MODELING AND FORECASTING FERTILITY RATES OF PAKISTAN IQRA FATIMA AYESHA LATIF FARAH YASMEEN ACTUARIAL SCIENCE & RISK MANAGEMENT DEPARTMENT OF STATISTICS UNIVERSITY OF KARACHI

FERTILITY Fertility is the major component of population dynamics World widely the size and structure of population is entirely dependent on fertility Fertility means bringing more people into the population.

ANALYZING FERTILITY Trends in fertility are rated as the most difficult of the demographic variables to project Information about women’s reproductive behavior and attitudes. Fertility rates represent the most important modeling variable in any population model These models are of critical importance

Pakistan ranks 6 th among the most populous countries of the world en.wikipedia.org/wiki/List_of_countries_and_dependencies_by_population

Crude birth rate General fertility rate Child women ratio Standardized birth rate Total fertility rate Age specific fertility rate Gross reproduction rate Net reproduction rate Replacement index BASIC MEASURES OF FERTILITY

TOTAL FERTILITY RATE (TFR) TFR is the most widely used fertility measure It is unaffected by differences of changes in age-sex composition It is an easily understandable measure of hypothetical completed fertility A TFR is a measure of the average number of children a woman will have during her childbearing years

PREVIOUS WORK

WHICH FACTORS EFFECT FERTILITY Education Age at first marriage Age at first birth Work status of women Family planning

TFR OF PAKISTAN YearsTFRYearsTFRYearsTFRYearsTFR http :// data.worldbank.org/indicator/SP.DYN.TFRT.IN?

Functional Form Proper Transformation Form of Simple Linear Regression Exponential ModifiedExp onential Logarithm Reciprocal Logarithm Vapor Pressure APPLICATION OF VARIOUS NON LINEAR MODELS

ARIMA MODEL ACF and PACF of second differenced series

MODELS MEMAEPEMAPE Exponential Model Modified Exponential Model Logarithm Model Reciprocal Logarithm Model Vapor Pressure Model ARIMA(1, 2, 2) COMPARISON

FORECASTING TFR PointForecastLo 95Hi

CONCLUSION  ARIMA (1,2,2) is the best model for forecasting TFR of Pakistan  TFR will decline and slowly level off  TFR is expected to be approximately 1.4 children per woman in 2035

REFERENCES  Alkema L., Gerland P., Raftery A.E., Clark J. and Pelletier F. (2012) Estimating Trends in the Total Fertility Rate, Leontine Alkema et al 26 (15)  Brackwell P.J., Davis R.A. (2000) Introduction to Time Series and Forecasting, United State America, (2)  CIA: The World Fact Book (2011) Population Growth Rate.  Hakim A. (1994) Factors Affecting Fertility in Pakistan.  Khan A. (2009) Is Fertility Level In Less Developed Rapidly Growing Regions Really Influenced by Literacy Rate and Age at Marriage? A Case of Punjab, A Research Journal of South Asian Studies 24,  Sathar And Kazi (2000) Women’s Status And Fertility Change In Pakistan, Population and Development Review 14 (3) 415  Sathar Z.A. (2001) Workshop on Prospects for Fertility Decline in HIGH fertility Countries, Population Division, New York,  Sathar Z.A.,Casterline J.B. (1998) The Onset Of Fertility Transition In Pakistan, Population And Development Review (24)  Sathar Z.A., Kazi S. (1990) Women, Work and Reproduction In Karachi, International Family Planning Perspectives (16)  Siddiqui F.A. (2001) Importance of Population Policy in Pakistan, the Pakistan Development Review, 40(1)

 Shitan M., Ng Y.L. (2015) Forecasting the Total Fertility Rate in Malaysia, Pakistan Journal of Statistics, 31(5),  Wazir M. A. (2013) Population Dynamics in Pakistan: Past, Present and Future, Sustainable Development Policy Institute (35). WEBSITES       on

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