Unmarried 100 [56.8] 85 [52.1] 84 [46.9]

Employment full time 62 [35.2] 59 [36.2] 66 [36.9] 0.715

part time 38 [21.6] 41 [25.2] 36 [20.1]

retired 0 [0] 2 [1.2] 5 [2.8]

self employed 31 [17.6] 29 [17.8] 32 [17.9]

student/unemployed 45 [25.6] 32 [19.6] 40 [22.4]

Salary <50,000 114 [64.8] 105 [64.4] 115 [64.3] 0.991

50,000-100,000 41 [23.3] 37 [22.7] 44 [24.6]

100,000-250,000 18 [10.2] 16 [9.8] 11 [6.2]

>250,000 3 [1.7] 5 [3.1] 9 [5]

Mean Mean Mean

[Std dev] [Std dev] [Std dev]

Score (0-4) 1.949 1.908 1.866 0.849

[1.378] [1.374] [1.412]

Risk tolerance 5-point Likert scale 3.475 3.221 3.402 0.191

[0.97] [1.073] [0.923]

Social Behavior 5-point Likert scale 3.647 3.733 3.663 0.782

[0.736] [0.526] [0.576]

Financial literacy Score (0-3) 1.443 1.352 1.181 0.0269**

[0.873] [0.916] [0.899]

Investment Experience

Thi s tabl e s hows the des ci pti ve s tati s ti cs of two treatment groups and one control group al ong the di mens i ons of

control vari abl es i .e. demographi cs , i nves tment experi ence, ri s k tol erance and s oci al behavi or, and moderator

vari abl e i .e. fi nanci al l i teracy. P-val ues are obtai ned through Krus kal -Wal l i s rank tes t i s us ed to as s es s the

di fference among al l thes e groups col l ecti vel y.

***,** and * i ndi cate s i gni fi cance at 1%, 5% and 10% l evel s res pecti vel y.

South Asian Review of Business and Administrative Studies Vol. 5, No. 1, June 2023

The coefficient on each dummy shows the average treatment effect of that nudge as compare to

control group, and the sign of the coefficient shows the direction of the relationship, which is more

important in this case.

Table 2: Average Treatment Effects of Both Opt-in and Opt-out Defaults: Results of LPM and

Logit Models

The first nudge of this study is an opt-in default nudge. The results of the LPM model show that

the average treatment effect of an opt-in nudge on SRI decisions is highly statistically significant

at the 1% significance level. The sign of the coefficient is positive, which shows that an opt-in

nudge increases the probability of an SRI decision. The coefficient value of the opt-in nudge is

interpreted as that when the opt-in choice for default is available to participants, it increases the

probability of an SRI decision by 0.1744 as compared to the control group. In logit model, the

odds of selecting the default option SRI fund is 2.26 in the opt-in default nudge group. The odds

of taking a SRI decision in the opt-in treatment group are 1.26 times (126%) higher than the odds

in the control group. The marginal effect (M.E) of an opt-in nudge is interpreted as follows: when

an opt-in choice for default is available to participants, it increases the probability of an SRI

decision by 0.203 as compared to the control group. The effect of an opt-in nudge on SRI decisions

in logit model is also significant at the 1% significance level. These results align with the study by

Steffel et al. (2016), which shows that default nudges are still significant when they are less

manipulative.

The second nudge is an opt-out default nudge. The results of the LPM model show that the average

treatment effect of an opt-out nudge on SRI decisions is highly statistically significant at the 1%

significance level. The sign of the coefficient is positive, which shows that an opt-out nudge

increases the probability of an SRI decision. The coefficient value of the opt-out nudge is explained

as follows: when preselected an opt-out default choice is given to participants, it increases the

probability of selecting SRI fund by 0.5445 as compared to the control group. In logit model, odds

of continuing with the default option SRI decision are 11.5 in the opt-out treatment group. The

odds of making a SRI decision and opting out of the treatment group are 10.5 times higher in the

treatment group than in the control group. The marginal effect (M.E) value of the opt-out nudge

shows that when a SRI default opt-out choice is given participants as a treatment, it increases the

probability of an SRI decision by 0.608 as compared to the control group. The effect of an opt-out

nudge on SRI decisions is highly significant at the 1% significance level. These results support the

findings of previous studies that preselected default option have great significant impact on the

financial decision of an individual (Gajewski et al., 2021; Camilleri et al., 2019; and Madrian &

Shea, 2001).

p-value t-statistics Odds Ratio M.E P>׀z׀

Opt-in 0.1744*** 0.000 3.57 0.8145*** 2.258 0.203 0.000

(0.0489) (0.2337)

Opt-out 0.5445*** 0.000 11.96 2.4430*** 11.507 0.608 0.000

(0.0455) (0.2584)

Observations 518 Observations 518

R-squared 0.203 LR Chi Sq. 110.29

F-Statistics 73.76 P-value 0.000

F significance 0.000
Robus t Sta nda rd Error (LPM) /Sta nda rd Error (Logi t Mode l ) i n Pa re nthe s e s . Mode l i ncl ude the cons ta nt. So n-1

dummi e s a re a l l otte d a nd omi tte d ca te gory i s re fe re nce ca te gory. Al l the nudge coe ffi ci e nts a re re l a ti ve to

control group. M.E i s the ma rgi na l e ffe ct of e a ch i nde pe nde nt va ri a bl e i n Logi t Mode l , whi ch i s ca l cul a te d by

formul a M.E= P (1-P)*β.

***,** a nd * i ndi ca te s i gni fi ca nce a t 1%, 5% a nd 10% l e ve l s re s pe cti ve l y.

Linear Probability Model (LPM) Logit Model

South Asian Review of Business and Administrative Studies Vol. 5, No. 1, June 2023

The results of this study demonstrate that the opt-out nudge is nearly three times more successful

than the opt-in nudge when we examine the outcomes of both opt-in and opt-out nudges. These

findings align with those of Johnson and Goldstein (2003), who found that altering the default

increased organ donation rates in Europe from 15% (opt-in default) to 98% (opt-out default).

Another study by Aysola et al. (2016) discovered that opt-in strategy enrollment rates were 13%

while opt-out strategy enrollment rates were 38%. The opt-in nudge is also less manipulative and

less likely to limit the decision maker’s autonomy. Contrarily, the opt-out default limits an

individual's autonomy and is manipulative (Smith et al., 2013). As a result, the opt-in nudge is

socially and ethically more acceptable than the opt-out default. The empirical results of this study

reveal that although though the opt-in nudge effect is less powerful than the opt-out nudge, it still

influences SRI decisions significantly. So, it can take the place of the opt-out default nudge to

increase its legitimacy and face less ethical backlash. The opt-in default nudge, however, works

without undermining autonomy. (Loewenstein et al., 2014; Sunstein, 2016).

Table 3 illustrates the findings of the interaction terms in both the LPM and the logit models that

represent the moderating impact of financial literacy on the relationship between default nudges

and SRI decisions. In order to interpret coefficients in a way that makes sense, mean centering is

used. Prior to estimate, the moderator variable for financial literacy is mean-centered by deducting

means from the starting values. To get the interaction terms, this mean-centered variable is

employed. In order to interpret coefficients in a way that makes sense, mean centering is used.

Table 3: Moderating Effect of Financial Literacy for both Opt-in and Opt-out Defaults: Results of

LPM and Logit Models

The main effects of opt-in and opt-out nudges are at the reference level of financial literacy zero,

which is the mean value of financial literacy as a result of mean centering, and the main effect of

financial literacy displays the effect in the control group where there is no nudge. In moderated

regression, the interaction effects are more relevant. The positive sign of the opt-in interaction term

is explained as the financial literacy of an individual enhancing the effectiveness of the opt-in

nudge, but this effect is not significant. The negative sign of the opt-out interaction term is

explained as the financial literacy of an individual reducing the effectiveness of the opt-out nudge.

The opt-out nudge is significant for this moderating effect at the 10% level of significance. Hence,

the moderation effect of financial literacy is generally just partially significant. The coefficient

p-value t-statistics Odds Ratio M.E P>׀z׀

Opt-in 0.1613*** 0.001 3.270 0.7467*** 2.110 0.186 0.002

(0.0493) (0.2381)

Opt-out 0.5402*** 0.000 11.740 2.4635*** 11.746 0.613 0.000

(0.046) (0.2657)

Financial Literacy 0.0099 0.781 0.280 0.0544 1.056 0.014 0.782

(0.0356) (0.1963)

Opt-in×Financial Literacy 0.0732 0.178 1.350 0.2941 1.342 0.073 0.269

(0.0543) (0.2663)

Opt-out×Financial Literacy -0.0865* 0.093 -1.680 -0.4941* 0.610 -0.123 0.084

(0.0514) (0.2862)

Observations 515 Oservations 515

R-squared 0.213 LR Chi Sq. 116.28

F-Statistics 33.15 [P-value] 0.000

F significance 0.000

Linear Probability Model (LPM) Logit Model

Thes e are the res ul ts of moderati on effect of Fi nanci al Li teracy from both LPM and Logi t Model , whi ch provi de i nteracti ons on

addi ti ve and mul tl i pl i cati ve s cal es res pecti vel y. Robus t Standard Error (LPM) /Standard Error (Logi t Model ) i n Parenthes es .

Model i ncl ude the cons tant. So n-1 dummi es are al l oted and ommi tted categoey i s reference category. Al l the nudge

coeffi ci ents are rel ati ve to control group. M.E i s the margi nal effect of each i ndependent vari abl e i n Logi t Model , whi ch i s

cal cul ated by formul a M.E= P(1-P)*β.

***,** and * i ndi cate s i gni fi cance at 1%, 5% and 10% l evel s res pecti vel y.

South Asian Review of Business and Administrative Studies Vol. 5, No. 1, June 2023

value of the opt-out nudge in the LPM model is -0.0865, which is interpreted as when financial

literacy is increased by one unit from its mean value, the success probability of opt-out nudge is

reduced by 0.0865 and vice versa. Similar to this, the opt-out nudge's marginal effect (M.E) in the

logit model is -0.123, which may be translated as: when financial literacy is raised by one unit

from its mean value, the opt-out nudge's success probability is decreased by 0.123, and vice versa.

Dawson (2014) two-way logistic interaction plot is used to further evaluate the substantial

moderating effects, as illustrated in figure 1. The dotted line depicts a less significant influence,

whereas the solid slope depicts a significant effect. This figure shows that the moderation effect of

financial literacy is significant for an opt-out nudge at low levels. The nudge-ability of an opt-out

nudge is greater at low financial literacy levels and less at high financial literacy levels. In other

words, as financial literacy rises, opt-out default's efficacy decreases, as seen by the slopes in the

interaction plot.

Figure 1: Two-way Logistic Interaction Plot for Opt-out nudge

Conclusion

This study empirically investigates the impact of default nudges on SRI decision of investors in

Pakistan. This study used the opt-out default and opt-in default nudges to increase the SRI

investment by overcoming the complexity barrier. A sample size of N = 518, a representative

sample of potential private investors from Pakistan, who are recruited through a commercial online

panel, is used in this study. To collect the data, an incentivized online survey experiment is

conducted. In this experiment, nudges are assigned randomly through software to different groups

in such a way that only one nudge is given to each of the eight treatment groups and no nudge is

assigned to the control group. The covariates of the SRI decision are controlled automatically

through this randomization. The overall empirical findings of this research conclude that nudges

can be used as an effective strategy to enhance socially responsible investment. This study also

concluded that the financial literacy partially reduce the effectiveness of nudges.

Both nudges used in this study significantly increase the share of individuals who choose SRI as

an investment decision. The results of this study demonstrate that the opt-out nudge is nearly three

times more successful than the opt-in nudge when we compare the outcomes of both nudges. The

empirical findings of this study show that the opt-in nudge effect still has a considerable impact

on SRI decisions, albeit being less potent than the opt-out nudge. This might thus be used as a

counter-strategy to the opt-out default nudge in order to increase its legitimacy and encounter fewer

ethical concerns. In addition to the core relationship between nudges and SRI, this study also

examines the moderation effect of financial literacy on this relationship. The empirical findings

imply that opt-out nudge effectiveness is reduced if the individual is financially literate, and vice

versa. Financial literacy doesn’t have a significant impact on the effectiveness of an opt-in nudge.

The empirical findings of this study can aid state-level SRI policymakers in creating better

South Asian Review of Business and Administrative Studies Vol. 5, No. 1, June 2023

investment instruments that, when used in conjunction with other policy tools, can promote SRI

investment in society.

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Appendix 1:

Table 4: Results of LPM Model with Financial Literacy and Education Used as Control

Variable

Table 5: Results of Logit Model with Financial Literacy and Education Used as Control

Variable

_cons -1.712059 .3362246 -5.09 0.000 -2.371047 -1.053071

education .201846 .1032079 1.96 0.050 -.0004378 .4041298

FL -.0145678 .1134574 -0.13 0.898 -.2369401 .2078046

OO 2.393341 .2601849 9.20 0.000 1.883388 2.903294

OI .7435754 .2376638 3.13 0.002 .2777629 1.209388

SRI Coef. Std. Err. z P>|z| [95% Conf. Interval]

Log likelihood = -299.90553 Pseudo R2 = 0.1570

Prob > chi2 = 0.0000

LR chi2(4) = 111.75

Logistic regression Number of obs = 515

Iteration 3: log likelihood = -299.90553

Iteration 2: log likelihood = -299.90553

Iteration 1: log likelihood = -299.94131

Iteration 0: log likelihood = -355.78056

. logit SRI OI OO FL education

South Asian Review of Business and Administrative Studies Vol. 5, No. 1, June 2023

Table 6: Results of the LPM of financial literacy moderation using education as a control

variable

Table 7: Results of the Logit model of financial literacy moderation using education as a

control variable

_cons .1400775 .0615456 2.28 0.023 .0191623 .2609927

education .0370341 .020013 1.85 0.065 -.0022843 .0763525

OOxFL -.0851137 .0514976 -1.65 0.099 -.1862881 .0160608

OIxFL .0697126 .0542091 1.29 0.199 -.0367891 .1762143

FL .0032331 .036838 0.09 0.930 -.0691404 .0756066

OO .5320354 .046502 11.44 0.000 .4406755 .6233953

OI .1513333 .0496412 3.05 0.002 .053806 .2488606

SRI Coef. Std. Err. t P>|t| [95% Conf. Interval]

Robust

Root MSE = .4441

R-squared = 0.2182

Prob > F = 0.0000

F( 6, 508) = 29.11

Linear regression Number of obs = 515

. regress SRI OI OO FL OIxFL OOxFL education, vce(robust)

_cons -1.692878 .3401941 -4.98 0.000 -2.359646 -1.026109

education .1966172 .1049472 1.87 0.061 -.0090755 .4023099

OOxFL -.5023187 .288322 -1.74 0.081 -1.06742 .0627821

OIxFL .277189 .2669143 1.04 0.299 -.2459535 .8003314

FL .0207238 .1970523 0.11 0.916 -.3654917 .4069393

OO 2.4423 .2667424 9.16 0.000 1.919495 2.965106

OI .6979464 .2400834 2.91 0.004 .2273915 1.168501

SRI Coef. Std. Err. z P>|z| [95% Conf. Interval]

Log likelihood = -295.87058 Pseudo R2 = 0.1684

Prob > chi2 = 0.0000

LR chi2(6) = 119.82

Logistic regression Number of obs = 515

Iteration 4: log likelihood = -295.87058

Iteration 3: log likelihood = -295.87058

Iteration 2: log likelihood = -295.87075

Iteration 1: log likelihood = -296.05145

Iteration 0: log likelihood = -355.78056

. logit SRI OI OO FL OIxFL OOxFL education

South Asian Review of Business and Administrative Studies Vol. 5, No. 1, June 2023

Appendix 2 (Questionnaire):

Nudges Questions Decision screenshots:

1. Hypothetical Investment scenario (control group)

2. Opt-in Default Nudge (type 1 transparent nudge)

(After presenting hypothetical investment task, the following option is given. If the participant

click this option, all choices with pre-selected SRI fund option is shown to them. If the

participant do not click this option, all choices with no pre-selected option is shown to

them)

3. Opt-out Default Nudge (type 1 non transparent nudge)

(After presenting the hypothetical scenario with pre-selected SRI fund option, the following

instructions are given)

South Asian Review of Business and Administrative Studies Vol. 5, No. 1, June 2023

Financial Literacy Questions

The following questions are about finance.

Q-1: Do you think the following statement is true or false? “Buying a single company stock

usually provides a safer return than a stock mutual fund.”

True False Refuse to

answer

Do not know

Q-2: Imagine that the interest rate on your savings account was 1% per year and inflation

was 2% per year. After 1 year, with the money in this account, would you be able to

buy

More than

today

Exactly the

same as today

Less than today Do not know Refuse to answer

Q-3: Suppose you had amount 100 in a savings account and the interest rate was 2% per

year. After 5 years, how much do you think you would have in the account if you left

the money to grow?

More than 102 Exactly 102 Less than 102 Do not know Refuse to answer

Suppose you had amount 100 in a savings account and the interest rate was 2% per year. After 5

years, how much do you think you would have in the account if you left the money to grow?

More than

today

Exactly the

same as today

Less than today Do not know Refuse to answer