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«Наука через призму времени»
Июнь, 2022 / Международный научный журнал
«Наука через призму времени» №6 (63) 2022
Автор: Жекенбай Даурен Биржанулы, студент магистратуры
Рубрика: Экономические науки
Название статьи: Survival analysis of grocery stores in Kazakhstan
Дата публикации: 9.06.2022
SURVIVAL
ANALYSIS OF GROCERY STORES IN KAZAKHSTAN
Zhekenbay Dauren Birzhanuly
master’s student
Suleyman Demirel
University, Kazakhstan
Abstract. When companies are faced with an economic shock, some
of them tend to better prepared than others. The aim of the paper is to assess
the effect of the financial/economic crisis on firm performance in Kazakhstan.
My research
isfocused on third largest industry by employment share in Kazakstan, which is Distribution
sector [1]. I trace the survival status of more than 19000 grocery stores from
2017 till 2021 and examine the determinants of stores survival across periods
of economic crisis. By applying the Cox proportional hazards model and OLS, I
explore the effects of different variables like location, region, sales size, type
of store regarded as key determinants of stores survival rate
Results of analysis
showed that due to Covid-19 crisis, 1.8 times higher retail outlets closed in
2020 comparing to previous years and that rural outlets with high sales value
has higher survival rates after Covid-19 crisis than urban outlets. In
addition, I found out that traditional grocery outlets have higher survival
rates than wholesale stores. This claim is supported by both non-parametric and
parametric tests in the survival analysis.
From a normative
perspective this finding could be important in softening the negative effects
of crisis during recessions. Additionally, this research can act as guide for grocery
stores as crisis management. There are very few research in this field in
Kazakhstan and proposed thesis can help companies to choose their strategic
directions in crisis management Kazakhstan.
Keywords: Firm failure; Effect of economic crises; Cox
proportional hazards model; Kazakhstan; Survival Analysis; Survival Data;
Covid-19 Crisis;
Introduction
The last decade was very stressful
for economic performance of Kazakhstan, with falling oil prices, with several
steep devaluation of tenge and high inflation rate for many consecutive years.
In this paper, I looked from the microeconomic perspective to the effect of
this crisis to grocery stores. Such an approach helps also better to understand
the importance of grocery stores characteristics for the macroeconomic
performance of Kazakhstan.In order to find out how to grow the economics, I
tried to find out what types of characteristics of outlets increase their
probability of survival from crisis. It is well known that firms that
successfully adapt to market fluctuations makes better off than during crisis.
I plan to
contribute to the literature in the following respects. First, research will be
on Kazakhstan. There is no survival analysis of firms yet on Kazakhstan.
Second, survival analysis will be conducted on Covid-19 crisis and will be
actual and on current interest. Since, Covid-19 crisis has many differences
comparing to previous recessions.
Literature
Review
There is now a lot
of interesting researches available in this field. Studies showed that
differences in ownership and corporate hierarchy have huge effect on firm
performance [2]. More specifically, typical characteristics of surviving
firmsoutsider ownership that includes foreigners and an independent board of
directors are suggested as typical characteristics of surviving firms. Firm
size and age also matter for firm survival. Large firms are less likely to
fail, whereas the effect of firm age is nonlinear [3]. In addition, there is
evidence that the orientation of firms affects their survival. Firms oriented
toward innovation, export, and diversification survive longer than those that
are not [4].
Another research
conducted by Mitton (2002) on East Asian financial crisis of 1997–1998 in
countries like Korea, Philippines, Malaysia
and Thailand revealed that firms that are more concentrated on less
product or services rather than other firms with diversified portfolio or
services performed better during recession [5]. Firms that have bid amount of
credits or affiliation with conglomerates have higher probability to fail
during the crisis period [6]. In addition, firms that has board of managers (or
several people in charge) has higher chance to fail than firms lead by
independent owners or managers [7].
Anpther survival
analysis conducted in Croatia showed that firms with long term investments
prior to crisis has between 60 and 70% higher survival rates than similar firms
that chose not to invest [8].Another survival analysis conducted in Russia found
out that companies which consult with international audit firm increases their
probability of firm survival [9].
Data
collection and research methodology
Data that I used in
this project are collected from tobacco company“Japan Tobacco International”
(JTI) sales to outlets. JTI’s
distributor has more than 30,000 outlets coverage in Kazakhstan and has
detailed sales data to for each outlet on daily basis. But I used only data for
19,466 traditional outlets (convenience shops) that are covered by JTI’s
internal sales representatives, excluding modern trade and key account sales. Data
used in research are from 2017 and survival analysis period starts from March
2020, period when Covid-19 quarantine started inKazakhstan.
The main component
in the standard survival analysis is failure event. In my research failure is
when grocery stores close their store or quit from the business. Another
important parameter is total time past until the failure. In my case this is
number of months which the grocery stores were continuing their operations.
Using our panel data, I estimate the survival functions and hazard rates for
our specified variables (nonparametricmethods). Then I apply Cox proportional
hazard model(parametric method) to estimate the survival rates for different
factors of grocery stores.
Analysis
and Findings
JTI has the highest market share %
in Kazakhstan with more than 40% share. In addition, JTI’s Distributor company
called “Megapolis” also delivers the cigarettes of second highest tobacco company
in Kazakhstan – “Philip Morris International”. So basically, distributor
company “Megapolis” covers more than 70% of volume of cigarettes in Kazakhstan.
In addition, cigarettes are one of the best sellingproduct in traditional outlets
making highest share of grocery store’s turnover. It means that if outlet do
not buy cigarettes of JTI at all for several consecutive months, I assume that it
is closed. So using JTI direct sales data to outlets, I can anticipate the
number of closed outlets and conduct survival analysis.
Note:All the graph is prepared by author using data from JTI sales
to outlets for 2020 and shows smoothed hazard rate for all 19,466 outlets.
Figure 1. Hazard rate for all outlets February to
December 2020
In
Figure 1, smoothed hazard estimate for 19,466 outlets show outlets started to
close after 3rd-4th months and incresing in the same
phase till 8th month , meaning that outlets started to close in
June-July 2020 and had peak of closing on October-November 2020
Figure 2. Kaplan-Meier survivor function of all
outlets survival probability
In Figure 2, we can
see that 4.8% of outlets from 19,466 outlets analyzed in research have closed
after Covid19 in 2020. If we compare the failure % with previous years, failure
% in 2020 in average 77% higher than 2019 and 2018. It means that higher
failure rate of grocery stores in Kazakhstan can be the result of Covid-19
crisis.
Figure 3. Kaplan-Meier survivor function of all
outlets survival probabilityby type of outlets
As it is shown in
Figure 3 above, % share of outlets closed in 2020 in wholesale type of outlets
is about twice higher than in traditional outlets. Failure % in
wholesaleis 8.7% and in regular outlet
groups is 4.8%. But in addition, we should consider that numeric coverage of
148 Wholesale outlets out of 19,466 outlets less than 1% of numeric coverage,
but making bigger contribution in weighted coverage.
Figure 4. Kaplan-Meier survivor function of all
outlets survival probabilityby type of location (urban or rural)
Figure 4 shows that
urban outletsfailure % = 5.2% is slightly higher that in rural = 4.1%. In
addition, the distribution of urban/rural is: 34.67% of coverage located in
rural areas and 65.33% in urban areas.
Figure 4. Kaplan-Meier survivor function of all
outlets survival probabilityby size of sales groups
Table 1. Distribution
of outlets and failure% by size of sales groups
Group
bysalessize |
Sales
range |
Share% |
Failure% |
Group
Size 1 |
above125 cartons |
23.63% |
1.52% |
Group
Size 2 |
80 to
124 cartons |
26.06% |
1.32% |
Group
Size 3 |
50 to
79 cartons |
25.95% |
2.86% |
Group
Size 4 |
below
50 cartons |
24.37% |
13.90% |
From the Figure 5
and Table 1 we can see that most outlets that have been closed have smallest
size of sales. Sales range where chosen to evenly distribute all into 4 groups.
Stores that buy less than 50 cartons per month considered as lowest category
stores and we can see that 13.90% of outlets which has less than 50 cartons of
sales per month in average have closed after Covid19. One of my assumptions why
possibly the smallest sales size stores closed after Covid19 that other bigger
stores, is that theycouldn’t cover all the cost in outlet with decreasing
profit after crisis.
Figure 5. Kaplan-Meier survivor function of all
outlets survival probabilityby region
Table 2. Distribution
of outlets and failure% by regions
Region |
Region ID |
Failure % |
Number ofoutlets |
Share % |
Nur-Sultan |
1 |
7.60% |
1 659 |
8.52% |
Almaty |
2 |
4.16% |
4 084 |
20.98% |
Akmola |
3 |
3.85% |
1 038 |
5.33% |
Aktobe |
4 |
6.37% |
926 |
4.76% |
Taldykorgan |
5 |
2.07% |
725 |
3.72% |
Atyrau |
6 |
8.94% |
615 |
3.16% |
West Kazakhstan |
7 |
6.50% |
646 |
3.32% |
Zhambyl |
8 |
5.78% |
813 |
4.18% |
Karaganda |
9 |
4.82% |
1 597 |
8.20% |
Kostanay |
10 |
3.30% |
1 269 |
6.52% |
Kyzylorda |
11 |
7.08% |
536 |
2.75% |
Mangystau |
12 |
4.68% |
704 |
3.62% |
Pavlodar |
14 |
4.28% |
1 214 |
6.24% |
North Kazakhstan |
15 |
3.02% |
597 |
3.07% |
East Kazakhstan |
16 |
2.54% |
1 652 |
8.49% |
Shymkent |
17 |
6.18% |
1 391 |
7.15% |
From the Figure 6
and Table 2 we can see that in Nur-sultan 7.6%, South region 6.7% in average
and in West region 6.5%, survival failure % of outlets is higher than national
average. The reason behind this numbers can be that in Nur-sultan, the rules
and control from government of quarantine regime is higher and possibly it
affected on failure of 7.6% of outlets. South region has highest number of
outlets per capita and after Covid-19, most of them closed due to the type of
outlets. Most of outlets in south region are opened in the houses or apartment
of owners. West region’s high survival failure % can be explained by closing of
oil production plants by decreasing of number of potential buyers.
Table 3. Cox proportional hazard model for sales
group, region,type of outlet (wholesale/outlet), location (urban/rural) and
target group
Table 4. OLS Regression model of sales volumes to
region, type of outlet (wholesale/outlet), location (urban/rural) and target
group
Note: The OLS Regression model is prepared by author in STATA by
using data from JTI sales to outlets for 2020.
Cox regression
model and OLS regression model results have showed that region is less affected
factor to the size of sales of outlets (p-value 0.254). Other factors like urban/rural,
wholesale/regular outlet, target group has very high effect on the sales volume
of outlet, as the P value for all of them are about 0.000
Conclusions
In conclusion, we
can see that Covid-19 crisis had definitely affected the survival rate of
outlets in Kazakhstan. Also research has shown that wholesale outlets, urban
outlets and outlets with small sales had affected most by crisis. In addition,
we found regions in Kazakhstan where Covid-19 crisis had higher effect on survival
of outlets.To test other hypothesis, I need data described in shortcomings of
research. Of course, this research have limitations. Since there can be
different factors which might also affected on higher failure rates of grocery
stores in Kazakhstan after Covid19: Number of new modern trade stores opened
after Covid19, emerging market of e-commerce in Kazahstan etc.
List of references:
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- Mata, J., and Portugal, P. (1994), Life Duration of New Firms, Journal of Industrial Economics, 42(5): 227-245.
- Dunne, J., and Hughes, A. (1994), Age, Size, Growth and Survival: UK Companies in the 1980s,Journal of Industrial Economics, 42(2): 115-140.
- Commander, S., and Svjenar, J. (2011), Business Environment, Exports, Ownership, and Firm Performance, Review of Economics and Statistics, 93(1): 309-337.
- Mitton, T. (2002), A Cross-firm Analysis of the Impact of Corporate Governance on the East Asian
- Baek, J-S., Kang, J.-K., and Park, K. (2004), Corporate Governance and Firm Value: Evidence from the Korean Financial Crisis, Journal of Financial Economics, 71: 265-313.
- Kang, J.-K., Lee, I., and Na, H, (2010), Economic Shock, Owner-manager Incentives, and Corporate Restructuring: Evidence from the Financial Crisis in Korea, Journal of Corporate Finance, 16: 335-351.
- Kovac, D., Vukovic, V., Kleut, N., & Podobnik, B. (2016). To invest or not to invest, that is the question: Analysis of firm behavior under anticipated shocks. PLoS ONE, 11(8), 1–18. https://doi.org/10.1371/journal.pone.0158782
- Iwasaki, I., & Kim, B.-Y. (2018). Firm failure in Russia during economic crises and growth : A large survival analysis. RRC Working Paper Series No. 76, 7.
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