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Unemployment rate and its relationship with government size, trade, inflation, urbanization, and economic growth in Romania
Ali Moridian, Magdalena Radulescu, Muhammad Usman, Seyed Mohammad Reza Mahdavian, Alina Hagiu, Luminita Serbanescu
PII: S2405-8440(24)17610-4
DOI: https://doi.org/10.1016/j.heliyon.2024.e41579 Reference: HLY 41579
To appear in: HELIYON Received Date: 4 August 2024 Revised Date: 27 December 2024 Accepted Date: 30 December 2024
Please cite this article as: A. Moridian, M. Radulescu, M. Usman, S.M.R. Mahdavian, A. Hagiu, L.
Serbanescu, Unemployment rate and its relationship with government size, trade, inflation, urbanization, and economic growth in Romania, HELIYON, https://doi.org/10.1016/j.heliyon.2024.e41579.
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1
Unemployment rate and its relationship with government size, trade,
1
inflation, urbanization, and economic growth in Romania
2
Ali Moridian1, Magdalena Radulescu2,3, Muhammad Usman4, Seyed Mohammad Reza 3
Mahdavian5, Alina Hagiu2, Luminita Serbanescu2 4
5
Ali Moridian1 6
1Department of Economics and Management, Urmia University, Urmia, Iran 7
(Email: [email protected]) 8
9
Magdalena Radulescu2,3 10
2 Department of Finance, Accounting, Economics, University of Pitesti, Pitesti, 110040, 11
Romania 12
3 Institute of Doctoral and Post-Doctoral studies, University Lucian Blaga of Sibiu, Sibiu, 13
Romania 14
(Email: [email protected]) 15
16
Muhammad Usman4 17
4 School of Economics and Management, and Center for Industrial Economics, Wuhan 18
University, Wuhan, 430072, China.
19
(Email: [email protected]) 20
21
Seyed Mohammad Reza Mahdavian5 22
5 University of Zabol, Iran 23
(Email: [email protected]) 24
25
Alina Hagiu2 26
2 National University of Science and Technology Politehnica Bucharest, Pitesti University 27
Center, Pitesti, Romania 28
(Email: [email protected]) 29
30 31
Luminita Serbanescu2 32
2 National University of Science and Technology Politehnica Bucharest, Pitesti University 33
Center, Pitesti, Romania 34
(Email: [email protected]) 35
36 37 38
Abstract 39
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2 Unemployment is an important phenomenon affecting all economies, especially during crises 40
such as the recent COVID-19 pandemic. Besides, the government size is significant for all the 41
formerly planned and centralized economies, like the Central and Eastern European countries.
42
This study aims to investigate the impact of government size, income tax, economic growth, 43
inflation, trade openness, and urbanization process on the unemployment level in Romania 44
from 1991 to 2021. After testing the nonlinearity property, the findings of the nonlinear 45
autoregressive distributive lag (NARDL), estimations show a positive influence of urbanization 46
on unemployment. Conversely, government size significantly reduces the unemployment rate 47
in Romania. Inflation and trade are negatively associated with unemployment, while taxation 48
has a positive impact. In this regard, the Romanian authorities should adopt appropriate 49
measures to support governmental expenditure, they need to collect budgetary tax that can 50
reduce the unemployment rate, unless some efficiency measures are adopted such as defeating 51
tax evasion which is high in Romania.
52
Keywords: Unemployment; Government Expenditure; Urbanization; Inflation; Economic 53
Growth; Romania 54
1. Introduction 55
During the last decades, global economies faced increasing unemployment that has determined 56
significant public policy measures to fight this phenomenon and cause large public deficits, 57
public indebtedness, and the resurge of inflation rate and economic growth (Soukaina and 58
Hammami, 2021). The relationship between inflation and the unemployment rate was 59
expressed by the Phillips curve, which stated a negative association between these two 60
macroeconomic variables in the short run. However, this negative relationship doesn't hold in 61
the long run, as the practice of numerous economies showed after the oil crisis of the 1970s, 62
because unemployment depends on an economy's structural variables, while inflation is a 63
monetary variable. After a severe crisis of consumption goods poverty faced during the 1980s, 64
Romania started its transition period in 1990. Among the transition costs, Romania bared high 65
inflation, high public deficits, and a high unemployment rate (Herman, 2010). The rise of 66
inflation in Romania was determined at the beginning of the transition by the corrections of the 67
distortions of the price system and by the collapse of economic growth because of the 68
restructuring of the Romanian economy on market principles. High unemployment was also 69
caused by the bad management of the restructuring process, maintaining the workforce in 70
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3 inefficient sectors with low labor productivity, increasing nominal net wages, and adopting 71
relatively passive employment policy actions that managed effects rather than causes of this 72
phenomenon. However, there is an insignificant relationship between inflation and 73
unemployment in Romania from 1990-2009 because unemployment was determined mainly 74
by productivity changes, and regulations (Herman, 2010).
75
Meanwhile, monetary factors impacted inflation not only labor market factors. So, in Romania, 76
it was difficult for fiscal or monetary policy to fight against the unemployment rate because of 77
the absence of an association between inflation and the unemployment rate. At the beginning 78
of the transition period, deferring privatization and restructuring of state-owned enterprises 79
determined a low unemployment rate because of concealed unemployment. Moreover, 80
Romanian unemployment rate was also determined by early retirements caused by the 81
restructuring of large state-owned enterprises, so the unemployment rate was under-evaluated 82
for a long time. The experience of Central and Eastern European (CEE) countries showed that 83
countries that allowed higher unemployment rate and rapid restructuring recovered their GDP 84
growth before 1989 (Poland, Slovakia, Hungary), while Romania allowed high inflation but 85
low unemployment rate and slow restructuring achieved only in 2004 the same level of 86
economic growth as the one of 1989.
87
Unemployment is not only associated with inflation but also with the economic growth rate.
88
When economic growth increases, unemployment typically decreases. So, there should be a 89
negative link between these time variables, and this negative relationship could be best 90
observed during the crisis periods. Economic crises generated low labor demand and a decrease 91
in income levels. In crisis times, poverty, unemployment, job insecurity, and social inequality 92
are increasing (OECD, 2014). Low consumption and production, especially during the crisis 93
periods, reduced access to external financing for an extended period, loss of some traditional 94
foreign markets, informal workers without work contracts, and low funding from education 95
and vocational training generated unemployment and low labor productivity in Romania. The 96
public sector adjusted slowly, deferring restructuring of the labor force after the election period 97
while cutting wages, while the private sector changed instantly. In this regard, many countries 98
liberalized their trade to boost their economic activity and for increasing foreign investment.
99
The positive relationship between trade openness and economic activity was demonstrated in 100
Romania (Damijan and Rojec, 2007). Nevertheless, in some cases, the relationship between 101
trade openness and investments is negative in Romania. Some other studies also demonstrated 102
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4 a negative relationship because foreign investment inflows depend also on tax rates, labor costs, 103
trade deficits, trade policies and restrictions, and inflation, besides trade openness.
104
Few studies elaborated for European countries on the nexus between taxation (corporate and 105
labor taxation) and unemployment rate found that corporate taxation determined fewer 106
detrimental effects on the performance of the European labor markets, compared to labor 107
taxation. It affected less labor supply. Still, it induced large welfare costs especially for 108
European countries with the highest trade openness (Bettendorf et al., 2009). For Romania, the 109
impact of tax on income on economic activity was found to be positive, against many other 110
studies elaborated for other countries or regions. Consequently, a higher tax rate on income 111
supports economic growth and can diminish the unemployment level (Moraru and Ionita, 112
2014).
113
It is also observed a direct association between the unemployment rate and public deficit, 114
inflation, and economic growth for the Middle East and North Africa (MENA) and Eurozone 115
countries (Soukaina and Hammami, 2021). Furthermore, it is also demonstrated that a strong 116
positive relationship between these variables in European countries (KureΔiΔ and KokotoviΔ, 117
2016). On the flip side, this relation can also be negative because a rise in public expenditure 118
for social purposes, reconversion, and training programs can cause a decrease in unemployment 119
in the medium and long run (Εahin et al., 2021).
120
As for the relationship between urbanization and unemployment, this was under-investigated 121
for EU economies. The rapid urbanization process is related to sustained and robust economic 122
growth that determines new jobs. This relationship for developed, and developing economies 123
and found that urbanization is negatively related to unemployment only in developed 124
economies but negatively related to unemployment for the rest of the country groups (Haq et 125
al., 2015). This can be explained by the fact that in developing and emerging economies, urban 126
areas expansion is not always linked to the growth of economic activity but is slightly driven 127
by rapid population growth and high social inequality detrimental to the low-skilled labor force 128
or for young people with no or few experiences in the labor market that acts mostly in the 129
informal sectors.
130
The unemployment rate has a lot of fluctuations, which had a downward trend from 1993 to 131
1997, and then went through an upward trend, and in the years 2015 to 2019, it again had a 132
downward trend, and then an upward trend. Its highest value was reached in 1993, and its 133
lowest was in 2019 (Figure 1). In 2022, the unemployment level was 5,44% against 6,2% in 134
the entire EU area. However, although unemployment peaked in 1993, Romanian 135
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5 unemployment levels were among the lowest in the Central and Eastern European Union 136
(CEEU) because of the early retirement system allowed by the state during the privatization of 137
state-owned enterprises. This process generated a massive exodus of the labor force to Western 138
European countries after the collapse of the communist regime. Concerning the per capita 139
income growth variable, the facts and figures show that it has gone through an upward trend 140
during the years, except for the years 1992, 1997-1999, 2009, 2010, and 2020. These years 141
represent periods when Romania faced deep crises. During 1992-1993 the price system was 142
liberalized after the communist era, and during 1997-1999 the exchange rate was liberalized.
143
During both periods, there were significant fluctuations in the inflation and exchange rates 144
simultaneously with the restructuring process of the entire Romanian economy. It can be seen 145
that 2007-2009 was the period when the international financial crisis erupted worldwide. In 146
2020, the pandemic crisis stopped economic activity generating unemployment, recession, and 147
inflation, while the public deficit increased to sustain the economies during the health crisis.
148
Regarding the urbanization variable, its graph is similar to the letter V, which shows a complete 149
downward trend from 1992 to 2002 and then upward. Urbanization trend was also determined 150
by the restructuring of the economy on sectors, increasing the role of the tertiary sector 151
significantly after 2000, and early retirements during the 90s' during the privatization process 152
in the first decade of transition. Inflation and government size also show fluctuations. The 153
inflation variable showed an almost downward trend when it reached its lowest level in 2014.
154
Governmental spending of GDP represented 39.7% in 2022 (against an EU average of 50%), 155
after its peak level of 41.5% in 2020. Trade openness displayed a dynamic increasing trend 156
during the entire investigated period, especially after 2007 when Romania joined the EU, 2022 157
reaching its highest level of 91.98%. Tax revenues (share of GDP) displayed a descending 158
trend, except for a short period after the financial crisis of 2008-2011, when they slowly 159
increased. In 2022 they represented 30.98% of GDP in Romania against the EU average of 160
45.4%. In 2022, corporate tax was 16%, the lowest level in the EU, except for Bulgaria (World 161
Bank database).
162
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6
2.6 2.7 2.8 2.9 3.0 3.1 3.2
1995 2000 2005 2010 2015 2020
TAXR
3.6 3.8 4.0 4.2 4.4 4.6
1995 2000 2005 2010 2015 2020
TRADE
-50 0 50 100 150 200 250 300
1995 2000 2005 2010 2015 2020
INF
2.4 2.5 2.6 2.7 2.8 2.9 3.0
1995 2000 2005 2010 2015 2020
GOVEXP
8.2 8.4 8.6 8.8 9.0 9.2 9.4 9.6
1995 2000 2005 2010 2015 2020
GDPPC
1.2 1.4 1.6 1.8 2.0 2.2
1995 2000 2005 2010 2015 2020
UN
3.96 3.97 3.98 3.99 4.00
1995 2000 2005 2010 2015 2020
URB
Fig. 1. Trend of the variables in Romania during 1991-2022 163
This study aims to investigate the determinants of the rate of unemployment in Romania based 164
on the non-linear ARDL model, which includes as explanatory variables economic growth, 165
inflation, urbanization, trade openness, and tax rate on income and government size for an 166
extended period from 1991-2022. The research gap that we are trying to fill is represented by 167
building a model by including urbanization and government size as explanatory variables 168
because this relation was under-investigated for the former communist countries, which display 169
large government sizes inherited from the former centralized structure of their economies. We 170
also added as control variables economic growth rate and inflation because these relations, 171
although investigated, have led to mixed results. Also trade openness is a significant variable 172
for a highly open economy such as Romania which has joined the EU and largely developed 173
its intra-trade. Government size is essential for former communist countries with a large public 174
sector. Also, Romania is a particular case considering that unemployment is determined by 175
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7 informal work, early retirements allowed by the legislation and an intense migration process.
176
Urbanization was forced during the communist era, and also faced a rapid trend ever since the 177
communist era as a result of the industrialization process Romania faced back then.
178
After a period of decreasing trend back in the 90s, the urbanization rate has started to increase 179
sharply as a result of the development of the services sectors in Romania, the tertiary sector, 180
while agriculture faced a decrease in its share of GDP and the rural area remained under- 181
developed. Income tax is also a definitory variable for the Romanian economy since tax evasion 182
was estimated at a level higher than 10% of GDP in Romania in 2023 (National Institute of 183
Statistics, 2023), the highest tax evasion among EU countries (Eurostat database). Romania 184
displayed high labor taxation, but lower capital taxation among European countries to attract 185
foreign investors. Starting with 2023, the Romanian government adopted a fiscal measures 186
package meant to increase taxation on income for the private sector to diminish excessive 187
budgetary deficit and adopted some measures that aim to diminish tax evasion here. However, 188
high taxes on income and tax evasion are highly positively correlated.
189
All the explanatory variables are intercorrelated and display their effects on the unemployment 190
rate. Urbanization can support unemployment decrease in the context of adequate labor skills 191
of people, skills that can be achieved by public investments in the education sector and training 192
sessions financed and organized by governmental organizations. Trade openness supports the 193
boosting of economic activity, and that can determine new jobs into the economy. Inflation can 194
support economic activity up to a certain level, and then, at high levels, the effects are 195
detrimental to the entire economy. Inflation also depends on taxes on income and government 196
expenditure. Higher taxation can reduce inflationary pressures on the economy, but it can 197
determine tax evasion and affect the economic growth rate. While government expenditure 198
feeds inflationary pressures and can reduce unemployment in the short run.
199
Considering all these explanatory variables, and their inter-correlations it is important to settle 200
their impact on the unemployment rate for Romania, which is an open European economy, with 201
high and robust economic growth rates, large government size, and forced urbanization 202
inherited from the communist era and with a low tax on income comparing to the rest of the 203
European countries. This is the first study investigating the relation between government size 204
and unemployment in Romania, considering these socio-economic determinants such as 205
economic growth, inflation, taxation, urbanization and trade openness.
206
2. Literature review 207
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8 2.1. Economic growth and unemployment nexus
208
Many studies have examined the link between unemployment and economic growth in 209
economics literature. One of the first notable theories to investigate the association between 210
real output unemployment is Okun's Law, which proved a reverse relationship between 211
unemployment and economic growth. Okun revealed that an increase in real GDP leads to an 212
increase in employment (Okun, 1962). Indeed, this rule generally demonstrated that with a one 213
percent rise in the growth rate, unemployment would decline by 0.5%, provided that the actual 214
GDP growth rate is lower than its potential growth (Panaite et al., 2022).
215
The linkage between unemployment and GDP growth demonstrates a correlation between 216
them. An increase in economic growth will lead to an expansion in employment or a diminution 217
in unemployment. The increase in the growth of any country in the economic dimension leads 218
to an increase in the volume of economic growth and productivity improvement, which can 219
ultimately create new and more job opportunities in the country (Hjzeen et al., 2021). In many 220
countries, the economy is not at full employment, or due to low productivity, the labor force 221
has low efficiency, and its full capacity is not used. In this case, it will grow production by 222
growing the labor force's productivity deprived of employing a new labor force if demand 223
increases. Likewise, unemployment specifies the incidence of poor economic performance and 224
shows that the available resources have not been used to the maximum levels (Siddiqa, 2021).
225
Unemployment is a macroeconomic variable of particular interest to countries due to its 226
importance in economic and social dimensions. Unemployment originates from the economic 227
structure of a nation, and the cause of its existence is dissimilar in different regions. For 228
example, unemployment in developed countries is caused by technological advances, and in 229
underdeveloped countries; a lack of capital causes it. Managing the economy in a situation with 230
a high unemployment rate and balancing the supply and demand of labor is very difficult.
231
Studies on the association between unemployment and economic growth can be divided into 232
three groups. Initially, this revealed a negative relationship between the study time series. In 233
this regard, Ceylan and Εahin (2010) demonstrated a negative association between economic 234
growth and unemployment from 1995 to 2005 in Turkey. Furthermore, Ruxandra (2015) 235
proved the existence of Okun's law during 2007-2013 by using the Hodrick-Prescott filter and 236
correlation method in the case of Romania. With the linear mixed-effects approach, Bartolucci 237
et al. (2018) showed an inverse linkage between unemployment and growth in developed and 238
developing economies. Besides, Olaniyi et al. (2021) surveyed the connection between 239
unemployment and GDP growth in Nigeria from 1980 to 2015. The estimated results indicated 240
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9 a negative and positive linkage between growth and unemployment in the short and long run, 241
respectively. Moreover, Siddiqa, (2021) investigated the factors affecting unemployment in 242
selected developing countries from 2000 to 2019 and showed that GDP negatively affects 243
unemployment. Moreover, Padder and Mathavan (2021) surveyed the link between GDP and 244
unemployment in India from 1990 to 2020. The results of linear regression and causality 245
methods indicated the negative relationship between mentioned variables.
246
Also, the causal relationship between them was not observed. Al-kasbah (2022) reviewed the 247
connection between GDP growth and unemployment in Jordan with the ARDL method and 248
confirmed the existence of Okun's law. It means that a 1% reduction in GDP leads to a 0.276%
249
rise in unemployment. Besides, Panaite et al. (2022) examined the causal link between GDP 250
and unemployment in Romania and showed unidirectional causality from unemployment to 251
GDP. Moreover, Liu et al. (2022) surveyed several variables affecting unemployment and 252
showed the negative linkage between GDP and unemployment in OIC countries by using the 253
DCCE method. Su et al. (2021) investigated the impact of the COVID-19 pandemic on 254
unemployment in European countries. This study found that slowing the pace of economic 255
activity in industry and services generated high unemployment rates in all European countries 256
that claimed a rapid public response in public policy worldwide to reduce the impact of growing 257
unemployment levels. Conversely, Leasiwal et al. (2022) studied the factors affecting 258
Indonesia's unemployment from 2001 to 2020. The outcome revealed a direct linkage between 259
unemployment and growth. Finally, the last group is studies that have shown an insignificant 260
relationship. Finally, Thapa et al. (2022) showed that the association between unemployment 261
and growth in Nepal is insignificant. Also, the existence of Okan's law was not confirmed in 262
Nepal.
263
Based on the previous findings we elaborated the following hypothesis:
264
Q1: Economic growth reduces unemployment.
265
2.2. Urbanization and unemployment nexus 266
Studies have shown that urbanization causes unemployment in urban areas due to high 267
population growth and rural-urban migration. Migration from the countryside to the city is 268
caused by the lack of jobs, which increases the percentage of urbanization. Urbanization is a 269
process in which the urban population increases through natural population growth, rural-urban 270
migration, and physical expansion of cities (Sati et al., 2017). Urbanization, which is the result 271
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10 of rapid and uncontrolled rural-urban migration, does cause the labor market to face severe 272
problems with the movement of the population from villages to cities.
273
In classical theories, urbanization is a phenomenon defined by the widespread rural-urban 274
migration in search of job chances in the non-agricultural sector and the transformation of rural 275
people into urban people. The lack of welfare and financial facilities and services leads to the 276
stimulation of people to rural-urban migration to find better jobs and higher income. This issue 277
affects young people more because urbanization increases the possibility of using technology 278
and better facilities (Saghir and Santoro, 2018). With the decrease in household income due to 279
unemployment and the emergence of social problems such as poverty and the increase in 280
marginalized and poor areas, the possibility of crime will also increase (Muhyiddin et al., 281
2017). Even though employment opportunities in urban areas are more than in rural areas, 282
employment in urban areas also requires higher skills. Consequently, the competition for 283
employment is more in urban areas. Planned urbanization can be considered a positive factor 284
in improving life quality (Sati et al., 2017). However, unplanned urbanization can harm the life 285
quality in rural and even urban areas through the change of land use and the reduced cultivated 286
area, increasing agricultural sector unemployment (Gossop, 2011). Additionally, Sati et al.
287
(2017) revealed that urbanization increases unemployment in rural areas and areas near the 288
urban region. Finally, it can be claimed that few researchers have directly surveyed the impact 289
of urbanization on the unemployment rate.
290
Q2: Urbanization decreases unemployment.
291
2.3. Inflation and unemployment nexus 292
The continuous general price level increase in the long term is called inflation. In other words, 293
inflation occurs when the general price level surpasses its average level, and the value of the 294
national currency decreases. Estimating and investigating the association between 295
unemployment and inflation is imperative for several reasons. Many central banks pursue the 296
goals of price stability and full employment. Nevertheless, these goals may be incompatible 297
and not in the same direction. Therefore, it is very important to fully and adequately understand 298
the relationship between these two variables.
299
Furthermore, after the financial crisis of 2008, unemployment had an increasing trend despite 300
the constant inflation rate. In the classical general equilibrium, there is no unemployment, 301
except where labor markets are subject to rules such as setting the minimum wage. But today's 302
world economy is not entirely classical. Since the early 1970s, the continuous rise in prices in 303
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11 the long run period in developed and developing countries caused the inflation problem to be 304
more attention. One of the first studies on the linkage between inflation and unemployment 305
was conducted by Phillips (1958). After the study of Phillips (1958), which has an inverse 306
linkage between unemployment and wages, the link between unemployment and inflation 307
became known as the Phillips curve. Samuelson and Solow (1960) extended the Phillips theory, 308
took inflation rather than wage in the model, and proved an inverse linkage between wage and 309
unemployment. Many researchers have claimed that the Phillips curve is only valid for 310
explaining the short-term relationship between inflation and unemployment (Aliu et al., 2021).
311
Friedman (1968) criticized the Phillips model and raised the issue that unemployment is related 312
to inflation and unanticipated inflation. Therefore, the issue was raised that the Phillips curve 313
is vertical (Bokhari, 2020; Aliu et al., 2021).
314
Despite the criticisms of the Phillips curve over the years, many studies have been conducted, 315
and each has presented different results. Additionally, Reinbold and Wen (2020) proved the 316
association between unemployment and inflation is still significant and inverse. However, 317
many other studies found a significant negative relationship between inflation and the 318
unemployment rate in Romania (CiurilΔ and MuraraΕu 2008; Jula and Jula, 2017). Moreover, 319
Iordache et al. (2016) investigated the Romanian case during 2004-2014, and they found a 320
negative relationship between inflation and unemployment. Still, its intensity modified over 321
time, especially in 2007, once the financial crisis erupted, so the slope of the Phillips curve was 322
diminished. Furthermore, Erdal et al. (2015) indicated a reverse link between inflation and 323
unemployment in Turkey and confirmed the existence of the Phillips curve by using the VAR 324
approach. Additionally, Bokhari (2020) argued that there is a long-term negative causal link 325
between inflation and unemployment in Saudi Arabia. But in the short run, there is an 326
insignificant association between the mentioned variables. Also, Aliu et al. (2021) surveyed 327
the association between inflation and unemployment in former Yugoslav countries. The 328
outcomes showed a reverse association between inflation and unemployment in Slovenia, 329
Croatia, and Montenegro. In contrast, the model showed a direct linkage between 330
unemployment and inflation in Kosovo, North Macedonia, Serbia, and Bosnia. In addition, 331
Fratianni et al. (2022) used the Wavelet method in the UK and proved the inverse and persistent 332
association between wage inflation and unemployment.
333
Q3: Inflation decreses unemployment.
334
2.4. Government size and unemployment nexus 335
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12 Over the past three to four decades, humanity has experienced rapid and extensive changes 336
across nearly all areas of social life. These developments have spurred the accumulation of 337
knowledge in various fields, from economics and finance to education, health, and international 338
relations. Consequently, this progress has inevitably influenced the political processes and 339
public administration systems of states (Keser et al, 2022). Economists have always tried to 340
determine the role of the government in the economy. In contrast to the Keynesians, who 341
believed in the effectiveness of the government in the path of economic growth, the classics 342
were against the enlargement of the government and believed in price mechanisms.
343
Consequently, the relationship between the government and unemployment is considered a 344
challenging issue (Almula-Dhanoon et al., 2020). Scully (1989) stated that the size of the 345
government and the unemployment rate are not only related, but for some reason, this 346
relationship has deleterious effects on economic growth. Regarding the reasons, it can mention 347
the things that affect the work motivation of the workforce. The first is that with the growth in 348
the size of the government, the tendency to increase taxes rises, which affects the workforce's 349
working hours and leisure time and has a diminishing effect on work motivation.
350
On the other hand, rising taxes increase unemployment by reducing disposable income and 351
aggregate demand (Almula-Dhanoon et al., 2020). Secondly, as the size of the government 352
increases, the costs of providing health insurance, unemployment insurance, and welfare 353
services will also enhance. This issue can have a reducing effect on the workforce's motivation 354
by reducing the cost of unemployment. Thirdly, pressure on private investment may lead to 355
unemployment. Also, government expansion may reduce labor market efficiency by tightening 356
regulations. Finally, increasing the size of the government will restrict the private sector from 357
employing the existing workforce because the salaries paid by the government will be 358
significantly different from the private sector. Furthermore, Ahmet and DΓΆkmen (2011) 359
showed a positive effect of Government expenditure (% of GDP) on unemployment. In 360
addition, Afonso et al. (2018), in emerging market countries using the DOLS approach, 361
revealed the direct linkage between Government size (expenditure) and unemployment.
362
Moreover, Abouelfarag and Qutb (2020) in Egypt, using data from 1980 to 2017 and the 363
VECM approach, demonstrated the negative effect of Government expenditure on 364
unemployment. By using the SUR method, Almula-Dhanoon et al. (2020) demonstrated the 365
reverse link between government size and unemployment in MENA countries. Likewise, 366
Shighweda (2020) also revealed the same result for Namibia using the ARDL method. Besides, 367
Anjande et al. (2020) applied the GMM and PMG methods and indicated a reverse linkage 368
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13 between government expenditure and unemployment in African countries. Reducing public 369
expenses during economic crises determines low financing of education, lower investments, 370
poverty, and an increase in unemployment because of austerity measures (Εahin et al., 2021).
371
Q4: Government size decrease unemployment.
372
2.5. Trade openness and unemployment nexus 373
Trade openness is also considered one of the most influencing factors affecting the process of 374
unemployment. In this pursuit, Dutt et al., (2009) studied the linkage between international 375
trade and unemployment and proved a positive and negative relationship in the short and long 376
run respectively in a panel of 90 economies. Similarly, Nwaka et al., (2015) showed a 377
significant role of economic growth in reducing unemployment and vice versa a surge effect 378
of trade openness in unemployment in Nigeria. Besides, Gozgor (2014) concluded that a rise 379
in trade openness leads to a decline in unemployment in G-7 economies. Indeed, the results 380
showed a negative association between trade and unemployment. Also, Anjum and Perviz 381
(2015) bid to detect the effect of trade openness on unemployment in labor and capital- 382
abundant economies. The results stated an inverse and direct linkage between the mentioned 383
variables in countries with an abundance of labor and capital respectively. Alkhateeb et al., 384
(2017) showed a positive impact of trade openness and economic growth on employment in 385
the long run by performing the ARDL technique in Saudi Arabia. KΔ±lΔ±Γ§ and Kutlu (2017) 386
exhibited a diminishing effect of trade openness on unemployment in 17 transition economies 387
by performing the dynamic panel approach. Additionally, Liu et al., (2022) carried research 388
out on the linkage between unemployment and trade openness in OIC countries and revealed a 389
reverse and direct relationship between the mentioned variables in the lower-income and 390
higher-income respectively. Furthermore, Ali et al., (2022) along with the DCCE method 391
proved an inverse linkage between trade openness and unemployment in low-income OIC 392
economies against the direct link in high-income economies. Nguyen (2022) along with the 393
VAR model showed that a rise in trade openness and GDP can lead to a surge in unemployment 394
in South Asia countries.
395
Q5: Trade openness decreases unemployment.
396
2.6. Tax rate and unemployment nexus 397
Planas et al., (2007) showed that dropping the taxation can help to reduce unemployment in 398
European countries. Seward (2008), using a panel approach in industrialized countries, found 399
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14 that a 1 percent increase in labor tax leads to a 0.53 percent increase in unemployment. Berger 400
and Everaert (2010) studied the influences of tax on unemployment in Nordic countries, the 401
EU, and the UK. The outcomes presented a positive impact of tax just in EU countries.
402
Aghazadeh et al., (2014) along with the VAR method proved that increasing taxes has a vital 403
role in the unemployment surge in Iran. Domguia et al., (2022) surveyed the effects of tax on 404
employment by GMM and Two Stage Least Square (2SLS) approaches in OECD and non- 405
OECD countries and caught the surprising results. The outcomes stated that the taxes have a 406
positive effect on total employment, fiscal policies will create new jobs in environmental 407
protection areas. Also, the employment of men will be positively stimulated by the imposition 408
of taxes. Zuo et al. (2023) claimed that in China a reduction in the income tax rate can boost 409
total employment by affecting employee wages and asset returns. Also, tax reduction can raise 410
the employment of private companies, but it has an insignificant effect on the employment of 411
government companies.
412
Q6: Tax increases unemployment.
413 414
2.7. Research gap 415
This study stands out in the literature on unemployment through its integration of innovative 416
aspects and focused analysis, offering a comprehensive and distinctive perspective. By 417
employing the nonlinear NARDL model, the study explores both short- and long-term effects 418
of key macroeconomic variables on unemployment, highlighting the asymmetric impacts of 419
taxation. This nuanced approach, which separates positive and negative effects, bridges a 420
critical gap in existing research that predominantly assumes linear relationships.
421
The inclusion of variables such as urbanization and government size as primary determinants 422
enriches the analysis, particularly for Romaniaβs unique context as a transition economy. These 423
factors, often overlooked in similar studies, are essential to understanding the structural 424
transformations and policy challenges of economies with post-communist legacies. Romaniaβs 425
economic historyβmarked by forced urbanization and centralized policiesβprovides a fertile 426
ground for investigating the interplay between macroeconomic factors and unemployment. By 427
examining this specific context, the study generates insights that extend beyond Romania, 428
offering lessons applicable to other transitioning economies.
429 430
3. Theoretical underpinning 431
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15 Government size is often measured by public sector employment or government expenditure 432
and may influence the rate of unemployment. Theoretically, a larger size of government can 433
generate more jobs in the public sector, leading to lesser rates of unemployment level.
434
Furthermore, government strategies, for example, regulations in the labor market and social 435
welfare agendas can affect the unemployment proportion. For instance, substantial 436
unemployment assistance may dishearten people from vigorously seeking jobs, possibly 437
leading to sophisticated unemployment rates. Additionally, international integration and trade 438
openness can distress the rate of unemployment. Hypothetically, augmented trade can tend to 439
profession formation in the course of foreign direct investment and export-oriented industries.
440
Economies that specialize in industries with a comparative advantage may practice lower rates 441
of unemployment own to augmented demand for their products and services. Nevertheless, in 442
a few cases, trade liberalization can also lead to job dislodgment, mainly in industries that 443
aspect augmented race from imports (Khan et al., 2023). The association between the 444
unemployment rate and inflation is designated by the Phillips curve theory. Conferring to the 445
Phillips curve, there is a negative association between unemployment and inflation in the short 446
run. This suggests that lower unemployment rates are linked with higher inflation rates, and 447
vice versa. The justification behind this association is that when the economy operates at low 448
unemployment levels, enlarged labor demand can lead to rising compression in wages, after 449
higher inflation (Zaman et al., 2021). However, urban areas offer more employment prospects 450
compared to rural areas owing to the meditation of services, industries, and infrastructure. As 451
urbanization rises, people residing in rural areas may migrate to cities in search of employment, 452
possibly dipping the rate of unemployment. Additionally, economic growth is frequently 453
connected with lower rates of unemployment. Theoretical influences recommend that as an 454
economy develops, it generates more job openings, leading to a decline in the rate of 455
unemployment (Zaman et al., 2023). Higher economic growth encourages investment, 456
upsurges productivity, and raises business development, all of which subsidize job formation.
457
On the other hand, economic recessions or downturns can tend to higher rates of unemployment 458
as industries diminish their labor force to cut budgets. The associations between these intended 459
time series in Romania may be influenced by precise appropriate features, policy outlines, and 460
other country-specific appearances. Consequently, directing an empirical analysis employing 461
suitable econometric methods, such as the NARDL test can help regulate the direction and 462
strength of such associations in the context of Romania.
463
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16 4. Data and research methods
464
4.1. Data description 465
This study examines the impact of government size, trade openness, economic growth, and tax 466
revenues on unemployment by controlling the other factors affecting unemployment such as 467
urbanization and inflation in the case of Romania. The period of the present study is 1991- 468
2022, which was chosen based on the availability of data. Table 1 shows symbols, index 469
definitions, and data sources. Equation 1 expressed the unemployment function which can be 470
drawn as follows:
471
ln(UN) = Ξ²0+ Ξ²1ln(GDPt) + Ξ²2ln(URBt) + Ξ²3ln(INFt) + Ξ²4ln(GOVEXPt) + Ξ²5ln(TAXRt) 472
+ Ξ²6ln(TRADEt) + Ξ΅t (1) 473
where, ln represents natural logarithm, URB represents urbanization and INF represents 474
inflation, GDP denotes economic growth, GOVEXP is government size, TAXR is tax income 475
and Trade is trade openness. The unemployment rate is the dependent variable and targets the 476
level of economic unemployment.
477
Table 1. Definition of index and data source 478
Variable symbol Definition of index Source
Unemployment UN Percentage of total labor force (modeled ILO
estimate) (WDI, 2023)
GDP GDP Constant 2015 US$ (WDI, 2023)
Urbanization URB Percentage of total population (WDI, 2023)
Inflation INF Consumer prices (annual %) (WDI, 2023)
Government
expenditure GOVEXP Percentage of GDP (WDI, 2023)
Tax revenue TAXR Percentage of GDP (WDI, 2023)
Trade Openness TRADE Sum of exports and imports of goods and
services measured as a share of GDP (WDI, 2023) The descriptive statistics of the variables are presented in Table 2. It is observed that there is 479
no big difference between minimum, maximum and mean values of all intended variables, 480
which depicts that the minimal chances of structural break in the data set.
481
Table 2: Descriptive Statistics and Correlation 482
TAXR TRADE INF GOVEXP GDPPC UN URB
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17 Mean 2.842944 4.173955 43.99277 2.701197 8.792653 1.877384 3.982460 Median 2.845857 4.146387 7.850803 2.718028 8.835452 1.918392 3.985812 Maximum 3.114488 4.467701 255.1669 2.926755 9.360310 2.125012 3.995058 Minimum 2.652135 3.667022 -1.544797 2.457289 8.308263 1.363537 3.966132 Std. Dev. 0.105067 0.210385 72.63372 0.119185 0.349021 0.191007 0.008243 Skewness 0.595357 -0.162223 1.909012 -0.368630 0.080152 -0.986513 -0.532260 Kurtosis 4.133945 2.300178 5.265360 2.499356 1.557377 3.580507 2.078947 This correlation analysis inspects the linear associations between the intended variables. Table 483
3 presents the bivariate correlation analysis between each pair of time series along with the 484
corresponding p-values. In this regard, TAXR has a negative correlation with TRADE and a 485
positive correlation with Inflation. Besides, TRADE has a negative correlation with INF.
486
Moreover, GOVEXP has a negative correlation with TAXR and a negative correlation with 487
INF. In addition, GDP has a negative correlation with TAXR and a positive correlation with 488
TRADE. However, the UN has a positive correlation with INF and a negative correlation with 489
TRADE. Furthermore, URB has a positive correlation with TRADE and a positive correlation 490
with GDP growth.
491
Table 3: Correlation analysis 492
TAXR TRADE INF GOVEXP GDP UN URB VIF
TAXR 1.00000 2.06
---
TRADE -0.51342 1.00000 5.43
(0.0031) ---
INF 0.67453 -0.57492 1.00000 5.87
(0.0000) (0.0007) ---
GOVEXP -0.41752 0.24041 -0.38463 1.00000 2.12
(0.0194) (0.1926) (0.0327) ---
GDP -0.62414 0.85962 -0.67858 0.52448 1.00000 8.18
(0.0002) (0.0000) (0.0000) (0.0025) ---
UN 0.71089 -0.59473 0.40801 -0.43873 -0.63437 1.00000 - (0.0000) (0.0004) (0.0227) (0.0135) (0.0001) ---
URB -0.04582 0.54035 0.17888 -0.03314 0.46265 -0.23379 1.00000 4.35 (0.8066) (0.0017) (0.3356) (0.8595) (0.0088) (0.2056) ---
Note: P-values are reported in parentheses.
493
4.2. Unit root and cointegration test 494
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18 In econometric modeling of time series, the stationarity of time series variables should be 495
examined. For this purpose, unit root tests, including the generalized Dickey Fuller (1979), 496
were used. In this test, an AR (1) autoregressive relationship is estimated as in eq.2:
497
βπ¦π‘= πΌ + π½π¦π‘β1+ β°π‘ ; ππ‘~π. π. π π(0, π2) (2) 498
The above relationship, βπ¦π‘ represents the first-order difference, Ξ± is the drift term and Ξ² is the 499
pattern coefficient. Also, the disturbance term (β°π‘) represents a white noise process with zero 500
mean and constant variance, where β°π‘ is uncorrelated for tβ s. In the univariate test, the t- 501
statistics of the residuals are compared with Dickey-Fuller's critical values (Saqib and Usman, 502
2023). In the Dick-Fuller test, the null hypothesis indicates a unit root and the opposite 503
hypothesis indicates the existence of a stable root (Usman et al., 2022). If the residuals remain 504
correlated in the first-order autoregressive model, the test is generalized by βπ¦π‘β1 to so-called 505
higher-order autoregressive processes (Saqib et al., 2023). If the process is null, then the null 506
hypothesis is rejected. Johansson and Juselius derived the likelihood ratio test for 507
autocorrelation vectors. The autocorrelation rank can be obtained with two Tris statistics and 508
the maximum eigenvalue, as per eq.3 and eq.4:
509
ππ‘ = πΌ + β π΅πππ‘βπ
π
π=1
+ β πΎπππ‘βπ+ π’1π‘
π
π=1
(3) 510
ππ‘= π½ + β ππππ‘βπ
π
π=1
+ β ππππ‘βπ+ π’2π‘
π
π=1
(4) 511
4.3. Nonlinear BDS test 512
Before using non-linear tests, this study performs the BDS non-linearity test proposed by Brock 513
et al. (1987, 1996) to determine the presence of non-linear dependence in the data. The BDS 514
test is applied to the residuals of the economic freedom series (Islam et al., 2023). If the test 515
statistic is greater than the critical value of the standard normal distribution at normal levels, 516
non-linearity is indicated. The BDS nonlinearity test is based on the correlation integral of the 517
time series as in equation 5:
518
ππ(π, π) =βπ[πΆπ(π, π) β πΆ1(π, π)π]
ππ(π, π) (5) 519
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19 where ππ(π, π) is the BDS test statistic, ππ(π, π) stands for the standard deviation of πΆπ(π, π), 520
m is the embedded dimension, while Ξ΅ represents the maximum difference between the 521
considered pair of observations in calculating the correlation integral. The BDS test statistic is 522
asymptotically distributed without mean and unit variance (for example [N (0,1)].
523
4.4. NARDL tests 524
The implication of Eq. 1 is that the unemployment rate is affected by the level of economic 525
growth, urbanization, inflation, Tax revenue and government size. This study aims to address 526
the following key research question:
527
How do economic factors such as government size, urbanization, trade openness, inflation, and 528
tax revenueβparticularly the asymmetric effects of tax revenueβaffect the unemployment rate 529
in Romania?
530
The traditional autoregressive distributed lag (ARDL) test is a widespread econometric 531
approach applied to examine the long-run and short-run associations between candidate 532
variables. Whereas it has numerous advantages, it is significant to deliberate its possible 533
assumptions and limitations made during the empirical analysis. In the framework of ARDL 534
precise examination, there are a few possible limitations such as the ARDL approach needs 535
stationarity assumption. If the variables are non-stationary, spurious regression results may 536
occur, leading to unreliable conclusions. Moreover, it also considers the omitted variable bias, 537
and endogeneity issues like ARDL adopts that the explanatory variables are exogenous, 538
meaning they are not influenced by the dependent variable or other variables in the model.
539
Moreover, ARDL requires specifying the appropriate lag lengths for the dependent and 540
independent variables. Choosing the optimal lag length can be subjective and may impact the 541
results. Additionally, ARDL estimates may suffer from low statistical power and imprecise 542
parameter estimates, when the sample size is relatively small. Finally, ARDL does not consider 543
the sensitivity analyses, robustness checks, and comparisons with alternative models that can 544
help strengthen the reliability of the findings. Finally, it also does not consider the nonlinear 545
relationship between variables. In this regard, to investigate nonlinear and asymmetric 546
cointegration between variables, the method of autoregression band test with multivariate 547
nonlinear distribution breaks (NARDL) developed by Shin et al. (2014) is used. To do this, 548
firstly we applied linear ARDL developed by (Pesaran and Shin, 1999) and modified by 549
(Pesaran et al., 2001) after that we converted it into the NARDL framework.
550
