Impact of Minimum Wage Policy on Income Inequality:

Azerbaijan Case

MAYIS GULALIYEV1,2,3*, SABINA VELIYEVA4, NAILA SULTANOVA5,

IRADA MEHDIYEVA6, EMIL GULALIYEV7

1Department of Management,

Azerbaijan Technological University, Ganja,

AZERBAIJAN

2Azerbaijan State University of Economics (UNEC),

Istiqlaliyyat str. 6, Baku,

AZERBAIJAN

3Western Caspian University,

Istiqlaliyyat str. 31, Baku,

AZERBAİJAN

4Department of Finance,

Azerbaijan Cooperation University,

Najaf Narimanov street 93, Baku,

AZERBAIJAN

5Department of Public Administration and Social Innovation,

Azerbaijan State University of Economics (UNEC),

Istiqlaliyyat str. 6, Baku,

AZERBAIJAN

6Department of Applied Economics,

Azerbaijan State University of Economics (UNEC),

Istiqlaliyyat str. 6, Baku,

AZERBAIJAN

7Department of Mathematics and Statistics,

Azerbaijan State University of Economics (UNEC),

Istiqlaliyyat str. 6, Baku,

AZERBAIJAN

*Corresponding Author

Abstract: - The effects of the minimum wage on income inequality in households were assessed in the article,

by using the differences-in-differences method. The authors claim that since the minimum wage in Azerbaijan

is much lower than the average wage, as well as because the number of waged (and salaried) employees has a

small share in the total employment, the impact of the minimum wage on the total income of households is not

felt. However, such an effect can be observed in the income inequality of low-income families. The authors

suggest that the minimum wage should be closer to the average wage and the level of self-employment in the

country should decrease. This can lead to a reduction in income inequality between households.

Key-Words: minimum wage, average wage, self-employment, income inequality, differences-in-differences

method

Received: April 28, 2023. Revised: September 9, 2023. Accepted: September 15, 2023. Published: September 29, 2023.

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1 Introduction

The implementation of the minimum wage (MW) in

all sectors of the economy or individual sectors is

considered state intervention in the economy. To

ensure that such intervention is effective and legally

based, in some countries, for example, in

Azerbaijan, a special law is adopted. In some

countries, the mechanisms for applying the MW are

different. The application of the MW in any field of

economic activity can be based on the decision of

the trade union organization formed in those fields.

Regardless of the mechanism by which it is

implemented, the implementation of the MW is part

of the state’s economic policy and the state

intervention in the economy is essential. As with

some forms of government intervention in the

economy, the full or partial implementation of the

MW is bipolar. The two-polarity of the state's

intervention in the economy with the (MW)

mechanism is related to its effects on both the

business environment and the social protection of

the employed. The state tries to prevent wages of

wage workers from falling below a certain level

through the policy of MW. This essentially means

ensuring the social protection of wage workers.

Determining the limit of the lower level of wages of

wage workers in all enterprises, regardless of

ownership, undoubtedly has a positive effect on the

social protection of low-skilled workers. However,

it should be taken into account that in some

countries, as well as in Azerbaijan, the level of the

MW also acts as a measure of the service fee in

some service areas. In this case, an increase in the

level of the MW leads to an increase in those service

fees. However, while the increase in the level of the

MW directly affects the level of wages of low-

skilled workers, the effect of the increase in various

service fees is indirect and is distributed among all

classes of the population as a whole. The other pole

of the MW as a mechanism of state intervention in

the economy is related to its effects on the business

environment. Thus, the adoption of a law or a

decision at any level regarding the increase in wage

(or salary) requires business subjects to look at the

wage system again. Since the change in the volume

of expenses related to labor wages in the enterprise

immediately affects the profit, it is required to take

appropriate measures to keep the profit, at least,

stable. However, the measures implemented in the

field of cost reduction should be such that the

competitiveness of the enterprise does not decrease.

That is, among such measures, increasing the selling

price of manufactured products or using cheaper and

lower-quality raw materials can be among the last

measures. Usually, in such cases, companies are

more inclined to reduce the number of employees or

cut back on incentive spending. In both cases or

other cases, the social protection of wage workers is

indirectly affected.

If a business entity reacts to an increase in the

MW by reducing its profits, this leads to a

weakening of the business environment. In all cases,

raising the MW forces businesses to create a higher

wage bill.

The effects of the MW on the social protection

of the employed are accompanied by an increase in

the wages of low-skilled workers. Increasing the

lower limit of wages through the policy of the MW

does not affect income inequality in the country.

Thus, the inflation process is unavoidable in almost

all countries of the world. This can happen as a

result of increasing the money supply, among other

factors, due to at least one factor, for example, in

connection with increasing the aggregate demand in

the country. The introduction of an MW allows

reducing of the effects of inflation to some extent by

increasing the wages of low-skilled workers. In this

case, income differences between low-income

workers and high-income workers are somewhat

reduced. But it cannot be claimed absolutely.

Because in most cases, especially in developing

countries, the incomes of highly skilled workers

increase faster than the wages of low-income

workers, and it is not possible to achieve a serious

reduction of this difference by increasing the MW.

Nevertheless, the MW is one of the economic

intervention mechanisms applied by the government

to reduce income inequality among the population.

It should be noted that income inequality can

arise for various reasons. These reasons may include

qualifications or educational level, as well as gender

and race. There are important reasons why the level

of MW does not have a univalent effect on income

inequality. So, at first, glance, if the level of MW

increases, then the wages of low-skilled workers

should rise, and the difference between the wages of

such a group of workers and the wage level of

middle-skilled workers should decrease. The

reduction of such differences should reduce the

level of inequality between the incomes of these

population groups. But in reality, the processes are

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more complicated. Thus, since the increase in MW

affects the business environment, the unemployment

rate of low-skilled workers may increase. In this

case, the income of households to which low-skilled

workers belong may decrease. A decrease in income

in such household groups can accelerate inequality

again.

Studying the relationship between MW level

and household income inequality is important for

every country. The importance of solving the

problem is, first of all, related to the efficiency of

decisions about MW. Decisions about MW are

effective when the welfare of the wider population,

especially the low-skilled workers, is improved. On

the other hand, such decisions should not harm the

business environment. One of the important

indicators in evaluating the efficiency of MW

decisions is the level of income inequality in

household groups. However, it should be noted that

the effects of MW decisions on income inequality

vary from country to country. Theoretically, the

study of the relationship between these two

indicators is not unambiguously evaluated, making

it necessary to use empirical methods.

2 Literature Review

The study of the effects of MW on income

inequality was carried out on the example of

different countries. In the case of Albania, these

problems are reflected in the research carried out by,

[1], the study shows that MW can act as an

important instrument in reducing income inequality.

But for its effectiveness, it is better to use it together

with other instruments. Thus, when MW is applied

together with collective agreements, better results

can be achieved in reducing income inequality. And

collective agreements can enable equal outcomes for

women and youth. According to the author's claim,

MW by itself cannot allow low-skilled workers to

receive higher wages. However, MW has the effect

of reducing the income inequality of workers in the

lower groups of the income distribution.

In, [2], the author studied the effects of MW on

income inequality through the channel of influence

on wages in the case of Turkey. In the study, the

changes in the wage level before and after the

increase of the MW level in the period 2003-2011

were analyzed. The main conclusion is that MW

played an important role in reducing wage

inequality between both women and men in the

period 2003-2005.

Note that income inequality is a serious problem

for developed countries, including the United States.

MW in this country varies from state to state.

According to the decision made at the federal level,

MW per hour is set at $7.25 in 2023, but in some

states, the level of MW is much higher. For

example, the level of MW in Colombia is even 16.5

dollars per hour. It's $15.74 in Washington and

$15.5 in California. In other states, the MW level

drops from $15.5 to $7.25 per hour of work. In the

United States, the MW instrument is also used to

reduce income inequality. In the example of the

USA, this problem has been analyzed by a large

number of researchers as a subject of scientific

research. For example, a study conducted by, [3],

argues that since the level of MW in the United

States after 2009 has not been changed by the

Federal government, its effects are weak, and states

make individual decisions about MW to strengthen

its influence. In the study, the Gini index was taken

as an indicator of income inequality. The study

shows that an effective MW policy cannot have

significant statistical and economic effects on the

level of income inequality. The main conclusion

reached by the author is that the MW does not affect

income inequality in US states if other economic

conditions do not change.

This issue was also explored by, [4], in the case

of the United States. The study takes into account

that although the level of wage inequality in the

United States has slightly decreased in recent years,

this level has tended to increase since the 1970s.

Some reasons stimulate wage inequality in the

country. Rising levels of inequality pose additional

challenges, particularly for low-wage workers.

Inequality in terms of wages also causes serious

inequality in the pension provision of employees.

The study argues that the policy implemented by the

US federal government regarding MW has been

aimed at mitigating the level of inequality in wages

and subsequently inequality in pension amounts.

The dynamic modeling method of income was used

in the study. According to this method, the level of

inequality is compared with the initial period against

the background of increasing wage inequality in the

long run. The study calculated incomes and levels of

inequality under conditions where MW would

increase from $7.25 to $12 an hour in 2017 and be

indexed to inflation. The study also predicted the

level of inequality caused by education in the level

of wages. Education wage increases are projected to

grow at a slower rate and continue to grow through

2070. And then such growth will remain unchanged.

After education supplements peak, wage inequality

will have increased by 15 percent compared to the

base year. During the study, household income was

taken into account in quintiles based on income

from various sources. During the evaluation, it was

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divided into two parts on the basis that a significant

part of household income is provided by the

husband and wife, that is, by two people.

In the study carried out by, [5], the effects of the

MW on inequality in the last 30 years were

evaluated using empirical methods in the example

of the United States. The income of men aged 25-61

was taken into account in the study. Data are based

on social security administrative correspondence for

the years 1981-2015. The obtained results confirm

that the increase in the national income tax reduces

the level of inequality in the annual distribution of

wages below the 12th percentile. On the other hand,

increasing the level of the minimum wage has a

stronger effect on the lower parts of the hourly wage

distribution. The obtained results confirm that the

increase of the national income tax, in addition to

the increase of the annual wage in the groups of

workers with low wages, also reduces the level of

inequality between these groups by 1.85% and is an

important instrument in the direction of reducing the

level of income inequality.

In, [6], the author argues in his research that the

implementation of MW is necessary to lift low-

skilled workers out of poverty. MW also has the

power to negotiate between the employee and the

business. In the study, the role of MW is justified to

strengthen the position of the employee in the

negotiations between the employee and the firm. A

panel analysis of US states in the study shows that

increasing MW helps reduce income inequality to

some extent. The study shows that the reduction of

the level of inequality in incomes occurs mainly at

the expense of the top 1% of the income

distribution.

In their study, [7], the authors not only note the

role of the MW mechanism in income distribution,

but also its unemployment-generating effects. The

usefulness of the MW mechanism in income

redistribution was studied by the microsimulation

method and the study of its effect on employment

elasticity. According to the obtained result, although

MW has the effect of creating unemployment at a

certain level, it also has the effect of reducing

poverty. However, due to these two effects, the

poverty reduction effect of MW is limited. Such

contradictory characteristics of MW are also

inherent in its impact on income inequality. Thus,

although the increase in MW reduces income

inequality, income inequality increases again due to

the creation of unemployment. The study argues that

the MW instrument should be implemented

considering its impact on unemployment.

A study by, [8], also focused on the effects of

MW on income inequality in the case of the United

States. The study shows that MW affects the bottom

of the wage income distribution. It is that part that

has been more exposed to changes in the labor

structure since 1980. In the study conducted by, [9],

a new method was proposed to estimate the impact

of an increase in MW on employment, based on the

comparison of vacant jobs with wages at or slightly

above MW and jobs with wages below MW. Note

that there are differences between the MW level at

the federal level in the United States and the MW

set at the various state levels. In the study, the

analysis of the 138-fold change of MW in the period

1979-2016 was carried out, and the conclusion was

reached that the number of workplaces with a low

wage level practically did not decrease. However,

the increase in MW led to an increase in the average

wage level. The fact that low-wage jobs are not

decreasing suggests that such jobs are difficult to

replace. The study also shows that an increase in

MW does not create unemployment at all. Thus, the

reduction of wage income inequality as a result of

MW does not lead to additional unemployment.

A study conducted by, [10], on the example of

the USA also proves that an increase in MW leads

to an increase in income in households at the lower

end of the household income distribution. The study

shows that the elasticity of poverty level with

respect to MW ranges from -0.220 to -0.459 for a

period of one hundred years or more. In, [11], the

author focused his study on the example of OECD

countries, claiming that the increase in MW in the

countries included in this union leads to a decrease

in income inequality. However, an important finding

in the study is that an increase in MW to a certain

optimal level, not a continuous increase, reduces

income inequality. Beyond this limit, the opposite

effect occurs.

In, [12], the author studied this problem in the

case of China. It should be noted that the Chinese

economy is developing rapidly. But at the same

time, income inequality in China is still high. As the

country developed, the incomes of the population

increased, and income inequality increased.

However, panel analysis for the period 2004-2009,

as well as household surveys, shows that the

increase in MW reduced inequality. This is due to

the reduction of differences between the median and

the bottom level of the income distribution.

The study, [13], analyzed the effects of real MW on

income distribution in Latin American countries.

The study included data covering the years 2000-

2012 for four countries, namely Argentina, Brazil,

Chile, and Uruguay. A semi-parametric technique

was used to estimate the distribution function. The

obtained results prove that MW has an inequality-

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reducing effect in Argentina, Brazil, and Uruguay.

As a result of the increase in MW in these countries,

crowding occurs in the lower parts of the income

distribution. Such agglomeration reduces the level

of income inequality. In the case of Chile, the MW

has no such effect on income inequality.

A similar study was conducted by, [14], on the

example of Latin American countries. Six countries

were included in this study. MW is different in these

countries. The study proves that the increase in MW

in these countries had a tightening effect on wage

distribution in the early 2000s. During that period,

rapid economic growth and revival in the labor

market took place in these countries. But in the

2010s, the labor market weakened a bit.

In, [15], the authors used regression analysis to

estimate the effects of a six percent increase in MW

levels on hourly wages and household income

distribution in the case of Ireland. The study

estimates that an increase in MW reduces the wage

rate ratio at the 90th and 10th percentiles by eight

percent and by four percent at the 75th and 25th

percentiles. The effects of MW on workers' wages

also vary by age. Thus, an increase in MW reduces

the ratio between the 90th and 10th percentile wages

of workers under the age of 25 by 24 percent.

A study by, [16], quantified the impact of MW

on employment, capital formation, wage

distribution, and other macroeconomic indicators.

According to the results of the research, the change

of MW at a low level does not affect the level of

employment. However, such changes lead to an

increase in wages by tightening the distribution of

wages. A study performed by, [17], quantified the

effects of the IPR legislation on the distribution of

wages in Germany. The obtained results show that

the adoption of MW legislation leads to an increase

in the lower part of the distribution of hourly wages.

In the example of Germany, the effects of MW on

income inequality are also reflected in the study

performed by, [18]. In the study, the effects of MW

on the change in monthly wages in the period

between 2000 and 2017 were quantitatively

assessed. The authors quantified the extent to which

MW had an effect in increasing wage inequality

during the period covered by the study, but then

decreasing to its previous level. The "differences in

differences" method was used during the research.

The obtained results prove that MW affects the

lower part of the wage distribution. Employment

dynamics do not affect the nature of the wage

distribution.

In, [19], the authors perform a quantitative

analysis of the impact of MW on the economic well-

being of low-skilled immigrants in the United States

and conclude that an increase in MW negatively

affects the employment of low-skilled immigrants.

However, such effects are gradually decreasing. The

state's migration policy causes low-skilled

immigrant workers to move from formal to informal

employment. Based on the calculations, the authors

conclude that MW is not an effective instrument in

reducing poverty among immigrants.

In, [20], the authors quantified the effects of MW on

income inequality in the case of Brazil. The study

shows that since 1994, the growth of MW has

played an important role in reducing income

inequality in Brazil. The results obtained in the case

of Brazil are slightly different from the results of the

studies we reviewed above. Thus, according to the

results of the study, MW affects even the upper

parts of the wage distribution. As a result, the

decrease in the logarithmic dispersion of logarithmic

returns in Brazil after 1994 is explained.

A comparative analysis of the large number of

studies devoted to the effects of MW on wage and

household income inequality suggests that such

effects do not lead to the same results. Depending

on the level of MW, the areas of economic activity

in which it is applied, the average wage level in the

country, the extent of its change, and other factors,

the effects on the level of inequality are also

different.

3 Methodology

There are some difficult aspects to quantifying the

impact of the MW on income inequality. The main

difficulty is that not all of the population's income

comes from wages. There is no country in the world

where all employed people receive wages.

According to this indicator, developed countries

lead the list. The nature of the relationship between

the volume of GDP per capita for 2021 and the

indicator "share of wage earners among the

employed population" also shows that there is a

positive relationship between these indicators. The

larger the wage-earning part of the working

population, the more the MW legislation can affect

the population.

Based on the structure of the incomes of the

population in Azerbaijan over the last 10 years,

wages do not constitute a high percentage of these

incomes (Graph 2). Thus, in the period between

2002 and 2021, the incomes obtained in connection

with salaried work have changed from 30% to 36%

of the total incomes of households. In 2001, this

number was approximately 40%. The share of

income from self-employment changed from 33% to

42% during that period. Income from property

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received current transfers and other income are

around 30% of household income.

Fig. 1: Dynamics of the share of households’

income from various sources in total income (%)

The fact that income from wage employment is

less than total household income significantly

weakens the effects of the MW on income

inequality. Taking into account the characteristics of

the economy of Azerbaijan, we can note that the

basis for choosing self-employment activities is the

very low MW and difficulties in finding paid work.

Such low employment levels are exacerbated when

the MW is raised due to the already small number of

waged (and salaried) jobs. Thus, during the

assessment of the impact of the MW on employment

in Azerbaijan, it was determined that the increase of

the MW has a negative effect on the employment

level of low-skilled workers. The Gini coefficient of

household income inequality will be used as an

indicator of income inequality in the study. The Gini

coefficient can also be calculated using the Gini

formula:

G=  











 (1)

Here, the number of the n-household group, 

and  – respectively, the share of the income of the

i-th and j-th households in the total income, - is

the average value of the share of the income of the

household in the total income, [21], to assess

income equality, we can use the distribution of

household income by deciles and quintiles.

However, according to the GINI coefficient

calculation methodology, the more income groups

there are, the more reliable the results are. With this

in mind, we will use data from the State Statistical

Committee of the Republic of Azerbaijan on

household monthly income per capita. In such a

distribution, income is distributed in groups of 20 or

more. However, one drawback of such distribution

is that the incomes are shown as sums after a certain

maximum volume, and in some years even 40-45%

of the total incomes of households are collected in

this group. To obtain a more reliable value of the

Gini coefficient, it is necessary to divide these

groups into as small groups as possible. On the other

hand, the source of household income is not taken

into account when calculating the Gini coefficient.

As we mentioned above, there is a significant

difference in the source of income. In such a case,

the difference between wages alone cannot be the

basis of inequality between household incomes.

Another distinguishing feature of households in

Azerbaijani society is that some young families are

supported by their parents, and some elderly parents

are supported by young families. Such patronage is

not reflected in household income. Also, during the

calculations, the monthly income of household

income (HHİ) was taken as a numerical average. Per

capita, income was calculated as the total income of

the population divided by the population. It should

be noted that when calculating the number of

households, a small group of the population, that is,

those in children's and old people's homes, was not

taken into account. According to the methodology

of the Gini coefficient, its numerical value indicates

a certain level of inequality in a certain interval

(Table 1). According to World Bank data for 2021,

among the 50 countries surveyed, Colombia and

Costa Rica have the highest levels of income

inequality, with 51.5 and 48.7 points, respectively.

Table 1. The relationship between the numerical

value of the Gini coefficient and the level of income

inequality

The numerical value of the

Gini coefficient

Level of income

inequality

  

Very low

    

down

    

medium

    

High

  

Very high

To calculate the effects of the MW on income

inequality, we will use the difference-in-differences

method. Periods of such policy changes will be

taken into account to determine the impact of MW

legislation or economic policy on household income

0,00

20,00

40,00

60,00

2001

2005

2008

2009

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

2021

Earnings from waged and salaried employment

from self-employment

from property

Current transfers received

Other income

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inequality. That is, we will compare the difference

between the level of inequality between household

incomes before the adoption of the law on the MW

and the inequality after the adoption of this law or

the change of the MW level. In this case:



󰆹

󰆹 



 

󰆹 󰇛



 





󰇜-󰇛



  



󰇜 (2)

Here 

󰆹 - is the “difference in difference” estimator

of exposure to the minimum wage.





  

– is the Gini coefficient after the

change in the level of the minimum wage in the

group affected by the minimum wage;





  

 is the Gini coefficient after the

change of the MW level in the control group;





  

-is the Gini coefficient before the

change of the MW level in the affected group;





  

󰆹is the Gini coefficient before the

change of the MW level in the control group;

According to the methodology, we can also use the

difference-in-differences regression format. This

time:

        

󰇛 󰇜  (3)

We can use the regression equation. Here - t

is the result of the ith observation at time t. This

observation can be either from the "affected group"

or from the "control group". -is an indicator

that is equal to "0" before the change of the MW

(i.e. when t=1) and "1" after the change of MW (i.e.

when t=2). is a dummy indicator and is

equal to “1” when the observation is from the

“exposed group” and “0” when it is from the

“control group”.

In this case, the regression function

If we compare the regression function (3) with (2),

A=; B=+; E= ; C=   .

Thus,

  󰇛  󰇜󰇛  󰇜 󰇛    )-

-( )-󰇛+󰇜   (5)

If we use the method of least squares,



󰆹 󰇟󰇛    )-( )]-󰇟󰇛+󰇜 

] (6)

According to econometric theory, the estimate

of δ in equation (5) is 

󰆹 in equation (6) and can be

calculated as the difference between the "control

group" and the "affected" groups in the MW change.

To assess the effects of the minimum wage on

income inequality in Azerbaijan using the

"difference in difference" method, "households

whose income is less than the average nominal

wage" as the "affected group" and "households

whose income is greater than the average nominal

wage" are the control group. we will take Of course,

the division of households into these groups requires

certain assumptions to be accepted. Thus, there are

no statistical data reflecting how many households

there are in these groups or what their income is. On

the other hand, it is much more difficult to

determine the number of wage earners in

households belonging to which income group.

Considering these or other shortcomings, we will

assume that:

1) The number of wage earners from each

household member is equal to the national average;

2) Income from wages of each household (HH) is

equal to the national average;

3) A change in the National Income Tax can directly

or indirectly affect all households. This assumption

allows us to calculate the Gini coefficient based on

the share of household income on wage income in

total wage income. In this case, we can take the Gini

coefficient of HHW as the "affected" group, and the

Gini coefficient of households on total income as

the "control" group.

Several statistical data will be used to calculate

the share of household income in total wage income:

a) the share of household income per capita in the

total number of households; b) the Monthly per

capita income of households; c) the total income of

the population in the country; d) share of income

from wages in total income; e) the number of

households; f) the number of population; g)

household size; j) the share of salaried employees in

the total number of employees; k) the share of the

employed in the total population.

Using these indicators, to calculate the Gini

coefficient for household wages (HHW), the share

of wage income in the income of i-th households in

the total monthly wage income for the country.

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 



 (7)

where  is the share of monthly wage income in

the year "t" in the i-th HHW in the total wage

income for the country,  in the i-th HHW

in the year "t" total monthly income from wages,

total monthly income from wages in the

year "t" for t-country,  average monthly income

per person in the year "t" in the i-th HHW, the

number of believers living in the i-th HHW in the

year "t",  is the share of income from wages

in the income of the HH in the year "t".

4 Results

4.1 Estimating the Impact of Minimum

Wages on Income Inequality in Some

Developing Countries: A Cross-Country

Analysis

The minimum wage is applied in most countries of

the world. However, as one of the forms of state

intervention in the economy, it cannot be confirmed

that it is always effective. Nevertheless, most

countries prefer to implement the minimum wage as

it is related to the social protection of low-skilled

workers. Despite the presence of heteroscedasticity

in Figure 1, which depicts the dependence of the

minimum wage on GDP p.c., it can be argued that

as the country's income increases, there is a

tendency to apply a higher minimum wage. For

example, according to the data of the International

Labor Organization (ILO, 2022), the monthly

minimum wage in Switzerland in 2021 was 4385

US dollars, and in Uganda, it was 1.67 US dollars.

In that year, the minimum wage in Azerbaijan was

147.06 US dollars. According to graph 2.5.1, which

shows the dependence of the minimum wage on the

GDP of 133 countries with different levels of

development, the minimum wage in most of these

countries is less than the average price, i.e. 522 US

dollars. The median in this ranking is 244.12 USD

(Samoa).

The dependence of GDP (current USD-

monthly) on GDP (current USD-annual) in some

countries for 2021 is presented in Figure 2.

Moreover, the dependence of the Gini index on

GDP volume for 50 countries with different levels

of development (Figure 3), shows that there is no

serious relationship between these indicators.

However, with the increase in GDP volume, the

tendency of the Gini index to decrease is felt to

some extent.

Fig. 2: Dependence of GDP (current USD-monthly)

on GDP (current USD-annual) in some countries

(2021)

Fig. 3: Dependence of the Gini index on GDP in

some countries (current US dollars) (2020)

The results of a 10-year panel analysis for 28

countries to assess the dependence of the Gini index

on the MW level in different income countries

suggest that there is an inverse, but much weaker

relationship between these indicators (Table 2 and

Table 3).

Table 2. The dependence of the Gini index on the

MW level in different income countries (PLS)

y = 0,0284x + 99,297

R² = 0,7787

0,00

1000,00

2000,00

3000,00

4000,00

5000,00

0 50000 100000 150000

y = -2,371ln(x) + 57,52

R² = 0,128

0 50000 100000 150000

Dependent Variable: GINI

Method: Panel Least Squares

Date: 01/03/23 Time: 23:22

Sample: 2011 2020

Periods included: 10

Cross-sections included: 28

Total panel (balanced) observations: 280

Variable Coefficient Std. Error t-Statistic Prob.

MW -0.001730 0.000497 -3.482842 0.0006

C 35.08298 0.513188 68.36282 0.0000

R-squared 0.041809 Mean dependent var 33.74107

Adjusted R-squared 0.038363 S.D. dependent var 5.784374

S.E. of regression 5.672337 Akaike info criterion 6.316197

Sum squared resid 8944.764 Schwarz criterion 6.342160

Log likelihood -882.2675 Hannan-Quinn criter. 6.326610

F-statistic 12.13019 Durbin-Watson stat 0.033451

Prob(F-statistic) 0.000576

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Table 3. The dependence of the Gini index on the

MW level in different income countries (RLS)

4.2 Household Income Inequalıty (Gini

Coefficient) in Azerbaijan

As we mentioned, the problems of the impact of the

MW on the inequality level of workers' wages have

attracted more attention in the example of different

countries. There are certain reasons for this. The

main reason is that wages dominate people's

incomes. On the other hand, the government's

intervention in the economy with the MW

instrument has a direct impact on wages. Although

the influence of the Ministry of Economic Affairs

on other sources of income is to some extent, such

effects are not direct. For example, income from

self-employment is not directly affected by the MW

level, even though it is a significant share of total

household income in some countries. However, it is

impossible to claim that there is no influence

between them. For example, some able-bodied

people who do not want to work for wages because

of the low MW or who are unemployed because of

the implementation of the MW may choose to be

self-employed.

To study the impact of the MW on household

income inequality, it can be useful in several ways

to assess the effects of the MW on income

inequality by wages as well as the effects of the

MW on the level of inequality in total household

income. First, such a comparison is useful if

household incomes have an important weight in

addition to wages. In the case of Azerbaijan, in the

last 20 years, the share of salary income in total

income is comparable to the share of income from

self-employment. Therefore, income other than

wages can be taken as an "unaffected" or "control"

group, regardless of the MW.

Despite such shortcomings in the calculation of

the level of inequality between household incomes,

when we calculate the Gini coefficient based on

Formula 1, we calculate the indicators and ,

that is, the share of income from wages of the i-th

and j-th households in the total income, respectively

for the last 12 years, we will consider 1) the income

of the population, 2) the number of households, 3)

the number of the population, 4) the size of the

household. The dynamics of these indicators in the

2009-2021 periods are given in Table 4. In Javal,

the division of households into different groups by

monthly income, the number of HH in households,

and other indicators indicate the distribution of meat

farms by income.

The problems of household income inequality in

Azerbaijan have been studied by various

researchers. Inequality in terms of household

income was evaluated in studies conducted by, [22],

and inequality in areas of economic activity was

evaluated by, Σφάλμα! Το αρχείο προέλευσης

της αναφοράς δεν βρέθηκε.. Based on the

indicators of the last 20 years, the inequality level of

household incomes (Gini coefficient) is given in

Figure 3. Specifically, Figure 3 shows that the Gini

coefficient G>0.350 in Azerbaijan in the last 10

years. That is, the level of total income inequality in

households in Azerbaijan is very high. During the

calculations, the division of ARDSK households

into different numbers of income groups based on

total income in different years was taken as a basis.

For example, in 2009, households were divided

into 20 different groups, and in 2021, they were

divided into 26 different groups. Calculations show

that the level of inequality in terms of total incomes

among HHI-s was much lower in the period

between 2002 and 2005 than in the following years.

Dependent Variable: GINI

Method: Robust Least Squares

Date: 01/03/23 Time: 23:20

Sample: 2011 2020

Included observations: 280

Method: M-estimation

M settings: weight=Bisquare, tuning=4.685, scale=MAD (median centered)

Huber Type I Standard Errors & Covariance

Variable Coefficient Std. Error z-Statistic Prob.

MW -0.001814 0.000517 -3.506331 0.0005

C 35.03668 0.534473 65.55369 0.0000

Robust Statistics

R-squared 0.041860 Adjusted R-squared 0.038413

Rw-squared 0.064819 Adjust Rw-squared 0.064819

Akaike info criterion 363.6556 Schwarz criterion 370.4976

Deviance 7190.889 Scale 4.474105

Rn-squared statistic 12.29436 Prob(Rn-squared stat.) 0.000454

Non-robust Statistics

Mean dependent var 33.74107 S.D. dependent var 5.784374

S.E. of regression 5.673730 Sum squared resid 8949.156

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Table 4. Dynamics of some indicators for calculating household income

incomes of the

population

(thousand manats*)

Share of income

from wages (%)

Number of HH

(number)

population

(thousands)

The size of the HH

(population/number

of HH)

The average share

of HHİ (%)

Number of HHİ

(number)

Share of salaried

employees in the

total number of

employees (%)

Share of the

employed in the

total population (%)

2009

1115300,0

33,76

1982756

8922,4

4,5

5,000

32,43

47,88

2010

1297453,9

34,67

1914383

8997,6

4,7

5,000

31,94

48,11

2011

1512442,6

32,77

1938532

9111,1

4,7

4,762

31,71

48,02

2012

1762980,6

32,53

1964915

9235,1

4,7

5,263

33,31

48,13

2013

2008840,6

32,93

1990745

9356,5

4,7

4,762

33,49

48,32

2014

2179733,0

32,52

2016404

9477,1

4,7

5,263

33,02

48,57

2015

2307116,5

32,52

2041064

9593,0

4,7

4,545

32,16

48,70

2016

2502103,7

33,48

2205818

9705,6

4,4

4,348

31,81

49,04

2017

2633004,0

33,35

2229545

9810,0

4,4

4,545

31,62

49,15

2018

2731875,6

33,48

2199578

9898,1

4,5

4,762

31,80

49,30

2019

2920586,9

33,94

2434512

9981,5

4,1

4,762

33,34

49,48

2020

2933552,9

35,72

2455390

10067,1

4,1

4,762

34,83

48,44

2021

3041902,7

36,05

2468073

10119,1

4,1

3,846

34,26

49,29

Note: collected and calculated by authors

*)manat is Azerbaijan’s national currency

Fig. 4: Dynamics of the Gini Index in Azerbaijan

(2001-2021)

Source: The Gini coefficient for 2001-2008 is taken from

the statistical database of the World Bank, [24]. The Gini

coefficient for the years 2009-2021 was calculated by the

author.

In the period between 2009 and 2021, the

number of employees receiving monthly wages in

Azerbaijan did not exceed 35% of the total number

of employees. That is why the share of wages in

household incomes changed from 32% to 36.05% in

those years. By affecting household wage income,

the minimum wage changes household wage

inequality. Calculations show that the Gini index of

these incomes in Azerbaijan has regularly increased

and decreased in the inter-annual period of 2009-

2021 (Figure 4). Although the inequality of total

incomes and wage incomes are very different in

HHI, the Gini index for both is very high.

In Azerbaijan, the impact of the national income

tax on the incomes of households can be when its

change increases incomes. However, a comparison

of the dynamics of the minimum wage and the

average wage in the country suggests that there is a

sharp difference between them (graph 2.5.4). This

difference has steadily increased in the period from

1999 to 2021. The ratio of the average wage to the

minimum wage decreased from 33 times (1999) to 3

times (2021). Therefore, the 354% (2001) change in

the national income tax and its increase from 1.1

manats to 5 manats could not have a serious impact

on the average salary of 52 manats. In the 2010-

2021 period covered by the study, the difference and

the ratio between the average salary and the average

salary was very high. After 2017, the increase of the

national income tax with a higher percentage

reduced this difference somewhat (Fıg. 5).

Nevertheless, in those years, the average wage in the

households located in the lowest parts of the

distribution of HHI was higher than the level of the

minimum wage. Thus, in 2010, the average salary of

the employed population in the HHI with an average

income of 73 manats was higher than 164 manats,

while the average wage was 78.3 manats. While it

was 85.7 manats in 2011, the average salary of the

employed population was higher than 200 manats.

Lastly, the dynamics of monthly nominal wages and

national income tax in Azerbaijan (manats) are

presented in Figure 5.

In the following years, the dynamics of the

average income of the minimum wage, the average

incomes of the HHI with the lowest incomes, and

0,000

0,200

0,400

0,600

0,800

1,000

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

2021

Total Income Inequality of HHI

Inequality of HHI on income from wages

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the wage volumes of the employed population in

those groups (Figure 6) show that even the incomes

of the HHI with the lowest incomes of the minimum

wage in 2010-2020 were not able to have a serious

impact in the interannual period. Only in 2020, the

level of the minimum wage (250 manats) was

comparable to the average salary of an employee in

the lowest-income HHI (264 manats). Although the

level of the minimum wage did not increase in 2020

and 2021, this volume created an incentive for a

certain increase in the average wage of occupation

in the lowest-income HHI in the country.

Fig. 5: Dynamics of monthly nominal wages and

national income tax in Azerbaijan (manats)

Fig. 6: The dynamics of the increase of the national

salary and the average salary in Azerbaijan (in

annual %)

4.3 Effects of the Number of Waged (and

Salaried) Employees on Per Capita GDP

As we mentioned above, in the last 20 years, the

number of salaried workers receiving monthly

wages in Azerbaijan has been 30-35% of the total

number of employees. A significant part of the

employed is self-employed. According to the data of

ARDSK for 2022, the unemployment level in the

country has changed around 5-7% in the last 10

years. In 2021, the unemployment rate in the

country was approximately 6%. However, the

positive effects of the number of wage earners on

economic development in each country suggest that

it is important to reduce the level of self-

employment. Thus, the calculations based on the

indicators of 228 and 229 countries for the years

2000 and 2019, respectively, show that there is a

positive relationship between the share of salaried

employees in the total number of jobs and the

volume of GDP per capita (according to 2.5. 6th and

2.5.7 graphs). In countries with a self-employment

rate of more than 40%, GDP per capita is less than

$10,000. In all countries with a GDP per capita

higher than 10,000 US dollars, the number of wage

earners exceeds 60% of total employment.

According to estimates for 2019, in all developed

and high-income countries (with GDP per capita

higher than 40,000 US dollars), the number of wage

earners is higher than 80% of the total employed

population, and the level of self-employment is less

than 20%. Due to the high level of self-employment,

the number of wage earners in households is also

low. This leads to a worsening of income inequality

in HHI. Lastly, the relationship between the share of

waged (and salaried) in total employment and GDP

per capita (2000) for 228 countries is shown in

Figure 7 whereas, the relationship between the share

of waged (and salaried) in total employment and

GDP per capita (2019) for 229 countries is

presented in Figure 8.

Fig. 7: The relationship between the share of waged

(and salaried) in total employment and GDP per

capita (2000) (228 countries)

Fig. 8: The relationship between the share of waged

(and salaried) in total employment and GDP per

capita (2019) (229 countries)

500

1000

1999

2001

2003

2005

2007

2009

2011

2013

2015

2017

2019

2021

average salary MW

100

200

300

400

500

600

700

800

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

2021

average salary

The lowest income in HHI

the average wage of an occupation in the lowest-incomeHHI

y = 167,71e0,0474x

R² = 0,721

20000

40000

60000

050 100 150

y = 385,1e0,0468x

R² = 0,7549

50000

100000

150000

050 100 150

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4.4 The Relationship between the Number of

Hired Workers and GDP Per Capita in

Azerbaijan

Unfortunately, the level of self-employment in

Azerbaijan was close to 65-70% in some years. This

means 30-35% of the total number of salaried

employees. Wages account for one-third of total

income in HHI, meaning that the vast majority of

households do not have steady wage earners. This

not only causes income inequality in those

households but also causes the country's GDP to

weaken. The effects of the indicator of the share of

the number of wage-earning employees in the total

number of employees on GDP per capita (current

US dollars) in Azerbaijan (1991-2018) are shown in

Graph 8. The graph shows that there is no

relationship between these two indicators. The main

reason for this is that a significant part of the GDP

volume in Azerbaijan is related to oil revenues. The

number of workers from the oil sector is

approximately 1% of the total number of employees.

Therefore, the effects of the level of self-

employment on the volume of added value created

in the oil sector, and consequently on the volume of

GDP, are not noticeable. Since self-employment is

mainly in the non-oil sector, its effects can be

reflected in the added value created in this sector.

Indeed, it can be seen from Graph 9 that the

indicator of the share of the number of salaried

workers in the total number of employees in

Azerbaijan has a positive effect on the volume of

GDP per capita in the non-oil sector. The regression

dependence of the logarithm of the volume of GDP

per person in the non-oil sector (󰇛󰇜) on

the share of the number of salaried workers in the

total number of employees () shows that

although the hypothesis  is rejected, there is

autocorrelation in this dependence. Nevertheless,

the positive relationship between these indicators is

consistent with the cross-country results based on

data from 129 countries.

Fig. 9: Effects of the share of the number of waged

(and salaried) in the total number of employment in

Azerbaijan (X-axis) on GDP per capita (Y-axis)

(current US dollars) (1991-2018)

Fig. 10: Effects of the share of the waged (and

salaried) in the total number of employment in

Azerbaijan (X-axis) on the volume of GDP per

capita (Y-axis) in the non-oil sector (current US

dollars) (1991-2018)

The effects of the share of the number of waged

(and salaried) in the total number of employment in

Azerbaijan (X-axis) on GDP per capita (Y-axis)

(current US dollars) regarding 1991-2018 are

presented in Figure 9. Similarly, the effects of the

share of the waged (and salaried) in the total number

of employment in Azerbaijan (X-axis) on the

volume of GDP per capita (Y-axis) in the non-oil

sector (current US dollars) regarding 1991-2018 are

shown in Figure 10.

The regression analysis between  and

, as well as between 󰇛󰇜 and

indicates that decreasing in the level of self-

employment and increasing in the number of waged

(and salaried) workers in Azerbaijan have a positive

effect on the volume of GDP per capita (Table 5 and

Table 6, respectively). The presence of waged (and

salaried) workers in the household has a serious

impact on household income. Unlike income from

self-employment, this source of income is more

stable. However, the self-employment of a

significant part of the employed population (65-

70%) deprives a significant part of households of

sustainable income.

2000

4000

6000

8000

10000

020 40 60

y = 1E-06e0,6642x

R² = 0,5319

0,00

2000,00

4000,00

6000,00

30,00 31,00 32,00 33,00 34,00

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Table 5. Results of regression analysis between

 and  indicators

Table 6. Results of regression analysis between

indicators 󰇛󰇜 and 

The number of waged (and salaried) workers

has a significant impact not only on the volume of

GDP per capita in the non-oil sector but also on the

formation of household incomes, thereby also on the

generation of income inequality between

households. Given that, in theory, the minimum

wage raises the wages of low-income workers, we

can hypothesize that:

Hypothesis 1: the MW also affects the formation

of household incomes;

Hypothesis 2: The MW affects income inequality

among HHIs.

Although the share of wages in household

income is small, the total income of HHI depends on

the number of wage workers. The positive effect of

the number of wage workers on the value created in

the non-oil sector is reflected in the total income of

households. A simplified model of this relationship







 

(8)

(4.557775)

(0.143792)

as we can express. Here,  - is the

logarithm of the income of the population "t",

 - is the share of the number of wage

earners in the year "t" in the total number of

employed people. The positive relationship between

the number of wage earners and the income of the

population suggests that an increase in wages for

any reason, including through the mechanism of the

minimum wage, will have a positive effect on the

volume of income. Indeed, according to the results

of the double regression analysis between the

minimum wage () and the population's income

from wages (), there is a positive

relationship between these two indicators (Table 7).

Although there is some autocorrelation in the model,

this model can be considered adequate. Moreover,

the regression analysis of the impact of the MW on

the population's income from wages is presented in

Table 8.

Table 7. Dependence of the logarithm of the income

of the population on the share of the number of

alaried employees in the total number of employees

Dependent Variable: D(NONOIL)

Method: Least Squares

Date: 01/21/23 Time: 15:01

Sample (adjusted): 2001 2019

Included observations: 19 after adjustments

Variable Coefficient Std. Error t-Statistic Prob.

D(WAGED) 115.2361 55.02475 2.094260 0.0515

C 211.1294 30.81800 6.850846 0.0000

R-squared 0.205085 Mean dependent var 214.5258

Adjusted R-squared 0.158325 S.D. dependent var 146.2200

S.E. of regression 134.1464 Akaike info criterion 12.73504

Sum squared resid 305919.4 Schwarz criterion 12.83446

Log likelihood -118.9829 Hannan-Quinn criter. 12.75187

F-statistic 4.385923 Durbin-Watson stat 1.915925

Prob(F-statistic) 0.051529

Dependent Variable: LOG(NONOIL)

Method: Least Squares

Date: 01/08/23 Time: 13:20

Sample: 2000 2019

Included observations: 20

Variable Coefficient Std. Error t-Statistic Prob.

WAGED 0.664232 0.146877 4.522353 0.0003

C -13.67318 4.655580 -2.936944 0.0088

R-squared 0.531880 Mean dependent var 7.371681

Adjusted R-squared 0.505873 S.D. dependent var 0.881167

S.E. of regression 0.619409 Akaike info criterion 1.974538

Sum squared resid 6.906017 Schwarz criterion 2.074111

Log likelihood -17.74538 Hannan-Quinn criter. 1.993976

F-statistic 20.45168 Durbin-Watson stat 0.362408

Prob(F-statistic) 0.000264

Dependent Variable: LOG(INCOME)

Method: Least Squares

Date: 01/08/23 Time: 13:38

Sample: 2000 2019

Included observations: 20

Variable Coefficient Std. Error t-Statistic Prob.

WAGED 0.644585 0.143792 4.482762 0.0003

C -12.79129 4.557775 -2.806476 0.0117

R-squared 0.527499 Mean dependent var 7.631089

Adjusted R-squared 0.501249 S.D. dependent var 0.858647

S.E. of regression 0.606396 Akaike info criterion 1.932074

Sum squared resid 6.618900 Schwarz criterion 2.031647

Log likelihood -17.32074 Hannan-Quinn criter. 1.951512

F-statistic 20.09516 Durbin-Watson stat 0.363866

Prob(F-statistic) 0.000288

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Table 8. Regression analysis of the impact of the

MW on the population's income from wages

As we mentioned above, although the number

of salaried workers has a positive effect on the

income of the population, the number of salaried

workers also depends on the MW level in certain

cases. Thus, when the MW is low, able-bodied

people prefer self-employment and do not want to

work in low-income jobs. This situation reduces the

supply in the labor market. On the other hand, the

increase of the MW above a certain level reduces

the demand in the labor market. The regression

analysis of the effects of the minimum wage ()

on the share of the number of waged (and) salaried

workers in the total employment () in the

example of Azerbaijan in the period covering the

years 2001-2019 suggests that a positive

relationship is observed between these indicators.

However, the MW in Azerbaijan did not have a

significant impact on the increase in supply in the

labor market (9). Because the level of the MW was

not at the level affecting the supply. This level is

achieved when the MW has the power to affect

wage income, at least in low-income HHI. Only in

2019, 2020, and 2021 was the level of the MW at

the level of affecting the income of low-income

HHI. The further increase of the MW in 2022 and

2023 will also affect the income of the HHI. The

channel of the main effects is related to the increase

in supply in the labor market. Another problem is

that the demand for low-skilled workers is not high

in the labor market.

One of the channels through which the

minimum wage affects household income inequality

is its effect on the number of waged (and salaried).

As we mentioned, the minimum wage increase can

also increase the number of waged (and salaried)

employment, under certain conditions. In this case,

the level of inequality may decrease in the lower

parts of the income distribution, as the incomes of

low-income HHIs increase first. The dependence of

the number of waged (and salaried) employment on

the minimum wage is presented in Table 9. The

regression analysis of the effect of the number of

waged (and salaried) employees on income

inequality related to the salary of this HHI in the

example of Azerbaijan is given in Table 10.

According to these results, as the number of waged

(and salaried) increases, the GINI index increases.

Serious differences between the average wage and

the minimum wage in Azerbaijan reduce the effects

of the minimum wage on the GINI index to almost

nothing.

Table 9. Dependence of the number of waged (and

salaried) employment on the minimum wage

The analysis of the effects of the MW on

income inequality by the "difference in difference"

method also confirms the above-mentioned result.

Thus, in the period between 2011 and 2021, we can

apply the "difference in difference" method, taking

the inequality of total HHI as the "control group"

and the wage inequality as the "affected group".

The results obtained for the period 2011-2021

(Table 11) show that in these years the MW did not

always have an effective effect on the reduction of

income inequality in households. Thus, in 2010-

2011, the Gini index for wages decreased faster than

the Gini index for total income. But in the following

four years (2011-2012, 2012-2013, 2013-2014, and

2014-2015), on the contrary, wage inequality grew

Dependent Variable: WINCOME

Method: Least Squares

Date: 01/08/23 Time: 14:17

Sample (adjusted): 2008 2021

Included observations: 14 after adjustments

Variable Coefficient Std. Error t-Statistic Prob.

MW 0.014436 0.003378 4.273414 0.0011

C 31.81484 0.467385 68.06983 0.0000

R-squared 0.603464 Mean dependent var 33.62023

Adjusted R-squared 0.570419 S.D. dependent var 1.141301

S.E. of regression 0.748036 Akaike info criterion 2.388833

Sum squared resid 6.714699 Schwarz criterion 2.480127

Log likelihood -14.72183 Hannan-Quinn criter. 2.380382

F-statistic 18.26207 Durbin-Watson stat 1.199626

Prob(F-statistic) 0.001082

Dependent Variable: WAGED

Method: Least Squares

Date: 01/08/23 Time: 15:12

Sample: 2001 2019

Included observations: 19

Variable Coefficient Std. Error t-Statistic Prob.

MW 0.013476 0.003481 3.871691 0.0012

C 30.70302 0.306518 100.1670 0.0000

R-squared 0.468584 Mean dependent var 31.68790

Adjusted R-squared 0.437324 S.D. dependent var 0.993745

S.E. of regression 0.745425 Akaike info criterion 2.349577

Sum squared resid 9.446202 Schwarz criterion 2.448992

Log likelihood -20.32098 Hannan-Quinn criter. 2.366402

F-statistic 14.98999 Durbin-Watson stat 0.537264

Prob(F-statistic) 0.001225

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faster. It even reached the level of 0.9 in 2015. This

can happen more with the devaluation of the manat.

Table 10. Dependence of household income inequality on the number of waged (and salaried) Employment

Table 11. Calculating the effects of the level of GDP on the income inequality of households using the

"differences in difference" method

MƏH

(azn)

Ə (azn)







































󰆹

2010

7,4

33,5

0,559

0,405

2011

7,8

32,7

0,512

0,253

0,512

0,405

0,512

-0,152

2012

3,8

34,2

0,569

0,362

0,569

0,253

0,569

0,109

2013

7,7

26,7

0,640

0,408

0,64

0,362

0,64

0,046

2014

19,4

0,648

0,525

0,648

0,408

0,648

0,117

2015

22,4

0,667

0,932

0,667

0,525

0,667

0,407

2016

32,9

0,743

0,432

0,743

0,932

0,743

-0,5

2017

28,7

0,567

0,331

0,567

0,432

0,567

-0,101

2018

16,1

0,642

0,525

0,642

0,331

0,642

0,194

2019

90,5

0,625

0,509

0,625

0,525

0,625

-0,016

2020

72,6

0,652

0,495

0,652

0,509

0,652

-0,014

2021

24,4

0,543

0,443

0,543

0,495

0,543

-0,052

Note: calculated by the authors

5 Conclusion

Thus, in 2015, the manat lost more than twice its

value compared to the US dollar. In 2016-2017 and

2017-2018, the level of wage inequality decreased

faster than the Gini index for total income. But in

2018-2019, the Gini index for total incomes

decreased faster. During that period, the increase in

the average wage and the increase in the MW were

comparable. However, it is impossible to say that

the MW had a serious effect on income inequality

during that period. Nevertheless, in 2018-2019,

2019-2020, and 2020-2021, wage inequality fell

faster than total household income inequality.

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Dependent Variable: GINI

Method: Least Squares

Date: 01/08/23 Time: 15:04

Sample (adjusted): 2001 2017

Included observations: 17 after adjustments

Variable Coefficient Std. Error t-Statistic Prob.

WAGED 4.108929 0.833959 4.927017 0.0002

C 29.88717 0.413433 72.29031 0.0000

R-squared 0.618082 Mean dependent var 31.77118

Adjusted R-squared 0.592621 S.D. dependent var 1.015461

S.E. of regression 0.648131 Akaike info criterion 2.080682

Sum squared resid 6.301099 Schwarz criterion 2.178707

Log likelihood -15.68580 Hannan-Quinn criter. 2.090426

F-statistic 24.27550 Durbin-Watson stat 1.085480

Prob(F-statistic) 0.000182

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Contribution of Individual Authors to the

Creation of a Scientific Article (Ghostwriting

Policy)

- Gulaliyev M: Writing - review & editing&

methodology

- Veliyeva S.: Data curation and resources

- Sultanova N.: Writing - original draft

- Mehdiyeva I.: Formal analysis and project

administration

- Gulaliyev E.: Investigation

Sources of Funding for Research Presented in a

Scientific Article or Scientific Article Itself

No funding was received for conducting this study.

Conflict of Interest

The authors have no conflict of interest to declare.

Creative Commons Attribution License 4.0

(Attribution 4.0 International, CC BY 4.0)

This article is published under the terms of the

Creative Commons Attribution License 4.0

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