Purpose ―Current paper assesses the impact of macroeconomic variables on Pakistan's economic growth. Method ― This study analyzed the data using the Markov Regime switching (MS) model using monthly data for 1981-2020. Firstly, BDS and CUSUM square tests were applied to detect the non-linearity of the model. Results ―The model is non-linear, so the Markov regime-switching model is used for analysis. Each regime's mean and variance are highly significant and show a high growth regime with high volatility and a low growth regime with low volatility. Furthermore, the results show that inflation, interest rate, and trade openness negatively impact while real effective exchange rates positively affect development in both regimes. The negative effect of interest rate, exchange rate, inflation, and trade openness become more pronounced in low growth regimes. Implication ― This study suggests that policymakers should consider the non-linear behaviour of macroeconomics. This will help to formulate better policies for the economy's economic growth. Originality ―The current research adds to the existing literature by identifying the non-linear effect of growth indicators on economic growth, which was previously neglected in the case of Pakistan.
Most research on monetary policy assumes availability of information regarding the current state of economy, at the time of the policy decision. A key challenge for policy-makers is to find indicators that give a clear and precise signal of the state of the economy in real time—that is, when policy decisions are actually taken. One of the indicators used to asses the economic condition is the output gap; and the estimates of output gap from real time data misrepresents the true state of economy. So the policy decisions taken on the basis of real time noisy data are proved wrong when true data become available. Within this context we find evidence of wrong estimates of output gap in real time data. This is done by comparing estimates of output gap based on real time data with that in the revised data. The quasi real time data are also constructed such that the difference between estimates of output gap from real time data and that from quasi real time data reflects data revision and the difference between estimates of output gap from final data and that from quasi real time data portray other revisions including end sample bias. Moreover, output gap is estimated with the help of five methods namely the linear trend method, quadratic trend method, Hordrick-Prescott (HP) filter, production function method, and structural vector autoregressive method. Results indicate that the estimates of output gap in real time data are different from what have been found in final data but other revisions, compared to data revisions, are found more significant. Moreover, the output gap measured using all the methods, except the linear trend method, appropriately portray the state of economy in the historical context. It is also found that recessions can be better predicted by real time data instead of revised data, and final data show more intensity of recession compared with what has been shown in real time data. JEL Classification: E320 Keywords: Data Uncertainty, Measurement Uncertainty, Output Gap, Business Cycle, Economic Activity
Human capital accumulation is one of the most important factors of economic growth for both developed and developing nations. The central research question of this paper is to evaluate the tendency of household educational spending vis-à-vis government spending on education, given the household’s credit constraints. For this purpose, use annual data of 40 countries from 2004 to 2018 in this paper. The intensity of government and household expenditures on education is a more appropriate indicator to analyze the impact of human capital on economic development. This paper has applied the Fixed effect and the random effect model. The Panel Corrected Standard error (PCSEs) model to tackle the problem of heteroscedasticity, Serial Correlation of AR (1), and Cross-sectional dependence. For testing stationarity of the variables, the second generation panel unit root test is Im-Pesaran and Shin (IPS) Test at level and difference. As a robustness test, I estimated a VAR (3) and computed the Impulse response function using Cholesky decomposition along with a 95% confidence interval. The current study concludes that the causality runs from household expenditures (HEX) to government expenditure (GEX) on education directly and not the other way round. This paper also finds a negative contemporaneous relationship between GEX and NPL at the 5% significance level. This means that as households become more credit-constrained, the government tends to spend less on education.
The purpose of this study is to identify the factors which affect organic food purchasing and to explore the factors in which Covid-19 affects consumers’ purchase intention regarding organic food. The study started with an exploratory exercise, whereby in-depth interviews were conducted with Eighteen customers, using mall intercept techniques. Thematic analysis was used to examine data. The study results showed that cost, perceived value, social values, health consciousness, and purchase behaviour, Covid-19 have a positive impact on consumers’ purchase intentions. The findings of this study will help to Improve distribution channels to extend their competitiveness in the organic food market in Pakistan. Prices are high with other conventional food because the production of organic food is low in Pakistan, That’s why producers demand higher prices. On the consumer side, if he is prepared to pay higher prices, he wants to make sure the food is Organic. The reason is that there is no authentication, and certification of organic food production is a major issue, in Pakistan, no institution gives surety that organic food farms are registered from a specific year. Government should give certification, in this way when consumers satisfy then consumers intentions toward organic food purchasing should become higher. Government should Promote Organic fertilizer and its production, that’s why prices of green food items become less.
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