assistant -  professor Assel K. Jumasseitova 

Kazakh British technical University

Socio -economic development and Integration. Causal effect. 

Economists have generally devoted their attention to the growth effects of economic integration.  There are ongoing debates about criteria for successful integration and the relationships between membership in integration blocks and subsequent sustainable development. 

Russian and Kazakhstani papers study integration through different criteria. Thus,  effectiveness of integration is seen through the high economic growth, while reducing the cost of inputs due to optimal utilization and increase the production (Radzhapova Z.K., 2005). The intensity of the integration of relations is based on such indicators as the share of exports relative to the total volume of exports, the commodity structure of mutual exports, indicating the extent of specialization and cooperation, the absolute and relative values of the reciprocal and direct investment (Shishkov Y.V.).  Quantitative assessment of factors affecting the macroeconomic indicators of development, industry structure, investment potential and living standards. The coefficient of economic dependence on external relations reflects the change in the final production of the percentage change in the external relations at the 1%  (Kazbekov B.K., 2003).  Model of Russian economist Dvorkovich A. (2000) is based on the relationship between the level of GDP, state budget, investment flows and the state of foreign trade.  Most of the  Kazakhstani authors pay much attention  on  theoretical aspects of integration.

The link between trade integration and economic growth has been emphasized by several authors (Edwards, 1993; Frenkel and Romer, 1999; Dollar and Kraay, 2001).  First of all, technological change would be positively correlated with country’s openness. In fact, ”globalized” countries can either learn more quickly how to produce new inputs or can import them at lower costs increasing total factor productivity, human capital accumulation, and overall national technological capacity (Grossmann and Helpmann, 1991; Romer, 1992). However, other authors do not pay much attention on the role and direction of causality  between trade and growth (Rodriguez and Rodrik, 2000). The empirical evidence from the East Asian Newly Industrialising economies, revealed that the adoption by governments of high level of trade protection and interventionist industrial policies promoted growth through investment and technological learning. Trade protection could raise long–run growth according to the old infant industry argument if protection is accompanied by strong incentives and policies to enhance factor accumulation and investment in research and innovation.

Experts have long discussed the issue of Kazakhstan’s competitiveness in the global context, identifying that greater competitiveness leads  to greater economic growth and material well-being and that growth always leads to  higher incomes for all income classes, including the poor. However, emerging countries should be aware of particular socio-economic vulnerability that might appear due to global integration, particularly concerning the trade.

         We identified many articles related to the topic published by Montalbano (2004, 2005, 2009). The basic concept was to measure the relationship between trade liberalization and socio-economic vulnerability. The result was that shock on trade openness directly reduced the resources available for private investment and consumption. The key point was that socio-economic well-being was worsening because of trade shocks that occurred at the beginning of the transition era, when observed countries were facing huge institutional and economic liberalization.   Montalbano et al. (2004)  outlined those countries with weak institutions and imperfect and incomplete internal market risk as  being worse off from international competition and globalization. Federici et al (2007) noticed that the focus was to develop options and strategies to help developing countries capture benefits of trade integration minimizing the risk of negative shocks.

Montalbano (2009) proved that the issue of trade openness in terms of economic crises was becoming more crucial, because openness raised vulnerability to foreign shocks. The author provided several explanations to support the statement: “the notion that a weakening in a country’s export performance can trigger a sudden stop in capital flows; the evidence that sudden stops in finance often extended to a loss in trade credit and that the resulting shrinkage in trade was more painful if trade represents a larger share of the economy; the empirical consideration that trade openness and financial openness go hand in hand in good and bad occurances” (Montalbano, 2009). The authors outlined that multilateral trade liberalization together with country’s global integration has impact on income and welfare (static effects) and on total factor productivity (dynamic effects). The research stresses the methodology to use improved qualitative and quantitative data, to create empirical validation, strengthen the dynamic dimension, emphasize the role of vulnerability and uncertainty, and move towards effective integration of the macro and micro level analysis.  

         To carry out our social development analysis in terms of integration union  we widened our measure of welfare, aggregating different aspects of the countries’ socioeconomic development  into a single index.  Idea of single index was implemented for socioeconomic vulnerability analysis of shocks associated to trade openness  (Triuzli U., Montalbano  P. ).

They  used a methodology of United Nations Development Programme (UNDP) for Human Development Index (HDI).  There are three  a unit-free index between 0 and 1, which allows different indices to be added together.  Having defined the minimum and maximum values, the subindices are calculated as follows:

 

 

Many indices have been developed to measure the social welfare or wellbeing of a nation, roughly equivalent to standard of living.

There are three dimensions represented social development  - living standards,  labor supply,  health. GDP per capita, unemployment rate and infant mortality  are three components  included for   index of social welfare.

The aggregation of unemployment and  infant mortality rates is supposed to give  a better and wider comprehension of the actual socio-economic well-being of the country. In particular, unemployment rate gives us a measure of the number of people excluded from labour market; infant mortality rate is as a proxy of the level of the basic sanitary conditions of the country, and quickly reacts to their improvement.  Infant mortality is the most sensitive index we possess of social welfare ( Lathrop,  1913).

However , methodology of HDI includes positive correlation of all subindeces. High level of GDP per capita is positive correlates with  our index,  unemployment and infant mortality rate  have negative correlation. In order to avoid  logical misunderstanding of index,  we change negative correlated components to employment rate and child survival rate. It means that  level of the social development index  reflects position  for each component.

 We analyze members of Eurasian Economic Community (EurAsEC)  for the period 1995-2008 on which reliable and complete statistics are available. Our index of well being has been computed for each year and each country as follows:

SWIti= wx1X1ti + wx2X2ti + wx3X3ti                                                               (2)

where SWIti is the composite index of socioeconomic development  in period t and country i; w is the weight of each component; X is the component.

 

Figure 1 – EurAzEC’s  SWIs

The standard deviation of SWI gives a measure of the volatility of well-being for each country in the time period analyzed, while the SWI percentage rate of change of the period gives us a measure of the socioeconomic performance of each country over time  and on average (Montalbano, 2009).  The comparison shows as level of volatility and worsening levels of well-being before EurAsEC and after.

 

Figure 2 - A comparison between SWI volatility and average % rate of change

of the EurAsEC  (1992-2008)

The comparison supports the view that Tajikistan and Kyrgyzstan    have experienced larger degrees of volatility and worsening levels of welfare  during the transition period  than other countries of EurAsEC. 

We estimate a cross country OLS regression model in the following way:

SWIi = β0 + β1 Tri.+ β2 GGDi + β3 FDIi ++ β4 LPi εi                                   (3)

 

Where i = 1,…,N and N is the number of countries that enter the sample;

Independent Variables represent Integration.

-         Tr  is a  trade terms ratio between Export and Import ;

-         GGD – General Government Debt ;

-         FDI – Foreign Direct Investment

-         LP – Labor Productivity

The error terms εi are assumed to be uncorrelated with zero mean and Var( εi) = σ2 .

After substantial testing using the variables , the regression results show some preferred  model which are presented in table  1.

Table 1 – OLS Regression results  for 6 members of  EurAsEC for the period 1992-2008

Dependent variable: swi

Robust (HAC) standard errors

 

Coefficient

Std. Error

t-ratio

p-value

 

const

0.611844

0.022225

27.5295

<0.00001

***

labour_producti

1.02457e-05

2.2636e-06

4.5263

0.00002

***

trade_ration

0.0236366

0.0160336

1.4742

0.14384

 

ggd

-0.000297371

0.000131902

-2.2545

0.02654

**

fdi

-0.00166177

0.000919636

-1.8070

0.07403

*

 

Mean dependent var

 0.698938

 

S.D. dependent var

 0.145448

Sum squared resid

 0.139523

 

S.E. of regression

 0.038943

R-squared

 0.934700

 

Adjusted R-squared

 0.928312

F(9, 92)

 146.3212

 

P-value(F)

 1.63e-50

Log-likelihood

 191.5878

 

Akaike criterion

-363.1757

Schwarz criterion

-336.9259

 

Hannan-Quinn

-352.5462

rho

 0.374689

 

Durbin-Watson

 0.815283

 

Test for differing group intercepts -

 Null hypothesis: The groups have a common intercept

 Test statistic: F(5, 92) = 91.6107

 with p-value = P(F(5, 92) > 91.6107) = 3.52467e-034

 

Specification of the Model  explains SWI as linear combination of the  export / import ratio (trade relationship), the general  government debt (public policy instrument), the foreign direct investment  stock as a percentage  of GDP , labour productivity .  This model suggest  positive correlation between labour productivity and social development, negative correlation between GGD, FDI and social welfare of the country and insignificant effect  of trade openness and social welfare.

 The results  need further tests to check their robustness.