Inflation and Coping Strategies: Analysis of Consumer in Penang, Kedah and Perlis
Nor Asmat Ismail
School of Social Sciences, Universiti Sains Malaysia, Minden, Penang, Malaysia
*Corresponding Author: [email protected] Accepted: 1 July 2020 | Published: 15 July 2020
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Abstract: Inflation has become a hot issue in Malaysia recently because it associated with the cost of living. This study helps policy maker to understand the coping strategies adopted by households in order to cope up inflation for the purpose of designing and implementing appropriate policies and programs for low income households. Therefore, this study has two objectives. First, to investigate households who are affected by inflation and second, to investigate the coping strategies adopted by households. This study conducted a survey in Penang, Kedah and Perlis using structured questionnaire to gather the information. The study concluded that almost 90% of the respondents were effected by inflation. The most effected were low income households, married and living in urban area and their coping strategies were buy less quantity, buy lower quality and less quantity and buy lower quality of goods according to the types of goods.
Keywords: Inflation, Coping strategy, Low income households
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1. Introduction
Central Bank of Malaysia projected that inflation in 2020 is higher than 2019 but remain modest (Zainul, 2020). A few years ago, Malaysia have focused on policy reform by gradually removing subsidies to shift the economy to become more market-determined prices. At the first stage, subsidies on cooking oil, sugar, and petrol has been removed. In December 2014, the government has implemented the managed-float pricing mechanism for petrol where petrol price is adjusted monthly according to changes in market prices. Another fiscal reform is the implementation of the Goods and Services Tax (GST) in 2015. Fiscal policy reform has changed the country’s inflation dynamics (Singh, 2016).
In the past few years, headline and core inflation have been stable and low. However, recently, inflation dynamics have been controlled by domestic supply-side shocks. Domestic supply shocks have been mainly determined by distractions in supplies and by irregular changes of control prices of certain goods (which comprise of 17% of the CPI basket).
Controlled prices for certain goods and subsidies had weakened the impact of external shocks on domestic inflation. Domestic inflations occurred mainly due to changes in the prices of petrol and foods, which comprised of about 40% of the CPI basket. The fast convergence of headline inflation to core inflation is a mainly due to supply shocks were temporary and exogenous. (Singh, 2016).
Not many researchers interested to relate inflation and consumer consumption pattern in the literature both theoretically and empirically. Therefore, the objectives of this paper is to investigate the relationship between inflation and consumption pattern of consumers by
focusing on two specific objectives. First, this study will investigate who will be effected by inflation and second to investigate how the effected consumer respond to inflation.
2. Literature Review
The issue of inflation in Malaysia recently has received significant attention from the media, economists, and general public, but not for development policy implication (Rafiqa Murdipi
& Siong Hook Law, 2016). There are two kind of research regarding inflation and consumer behaviour in the literature, first, studies using macro data and the second using micro data. In this section the researcher review studies using macro data first followed by studies using micro data. Inflation influence the consumer spending through dropping their purchasing power and increasing cost of living (Rafiqa Murdipi and Siong Hook Law, 2016). Short-run inflation is due to the increase in producer price index while the long-run inflation is due to demand-pull and imported inflation (Rafiqa Murdipi and Siong Hook Law, 2016).
Implementation of GST in 2015 causing negative impact on society as Malaysia is highly dependable on domestic consumption. GST has caused domestic market to shrink and it would undeniably cause to high inflation. Inflation also may cause negative impact on investment (Rabiul Islam, Ahmad Bashawir Abdul Ghani, Emil Mahyudin, and Narmatha Manickam, 2017). However, some studies found that inflation stimuluses consumers to save rather than consume (Rabiul Islam et al., 2017). If consumers assume inflation rates to be higher, and if they have higher marginal propensities to consume out of their wealth, they will increase present consumption (Doepke and Schneider, 2006); and (Mian, Rao, and Sufi, 2013). Inflation may change household’s income distribution (Howard, 1978) and may reduce the real value of nominal assets and wealth held in those assets.
Higher inflation function as hidden tax on paper money used by households as medium of exchange. As a results, disposable income decreases and in higher inflation periods, consumer spending would be lower (Aruoba and Schorfheide, 2011) but Nyamekye and Poku (2017) found the positive relationship between inflation and consumer spending. Inflation also influence consumer spending through the precautionary savings (Bloom, 2009; P´astor and Veronesi, 2013).
High levels of inflation and unemployment have generated severe demands on consumers' resources. Consumers have adopted several strategies to manage their resources. Gross, Crandall, and Knoll (1980) have identified four ways of resource management strategies: (1) increase supply of resources, (2) convert or create resources, (3) change the amount of resources used, and (4) make use of alternative resources. The implementation of resources management strategies differed among metropolitan, urban, and rural families (Hogarth, Krein, and Rettig, 1984). Different strategies adopted by the consumers not only because of the location but also depend on the availability of the resources (Jelani Razali and Nurhani Aba Ibrahim, 2010; Tandon and Landes, 2014).
The choice of a coping strategy also depends on the knowledge and awareness of the household on future values of chosen strategies. Furthermore, households’ expectation on the seriousness of the crises and the nature of the external setting i.e., technical, biophysical, social and political aspects also contributed to the households’ decision making. Since different strategy has different effects on the sustainability of households, the success of household coping strategies depends on the suitability of the strategy (Sarath, and Jeevika,
insecure households may use food or non-food based coping strategy or a combination of both to maintain their basic needs (Fahmida Dil Farzana, Ahmed Shafiqur Rahman, Sabiha Sultana, Mohammad Jyoti Raihan, Md Ahshanul Haque, Jillian L. Waid, Nuzhat Choudhury
& Tahmeed Ahmed, 2017).
Policy makers need to understand the coping strategies chosen by households to formulate appropriate policies and design related programs for the targeted income group. Therefore, the objective of this study are to investigate the most effected households by inflation and coping strategies taken by effected households in Penang, Kedah and Perlis. This study is different from other study because this study was conducted in three different states with different cost of living and different standard of living covering urban and rural areas.
Furthermore, the this study adopted two types of analyses, the first, investigates which households are the most effected by inflation and second, analyses the coping strategies adopted by most effected households in order to maintain their basic needs.
3. Methodology
Wellbeing of the households and the choice of the coping strategy may differ according to locality. Therefore, the sampling of this study was design to cover different sectors (urban and rural) of a given geographic location. Penang, Kedah and Perlis were chosen as the geographical area to consider different types of municipalities (high, moderate and low), different cost of living and standard of living. Face-to-face interviews were conducted with 600 households (100 urban and 100 rural) in Penang, Kedah and Perlis respectively using a structured questionnaire. The data was analysed using ordered logistic regression to determine the most effected households and multinomial logistic regression to determine the coping strategies adopted by households. Households were categorised into 3 groups, highly impacted, moderately impacted and mild impacted by inflation. Coping strategies were grouped into 4 groups, buy less quantity, buy lower quality, both (buy less quantity and buy lower quality) and no coping strategy. In the regression models, variables were considered as significant predictors if the p-value was less than 0.05 with Relative Risk Ratio (RRR) 95%
CI. The outcome variable was categorized into buy less, buy lower quality, buy less and lower quality and no coping strategy with “no coping” as the base outcome.
Results and Discussions
Demographic characteristics of the study sample are presented in Table 1. 49.5% of the respondents are male and 50.5% are female. Almost 70% of the respondents are below 40 years old. Instead of monthly income, this study use category of household income which is B40, M40 and T20. B40 income category is defined as households whose income below RM 4000 per month. M40 income category is defined as households whose income between RM 4001 to RM 9000 per month and T20 income category is defined as households whose income more than RM 9001 per month. Almost half of the respondents are in B40 income group (64.3%), M40 income group (34.2%) and T20 income group (1.5%). 56% of the respondents are working in public sector, 26% are working in private sector and 18% are self- employed.
Table1: Demographic profile of the respondent
Variable Description Percentage
Gender Male
Female
49.5 50.5
Age 21-30
31-40 41-50 51-60 61 and above
34 37.8 18 6.8 3.3
Occupation Public sector
Private sector Self employed
56 26 18
Income group B40
M40 T20
64.3 34.2 1.5
Marital status Single
Married
Widow/Widower Divorce
33.3 63.2 1.2 2.3
Household members 0
1 2 3 4 5 6 7 8
24.5 15 22.2 14.7 13.3 7.7 1.5 0.8 0.3
Descriptive statistics in Table 2 shows that 11% of the respondents are not impacted by inflation. 37.2% are impacted and 51.8% are highly impacted by inflation. Out of all impacted respondents, 20.5% are choosing to buy less quantity of goods when facing inflation. 10% are choosing to buy lower quality of goods and 41.3% are choosing to buy less quantity and lower quality of goods when facing inflation.
Table 2: Descriptive statistics of households effected by inflation and coping strategy
N Marginal percentage
Effected No effect 66 11.0%
Mildly Effected 223 37.2%
Highly effected 311 51.8%
Coping strategy Buy less quantity 123 20.5%
Buy lower quality 60 10%
Buy less quantity and lower quality
248 41.3%
No coping 169 28.2%
To analyse whether the consumers are mildly effected, highly effected or not effected by the increase in price of goods, this study conducted Ordered Logit regression. The results are shown in Table 3. Model fitting information shows that the final model is significant with the p value less than 0.05, consistent with the results in Goodness of fit and Pseudo R-Square, meaning that the model is good.
Table 3: Ordered Logit Regression Result Model Fitting Information Model
Fitting Criteria
Likelihood Ratio Tests
Model -2 Log
Likelihood
Chi- Square
df Sig.
Intercept Only 407.883
Final 324.462 83.421 9 .000
Goodness-of-Fit
Chi-Square df Sig.
Pearson 150.787 127 .074
Deviance 149.221 127 .087
Pseudo R-Square
Cox and Snell .130
Nagelkerke .153
McFadden .073
Parameter Estimates Estimate Std.
Error
Wald df Sig. 95% Confidence Interval Lower Bound
Upper Bound Threshold [impacted =
1.00]
-1.376 .896 2.360 1 .125 -3.132 .380
[impacted = 2.00]
.847 .895 .897 1 .344 -.906 2.601
Location [Penang=1] -.798 .202 15.641 1 .000 -1.193 -.402
[Kedah=2] .418 .208 4.055 1 .044 .011 .825
[Perlis=3] 0a . . 0 . . .
[Urban=1] .388 .166 5.467 1 .019 .063 .714
[Rural=2] 0a . . 0 . . .
[Male=1] .389 .167 5.437 1 .020 .062 .716
[Female=2] 0a . . 0 . . .
[B40=1.00] 2.191 .658 11.099 1 .001 .902 3.480
[M40=2.00] 1.211 .654 3.429 1 .064 -.071 2.493
[T20=3.00] 0a . . 0 . . .
[Married=1] -.957 .603 2.523 1 .112 -2.138 .224
[Single=2] -1.642 .619 7.039 1 .008 -2.855 -.429
[Widow=3] .076 1.189 .004 1 .949 -2.253 2.406
[Divorce=4] 0a . . 0 . . .
Link function: Logit.
a. This parameter is set to zero because it is redundant.
Table 3 shows that all independent variables are significant at 5% confident level. Compared to households in Perlis, households in Penang are 0.798 times less effected by inflation.
Compared to households in Perlis, households in Kedah are 0.418 times more effected by inflation. Compared to households in rural area, households in urban area are 0.388 times more effected by inflation. Compared to T20 households, B40 households are 2.191 times more effected by inflation. There is no significant different between M40 and T20 in the case of effected by inflation at 5% significant level. However, at 10% significant level, compared to T20 households, M40 households are 1.211 times more effected by inflation. Compared to divorced households, single households are 1.642 times less effected by inflation at 5%
significant level. There is no significant difference between married, widow/widower and divorce households in the case of effected by inflation in Penang, Kedah and Perlis.
To determine how the effected households in Penang, Kedah and Perlis adopted coping strategy to get their necessities, this study conducted Multinomial Logistic regression. The results are shown in Table 4. Model fitting information, goodness of fit and Pseudo R Square show that the model is good.
Table 4: Multinomial Logistic Regression Results Model Fitting Information
Model Fitting Criteria
Likelihood Ratio Tests
Model -2 Log
Likelih ood
Chi- Squa
re
df Sig
. Intercept Only 405.316
Final 292.610 112.7
06
24 .00
0 Goodness-of-Fit Chi-
Square
df Sig.
Pearson 100.476 99 .440
Deviance 106.754 99 .279
Pseudo R-Square
Cox and Snell .171
Nagelkerke .186
McFadden .074
Parameter Estimates
copinga B Std.
Error
Wald df Sig. Exp(B) 95% Confidence Interval for Exp(B) Lower
Bound
Upper Bound buy
less
Intercept -15.811 1.140 192.1 86
1 .000
[Penang=1] .005 .285 .000 1 .985 1.005 .575 1.759
[KedahI=2] .551 .326 2.853 1 .091 1.736 .915 3.292
[Perlis=3] 0b . . 0 . . . .
[Urban=1] .083 .245 .114 1 .736 1.086 .672 1.755
[Rural=2] 0b . . 0 . . . .
[B40=1.00] 17.620 .273 4153.
583
1 .000 448941 74.440
262709 67.470
7671917 3.010
[M40=2.00] 16.933 .000 . 1 . 225845
40.530
225845 40.530
2258454 0.530
[T20=3.00] 0b . . 0 . . . .
[Married=1] -1.897 1.136 2.788 1 .095 .150 .016 1.391
[Single=2] -2.236 1.154 3.757 1 .053 .107 .011 1.025
[Widow=3] 14.206 2559.
591
.000 1 .996 147712 0.026
.000 .c
[Divorce=4] 0b . . 0 . . . .
buy lowe r quali ty
Intercept -.353 1.844 .037 1 .848
[Penang=1] -1.118 .397 7.942 1 .005 .327 .150 .711
[Kedah=2] .333 .375 .785 1 .376 1.394 .668 2.910
[Perlis=3] 0b . . 0 . . . .
[Rural=2] 0b . . 0 . . . .
[B40=1.00] 1.658 1.146 2.092 1 .148 5.249 .555 49.630
[M40=2.00] .477 1.150 .172 1 .678 1.612 .169 15.348
[T20=3.00] 0b . . 0 . . . .
[Married=1] -1.368 1.447 .894 1 .344 .255 .015 4.338
[Single=2] -2.148 1.472 2.130 1 .144 .117 .007 2.089
[Widow=3] 15.880 2559.
591
.000 1 .995 788051 9.132
.000 .c
[Divorce=4] 0b . . 0 . . . .
buy less and lowe r quali ty
Intercept .419 1.358 .095 1 .758
[Penang=1] -.757 .263 8.272 1 .004 .469 .280 .786
[Kedah=2] .950 .276 11.87
4
1 .001 2.586 1.506 4.440
[Perlis=3] 0b . . 0 . . . .
[Urban=1] .789 .219 12.90
8
1 .000 2.200 1.431 3.383
[Rural=2] 0b . . 0 . . . .
[B40=1.00] 2.318 .797 8.462 1 .004 10.153 2.130 48.394
[M40=2.00] 1.008 .790 1.628 1 .202 2.740 .582 12.887
[T20=3.00] 0b . . 0 . . . .
[Married=1] -1.992 1.104 3.255 1 .071 .136 .016 1.187
[Single=2] -2.979 1.121 7.057 1 .008 .051 .006 .458
[Widow=3] 14.317 2559.
591
.000 1 .996 165151 6.640
.000 .c
[Divorce=4] 0b . . 0 . . . .
a. The reference category is: no coping.
b. This parameter is set to zero because it is redundant.
c. Floating point overflow occurred while computing this statistic. Its value is therefore set to system missing.
Buy less quantity coping strategy
Compared to household in Perlis, households in Penang are 0.005 times more likely to choose buy less quantity coping strategy in order to cope up inflation. However, there is no significant difference between households in Kedah and Perlis to choose buy less quantity coping strategy. The result also indicates that there is no significant difference between urban and rural households in choosing buy less quantity coping strategy. Compared to T20 households, B40 households are 2.318 times more likely to choose buy less quantity coping strategy. There is no significant difference between all marital status in choosing buy less quantity coping strategy when facing inflation.
Buy lower quality coping strategy
Compared to households in Perlis, households in Penang are 1.118 times less likely to buy lower quality of goods when facing inflation. There is no significant difference between households in Kedah and households in Perlis in choosing buy lower quality strategy in order to cope for inflation. There is no significant difference between households in urban and rural areas, income group and marital status in choosing lower quality products when facing inflation.
Buy less quantity and lower quality coping strategy
Compared to households in Perlis, households in Penang are 0.757 less likely to choose buy less quantity and buy lower quality coping strategy when facing inflation. Compared to households in Perlis, households in Kedah are 0.95 more likely to choose buy less quantity and buy lower quality coping strategy when facing inflation. Compared to rural households, urban households are 0.789 more likely to choose buy less quantity and buy lower quality
coping strategy when facing inflation. Compared to T20 households, B40 households are 2.318 more likely to choose buy less quantity and buy lower quality coping strategy when facing inflation. However, there is no significant difference between M40 and T20 in choosing buy less quantity and buy lower quality coping strategy when facing inflation.
Compared to divorced households, single households are 2.979 less likely to choose buy less quantity and buy lower quality coping strategy when facing inflation.
The findings of this study consistent with the results of FAO (2009) that households choose to buy cheaper food and reduce spending on other less important goods when they are facing declining in wages. This study also consistent with the findings of Jelani Razali and Nurhani Aba Ibrahim (2010) and Sarath and Jeevika (2011) that households cut down on their expenses to cope up inflation since the resources available for them are limited. Households will reduce the frequency and quantity of food taken as well as substitute into a lower quality product.
4. Conclusion
Regression results show that households in urban area, B40 and married are more effected by inflation. Their coping strategies are buy less quantity and buy lower quality of goods. Low income households have to adopt variety of coping strategies to fight inflation especially when food prices become very high and they need to meet their necessities. The choice of a coping strategy depends on the location of the household, income and marital status. Some of the coping strategies they have adopted, such as shifting to lower quality and less quantity of foods, must have led to the deterioration of nutritional status of household members.
Therefore, this study suggested that further investigation should be done to show empirically the impact of changes of food to nutrient intake of low income households. This information is crucial for the policy makers to develop and design appropriate policies to help low income households.
Acknowledgement
This research was funded by PNB Research Institute and Universiti Sains Malaysia Grant Number PSOSIAL650945.
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