tel Theoretical Economics Letters 2162-2086 2162-2078 Scientific Research Publishing 10.4236/tel.2026.163031 tel-151738 Article Business Economics Empirical Evidence of Lilien Hypothesis from African Countries Gbagbeu Kando Serge 1 Department of Economics, Pelefero Gon Coulibaly University, Korhogo, Côte d’Ivoire

The author declares no conflicts of interest regarding the publication of this paper.

 03 06 2026 06 2026 16 03 535 548 13 02 2026 01 06 2026 04 06 2026 © 2026 by the authors and Scientific Research Publishing Inc. 2026 This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( https://creativecommons.org/licenses/by/4.0/ ). https://doi.org/10.4236/tel.2026.163031

The aim of this study is to investigate the validity of the sectoral shift hypothesis in African countries for the period 1991-2023. It analyses the long-run relationship between unemployment and sectoral shift of labour for the countries using the panel dynamic ordinary least squared (DOLS) model. It contributes to literature on the Lilien hypothesis for African countries The results conclude to evidence of a trade-off between sectoral shift of employment and unemployment in long run. An implication for economic policies is that we can reduce unemployment rate while working toward shifting employment from low productive sector to the high-productivity sector. Consequently, it is recommended that policymakers implement strategies to expand manufacturing and other high-productivity sectors, to invest in education and skills upgrading aligned with industrial and modern needs, to create more decent work conditions in industrial and service sectors.

 Unemployment Sectoral Shift Panel DOLS African Countries 1. Introduction

Achieving low level of unemployment rate or high level of structural change have been some important priorities for policymakers. Structural change matter because it affects productivity growth, unemployment and other macroeconomic variables ([6]). Unemployment and structural change have also been a major challenge for scholars in developing countries as well as emerging or in advanced economies. Unemployment can lead to a negative impact on economic and social welfare. Structural change, while dealing with reallocation resources between sectors, esteemed in reducing unemployment rate ([8]).

While recommending or implementing economic policies on unemployment or structural change researchers and policymakers need to come out simultaneously with the reduction of persistent unemployment and the shift of labour from low‑productivity activities toward high-productivity activities. A complex challenge appears when they must manage unemployment, structural change, inflation, economic growth simultaneously with respect to [24], [18], [13] and [14] among others.

The main concern in this paper is to deal with unemployment and sectoral shift. Linking unemployment to sectoral shift has traditionally been and is contemporarily an area of discussion and controversy. The Sectoral Shifts Hypothesis (SSH), introduced by [14], quickly became controversial, particularly with [1].

The aim of this study is to investigate the validity of sectoral shift hypothesis in African countries for the period 1991-2023. More objectively, it analyses the long-run relationship between unemployment and sectoral shift of labour for the countries over the time span. This study is a contribution to fill the gap of the related literature for African countries. The relationships established may serve as reference in employment and structural policy.

Our study consists of fourth sections including the introduction. The theoretical framework and the related literature are briefly presented in the following section. The next section consists of the model specification, the variable and data description, the methodology and the empirical results. And in the last section, we provide concluding remarks.

 2. Theories and Literature Review

The theoretical framework of this study is the Sectoral Shifts Hypothesis proposed by [14]. It claims that there is a link between labour reallocation and unemployment. According to this theory, as labour move across sectors the unemployment rate tend to increase. The theory could be extended with insights from [19], [16], [9]. The key insight of these studies is in favour of the validity of the Lilien hypothesis. However, there is a challenge of Lilien framework from [17] who finds that exogenous intersectoral shifts do not significantly affect unemployment. Together, these frameworks provide a comprehensive basis for African studies. Indeed, the countries are mainly underdeveloped economies with two sectors: a traditional sector with low productivity, and a modern sector with high productivity. The countries are also overpopulated and characterized by maladaptive labour force to modern sectors and overall high employment rate.

As well as the extension of the theoretical framework, empirical evidence on the relation between sectoral shift and unemployment, even not so large, remains controversial in the literature. Recent research highlight that temporal dynamics is a main source of divergence in evidence. [25] found there is significant short-term effects of sectoral shifts on unemployment while in long-term there is no evidence of relationship between unemployment and sectoral shifts. Similarly, [26] highlights that changes in industrial structure has a short run positive and significant effect on the level of unemployment. Despite the consensus around the short-term friction, the long-term relationship remains contested. Indeed, [8] came out that sectorial employment, can raise unemployment in the short-run and long run. In same fashion, [5] argue that in the short run, job reallocation raises unemployment but in the long-run, job reallocation reduces unemployment as emerging sectors develop and the workforce adapts.

A second raison of mixed results when checking the validity of sectoral shift hypothesis lies is related to the methodological approaches. Indeed, econometric specifications substantially influence results of studies. The studies using ADRL model like [5] or VECM model like [7] emphasize that an important source of the persistence of unemployment comes from sectoral shifts. In panel framework the use of panel unit root and dynamic panel data regression approach ([3]; [4]) concludes that impact of sectoral shift depends on whether the level of unemployment is low or higher. The results also consider the aggregate level, the state levels, the heterogeneity and cross-sectional dependence.

Another explanation of non-consensual evidence about the sectoral shift hypothesis lies on regional group or level of development of countries. Evidence from [2] finds a positive and significant relationship between employment dispersion and unemployment for 15 European countries and across U.S. states. This aligns with [10] who identity through EU countries comparison that the relationship between sectoral reallocation and unemployment is strong in Spain but weak in Italy. However, they found no significant relationship in France, Ireland or Portugal. Overall, for OECD countries there is solid evidence of sectoral shift hypothesis, but these effects seem to be country‑specific. As regards developing regions, [11] using data from 18 sub‑Saharan African countries shows that sectoral shifts generally increase unemployment. Overall, for developing countries, there are supports of the sectoral shift hypothesis. However, evidence for African countries remains scarce and mainly underexplored in the literature. Hence, recent research covering almost all African countries could provide a clear-cut view of evidence of the sectoral shift hypothesis from Africa. Indeed, without the data limitations constraints, a rigorous empirical assessment focusing on the full panel of African would not only contribute to the debate on sectoral shift hypothesis but also would help understanding the labour market implication on structural transformation in developing countries.

 3. Empirical Analysis and Results 3.1. Model Specification

Our empirical model to check the evidence of Lilien hypothesis for the selected countries in Africa is the following equation

y i t = a i + x i t β + ∑ j = − q 1 q 2 c i j Δ x i , t + j + v i t

where i = 1 , ⋯ , N for each country in the panel, t = 1 , ⋯ , T denotes the time period, q1 represents the maximum lag length, q2 represents the maximum lead length and vit denotes the Gaussian vector error terms process.

 3.2. Variables and Data

To carry our empirical analysis, data from 50 African countries (see Appendix) out of 54 over 1991 to 2023 are used. The variables with their short description and the data sources are presented in Table 1. The variable mli measuring the sectoral shifts is constructed as follows:

M L I = ∑ t = 1 n x i t ⋅ x i s ( ln x i t x i s ) 2

where x is the broad sectoral shares of employment for the sector i at two points in time s and t.

Table 1.Variables and sources.

Variable Description Source
une Unemployment rate (age 15+) International Labour Organization (ILO)
mli Modified Lilien index(using sectoral employment share) International Labour Organization (ILO)
inf Inflation rate, average consumer prices(annual percent change) International Monetary Fund (IMF)
gdp Gross Domestic Product per capita(annual growth rate) United Nations Statistics Division (UNSD)

Table 2 presents descriptive statistics for the data. The statistics reveal substantial heterogeneity across the variables in the sample. While unemployment and sectoral shift show moderate dispersion, inflation and economic growth exhibit strong volatility and the presence of extreme values. Such outliers are retained as they reflect genuine observed values rather than measurement errors.

Table 2.Descriptive statistics.

Variable Obs Mean Std. Dev. Min Max
une 1,650 9.02 7.46 0.20 37.94
mli 1,650 1.08 1.04 0.00 8.64
inf 1,650 36.90 612.45 −72.70 23773.10
gdp 1,650 1.82 7.49 −50.79 91.78

Figure 1 displays the relation between sectoral shift and unemployment for region under the study. The graphical analysis reveals that increases in sectoral shift of employment were associated with a decline in unemployment.

Figure 1. Relation between unemployment and sectoral shift.

To make sure that we can specify a linear relationship with the variables used in this study, we check for multicollinearity within the dataset. The result from the variance inflation (VIF) test for multicollinearity is presented in Table 3. The mean VIF less here than 5 denotes that the variables do not have problems of multicollinearity between them, and they can specify econometric models.

Table 3.Multicollinearity test.

Variable VIF 1/VIF
mli 1.01 0.99
inf 1.00 1.00
gdp 1.01 0.99
Mean VIF 1.01
3.3. Methodology and Results

3.3.1. Cross-Sectional Dependence

Given the nature of our dataset, it is important to test the presence of cross-sectional dependence in the series. Ignoring interconnectedness between countries can lead to biased and inconsistent results. Therefore, this study employed the [21] CD test to check for potential cross-sectional dependence problem. Table 4 provides the results of such diagnostics test. It shows that the Pesaran statistic for all variables are significant at 1% level, meaning that there is cross-sectional dependence across countries in the panel.

Table 4.Cross-sectional dependence test.

Variable CD-test p-value
une 24.56 0.000
mli 41.71 0.000
inf 50.87 0.000
gdp 24.50 0.000

3.3.2. Slope Homogeneity

Following the above test, it is also essential to examine the existence of homogeneity of the slopes of the countries. In practice, assuming that slope coefficients across cross-sectional units is highly restrictive. Therefore, this study used the homogeneity test of [23] to evaluate the homogeneity of slope coefficients. Table 5 provides the results of such diagnostics test. It shows that the Delta statistics are significant at 1% level, meaning that there are heterogeneous country-specific effects across African countries.

Table 5.Slope Homogeneity test.

Delta p-value
31.82 0.000
35.30 0.000

3.3.3. Panel Unit Root

Before moving forward with the econometric analysis is essential to examine the stationarity properties of the variables. Series containing a unit root (non-stationary series) can lead to spurious and misleading findings. Therefore, this study used the second-generation [22] unit root test to account for the cross-sectional dependence across countries in the panel. Table 6 presents the results of such stationarity test. It shows that only the statistic for unemployment rate in not significant at level. However, all statistics are significant at the 1% significance level in the first difference. Hence, the series are, at most, integrated of order one I(1), meaning that there could exist a long run relationship among the variables.

Table 6.Panel unit root tests.

CIPS statistic
lags une mli inf gdp
no constant nor trend 1 −1.15 −3.49*** −2.78*** −4.46***
with constant only 1 −1.71 −3.73*** −3.67*** −4.62***
with constant and trend 1 −1.86 −3.73*** −3.87*** −4.96***
lags Δ une Δ mli Δ inf Δ gdp
no constant nor trend 1 −4.04*** −5.85*** −5.65*** −6.08***
with constant only 1 −4.16*** −5.86*** −5.69*** −6.14***
with constant and trend 1 −4.19*** −5.96*** −5.88*** −6.33***

Note: *, **and *** indicate 10%, 5% and 1% significance level respectively.

3.3.4. Panel Cointegration

To check a potential long run equilibrium relationship among the variables, this study uses the panel error correction cointegration test of [27] which assumes cross-sectional dependence. Table 7 presents the results from the Westerlund Panel cointegration test. It shows that the Variance ratio statistic is significant at 1% significance level. Hence, we found existence of a cointegration relationship between unemployment and sectoral shift employment. Thus, the overall result implies that these variables move together in the long run.

Table 7.Panel Cointegration test.

Statistic p-value
Variance ratio 17.67 0.000

3.3.5. Panel Dynamic Ordinary Least Squared (DOLS)

Considering the results of the cross-section dependence test, homogeneity test of the slope coefficients, unit root test, and cointegration test, the study uses the panel Dynamic Ordinary Least Squared method ([12]; [20]; [15]) for estimating cointegrating relationships between unemployment and sectoral shift of employment. This method eliminates the asymptotic endogeneity and serial correlation, provides consistent estimates is also preferred Common Correlated Effects (CCEMG) or Augmented Mean Group (AMG) estimators. Table 8 presents the results of estimations. It shows that there is a negative long-run relationship between unemployment and sectoral shift of employment for the full model. These results deviate from the typical interpretation of Lilien’s Hypothesis. Indeed, an increase in sectoral shift of employment of 1 point will decrease unemployment by 0.47% in long run for the full sample. It is also what reflect sub-Saharan countries. While confirming the impact of labour shift unemployment in long run, these results show divergence with [4] and [11] about the sign of the impact.

Table 8.Panel DOLS - panel sample.

une Full Model North Africa Sub − Saharan Africa
mli −0.47*** −0.01 −0.47***
(−3.54) (−0.04) (−3.54)
inf 0.00 0.03 0.00
(1.47) (0.42) (1.47)
gdp 0.01 −0.41*** 0.01
(1.49) (−8.26) (1.49)
cons 5.82*** 15.87*** 5.82***
(32.88) (41.81) (32.88)
R-squared 0.43 0.43 0.43

Note: *, **and *** indicate 10%, 5% and 1% significance level respectively.

3.3.6. Robustness Check

As regards of region in Africa, the results of estimations are summarized in Table 9. There is a significant negative long-run relationship between unemployment and employment for Eastern and Southern Africa. Indeed, an increase in sectoral shift of employment of 1 point will decrease unemployment by 0.47% and 2.22% in long run for Eastern Africa and Southern Africa, respectively.

Table 9. Panel DOLS - region.

une Full Model Eastern Africa Middle Africa Northern Africa Southern Africa Western Africa
mli −0.47*** −0.47*** 0.04 −0.01 −2.22*** 0.06
(−3.54) (−3.54) (0.05) (−0.04) (−5.29) (0.86)
inf 0.00 0.00 −0.12*** 0.03 −0.20 0.02
(1.47) (1.47) (−3.16) (0.42) (−1.22) (1.37)
gdp 0.01 0.01 −0.05 −0.41*** 0.01 −0.08***
(1.49) (1.49) (−1.05) (−8.26) (0.07) (−2.67)
cons 5.82*** 5.82*** 2.13*** 15.87*** 32.05*** 1.91***
(32.88) (32.88) (4.28) (41.81) (22.39) (18.89)
R - squared 0.43 0.43 0.61 0.43 0.70 0.77

Note: *, ** and *** indicate 10%, 5% and 1% significance level respectively.

When focusing on income level in Africa, the results of estimations are summarized in Table 10. There is a significant negative long-run relationship between unemployment and sectoral shift of employment for all middle-income countries. Indeed, an increase in sectoral shift of employment of 1 point will decrease unemployment by 0.47% and 2.22% in long run for lower middle-income countries and upper middle-income countries, respectively.

Table 10. Panel DOLS - income level.

une Full Model Low-income countries Lower middle-income countries Upper middle-income countries
mli −0.47*** −0.08 −0.47*** −2.22***
(−3.54) (−0.76) (−3.54) (−5.29)
inf 0.00 −0.08* 0.00 −0.20
(1.47) (−1.81) (1.47) (−1.22)
gdp 0.01 0.25*** 0.01 0.01
(1.49) (3.01) (1.49) (0.07)
cons 5.82*** 2.21*** 5.82*** 32.05***
(32.88) (5.98) (32.88) (22.39)
R-squared 0.43 0.59 0.43 0.70

Note: *, ** and *** indicate 10%, 5% and 1% significance level respectively.

The results of estimations according to the industrial level of counties in Africa are summarized in Table 11. There is a significant negative long-run relationship between unemployment and sectoral shift of employment for all industrializing and industrial countries. Indeed, an increase in sectoral shift of employment of 1 point will decrease unemployment by 0.47% and 2.22% in long run for middle-income industrializing economies and middle-income industrial economies, respectively. These results are identical to those obtained for the income-level classification, indicating the robustness of the findings across different classifications.

Table 11. Panel DOLS - industrial level.

une Full Model Low-income economies Middle-income industrializing economies Middle-income industrial economies
mli −0.47*** −0.08 −0.47*** −2.22***
(−3.54) (−0.76) (−3.54) (−5.29)
inf 0.00 −0.08* 0.00 −0.20
(1.47) (−1.81) (1.47) (−1.22)
gdp 0.01 0.25*** 0.01 0.01
(1.49) (3.01) (1.49) (0.07)
cons 5.82*** 2.21*** 5.82*** 32.05***
(32.88) (5.98) (32.88) (22.39)
R-squared 0.43 0.59 0.43 0.70

Note: *, ** and *** indicate 10%, 5% and 1% significance level respectively.

The profiles of countries in the sample are provided in Table 12. There is a significant negative long-run relationship between unemployment and sectoral shift for some countries (see country list in appendix). There is also positive long-run relationship for other countries. This latter result aligns with the work of [4] and [11].

Table 12. Countries profiles.

AGO BDI BEN BFA BWA CAF CIV CMR COD COG
(−)*** (−) (+)*** (−) (−) (−)** (−)*** (−) (+) (−)
COM CPV DJI DZA EGY ERI ETH GAB GHA GIN
(−) (+)* (−) (−)*** (−) (+)*** (−)*** (+) (−) (−)
GMB GNB GNQ KEN LBR LBY LSO MAR MDG MLI
(−) (+) (+) (+) (−)*** (−)* (+) (−) (−)* (+)
MOZ MRT MUS MWI NAM NER NGA RWA SEN SLE
(+)** (−)** (+)** (+) (−) (+) (−) (+)*** (+) (+)***
STP SWZ TCD TGO TUN TZA UGA ZAF ZMB ZWE
(+)*** (−)** (+) (+) (−) (+)*** (−) (−)*** (−) (−)***

Notes: (+) and (−) indicate positive, negative relation respectively; *, **and *** indicate 10%, 5% and 1% significance level respectively.

 4. Conclusions

Achieving low level of unemployment rate or high level of structural change have been some important priorities for policymakers. Unemployment and structural change have also been a major challenge for scholars in developing countries as well as emerging or in advanced economies. While recommending or implementing economic policies on unemployment or structural change researchers and policymakers need to come out simultaneously with the reduction of persistent unemployment and the shift of labour from low‑productivity activities toward high-productivity activities.

The main concern in this paper is to deal with unemployment and sectoral shift with regards of the sectoral shift hypothesis. The aim of this study is to investigate the validity of sectoral shift hypothesis in African countries for the period 1991-2023. It analyses the long-run relationship between unemployment and sectoral shift of labour for the countries over the time span. The results conclude to evidence of a trade-off between sectoral shift of employment and unemployment in long run. These results deviate from the typical interpretation of Lilien’s Hypothesis. An implication for economic policies is that we can reduce unemployment rate while working toward shifting employment from low productive sector to high productive sectors. Consequently, it is recommended that policymakers implement strategies to expand manufacturing and other high-productivity sectors, to invest in education and skills upgrading aligned with industrial and modern needs, to create more decent work conditions in industrial and service sectors.

While this research has produced important findings, there are still limitations that can be addressed in future research. First, the unavailability of data for 4 African countries from labour statistics lead use to ignore them in the analysis. As soon available, the sectoral shift hypothesis could be check. Second, the study focuses only on Africa countries and compare them according to regional and economic classification. Furthermore, expand country group analysis to other countries in the world could enrich the overall findings. Third, regarding the variables in this study, and rather to check only the labour shift hypothesis, other studies could explore the relationship between the variables aligned Philipps curve and Okun’s law.

 Appendix
Code Country Classification
AGO Angola Sub-Saharan Africa Middle Africa Lower middle-income Middle-income industrializing
AGO Angola Sub-Saharan Africa Middle Africa Lower middle-income Middle-income industrializing
BDI Burundi Sub-Saharan Africa Eastern Africa Low-income Low-income
BEN Benin Sub-Saharan Africa Western Africa Lower middle-income Middle-income industrializing
BFA Burkina Faso Sub-Saharan Africa Western Africa Low-income Low-income
BWA Botswana Sub-Saharan Africa Southern Africa Upper middle-income Middle-income industrializing
CAF Central African Republic Sub-Saharan Africa Middle Africa Low-income Low-income
CIV Côte d'Ivoire Sub-Saharan Africa Western Africa Lower middle-income Middle-income industrializing
CMR Cameroon Sub-Saharan Africa Middle Africa Lower middle-income Middle-income industrializing
COD Congo, Dem. Rep. of the Sub-Saharan Africa Middle Africa Low-income Low-income
COG Congo, Republic of Sub-Saharan Africa Middle Africa Lower middle-income Middle-income industrializing
COM Comoros Sub-Saharan Africa Eastern Africa Lower middle-income Middle-income industrializing
CPV Cabo Verde Sub-Saharan Africa Western Africa Lower middle-income Middle-income industrializing
DJI Djibouti Sub-Saharan Africa Eastern Africa Lower middle-income Middle-income industrializing
DZA Algeria North Africa Northern Africa Upper middle-income Middle-income industrializing
EGY Egypt North Africa Northern Africa Lower middle-income Middle-income industrializing
ERI Eritrea Sub-Saharan Africa Eastern Africa Low-income Low-income
ETH Ethiopia Sub-Saharan Africa Eastern Africa Low-income Low-income
GAB Gabon Sub-Saharan Africa Middle Africa Upper middle-income Middle-income industrializing
GHA Ghana Sub-Saharan Africa Western Africa Lower middle-income Middle-income industrializing
GIN Guinea Sub-Saharan Africa Western Africa Lower middle-income Middle-income industrializing
GMB Gambia, The Sub-Saharan Africa Western Africa Low-income Low-income
GNB Guinea-Bissau Sub-Saharan Africa Western Africa Low-income Low-income
GNQ Equatorial Guinea Sub-Saharan Africa Middle Africa Upper middle-income Middle-income industrializing
KEN Kenya Sub-Saharan Africa Eastern Africa Lower middle-income Middle-income industrializing
LBR Liberia Sub-Saharan Africa Western Africa Low-income Low-income
LBY Libya North Africa Northern Africa Upper middle-income Middle-income industrializing
LSO Lesotho Sub-Saharan Africa Southern Africa Lower middle-income Middle-income industrializing
MAR Morocco North Africa Northern Africa Lower middle-income Middle-income industrializing
MDG Madagascar Sub-Saharan Africa Eastern Africa Low-income Low-income
MLI Mali Sub-Saharan Africa Western Africa Low-income Low-income
MOZ Mozambique Sub-Saharan Africa Eastern Africa Low-income Low-income
MRT Mauritania Sub-Saharan Africa Western Africa Lower middle-income Middle-income industrializing
MUS Mauritius Sub-Saharan Africa Eastern Africa Upper middle-income Middle-income industrial
MWI Malawi Sub-Saharan Africa Eastern Africa Low-income Low-income
NAM Namibia Sub-Saharan Africa Southern Africa Upper middle-income Middle-income industrializing
NER Niger Sub-Saharan Africa Western Africa Low-income Low-income
NGA Nigeria Sub-Saharan Africa Western Africa Lower middle-income Middle-income industrializing
RWA Rwanda Sub-Saharan Africa Eastern Africa Low-income Low-income
SEN Senegal Sub-Saharan Africa Western Africa Lower middle-income Middle-income industrializing
SLE Sierra Leone Sub-Saharan Africa Western Africa Low-income Low-income
STP São Tomé and Príncipe Sub-Saharan Africa Middle Africa Lower middle-income Middle-income industrializing
SWZ Eswatini Sub-Saharan Africa Southern Africa Lower middle-income Middle-income industrial
TCD Chad Sub-Saharan Africa Middle Africa Low-income Low-income
TGO Togo Sub-Saharan Africa Western Africa Low-income Low-income
TUN Tunisia North Africa Northern Africa Lower middle-income Middle-income industrializing
TZA Tanzania Sub-Saharan Africa Eastern Africa Lower middle-income Middle-income industrializing
UGA Uganda Sub-Saharan Africa Eastern Africa Low-income Low-income
ZAF South Africa Sub-Saharan Africa Southern Africa Upper middle-income Middle-income industrial
ZMB Zambia Sub-Saharan Africa Eastern Africa Lower middle-income Middle-income industrializing
ZWE Zimbabwe Sub-Saharan Africa Eastern Africa Lower middle-income Middle-income industrializing
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