**3. Materials and Methods**

In order to analyze the inclusive and sustainable development of the EU countries, we used gross domestic product (GDP) per capita (in purchasing power standards (PPS) as a percentage of EU-28 average GDP =100%), Human Development Index (HDI), Europe 2020 Competitiveness Index, and Inclusive Development Index (IDI). In our paper, we focus on the economic and human dimension of sustainable development. According to the Human Development Report [58], HDI integrates three basic dimensions of human development: the ability to lead a long and healthy life, the ability to acquire knowledge, and the ability to achieve a decent standard of living. As the World Economic Forum report [59] highlights, IDI is a composite index which comprises three pillars: growth and development, inclusion, and equity and sustainability. Four main indicators were chosen within each of the three pillars. Thus, the growth and development pillar consisted of GDP per capita, labor productivity, employment, and healthy life expectancy. The inclusion pillar included median household income, poverty rate, income Gini, and wealth Gini. The intergenerational equity and sustainability pillar incorporated adjusted net saving, public indebtedness as a share of GDP, dependency ratio, and carbon intensity of GDP [59]. The Europe 2020 Competitiveness Index "is grouped into three sub-indexes that monitor Europe's progress toward becoming an increasingly smart, inclusive, and sustainable economy" [59] (p.8), including seven pillars that reflect the spirit of the seven flagship initiatives: enterprise environment, digital agenda, innovative Europe, education and training, labourlabor market and employment, social inclusion, and environmental sustainability. IDI scores and Europe 2020 Competitiveness Index scores are based on a 1–7 scale, where 1 = worst and 7 = best.

Productive employment was analyzed based on five indicators related to employment issues: employment rate, labor productivity (GDP per employed person, as a percentage of EU-28 average = 100%), employment in services, employment in knowledge-intensive activities (KIA), and compensation of employees (percentage of GDP). A high level of these interrelated indicators reflects a high level of productive employment [16,33]. According to the Eurostat definition, "an activity is classified as knowledge-intensive if tertiary-educated persons employed (according to ISCED97, levels 5–6) represent more than 33% of the total employment in that activity" [21].

Employment in agriculture, vulnerable employment (own-account workers and unpaid family workers as a percentage of total employment), precarious employment (involuntary part-time employment), in-work at-risk-of-poverty rate, and at-risk-of-poverty rate were used for analyzing the deficit in productive employment and poverty (Table 1).

**Table 1.** Variables included in the principal component analysis (PCA) and cluster analysis (*N* = 25). Descriptive statistics (2007–2016 average).


Notes: <sup>1</sup> percentage of population aged 20 to 64; <sup>2</sup> per person employed (EU28 = 100%); <sup>3</sup> percentage of total employment; <sup>4</sup> percentage of GDP; <sup>5</sup> the share of own-account workers and contributing family workers, in total employment; <sup>6</sup> percentage of total part-time employment; <sup>7</sup> in purchasing power standards (PPS) as percentage of EU-28 average GDP = 100%; <sup>8</sup> "the share of persons who are at work and have an equivalized disposable income below the risk-of-poverty threshold" [21]; <sup>9</sup> people at risk of income poverty after social transfers; \* "an activity is classified as knowledge-intensive if tertiary educated persons employed (according to ISCED97, levels 5–6) represent more than 33% of the total employment in that activity" [21]; \*\* at-risk-of-poverty threshold iswas set at 60% of the national median equivalized disposable income [21]; BG—Bulgaria, CZ—Czech Republic, DK—Denmark, EE—Estonia, EL—Greece, FI—Finland, IE—Ireland, NL—Netherlands, RO—Romania, SE—Sweden, UK—United Kingdom. Source: own calculations based on References [21,36,58,60].

Our sample consisted of 25 countries from the EU, without Malta, Cyprus (the countries for which more statistical data are unavailable), and Luxembourg (an outlier in many variables). Statistical data on the analyzed variables were collected from the Eurostat Database [21], United Nations Development Program (UNDP) Report [58], and World Economic Forum (WEF) reports [36,60] for the 2007–2016 period.

In order to test the research hypotheses, we used descriptive statistics, correlation and regression analysis, principal component analysis (PCA), and cluster analysis (CA).

We applied the Pearson correlation coefficient (*r*) to study the intensity of the relationship between variables. The value of the correlation coefficient was situated in the interval (−1, +1). A value of +1 indicates a perfect positive linear relationship between variables. Conversely, a value of −1 indicates a perfect negative linear relationship between variables. Independence between the variables implies that the value of *r* is equal to zero [61].

We employed a simple linear regression analysis (Y = α + β × X + ε, where Y is the dependent variable, X is the explanatory variable, α and β are regression coefficients, and ε is the residual or error) and multiple linear regression analysis (Y = α + β<sup>1</sup> × X1 + β<sup>2</sup> × X2 + ε) to identify a functional relationship between the analyzed variables. The regression coefficients were estimated using the least-squares method [62]. To assess the validity of the regression model, the Fisher Snedecor (*F*) statistic was used. Based on the *R<sup>2</sup>* (the coefficient of determination) value, the quality of prediction was assessed. The value of *R2* indicates the proportion of the variance in the dependent variable that the independent variables explain. The variance inflation factors (VIF) and the tolerance of the independent variables were tested in order to check if the results were affected by multicollinearity. As Hair et al. [63] stated, a high multicollinearity could be met when the VIF has a value which is higher than 10 and the tolerance records a value which is less than 0.1.

In order to classify the EU countries and obtain a comparative view of their interrelation between productive employment, and inclusive and sustainable development, principal component analysis (PCA) and cluster analysis (CA) were used.

In the first step, PCA with Varimax rotation and Kaiser normalization was used to reduce the dimensionality of a dataset consisting of a large number of interrelated variables (14 variables) to a few factors or principal components [64]. The advantage of this multivariate technique consists of the reduction of the complexity of the data, producing a small number of derived variables that can be used instead of the larger number of original variables in order to simplify the subsequent analysis of the data [62]. In order to choose the number of principal components, we used multiple criteria such as the Kaiser criterion or eigenvalue-greater-than-one rule, as well as Catell's scree plot criterion and percentage of cumulative variance, based on which only the components which capture a large percentage of the total variation of the original variables (between 70 and 90%) are retained [62,64]. The choice of using PCA in this research took into consideration the fact that, in recent years, PCA was widely applied to the study of the social and economic differences and similarities between various nations [22,65–67]. Furthermore, PCA is recognized as a multivariate statistical method which contributes to solving the inconveniences generated by different measuring of original variables, data seasonality, and high variations of the covariance coefficients [65,68].

In the second step, the principal components that resulted from the PCA were used for the cluster analysis and this helped us identify the homogenous groups of countries. Therefore, at first, we used a hierarchical cluster analysis, using Ward's method and the Euclidian distance in order to determine the number of clusters. This method was followed by a k-means cluster analysis. Then, to identify the relatively homogeneous groups of cases based on the selected characteristics, k-means cluster analysis was used [22,69]. We used the SPSS statistical package for all statistical analyses.

#### **4. Results and Discussion**

In order to achieve an inclusive and sustainable development, it is necessary for economic growth to be accompanied by employment growth, on the one hand, and for the benefits of economic growth to be more equitably distributed, on the other hand. Based on the data provided by Figure 1 in the EU, during the 2007-2016 period, the process of economic growth (expressed by real GDP growth rate) varied substantially across countries, and this process was not accompanied by employment growth (in all 25 EU countries analyzed). Thus, in six of the 25 countries, despite an economic growth process, employment decreased, but labor productivity increased (Romania, Bulgaria, Spain, Estonia, Lithuania, and Latvia). It is confirmed that economic growth is a necessary but not sufficient condition for achieving substantial progress in living standards [10,59].

**Figure 1.** Real economic growth, employment growth, and labor productivity growth, in European Union (EU-25) countries, 2007–2016. Source: own calculations based on Reference [21].

Also, the "jobless growth" process is noticeable in 15 of the 25 countries, emphasizing the small capacity of economic growth to generate employment growth (annual average economic growth is higher than annual average employment growth). Four of the 25 countries recorded a decrease in both average employment growth and average economic growth (Portugal, Italy, Greece, and Croatia). It is worthwhile mentioning that the relationship between employment growth and economic growth is more complex, as large numbers of jobs are being created and destroyed simultaneously in the context of structural change and spatial labor reallocation [8,14].

In the same period, all countries (except Greece and Italy) recorded labor productivity growth which varied significantly across these countries (Figure 1). Moreover, statistical data point out the significant differences in terms of the level of labor productivity (Figure 2), which ranged from 42% to 150% (EU-28 = 100%). However, significant gaps can still be noted between the new member states and the old member states.

**Figure 2.** Relationship between labor productivity and employment in knowledge-intensive activities (KIA) and services, 2007–2016 average. Source: own calculations based on Reference [21].

The results of the correlation analysis (Table 2) show that, in the EU member states, during the 2007–2016 period, labor productivity was positively correlated with Europe 2020 Competitiveness Index (*r* = 0.693), GDP/capita (*r* = 0.952), HDI (*r* = 0.872), and IDI (*r* = 0.577). Thus, in the EU countries where labor productivity is higher (especially the developed economies of EU), the level of competitiveness, level of economic and human development, and level of inclusive development are also higher and vice versa, which confirms Hypothesis H1.


**Table 2.** Correlation between productive employment and inclusive and sustainable development.

Notes: \* correlation is significant at the 0.01 level (two-tailed); \*\* correlation is significant at the 0.05 level (two-tailed); IDI—Inclusive Development Index; HDI—Human Development Index. Source: Own calculations based on References [21,36,58,60].

As Table 2 and Figure 2 show, it appears that, in the EU, higher levels of labor productivity are linked to an efficient sectoral structure of employment, expressed by a higher share of employment in services (*r* = 0.807) and KIA (*r* = 0.832) in total employment, and by a lower employment in agriculture (*r* = −0.679).

The results of the simple linear regression analysis (Figure 2 and Table 3) show that, in the EU countries, the level of labor productivity is positively influenced by the level of employment in services. The simple linear regression model (labor productivity = −56.492 + 0.808 × employment in services) was statistically significant (*F* (1, 23) = 43.174; *p* = 0.000) and accounted for 65.2% of the variance of labor productivity (*R2* = 0.652).


**Table 3.** Simple regression results: the impact of employment in services on labor productivity.

Note: <sup>1</sup> labor productivity; *R<sup>2</sup>* = 0.652, adjusted *R<sup>2</sup>* = 0.637; standard error of the estimate = 15.818; *F* (1, 23) = 43.174, *p* < 0.001. Source: own calculations based on Reference [21].

Moreover, we analyzed the impact of the level of employment in KIA on labor productivity (Figure 2 and Table 4). The estimated simple linear regression model for the impact of the level of employment in KIA on labor productivity in the EU countries, during the 2007–2016 period (labor productivity = 42.640 + 0.830 × employment in KIA), highlights that employment in KIA positively influenced labor productivity (β = 0.830). This model was statistically significant (*F* (1, 23) = 50.93; *p* = 0.000 *p* = 0.000) and accounted for 67.5% of the variance of labor productivity (*R2* = 0.675).



Note: <sup>1</sup> labor productivity; *R<sup>2</sup>* = 0.689, adjusted *R<sup>2</sup>* = 0.675; standard error of the estimate = 14.966; *F* (1, 23) = 50.932, *p* < 0.001. Source: own calculations based on Reference [21].

These results show that, in the EU countries where employment in services and employment in KIA are higher, the level of labor productivity is high too, which confirms Hypothesis H2. This fact reflects the need to make the sectorial structure of employment more efficient in some EU countries, especially in new EU member states, so that they become developed economies in the context of sustainable development.

As it can be noted in Table 5, all four variables which reflect deficit in productive employment are negatively correlated with variables specific to economic, and inclusive and human development and competitiveness. A negative and significant correlation was identified between inclusive development (expressed by IDI), on the one hand, and working poverty, as a form of deficit in productive employment (*r* = −0.654, *p* < 0.01, Table 5) and overall poverty (*r* = −0.705, *p* < 0.01, Table 5), on the other hand.

**Table 5.** Correlation between deficit in productive employment and inclusive and sustainable development.


Note: \* correlation is significant at the 0.01 level (two-tailed); \*\* correlation is significant at the 0.05 level (two-tailed); IDI—Inclusive Development Index; HDI—Human Development Index. Source: own calculations based on References [21,36,58,60].

Using a simple regression of IDI on in-work poverty rate (Figure 3 and Table 6) points out that working poverty influenced IDI negatively and significantly (*β* = −0.654, *p* = 0.000). The IDI regression model was statistically significant (*F* (1, 23) = 17.195, *p* = 0.000; *R2* = 0.428). Thus, the low level of inclusive development in the EU countries can be explained by the existence of high working poverty rate. Taking account of these results, Hypothesis H3 is confirmed.


Note: <sup>1</sup> IDI, Inclusive Development Index; *R<sup>2</sup>* = 0.428, adjusted *R2* = 0.403; standard error of the estimate = 0.408; *F* (1, 23) = 17.195, *p* < 0.001. Source: own calculations based on Reference [21].

Furthermore, Figure 3 shows how the level of working poverty varies across countries. The highest in-work at-risk-of-poverty rate from EU-28 was recorded in Romania (18.44%), followed by the southern countries (Greece, 13.71%; Spain, 11.5%; Italy, 10.52%; Portugal, 10.47%) and Poland (10.04%). In-work at-risk-of-poverty rate in Romania was 2.07 times higher than the European average (8.89%) and almost five times higher than in the Czech Republic (3.77%), the most efficient European country from this perspective. Thus, working poverty is a real socio-economic challenge at the European level, confirmed by other studies [16,22,45,49].

**Figure 3.** In-work poverty rate and Inclusive Development Index (IDI) in EU countries. Source: own calculations based on References [21,60].

In order to test Hypothesis H4, we estimated the influence of vulnerable employment (own-account workers and unpaid family workers) and precarious employment (involuntary part-time employment), as independent variables, on working poverty, using multiple regression analysis (Table 7). The regression model was statistically significant (*F* (2, 22) = 19.531, *p* < 0.001) and accounted for over 60% of the variance of working poverty (*R2* = 0.640, adjusted *R2* = 0.607). As can be seen by examining the beta weights (β), vulnerable employment received the strongest weight in the model (β = 0.489), followed by involuntary part-time employment (β = 0.434), implying that vulnerable employment has a greater impact on working poverty. In the EU countries, during the 2007–2016 period, higher vulnerable and precarious employment determined a high risk of working poverty. Thus, hypothesis H4 is confirmed and supported by other empirical results [22,45,49]. Corroborating these results with the positive link between vulnerable employment and employment in agriculture (*r* = 0.644, *p* < 0.01), it is shown that, in EU countries, during the 2007–2016 period, vulnerable workers, mainly those who work in agriculture, suffered the consequences of working

poverty risk. Moreover, this implies that both agricultural productivity and the income for agricultural workers need to increase to reduce working poverty [44].


**Table 7.** Multiple regression results.

variance inflation factors (VIF). Source: own calculations based on Reference [21].

In order to test Hypothesis H5, we took into consideration the cumulative influence of 14 socio-economic variables selected, the inclusive and sustainable development indicators, productive employment indicators, and indicators which reflect the deficit in productive employment and poverty (see Table 1), employing complex statistical methods of data analysis, principal component analysis (PCA), and cluster analysis, respectively. Based on PCA (rotation method: Varimax with Kaiser normalization; rotation converged in three iterations), the 14 variables were grouped into two components (factors), which explain 76.63% of the total variance of the 14 initial variables (Tables 8 and 9).

**Table 8.** Total variance and eigenvalues explained.


The first principal component (PC1), which explains 64.23% of total variance, includes seven variables (Table 9). Six of these variables can be specific to an efficient employment, having a positive influence on national competitiveness and development: employment in services, KIA employment, labor productivity, GDP/capita, HDI, and Europe 2020 Competitiveness Index. This component (PC1) is negatively correlated with the employment in agriculture. Thus, a high level of employment in agriculture cannot be associated with economic and human development. The second principal component (PC2) explains 12.4% of total variance of the 14 original variables and includes seven variables: involuntary part-time employment, in-work poverty rate, vulnerable employment, IDI, employment rate, total poverty rate, and compensation of employees (Table 9). Three of these variables reflect a deficit in productive employment directly (in-work at-risk-of-poverty rate) or indirectly (vulnerable employment and involuntary part-time employment) and have a negative contribution to the creation of this component. Other variables (employment rate, IDI, and compensation of employees) have a positive contribution to the creation of PC2 (Table 9).

The two principal components were used in the cluster analysis to classify the EU countries. We used hierarchical cluster analysis, Ward's method, and Euclidean distance to define the number of clusters in which the 25 countries were classified. Then, we used the k-means analysis to actually form the clusters. According to the results of the ANOVA analysis (*F* (2, 22) = 31.084, *p* < 0.001; *F* (2, 22) = 28.419, *p* < 0.001; Table 10), the formed clusters were statistically significant. As can be seen in Figure 4, the analyzed countries were classified into three clusters.



Note: extraction method: principal component analysis; rotation method: Varimax with Kaiser normalization; rotation converged in three iterations.



**Figure 4.** EU cluster analysis results.

Cluster 1 was positively correlated with factor 1 (PC1), but also with factor 2 (PC2) (0.913 and 0.501, respectively, Table 10). All ten countries included in this group (Austria, Belgium, Germany, Denmark, Finland, France, Ireland, Netherlands, Sweden, and United Kingdom) are old EU member states with the highest level of economic, human, and inclusive development. In the case of these countries, Europe 2020 Competitiveness Index level and labor productivity are also very high (Figures 5 and 6). As regards the employment indicators, it is noted that this cluster has the highest level of employment rate (73.83%).

**Figure 5.** Inclusive and sustainable development and competitiveness. Source: own calculations based on References [36,58,60].

Over 77% of jobs are created in services, and 39.5% in knowledge-intensive activities (KIA), demonstrating a high role of knowledge in these economies (Figure 6). Employment in agriculture, vulnerable employment, involuntary part-employment, and poverty rate (working poverty and overall poverty) recorded the lowest level compared to the other clusters (Figure 7). Countries from this cluster (except Ireland) were placed in the first quadrant (Figure 4). Ireland was placed in the fourth quadrant, but close to PC1, because employment in agriculture, vulnerable employment, and involuntary part-time employment are higher compared to the average of this cluster, but lower than in other clusters.

**Figure 6.** Productive employment and economic development (average values per cluster). Source: own calculations based on Reference [21].

Cluster 2 included five countries (Greece, Italy, Spain, Portugal, and Romania) with the highest working poverty rate, vulnerable employment, employment in agriculture, and involuntary part-time employment (Figure 6). This cluster was strongly negatively correlated with factor 2 (−1.657, Table 10). From the perspective of IDI and Europe 2020 Competitiveness Index, this cluster was the lowest. Within this cluster, the countries showed high heterogeneity. The southern countries (Italy, Spain, and Greece) are situated in the fourth quadrant (Figure 4) close to the negative sense of PC2, achieving both a higher level of economic performance (GDP/capita, labor productivity) and a higher level of deficit in productive employment compared with the average of this cluster. Greece is in first place in EU-28 in terms of involuntary part-time employment (60.29%), in second place in terms of vulnerable employment (28.16%) and working poverty (13.71%), and in third place as regards poverty rate (21.25%; Bulgaria is in second place). Also, this country achieved the worst performance in IDI. Portugal is situated very close to PC1 in a negative sense and tends to be closer to cluster 3. This means that economic development, labor productivity, and employment in services are more reduced than in Italy, Spain, and Greece. Romania's position in the third quadrant and outside the "correlation

circle" (Figure 4) is due, on the one hand, to the highest values of indicators which reflect deficits in productive employment and poverty, and, on the other hand, to the lowest values of indicators which reflect productive employment and inclusive and sustainable development, compared with the peer countries in the cluster. Thus, during the 2007–2016 period, Romania was EU-28's leader in terms of poverty rate (23.59%), in-work poverty rate (18.4%), vulnerable employment (30.88%), and employment in agriculture (29.24%). This country was ranked last in EU-28 in terms of employment in services (41.54%), employment in KIA (20.8%), compensation of employees (as a percentage of GDP), and Europe 2020 Competitiveness Index (3.64). Our results show that Romania is confronted with critical challenges in terms of productive employment and its impact on inclusive and sustainable development, taking into consideration that it is ranked last in both cluster 2 and in the EU countries.

**Figure 7.** Deficit in productive employment and poverty (average values per cluster). Source: own calculations based on Reference [21].

Cluster 3, which consisted of the Baltic states and some CEE States (Estonia, Lithuania, Latvia, Poland, Czech Republic, Hungary, Slovenia, Slovakia, Croatia, and Bulgaria), was very far from PC1 (−0.966). Thus, some of the indicators such as labor productivity, economic and human development, and employment in services had the lowest values. A low level of economic performance can be observed. The GDP/capita average was only 54.9% of the GDP/capita realized by the countries from cluster 1 and labor productivity was 59.1%. Within this cluster, the countries showed some heterogeneity. Bulgaria and Poland were situated in the third quadrant (Figure 4) because, in these countries, employment in agriculture, compensation of employees, and poverty rate were higher compared with the average of this cluster. Bulgaria was in last place in EU-28 in terms of human and economic development, and labor productivity, and the next to last as regards Europe 2020 Competitiveness Index (3.75). The Czech Republic recorded, during the 2007–2016 period, the lowest in-work poverty rate in EU-28, which can be explained by the highly distributive effects of its welfare system [17,22]. Our results (Figures 5 and 6) show that countries enrolled in this cluster had a lower level of deficit in productive employment, and a lower level of inclusive development and competitiveness than cluster 2, but higher than cluster 1. Thus, cluster 2 was ranked second in terms of productive employment for inclusive and sustainable development.

In the light of these results, it was proven that there are common features and differences between EU member states based on their interrelationship among productive employment, and inclusive and sustainable development; thus, Hypothesis H5 is confirmed. Therefore, different and specific measures are needed to support the improvement of this interrelationship.

### **5. Conclusions and Main Implications**

One of the real challenges of economies in the current economic and social global environment is to generate high productive employment in order to achieve inclusive and sustainable development. In this context, this paper highlighted the main characteristics and mechanisms of productive employment, focusing on the interrelationships among productive employment, and inclusive

and sustainable development in the EU countries, during the recent economic crisis and recovery period (2007–2016). Moreover, it is essential to assess productive employment and to identify key opportunities and barriers in order to create productive employment, taking into account that productive employment can be a driving force for reducing gaps between the EU countries in order for these to really integrate into the European Union [8].

The hypotheses in the study were successfully supported by empirical data. The research results showed that economic growth in some EU countries (countries in clusters 2 and 3) should be more sustained and inclusive, and sufficiently employment intensive, such that more job opportunities for a larger workforce are created, and workers, especially those who are poor, benefit from improvements in standards of living [16]. Thus, an important challenge for policy-makers is to mix pro-growth and pro-poor policies [32]. As WEF report [60] (p. 6) states, "a new growth model that places people and living standards at the center of national economic policy and international economic integration is required to transform inclusive growth from aspiration into action in the Fourth Industrial Revolution".

The results of the correlation and regression analysis reflected, on the one hand, the positive influence of labor productivity on inclusive and sustainable development (expressed by GDP/capita, HDI, IDI and Europe 2020 Competitiveness Index), and, on the other hand, the positive influence of the level of employment in services and of employment in knowledge-intensive activities on labor productivity. Also, the results showed that, in the EU countries during the period analyzed, inclusive development was negatively influenced by working poverty (as a deficit in productive employment), and that vulnerable employment and precarious employment were important drivers for a high level of working poverty. Thus, the results showed that gaps in the level of development can be explained by the levels and characteristics of productive employment and that there are large cross-country differences in terms of the interlink between productive employment and inclusive and sustainable development, which emphasizes the need to take specific actions to translate unproductive employment into productive employment.

Results of the PCA and cluster analysis, for the 2007–2016 period, emphasized that the highly developed European countries proved to be more homogeneous in terms of the interrelationship between productive employment, and inclusive and sustainable development, as they were enrolled in the same cluster (cluster 1). On the contrary, European countries with medium and low levels of development, mostly new member states, recorded different results, divided into two clusters. Southern countries (Greece, Italy, and Spain), together with Portugal and Romania, were grouped in the most unproductive cluster (cluster 2) characterized by the highest working poverty, vulnerable employment, employment in agriculture, and involuntary part-time employment, with negative consequences on inclusive and sustainable development.

These findings reflect, on the one hand, the need to accelerate labor productivity growth based on productive structural transformation, characterized by a shift from low-productivity sectors to high-productivity sectors (especially in the countries in cluster 2 and 3). If the European countries, where the agricultural sector still generates significant jobs (Romania, 29.2% of total employment; Bulgaria, 19.2%; Poland, 12.6%; Croatia, 11.7%; Greece, 11.5%), are aware of the potential of the agriculture sector and they act through strategic investments in this sector, agricultural jobs could be more productive; consequently, gains in productivity can raise incomes to a level that enables agricultural workers to escape the poverty trap. On the other hand, it is essential that poor workers significantly benefit from the gains in labor productivity.

Moreover, policies for prematurely deindustrialized countries and for those with a high share of employment in the agricultural sector should target a substantial subsidy for traditional agriculture, focusing on the ecological component of sustainable development. Agricultural jobs should become more productive, but also better paid. Furthermore, these sectoral changes should be accompanied by similar changes from vulnerable employment to wage and salaried employment, having positive consequences on the share of compensation for employees in total income, and an implicit effect on working poverty reduction.

The findings of this research can be useful for policy-makers in order to formulate policies that support the improvement in productive employment within a framework of inclusive and sustainable development.

**Author Contributions:** Both authors equally contributed to this paper, with both considered as first authors.

**Funding:** This research received no external funding.

**Conflicts of Interest:** The authors declare no conflict of interest.
