Guner, Nezih
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Publication Technology and the Changing Family: A Unified Model of Marriage, Divorce, Educational Attainment and Married Female Labor-Force Participation(2012-01-03) Greenwood, Jeremy; Guner, Nezih; Kocharkov, Georgi; Santos, CezarMarriage has declined since 1960, with the drop being bigger for non-college educated individuals versus college educated ones. Divorce has increased, more so for the non-college educated vis-a-vis the college educated. Additionally, assortative mating has risen; i.e., people are more likely to marry someone of the same educational level today than in the past. A unified model of marriage, divorce, educational attainment and married female labor-force participation is developed and estimated to fit the postwar U.S. data. The role of technological progress in the household sector and shifts in the wage structure for explaining these facts is gauged.Publication Marry Your Like: Assortative Mating and Income Inequality(2014-01-12) Guner, Nezih; Kocharkov, Georgi; Greenwood, Jeremy; Santos, CezarHas there been an increase in positive assortative mating? Does assortative mating contribute to household income inequality? Data from the United States Census Bureau suggests there has been a rise in assortative mating. Additionally, assortative mating affects household income inequality. In particular, if matching in 2005 between husbands and wives had been random, instead of the pattern observed in the data, then the Gini coefficient would have fallen from the observed 0.43 to 0.34, so that income inequality would be smaller. Thus, assortative mating is important for income inequality. The high level of married female labor-force participation in 2005 is important for this result.Publication The Downward Spiral(2022-09-14) Greenwood, Jeremy; Guner, Nezih; Kopecky, Karen A.There have been more than 500,000 opioid overdose deaths since 2000. To analyze the opioid epidemic, a model is constructed where individuals, with and without pain, choose whether to misuse opioids knowing the probabilities of addiction and dying. These odds are functions of opioid use. Markov chains are estimated from the US data for the college and non-college educated that summarize the transitions into and out of opioid addiction as well as to a deadly overdose. A structural model is constructed that matches the estimated Markov chains. The epidemic's drivers, and the impact of medical interventions, are examined.