The Environmental Philips Curve from a Gender Perspective: Empirical Evidence from India

doi:10.21203/rs.3.rs-1650427/v1

The trade-off between environmental degradation and unemployment has been recently termed as Environmental Philips Curve (EPC) .This research attempts to investigate the presence of EPC in the Indian context utilizing time series data for the period 1990-2019. Besides contributing to the meager empirical literature on this issue, the present study makes a novel contribution by introducing a gender dimension to this investigation. In particular, this study investigates whether the nexus between environmental degradation and unemployment is different for men and women. We examine the presence of the EPC by estimating a model that considers growth in CO₂ emission as a function of economic growth, trade openness and gender segregated unemployment rates. Our findings based on ARDL, FMOLS and DOLS estimators generate robust evidence for a negative impact of male unemployment rate on growth in CO₂ emission that validates the existence of Environmental Philips Curve for male unemployment rate . However, there is no trade-off between environmental quality and women’s employment. In fact, the results point to a favourable effect of reduction in female unemployment on environmental quality. The results of Block Exogeneity test indicate a unidirectional causality from male unemployment rate to environmental degradation. However, bidirectional causal relationship , exists between female unemployment and environmental degradation. The existence of trade-off between environmental quality and male employment suggests that India is yet to find viable technologies that can curtail pollution without compromising the livelihood. An optimistic conclusion emanating from our findings is the existence of a virtuous cycle between female employment and environmental quality. An integrated approach to improve environmental quality and increase women’s economic activity may facilitate a speedy realization of sustainable development goals for India as both the goals compliment and reinforce each other.

Environmental Philips Curve

Unemployment

Gender

India

ARDL

FMOLS

DOLS

Block Exogeneity Test

Environmental degradation is one of the defining challenges of our times that has attracted both local and global attention. In response to the issues caused by deteriorating environmental quality, a general consensus on the need to achieve environmental sustainability has risen. A major challenge confronting the policymakers is to curtail environmental pollution without compromising the livelihood of the masses.

A voluminous literature indicates a positive relationship between economic growth, energy consumption and pollution ( see e.g., Grossman and Kruger 1995; Farhani and Rejeb 2012; Farhani and Ozturk 2015,Mohapatra and Giri 2015, Shastri et al. 2020) suggesting that higher output generates higher pollution. Given that employment generation is directly linked with output growth, a trade-off between controlling environmental degradation and sustaining livelihood arises. This nexus or the trade-off between environmental degradation and unemployment is termed as Environmental Philips Curve (EPC) by Kashem and Rehman(2020).

The EPC is a novel concept with limited empirical validation. In the past, studies have tried to investigate society’s perception about the trade-off between environmental objectives and reduction in standard of living ( see e.g., Veisten et al. 2004; De Silva and Pownall 2014). A few survey based studies look at the relationship between employment status and pro environmental behaviour of individuals (see e.g., Torgler et al. 2007 ; Kahn and Kotchen 2011; Meyer 2016) For instance, Meyer (2016) using survey data from European countries, found a positive impact of unemployment on time and effort intensive pro environmental behaviours. At the same time unemployment decreases income and hence reduces the pro environmental behaviours requiring monetary contributions. However, not much effort has been made to quantify the trade-off between environmental quality and employment on the macro level. To the best of authors’knowledge, only three studies have attempted to empirically examine the existence of EPC. Kashem and Rehman (2020) for a panel of thirty OECD and newly industrialised Asian economies confirmed the existence of EPC. Anser et al. (2021) investigated the presence of EPC in context of panel of BRICS countries and Turkey and affirmed a significant trade-off between unemployment and environmental degradation. Tariq et al. (2022) explored the case of South Asian countries. Using both panel as well as country-specific estimations, the authors validated the conflict between reducing environmental degradation and sustaining employment in South Asian countries.

The present study investigates the existence of EPC utilizing annual time series data for the period 1990–2019 for the Indian economy. Besides contributing to the meagre empirical literature on this issue, the present study makes a novel contribution by introducing a gender dimension to this investigation. In particular, this study investigates whether the nexus between environmental degradation and unemployment is different for men and women. The motivation for this investigation stems from the proposition that environmental degradation and the consequent climate change are experienced differently by men and women (Jerneck 2018). Women are not inherently vulnerable, however, the intersection between gender, socio economic structures, societal norms and expectations lead to differential climatic impacts on women (Andrijevic et al. 2020). Environmental degradation affects labour market outcomes through two channels: deterioration in workers’ own health and burden of caregiving to the dependents (Montt 2018). While the first effect is common on both men and women, the second effect is particularly relevant to women. When pollution is high children and the elder people fall sick more often (Montt 2018).Global evidence suggest that the distribution of care work is highly gender-biased, with the bulk of care work falling on women (Ferrant et al. 2014).

The gender bias in the distribution of care work significantly affects women’s ability to participate in the labour market and the type and quality of employment opportunities available to them. Every minute that a woman devotes to the unpaid care work implies a minute less that she could potentially spend on labour market activities or invest in her educational and vocational skills which in turn adversely affect women’s employment prospects. Another linkage between environmental degradation and women’s employment opportunity is the depletion of natural assets caused by air pollution. Airborne pollutants affect plant growth and agricultural productivity (Unsworth and Ormrod 1982).This may in turn reduce availability of work in agriculture sector where women are overrepresented.

The aforementioned arguments suggest that rather than presenting a trade-off, efforts to reduce environmental degradation may in fact be conducive to women’s employment. Hence it is pertinent to examine the validity of EPC using a gender disaggregated unemployment data. India offers an interesting case for this investigation. Evidence clearly suggest that India has not augured well on environmental norms (Chauhan 2021). At the same time unemployment is an enormous challenge for the country. As per the recent reports, India has 53 million unemployed people as of December 2021 and a major chunk of this unemployed population is women (CMIE 2022). Battling with the twin challenge of environmental degradation and unemployment, an investigation of the nexus between the two may provide relevant insights to the policymakers to formulate appropriate policies.

The remainder of the paper is organised as follows. In section two we describe the empirical model, data and econometric methods employed. Section three discusses the empirical findings. Section four concludes the study with policy recommendations.

Following Kashem and Rehman(2020) we define an estimable model in which environmental degradation is defined as a function of unemployment and a set of control variables.

The popular models of environmental degradation posit a positive relation between income and pollution levels as

$$p=a+by$$

1

where p is pollution and y represents output.

According to Okun’ s Law, unemployment is postulated as an inverse function of output or income

$$u=c-dy$$

2

Where u represents unemployment rate.

With the above two relationships we purport that pollution levels / environmental degradation and unemployment are inversely related

$$p=e-gu$$

3

Following earlier literature, we consider output growth and trade openness as additional control variables in the model (see e.g., Batool et al. 2022; Yousaf et al. 2022; Khalid et al. 2021; Kashem and Rehman 2020; Dogan and Seker 2016; Mercan and Karakaya 2015; Hamilton and Turton 2002; Hossain 2012; Rahman et al. 2017). The data for all the variables for the period 1990–2019 has been obtained from World Development Indicators, World Bank.

To begin with, we investigate the presence of unit root with the help of Augmented Dickey Fuller (ADF) and KPSS test by Kwiatkowski et al. (1992). The long run relationship amongst the series is examined through autoregressive distributed lag (ARDL)bounds testing approach .The approach developed by Pesaran et al. (2001) has several advantages over the conventional cointegration tests propounded by Engle and Granger (1987) and Johansen and Juselius (1990). First, the ARDL approach yields estimates of long-run and short-run parameters simultaneously. Defining the ARDL with the appropriate lags takes care of autocorrelation and endogeneity problem (Pesaran and Shin 1999). Furthermore, the ARDL procedure may be applied on series with different orders of integration. Most importantly, the small sample properties of the approach are superior to multivariate cointegration techniques (Narayan 2004).

The basic ARDL specification used for testing the long-run relationship among the relevant variables is given as:

Where environmental degradation (proxied by growth in CO₂ emission) is considered as a function of economic growth (GDP_GR), trade openness (TO), and gender segregated unemployment rate i.e., unemployment rate for male (UNEMP_M) and unemployment rate for male (UNEMP_F).

At the outset, the bounds testing procedure estimates Eq. (4) through the Ordinary Least Squares. The long-run relationship is said to exist if the F-statistic value based on Wald test is higher than the upper critical value. The third step involves the estimation of long-run coefficients and reparameterization of ARDL model into Error correction form. The error correction term assesses the speed of adjustment toward the equilibrium in the long run.

To check the robustness of the results obtained from the ARDL model, we also use Fully Modified Ordinary Least Squares (FMOLS) and Dynamic Ordinary Least Squares (DOLS) estimators. Originally employed by Phillips and Hansen (1990), FMOLS has the benefit of erasing long run correlation problems between stochastic regressor’s innovation and cointegrating equations while the DOLS delivers estimates which are efficient and unbiased or a small sample size (Kirikkaleli et al. 2021). Also, the DOLS corrects for regressor endogeneity by including leads and lags of first difference of the regressors (Shastri et al. 2018) .

In a Vector Error Correction Model (VECM), short-term causality is indicated by changes in other differenced explanatory variables. After obtaining co integration test results, VECM block exogeneity test has been applied. The VEC model may be described as follows:

$\varDelta {y}_{1t}={\mu }_{1}+{\gamma }_{1}{z}_{t-1}+\sum _{i=1}^{p-1}{\theta }_{1i}\varDelta {y}_{1t-i}+\sum _{i=1}^{p-1}{\delta }_{1i}\varDelta {y}_{2t-i}$ +${\epsilon }_{1t}$ (5)

$\varDelta {y}_{2t}={\mu }_{2}+{\gamma }_{2}{z}_{t-1}+\sum _{i=1}^{p-1}{\delta }_{2i}\varDelta {y}_{2t-i}+\sum _{i=1}^{p-1}{\theta }_{2i}\varDelta {y}_{1t-i}+$ +${\epsilon }_{2t}$ (6)

where ${z}_{t-1}\text{i}\text{s}$the error correction term derived from the cointegrating vector. θ and δ are the short-run parameters to be estimated, p is the lag length. “For each equation in the VEC Model, short-term Granger causality to assess whether endogenous variables can be treated as exogenous may be tested by the joint significance of the coefficients of each of the other lagged endogenous variables in that equation. The short term significance of sum of the each lagged explanatory variables (θ’s and δ’s) can be exposed either through joint F or Wald χ2 test”(Singh and Shastri,2019 pp.669).

The empirical analysis of time series data conventionally begins with an examination of the order of integration of the series. Towards this end we apply the ADF and KPSS tests. As shown in the Table 1, all the series are integrated of order one as per the ADF test. The results of KPSS test reveal that while unemployment rate for men (UNEMP_M) and trade openness (TO) are stationary at levels, the rest of the series are I(1).

After ensuring that none of the variables is I(2), we conduct the cointegration analysis in the ARDL Bounds framework.

Table 1

Results of ADF and KPSS Tests
Variable	ADF Test		KPSS Test
	Lags	t-statistics	Bandwidth	LM statistics
GCO₂	0	3.751	4	0.189
∆GCO₂	0	-5.655***	2	0.079
UNEMP_M	1	-2.863	3	0.073
∆UNEMP_M	0	-4.378***	1	0.072
UNEMP_F	2	-0.819	4	0.528
∆UNEMP_F	1	-8.173***	8	0.183
GDP_GR	0	-1.668	0	0.159
∆GDP_GR	0	-4.747***	0	0.085
TO	0	-0.380	4	0.141
∆TO	0	-4.892***	0	0.134
Note : Δ represents first difference. *** represents significance at 1%.For ADF test, the null hypothesis is of unit root. For KPSS test, the null hypothesis is of stationarity of series.

Since we have annual time series data, we consider two as the maximum order of lags in the model (Narayan ,2004). The Akaike information criterion is used to determine the optimal number of lags. The results contained in Table 2 reveal that the F-statistic exceeds the upper bound critical value at all the conventional levels of significance. Therefore, the null hypothesis of no cointegration is rejected and we conclude that the variables are cointegrated.

Table 2

**Results of ARDL bound test for Cointegratio**n
	Critical Values
F-statistic = 16.022	α = 0.01		α = 0.05		α = 0.10
	I(0)	I(1)	I(0)	I(1)	I(0)	I(1)
	4.590	6.670	3.354	4.774	2.752	3.994
Note : The lag order of the model is based on AIC

Tables 3 and 4 report the estimated long run and short run coefficients of the model. As evident from Table 3, there is a significant negative impact of male unemployment rate (UNEMP_M) on the growth in CO₂ emission in the long run. In particular, a one percentage point increase in male unemployment rate results in 0.0073 percentage point reduction in CO₂ emission growth. The results thus confirm the presence of trade-off between male employment and pollution control. In other words, a decrease in CO₂ emission growth may be achieved at the expense of employment of male workers.

Table 3

Estimated long-run coefficients
Variable	Coefficient	t-statistics	p value
UNEMP_M	-0.0073	-2.964	0.009
UNEMP_F	0.0114	2.964	0.009
GDP_GR	0.199	6.088	0.000
TO	0.060	1.228	0.230
R²	0.88	Breusch-Godfrey LM Test for Serial Correlation	F = 0.413 (p val = 0.669)
Adj R²	0.78	Breusch-Pagan-Godfrey Test for Heteroskedasticity	F = 0.247 (p val = 0.987)
		Ramsey Reset Test	F = 1.424 (p val = 0.251)

Table 4

Estimated short-run coefficients
Variable	Coefficient	t-statistics	p value
UNEMP_M	-0.014	-2.092	0.016
UNEMP_F	0.005	3.2014	0.005
GDP_GR	0.183	2.083	0.050
TO	0.292	1.516	0.153
ECT	-1.568	-10.548	0.000

Interestingly, the coefficient associated with female unemployment rate is significant and positive in the long run. A one percentage point increase in unemployment of women in India leads to increase in the environmental pollution by 0.011 percentage points. In other words, a reduction in female unemployment results in lower environmental degradation and there is no trade-off relationship between female employment and environmental quality in the long run. The results also indicate that in the long run, while the GDP growth tends to increase the CO₂ emission growth, the trade openness does not exert any impact on environmental degradation.

All the variables retain their sign and significance in the short run as well. As shown in Table 4, the coefficient associated with male unemployment rate is negative indicating a short run trade-off between male employment and environmental quality. In short run too, the regression coefficient on female unemployment rate is positive suggesting that a reduction in female unemployment is associated with lower CO₂ emission in the short run.

The negative and significant error correction term in Table 4 confirms the cointegration amongst the variables. The ECT suggests that the system corrects its previous period’s disequilibrium at a speed of 156.8% annually. This speed may appear overadjusted as ECT conventionally lies between one and zero. According to Narayan and Smyth (2006), an ECT lying between one and two suggests that instead of a monotonic convergence to the equilibrium path, the error correction process fluctuates around the long-run value in a dampening manner. Once the path is complete, there is a rapid convergence to the equilibrium path (Singh and Shastri,2019).

Various diagnostic tests were applied to assess the appropriateness of the specification of the model. The Breusch Godfrey LM test for autocorrelation and Breusch-Pagan-Godfrey Test for Heteroskedasticity fail to reject the null hypothesis of no autocorrelation and homoskedasticity respectively. Further, the Ramsay reset test suggests that the functional form of the model is appropriate. Finally, Fig. 1 presents plots of CUSUM and CUSUMSQ test statistics that fall inside the critical bounds of 5% significance suggesting that the parameters in the model are stable.

In the interest of checking the robustness of the above evidence, we also apply Fully Modified Least Square (FMOLS) and Dynamic Ordinary Least Squares (DOLS) estimators. The results of FMOLS and DOLS provide the same evidence for a negative, significant impact of male unemployment rate on CO₂ emission growth and a positive impact of female unemployment rate on CO₂ emission growth.

Table 5

Long Run Coefficients under Alternative Estimators
Variable	FMOLS		DOLS
	Coefficient	t-statistics	Coefficient	t-statistics
UNEMP_M	-0.0060	-2.208 (0.037)	-0.0059 (0.025)	-2.309
UNEMP_F	0.0115	2.244 (0.034)	0.016	2.458 (0.022)
GDP_GR	0.179	2.908 (0.050)	0.193	5.666 (0.000)
TO	0.061	1.545 (0.134)	0.075	1.695 (0.102)
R²	0.855		0.871
Adj R²	0.753		0.742
Note : Figures in parenthesis are p values

All three methods, namely , ARDL,FMOLS and DOLS provide approximately the same outcomes, which validate existence of Environmental Philips Curve for male unemployment rate while the Environmental Philips Curve does not exist for women unemployment.

A few reasons explain this discrepancy and the absence of trade-off between female employment and environmental quality. First is the occupational distribution/segregation between men and women in India. The NSSO data (1970–2018) exhibits that women have largely been undertaking labor-intensive and informal work, concentrated in low-productivity sectors. While nearly 77% of rural women are employed in agriculture sector, for the urban women, service sector is the key employment provider with its share in employment rising from 35% in 1977-78 to 60% in 2017-18. In this sector, women are largely concentrated in professions such as teaching and nursing considered to be the cleaner sectors of the economy. Hence, women’s employment does not directly result in environmental degradation.

Not only that we find the absence of any trade-off between environmental quality and employment, we find a positive effect of women’s employment on the pollution control. As the female unemployment decreases, environmental degradation also reduces. The favourable effect of female employment on the environment may emerge from two channels. First, with the reduction in unemployment or access to work, women tend to go for family planning and birth control and wish to have children later in life. This may slow down population growth and relieve stress on the environment (Jameel et al., 2022).

Second, the employment of women increases women’s autonomy towards economic resources and results in women empowerment (Guinée, 2014; Peinado and Serrano 2018) giving them a greater role in decision making at various levels. Researches show that in capacity of decision makers, women are more sensitive towards environmental issues. For instance, illustrating the case of agriculture sector, O’Connor (2019) observes that giving women leadership roles leads to increased innovations to make farming more sustainable. Women often possess valuable knowledge and potential for sustainable innovation particularly with regard to agriculture technology. Desrochers et al.( 2019) opine that since women are more conscientious (i.e. goal directed, organized) than men, they are more likely to support environmental protection and indulge in pro environment decisions. Hence, an increase in women’s economic activity is conducive to more environment friendly decision making at various levels.

We next employ the block exogeneity Wald test (based on VECM) to examine causal relationship amongst the series. From the results presented in Table 5 it is evident that there is a unidirectional causality from male unemployment rate and GDP growth rate to environmental degradation. Interestingly, there is a bidirectional causality between female unemployment rate and environmental degradation. This implies that in the Indian context, it is not only the female employment that may reduce the environmental degradation, an improvement in environment also facilitates reduction in female unemployment^[1]. This effect may come from two channels. First, an improvement in environmental quality is associated with lesser care work (as children and elders have lower probability of falling sick). This results in a greater availability of time to be invested in educational and vocational skills which in turn favourably affect women’s employment prospects. Also, environmental degradation often leads to curtail women’s employment opportunity owing to the reduction in natural assets caused by air pollution. (Unsworth and Ormrod, 1982).An improvement in environmental quality may in turn increase availability of work in agriculture sector where women are overrepresented.

Table 6

Results of Cuasality Test
Excluded	Chi Square	p value
Dependent Variable : GCO₂
UNEMP_M	3.750	0.050
UNEMP_F	6.331	0.011
GDP_GR	7.437	0.006
TO	0.011	0.913
Dependent Variable : UNEMP_M
GCO₂	1.115	0.290
UNEMP_F	0.0484	0.486
GDP_GR	14.569	0.000
TO	15.006	0.000
Dependent Variable : UNEMP_F
GCO₂	8.511	0.014
UNEMP_M	7.001	0.008
GDP_GR	6.370	0.041
TO	6.751	0.009
Dependent Variable : GDP_GR
GCO₂	0.040	0.841
UNEMP_M	13.060	0.000
UNEMP_F	9.020	0.011
TO	3.628	0.053
Dependent Variable : TO
GCO₂	0.459	0.497
UNEMP_M	18.877	0.000
UNEMP_F	21.246	0.000
GDP_GR	13.476	0.000

Furthermore, we find that all three variables, namely, trade openness, unemployment rate for males and females cause CO₂ emission growth via the GDP growth channel. Also, we find a bidirectional causality between trade openness and male unemployment rate as well as between trade openness and female unemployment rate. Bidirectional causality also exists between GDP growth and both male and female unemployment rates. Additionally, there is a unidirectional causality running from male unemployment rate to female unemployment rate.

A major challenge confronting the policymakers across the globe is to curtail environmental pollution without compromising the livelihood of the masses. The trade-off between environmental degradation and employment has been recently termed as Environmental Philips Curve (EPC) .This study is a novel attempt to investigate the presence of Environmental Philips Curve in the Indian context using time series data for the period 1990-2019. Besides contributing to the meagre empirical literature on this issue, the present study makes a novel contribution by introducing a gender dimension to this investigation. In particular, this study investigates whether the nexus between environmental degradation and unemployment is different for men and women.

Our empirical results based on ARDL, FMOLS and DOLS estimators generate robust evidence that validate existence of Environmental Philips Curve for male unemployment rate while there is no trade off between environmental quality and women’s employment. In fact, the results point to a positive effect of reduction in female unemployment on environmental degradation.

The results of Block exogeneity test indicate a unidirectional causality from male unemployment rate to environmental degradation. A bidirectional causal relationship , however, exists between female unemployment and environmental degradation.

The results of the study offer some important implications for literature as well as policymakers. From the view point of literature , the results underscore the importance of conducting a gender segregated analysis while investigating the trade off between environmental quality and standard of living.

For policymakers, the following implications may be drawn from our empirical analysis. First, the existence of trade-off between environmental quality and male employment suggest that India is yet to find viable technologies that can curtail pollution without compromising on the livelihood. To sustain employment and facilitate the process of poverty reduction , India must strengthen her efforts to devise technologies that are less polluting and employment generating at the same time.

An optimistic conclusion emanating from our findings is the existence of a virtuous cycle between female employment and environmental quality. Women’s work force participation and economic empowerment has not received much public attention as a climate solution in India. Our results suggest that the strategies to improve environmental quality in India must also consider women’s economic activity as a potential tool. At the same time, efforts to improve the work force participation of women must also focus on improvement in environmental quality. An improvement in environmental quality (resulting in improved health of families) reduces the care burden of women and gives them greater time to hone their vocational skills and improve their employment prospects. Also, an improvement in environmental quality may in turn increase availability of work in agriculture sector where rural women are overrepresented. An integrated approach to improve environmental quality and increase women’s economic activity may facilitate a speedy realization of sustainable development goals for India as both the goals compliment and reinforce each other.

Notes

[1] An ARDL based regression shows a positive effect of environmental degradation on female unemployment. The results are not reported to conserve space.

Ethics approval and consent to participant

This article does not contain any studies of human participants or animals by any of the authors and we used published data. These data have been widely used in research and are generally available. Therefore, we confirm that all aspects of the work covered in this manuscript were carried out in accordance with ethically approved consent.

Consent to Publish

Not applicable.

Author Contribution

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by all the authors together. The first draft of the manuscript was written by Shruti Shastri and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.”

Funding

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

Availability of data and Materials

Data sharing not applicable to this article as no datasets were generated during the current study.

Declaration of Competing Interest: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Note on preprint server: We have submitted my manuscript to a preprint server before submitting it to Environmental Science and Pollution Research”

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The Environmental Philips Curve from a Gender Perspective: Empirical Evidence from India

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Version 1

Abstract

Figures

1. Introduction

2. Model Specification And Econometric Methodology

3. Empirical Findings And Discussion

4. Conclusion And Policy Implications

Declarations

References

Status:

Version 1