Measuring Inclusive Growth

To study the dual objective of efficiency and equity under one umbrella, the idea of generalized concentration curve is most commonly used, introduced initially by Ali et al. (2007). According to this, the population is arranged in ascending order in accordance with the opportunity they attain. Let ${\stackrel{-}{Y}}_i$ be the average opportunity available to bottom i^th percentile where i ranges from 0 to 100 and $\stackrel{-}{Y}$ is the mean of that opportunity. Figure 1 depicts the same level of average opportunities $\stackrel{-}{Y}$ with a different pattern of distribution ${\stackrel{-}{Y}}_i$ , here we can see that a higher level of social mobility and inclusive growth is dependent on (i) if the curve moves up at all points (growth), (ii) if the distribution of the opportunity changes (equity) ² .

By calculating the area under the social mobility curve, we can develop social opportunity index that is;

The above equation presents the following points.

1. $\overline{Y}$ will be equal to ${\overline{Y}}^{*}$ in a completely equitable society.

2. If ${\overline{Y}}^{*}$ is lower than $\overline{Y}$ the distribution of income will be inequitable.

3. A higher ${\overline{Y}}^{*}$ depicts the higher level of income across all segments.

Here, $\overline{Y}$ shows on average availability of opportunities in a society ${\overline{Y}}^{*}$ shows the average opportunities available at each income quintile; hence, the difference between ${\overline{Y}}^{*}$ and $\overline{Y}$ shows an unequal distribution of opportunities. Based on thisAli et al. (2007) proposed the Income Equity index (IEI),

In a completely equitable society, value of IEI will be equal to one.

Equation 2.b defines that inclusive growth depends on average income and its distributional equity measured by the Income Equity index (IEI). This suggests that inclusive growth can be achieved by increasing $\overline{Y}$ or by increasing the value of the equity index ${(\omega }_{i })$

Here, $d{\overline{y}}^{*}$ represents the change in the degree of growth inclusiveness. This suggests that growth is more inclusive if $d\stackrel{-}{y}*>0$ . This increase in $d\stackrel{-}{y}*$ can be achieved by adopting any of these two policy measures i) growth-oriented policies that increase the average income $\left(d\stackrel{-}{y}\right)$ ii) policies that make the distribution of the resources or opportunities more equitable $\left(d{\omega }_{i }\right)$ (Klasen, 2010).

Following more interpretations can be drawn from equation (iii)

1- $d\stackrel{-}{y}> 0$ and $d\omega >0$ — growth is unambiguously inclusive

2- $d\stackrel{-}{y}< 0$ and $d\omega <0$ — growth is unambiguously non-inclusive

3- $d\overline{y}>0$ and $d\omega < 0$ — can be inclusive (if the percentage change in average opportunities is greater than the percentage change in $\omega$ )

4- $d\stackrel{-}{y}< 0$ and $d\omega <0$ — can be inclusive if the percentage change in $\omega$ is greater than the percentage change in average opportunities.

The above discussion helped us to calculate our inclusive growth variable. ³ To calculate, we took GDP per capita data as the average opportunity available in the society $\left(\overline{Y}\right)$ , for $\left({\overline{Y}}^{*}\right)$ we averaged income share held at different income quintiles. ⁴

Measuring Institutional Quality Index

To calculate the institutional quality index, we applied Principal Component Analysis (PCA) on all six WGI indicators (government effectiveness, voice and accountability, regulatory quality, control of corruption, rule of law and political stability and violence) introduced by Kaufmann and Kraay (1999). PCA uses an orthogonal transformation to convert a large number of correlated variables to a smaller number of uncorrelated variables called principal components. PCA also helps to mitigate bias which any objective weight assigning can create, as it assigns the weights for each indicator from its data.

Regression framework for IG and IQI

We have used the following regression to determine the role of institutional quality in growth inclusiveness.

In the above equation Y* _{i, t} -Y* _{i, t-1} is the log difference of ${\overline{Y}}^{*}$ ; IQI is the institutional quality index, measured using WGI indicators, $X_{it}$ is the set of control variables which includes domestic credit to the private sector, inflation, merchandise trade statistics as a percentage of GDP, age dependency ratio, Government’s Final consumption expenditure, and fixed investment as a percentage of GDP, while ${\epsilon }_{it}$ is the error term.

To analyze which type of institutional quality mostly describes the variation in growth inclusiveness, we have regressed Inclusive growth on Political, economic, and legal quality of institutions from 1996 to 2010 (unbalanced panel) along with the same vector of control variable.

Data Sources

The study uses the unbalanced panel data for 86 countries from 1996 to 2015 with few gaps in the initial six years. The data for two key variables (Inclusive growth and Institutional quality) has been constructed by using different methods suggested by the literature as discussed in the previous section. For constructing Inclusive growth, we have used data of Per capita Income and Income distribution held at each 20% (to assess distributional pattern) from the database of World Bank. For the construction of the Institutional quality index, we utilized WGI data sets (1996-2015), whereas to establish a deep understanding of institutional quality data for different kinds of institutional quality (economic, political and legal) has been obtained fromKunčič (2014) from 1990 to 2010. The selection of time period and the country was mostly dictated by the availability of data for our two key variables. The data of the control variable is taken from several international organizations which include World Trade Organization, World Bank National Accounts database, International financial statistics, International Monetary Fund and United Nations’ population division data. The list of control variables includes; FDI, Gross fixed capital formation (% of GDP) as a proxy for fixed investment and general government final consumption expenditure (% of GDP), inflation measured by consumer price index and domestic credit to private sector and age dependency ratio.

Assessing Inclusive Growth

This section provides the empirical assessment of the results obtained by using the concept of the social opportunity curve. As per our earlier discussion, an increase in inclusive growth can be achieved by increasing $\left({\overline{Y}}^{*}\right)$ which depends on two factors; an Increasing average level of overall opportunities $\left(\overline{Y}\right)$ and increasing the equity index of opportunities $\left(\omega \right)$ For conducting an assessment we have calculated change in inclusive growth $d{\overline{y}}^{*}$ , change in per-capita income $d\overline{y}$ and change in equity index of income $\left(d{\omega }_{i }\right)$ .

A few of the major findings are as follows:

1- Few of the world’s major economies despite having higher standing on the growth map are performing quite unsatisfactorily in terms of equity surprisingly the United States managed to be in that list. From 2004 to 2016 USA’s equity growth index has declined by 1.53%. Australia with a small increase in overall opportunities (1.62%) and lower equity index (-0.70%), South Africa’s decline in growth of Equity index of income by 4.32%, and Germany’s by 2.92% brought them in the list of lower-performing economies in terms of inclusive growth.

2- Other countries such as countries like Ethiopia, Zambia, Tajikistan, Lao PDR, Vietnam, Bosnia, and Herzegovina, and Magnolia; despite showing negative growth in terms of equity, successfully performed quite well in terms of inclusive growth which is shown by higher growth in GDP Per capita. This suggests that inclusive growth is achieved by increasing the overall opportunity whereas lower attention has been paid to the equity side of the growth.

3- Among the list of countries with higher inclusive growth very few of the high-income economies successfully secured any position.

These findings helped us to conclude that countries with a low level of an initial endowment of resources should prioritize increasing the overall resource availability rather than the distributional pattern. Secondly, countries that already have achieved a substantial rate of economic growth or made an efficient number of resources available on average to their people need to start considering the pattern of the distribution more than compared to pace.

The results suggest that neither the same level of growth nor the higher pace of growth in the two countries ensures a similar level of growth inclusiveness. As the distributional heterogeneity depends on the economy’s policy making. Additionally, there are strong instances in which growth is achieved without compromising equity.

Assessing Institutional Quality

Based on the discussed methodology in the above section, we implied PCM, to identify principal components, which account for most of the variation in the data. All the three criteria discussed [See Appendix B 2] suggest selecting only the first principal component. The first principal component explains 88.29% of the variation in the data. Later we transformed the original WGI data into a single composite institutional index by using the following method.

Equation 8 shows that PC1 is obtained by multiplying data with factor loading later it is rescaled by dividing with the largest element to develop the composite index for institutional quality.

The resulting index from the above empirical implication brought several conclusions discussed as under:

1- The majority of the high-income economies ⁵ stood at a higher rank on the institutional quality index. Such as the United Kingdom, Finland, Sweden, Ireland, Netherlands, Australia, Switzerland, Denmark, Canada, Germany, Estonia, Luxembourg, Norway, Austria, Chile, Belgium, Lithuania, Iceland, Israel, and United States ranked among the top 20 on our IQI ranking.

2- Despite the top ranks being held by high-income countries yet their performance over almost two decades remained quite stagnant or either deteriorated. Countries like Italy, Greece, Slovenia, Uruguay, Spain, Cyprus, Austria, United Kingdom, Luxemburg, Hungary, and Denmark have shown negative growth in the IQI index while computing the change over the periods of 1996 to 2015.

3- The bottom 20 positions were majorly held by lower middle income (Nicaragua, Honduras, Tunisia, Zambia, Vietnam, Côte d’Ivoire, Ukraine, Pakistan, Iran, Islamic Rep., Argentina, Egypt, Arab Rep. Bolivia & Bangladesh) and low-income countries (Niger, Madagascar & Guinea) with an exception of Belarus, Ethiopia, Ecuador, Venezuela and RB Russian Federation belonging to the upper-middle-income country.

4- Compared to others few lower-middle-income economies and low-income economies have shown positive improvements such as Ethiopia (27.01%), Niger (18.94%), El-Salvador (18.48%), Honduras (18.06%), Madagascar (10.07%), and Vietnam (7.06%) etc.

Institutional Quality and Inclusive Growth Nexus

To identify the role of institutional quality in promoting inclusive growth, the study utilized unbalanced panel data of 86 countries from 1996 to 2015. In Table 2 Column 1 presents the result of fixed effect estimates corrected for heteroscedasticity. The robust results were obtained due to the presence of heteroscedasticity. The results of the Modified Wald test for group-wise heteroscedasticity reflect the presence of heteroscedasticity (Engle, 1984) (Appendix C 3). The test presented the evidence against our null hypothesis of homoscedasticity (or constant variance) hence, the robust results for fixed effect were obtained which automatically corrects standard errors for heteroskedasticity. For the selection of an appropriate panel data model for our objective, we have applied the Hausman specification test to choose between FE and RE (Hausman, 1978). The results obtained from the Hausman test suggested FE technique is more appropriate. The basic null hypothesis for Hausman’s preferred model is random effect against the alternative hypothesis of fixed effect. The Chi square probability of the Hausman test is less than 0.05 hence fixed effect coefficients and results are more appropriate for our analytical study Appendix C 3.

Our results for control variables suggest that inflation, government final consumption and age dependency ratio have a negative and significant effect on inclusive growth. This suggests that inflation by reducing the real purchasing power further deprives the masses of accessing certain resources hence reducing the inclusivity while; a higher dependency ratio itself reflects the exclusion of a high number of individuals from the growth process (Anand et al., 2013) Another set of control variables by showing significant positive association such as trade openness, financial deepening, and fixed investment suggests that they have helped countries achieve growth inclusiveness. However, FDI does not appear to have any positive and significant association. Our key variable Institutional quality has shown a significant and positive association with inclusive growth. As the institutional quality variable is calculated by implying PCM on WGI indicators which predicts the complete social, economic, political, and legal aspects of institutional quality hence, our variable depicts the relationships among the overall quality of institutions without being specific to any particular type of institution or neglecting any of them and inclusive growth. Column 2 to 4 of Table 2 shows that economic and political institutions influence inclusive growth positively and significantly while legal institutions are found to have an insignificant negative association with growth inclusiveness. As average opportunities availability majorly relies on economic institutions, the difference in efficacy of economic institutions is translated by differences in political institutions. (Acemoglu et al., 2008). The results agree with the theoretical understanding ofAli et al. (2007) as they argued that the initial and first most requirement for inclusive growth is to take political and economic institutions on board to make result-oriented policies.