Cointegration and causality analyses

After checking the order of integration of all the variables, Johansen maximum likelihood information method is applied to estimate the number of cointegrating vectors. The results of the number of cointegration vectors estimated using Eigen value and Trace test is given in the table 7.2 below.

Table 7.2: Eigen value and Trace statistics (Tests for no. of cointegrating vectors)

Rank Eigen value Log likelihood H0 : rank <= Trace test P-value

236.634 0 87.935** 0.001 pci 0.703 258.504 1 44.195 0.105 pche 0.467 269.808 2 21.587 0.332 calori 0.314 276.597 3 8.009 0.472 agrilf 0.196 280.532 4 0.139 0.710 invgdp 0.003 280.602 5 Test Summary Vector Portmanteau (5) 117.206 Vector AR 1-2 test F(50,76) 1.124 (0.320) Vector Normality test Chi2 (10) 8.770 (0.554) Vector Hetero test F(150,60) 0.901 (0.697) Vector Hetero-X test Chi2 (300) 329.28 (0.118)

** Shows the rejection at 1 % level of critical values.

Number of lags included in the analysis: 1. Constant is unrestricted.

The results of trace test and Eigen value statistics (table 7.2) confirm that there is only one cointegrating vector between per capita income and other variables. It means that the variables of the model are bound to a long run relationship and are moving together on a long run mean and their variance is time invariant. Given that cointegration is established in the variables and it is therefore necessary to test

α

and

β

matrix restricting them equal to zero. Because,

α

and

β

matrix are very important for further testing.

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In our model we have estimated an equation in which dependant variable is development proxy by income per capita at national level, hence it can be written as a function of the remaining variables and the requirement of these remaining variables in the

α

matrix to be equal to zero. We have estimated

α

and

β

-coefficients in this way by restricting them equal to zero which is a test of weak exogeneity. This test is conducted using likelihood ratio test (LR-test) having chi square distribution and the results of both

α

and

β

-coefficients are presented in table 7.3.

The significant value of per capita health in panel 1 (

α

-coefficients) in table 7.3 explains that one can also model per capita health expenditures as dependant variable as well as labor force in agriculture.

Table 7.3: Restriction test on the long run parameters (

α

and -

β

coefficients)

1).

α

-coefficients

Variables pci pche calori agrilf invgdp

α

-coefficient -0.130 -0.09 0.17 -0.02 0.029 LR test: chi square 1.623 13.73 0.41 4.552 0.188 P-value 0.202 0.0002** 0.523 0.033* 0.665 2).

β

-coefficients

β

-coefficient 1.000 0.41 0.12 -1.45 0.132 LR test: chi square 18.68 1.32 1.361 4.134 20.47 P-value 0.000** 0.251 0.244 0.042* 0.000**

**show rejection at 1 percent level of significance and * at 5 percent level of significance.

Our aim here is to estimate the impact of these variables on income per capita therefore we normalized the remaining variables on per capita income and estimated the long run elasticity of the remaining variables, elasticity estimates are presented in table 7.4 for different models estimated.39 The long run coefficients (

β

-coefficients) are also estimated using zero restriction test this is done to see which variable uniquely constituting the single cointegrating vector. The results for

β

-coefficients are presented in the panel 2 of table 7.3. The variables like per capita income, labor force in agriculture and investment to GDP ratio is significant. Investment is significant in the long run because it has a lagged economic impact, as investment is a long term process.

39

Here we are only presenting restriction test results for the first model, as this model is our basic model the rest of the model are estimated and used to calculate the long run elasticity for comparing it with the first model.

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The Johansen normalized estimates (long run elasticity) are calculated and are given in table 7.4 below. First, the magnitude of the per capita health expenditure is relatively low around 0.23. Our elasticity estimate implies that a one percent increase in health expenditures in Pakistan (measured in per capita terms), leads to an increase in income of around 0.23 percent. The estimated elasticity of health expenditures per capita for OECD countries using panel data approaches is around 0.5 to 0.6 (Narayan, 2007). While for a panel of Asian countries Narayan (2010) estimated elasticity of around 0.16 to 0.26. Interestingly, our estimate falls in the range of Asian economies.

Elasticity of investment (measured as investment over GDP ratio) is well above unity and is 1.42 which is higher than the estimated elasticity for OECD countries around 0.3 to 0.5 (Narayan, 2007). This measure is also in conjunction with estimated elasticity of Asian economies estimated by Narayan (2010) around 1.36 to 2.32.

The value of the coefficient of nutrition availability is above unity which is very nice result in case of Pakistan. As a developing as well as agriculture dependent economy majority of economic activities in Pakistan are related to agriculture which is a labor intensive sector and hence needs more energy in terms of calorie requirement. Webber (2002) estimated that the effect of calories per head on economic growth was positive but insignificant and concluded that investing in health in the form of nutrition might not have impact on economic growth. One reason for this result can be that the study of Webber (2002) used panel data from heterogeneous countries from less developed to industrialized and transition economies, so it might have the problem of aggregation of countries at different level of development. Whereas, the results of the Wang and Taniguchi (2002) estimated that there is a positive and significant impact of nutrition on economic growth.

The impact of the health outcome variables like infant mortality and fertility is with a priori sign but the magnitude of fertility is higher than that of mortality which is interesting result. One reason might be that Pakistan already has a high unemployment rate an increasing fertility means increase in dependency ratio and hence a negative impact on per capita income. While the same negative impact of infant mortality is observed on income per capita in Pakistan but the magnitude is less than 0.5.

Test for serial correlation and normality as well as conditional heteroskedasticity are also conducted to check any of these problems in the estimation of different equations that may exist and the summary statistics is given in lower panel of table 7.2. There is no such problem of normality of residual as well as of heteroskedasticity exists. Once the long run relation among variables is established, in Johansen

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framework, one can also estimate Granger bivariate causality using simple OLS regression with difference form of the dependant as well as independent variable.

Table 7.4: Long run elasticity estimates (Johansen normalized estimates)

Variables Model 1 Model 2 Model 3

calorie 1.088 - - agrilf 1.752 0.711 1.373 invgdp 1.42 1.275 1.433 pche 0.23 0.47 0.36 imr - - -0.480 tfr - -0.89 -

Use of error term, estimated during cointegration analysis, can be made by including its lagged value. The results of Granger bivariate causality40 analysis are presented in table 7.5. Causality analysis shows that there is unidirectional causality running from nutrition per capita to income per capita. This is an interesting result in case of Pakistan because as an agricultural and labor intensive economy Pakistan needs a healthy labor force (due to labor intensive nature of agricultural work) which is directly affected by the availability of diet and nutrition to them. Income is not affecting calorie demand because family ties are very strong in rural areas of Pakistan and that social fabric leads to gifts and barter type of exchange of food commodities at the time of needs due to which there is less influence of income in determining calorie demand.

Table 7.5: Granger bivariate causality test results

Direction F-statistics (P-value) Lag length Results

pci→calorie 1.909 (0.135) 1 No

calorie→ pci 2.708 (0.049)* 1 Yes

pci→invgdp 6.898 (0.00)** 3 Yes

invgdp→ pci 7.651 (0.00)** 3 Yes

pci→ pche 5.478 (0.001)** 3 Yes

pche→ pci 6.158 (0.00)** 3 Yes

* and ** shows level of significance at 1 percent and 5 percent. → Shows the direction of causality.

40

For detailed methodological description of causality analysis and F-test used to estimate the causal relation see methodology chapter of this dissertation.

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This is in contrast with Dawson and Tiffin (1998), Dawson (2002) and Neeliah and Shankar (2008).41 Causality is also running, both ways, between per capita health expenditures and income confirming that investing in health is a viable option for government of Pakistan that triggers economic development which is voiced in World Health Organization’s Macroeconomic commission report (2001) that investing in health is justified on the grounds of human development that will guide the path to economic development.

Nutrition is an important aspect of productivity increase and also improving health conditions of human being. The relation between income and different measures of health like mortality, fertility, health expenditures and nutrition availability is controversial. This study finds a unidirectional causality relation running from calorie to income hence proving the efficiency wage hypothesis. While the case of reverse causality that is income causing calorie intake is not proved hence rejecting the calorie demand hypothesis that is widely investigated by the researchers (Dawson, 2002 and Dawson and Tiffin, 1998).

7.4

Summary

This chapter is dealing with the affect of nutrition availability and other health related input and outcome measures on economic development of Pakistan using annual data. This chapter aims at identifying the role that nutrition (as a health status variable) can play in economic development of Pakistan and comparing it with traditional health related variables like; infant mortality, fertility and health expenditures.

The analysis is carried out using time series data and applying three step procedures i.e., estimating order of integration of each variable in the first step. If variable are integrated of the same order than we can have estimated a long run cointegration relationship, thus, Johansen cointegration methodology is employed to empirically examine the long run relationship among variables of interest. In the third and final step, Granger bivariate causality analysis is carried out to see the direction of causality. It is estimated that the per capita income and other variables of health measures are cointegrated and hence are in long run relationship. The magnitude of nutrition is appeared to be statistically significant and stronger than that of the other health related variables. It is found that per capita income is caused

41

The aim of these studies mentioned above was to estimate the calorie income relationship as opposed we are trying to estimate the efficiency wage hypothesis type of equation in this chapter. All these studies cited above used time series data from a single country.

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by calorie intake and there is no reverse causality between income and calorie intake meaning that increasing food availability has enhancing impact on income at national level.

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In document Public health sector expenditures, health status and their role in development of Pakistan (Page 111-117)