The Effects of Female Wage on Fertility in Korea (여성의 임금수준이 출산율에 미치는 영향 분석)
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- KDI Journal of Economic Policy
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- v.31 no.1
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- pp.105-138
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- 2009
Although the decline in fertility rate is generally observed along the history of economic development throughout the world, the continuing decline hitting below the replacement level in Korea over the recent years gathered serious social concerns on the ground that it accelerates the process of population aging. The total fertility rate in Koreareached 2.08 in 1983, and gradually fell to the levels of 1.08 in 2005 and 1.26 in 2007. The policy debate over the role of the government has been focused mainly on the level of theoretical discussion without substantial basis on firm empirical evidence and the determinants of fertility. The objective of the paper is to empirically investigate the fertility effect of the female wage, which is understood as one of the most important determinants of fertility in Koreasince 1980 focusing on one aspect of fertility, namely birth spacing. Using the Korean National Fertility Survey conducted in 2006, I estimate a duration model of first and second births taking into account individual heterogeneity, which turned out to be an important factor to control for. Compared with previous studies in the literature on the Korean fertility, the study has an advantage of using the complete pregnancy history of women in a more representative sample. Unlike the previous studies, the analysis also deals with the endogeneity of marriage by treating a certain age, rather than age at marriage, as the time in which a woman becomes exposed to the risk of pregnancy. The study shares the common problem in the literature on birth spacing of lacking relevant wage information for respondents in a retrospective survey. I estimate the wage series as a function of the basic characteristics using the annual Wage Structure Survey from 1980 to 2005, which is considered as a nationally representative sample for wage information of employees. The results suggest that the increase in female wage by 10 percent leads to a decrease in second birth hazard by 0.56~0.92 percentage points and that the increase in spouse's wage by the equal amount is accompanied by the increase in second birth hazard by 0.36~1.13 percentage points. These estimates are more precisely estimated and of smaller magnitude than those presented by the previous studies. The results are robust to the different specifications of the wage equation. The simulation analysis based on the predicted values shows that about 17% of the change in the second birth hazard over the period 1980 to 2005 was due to the change in the female wage. Although there is some limitation in data, the results can be viewed as one estimate of the role of female wage on the recent fertility decline in Korea. The question raised by the paper is not a normative one of whether a government should promote childbearing but a positive one thatexplains fertility decline. Therefore, if there is a wide consensus on promoting childbearing, the finding suggests that the policies designed to reduce the opportunity cost of women in the labor market would be effective. The recent movement of implementing a wide range of family-friendly policies including child care support, maternity leave, parental leave and tax benefit in developed countries should be understood in this context.
To create and maintain comparative supremacy as a strategic tool of business, many organizations have introduced informational technology and system. By using this system, Some companies got a beneficial value for achieving organizational goals but others could not obtain their effectiveness and efficiency. In particular, a lot of organizations that tried to make strategic supremacy with e-commercial trade are under hard condition because of poor profit. It implies that it is essential to identify and analyse the consumer who uses e-commercial trade. This paper, therefore, focusing on internet shopping malls between business and consumer as one of areas of e-commercial trades, shows the difference between consumer expectation and performance. The results of this study are as follows: First, as for the significant difference of influencing factors to consumer satisfactions according to the types of internet shopping malls, there is a meaningful difference in consumer anxiety and internet usefulness, but not in consumer service. Prior to verify the differences in detail on consumer's anxiety and internet usefulness, we examined that there is any difference between expectation and performance. T-test was used for the variants of consumer anxiety and internet usefulness, and its meaningful probability was 0.000, which means that both showed statistically significant difference. Based on the results, we also found that regardless of the types of internet shopping malls, consumer expectation was greater than performance. although the difference between expectation and performance was not equal according to the internet shopping malls. Second, a regression analysis was performed to understand the relation between consumer service, internet usefulness, consumer anxiety, and consumer satisfaction, it was found that consumer service, internet usefulness, consumer anxiety had significantly effected on consumer satisfaction. Third, To verify the relation between consumer satisfaction and repurchase-intentions, intentions to spread out, Pearson correlation analysis was used. it was found that consumer satisfaction had positive effect on both intentions. This study has some limitations because of the shorts of money and time. since the sample of this study was consumers who have ever bought one or more products via internet shopping mall, this sample was appropriate. but the major parts of sample were college students, and the sample size was so small. therefore this results should carefully be generalized. For further study, it is required to select more precise samples and to include more variables.
The scarcity is pervasive aspect of human life and is a fundamental precondition of economic behavior of consumers. Also, the effect of scarcity message is a power social influence principle used by marketers to increase the subjective desirability of products. Because valuable objects are often scare, consumers tend to infer the scarce objects are valuable. Marketers often do base promotional appeals on the principle of scarcity to increase the subjective desirability their products among consumers. Specially, advertisers and retailers often promote their products using restrictions. These restriction act to constraint consumers' ability th take advantage of the promotion and can assume several forms. For example, some promotions are advertised as limited time offers, while others limit the quantity that can be bought at the deal price by employing the statements such as 'limit one per consumer,' 'limit 5 per customer,' 'limited products for special commemoration celebration,' Some retailers use statements extensively. A recent weekly flyer by a prominent retailer limited purchase quantities on 50% of the specials advertised on front page. When consumers saw these phrase, they often infer value from the product that has limited availability or is promoted as being scarce. But, the past researchers explored a direct relationship between the purchase quantity and time limit on deal purchase intention. They also don't explored that all restriction message are not created equal. Namely, we thought that different restrictions signal deal value in different ways or different mechanism. Consumers appear to perceive that time limits are used to attract consumers to the brand, while quantity limits are necessary to reduce stockpiling. This suggests other possible differences across restrictions. For example, quantity limits could imply product quality (i.e., this product at this price is so good that purchases must be limited). In contrast, purchase preconditions force the consumer to spend a certain amount to qualify for the deal, which suggests that inferences about the absolute quality of the promoted item would decline from purchase limits (highest quality) to time limits to purchase preconditions (lowest quality). This might be expected to be particularly true for unfamiliar brands. However, a critical but elusive issue in scarcity message research is the impacts of a inferred motives on the promoted scarcity message. The past researchers not explored possibility of inferred motives on the scarcity message context. Despite various type to the quantity limits message, they didn't separated scarcity message among the quantity limits. Therefore, we apply a stricter definition of scarcity message(i.e. quantity limits) and consider scarcity message type(general scarcity message vs. special scarcity message), scarcity depth(high vs. low). The purpose of this study is to examine the effect of the scarcity message on the consumer's purchase intension. Specifically, we investigate the effect of general versus special scarcity messages on the consumer's purchase intention using the level of the scarcity depth as moderators. In other words, we postulates that the scarcity message type and scarcity depth play an essential moderating role in the relationship between the inferred motives and purchase intention. In other worlds, different from the past studies, we examine the interplay between the perceived motives and scarcity type, and between the perceived motives and scarcity depth. Both of these constructs have been examined in isolation, but a key question is whether they interact to produce an effect in reaction to the scarcity message type or scarcity depth increase. The perceived motive Inference behind the scarcity message will have important impact on consumers' reactions to the degree of scarcity depth increase. In relation ti this general question, we investigate the following specific issues. First, does consumers' inferred motives weaken the positive relationship between the scarcity depth decrease and the consumers' purchase intention, and if so, how much does it attenuate this relationship? Second, we examine the interplay between the scarcity message type and the consumers' purchase intention in the context of the scarcity depth decrease. Third, we study whether scarcity message type and scarcity depth directly affect the consumers' purchase intention. For the answer of these questions, this research is composed of 2(intention inference: existence vs. nonexistence)
The government recently announced various policies for developing big-data and artificial intelligence fields to provide a great opportunity to the public with respect to disclosure of high-quality data within public institutions. KSURE(Korea Trade Insurance Corporation) is a major public institution for financial policy in Korea, and thus the company is strongly committed to backing export companies with various systems. Nevertheless, there are still fewer cases of realized business model based on big-data analyses. In this situation, this paper aims to develop a new business model which can be applied to an ex-ante prediction for the likelihood of the insurance accident of credit guarantee. We utilize internal data from KSURE which supports export companies in Korea and apply machine learning models. Then, we conduct performance comparison among the predictive models including Logistic Regression, Random Forest, XGBoost, LightGBM, and DNN(Deep Neural Network). For decades, many researchers have tried to find better models which can help to predict bankruptcy since the ex-ante prediction is crucial for corporate managers, investors, creditors, and other stakeholders. The development of the prediction for financial distress or bankruptcy was originated from Smith(1930), Fitzpatrick(1932), or Merwin(1942). One of the most famous models is the Altman's Z-score model(Altman, 1968) which was based on the multiple discriminant analysis. This model is widely used in both research and practice by this time. The author suggests the score model that utilizes five key financial ratios to predict the probability of bankruptcy in the next two years. Ohlson(1980) introduces logit model to complement some limitations of previous models. Furthermore, Elmer and Borowski(1988) develop and examine a rule-based, automated system which conducts the financial analysis of savings and loans. Since the 1980s, researchers in Korea have started to examine analyses on the prediction of financial distress or bankruptcy. Kim(1987) analyzes financial ratios and develops the prediction model. Also, Han et al.(1995, 1996, 1997, 2003, 2005, 2006) construct the prediction model using various techniques including artificial neural network. Yang(1996) introduces multiple discriminant analysis and logit model. Besides, Kim and Kim(2001) utilize artificial neural network techniques for ex-ante prediction of insolvent enterprises. After that, many scholars have been trying to predict financial distress or bankruptcy more precisely based on diverse models such as Random Forest or SVM. One major distinction of our research from the previous research is that we focus on examining the predicted probability of default for each sample case, not only on investigating the classification accuracy of each model for the entire sample. Most predictive models in this paper show that the level of the accuracy of classification is about 70% based on the entire sample. To be specific, LightGBM model shows the highest accuracy of 71.1% and Logit model indicates the lowest accuracy of 69%. However, we confirm that there are open to multiple interpretations. In the context of the business, we have to put more emphasis on efforts to minimize type 2 error which causes more harmful operating losses for the guaranty company. Thus, we also compare the classification accuracy by splitting predicted probability of the default into ten equal intervals. When we examine the classification accuracy for each interval, Logit model has the highest accuracy of 100% for 0~10% of the predicted probability of the default, however, Logit model has a relatively lower accuracy of 61.5% for 90~100% of the predicted probability of the default. On the other hand, Random Forest, XGBoost, LightGBM, and DNN indicate more desirable results since they indicate a higher level of accuracy for both 0~10% and 90~100% of the predicted probability of the default but have a lower level of accuracy around 50% of the predicted probability of the default. When it comes to the distribution of samples for each predicted probability of the default, both LightGBM and XGBoost models have a relatively large number of samples for both 0~10% and 90~100% of the predicted probability of the default. Although Random Forest model has an advantage with regard to the perspective of classification accuracy with small number of cases, LightGBM or XGBoost could become a more desirable model since they classify large number of cases into the two extreme intervals of the predicted probability of the default, even allowing for their relatively low classification accuracy. Considering the importance of type 2 error and total prediction accuracy, XGBoost and DNN show superior performance. Next, Random Forest and LightGBM show good results, but logistic regression shows the worst performance. However, each predictive model has a comparative advantage in terms of various evaluation standards. For instance, Random Forest model shows almost 100% accuracy for samples which are expected to have a high level of the probability of default. Collectively, we can construct more comprehensive ensemble models which contain multiple classification machine learning models and conduct majority voting for maximizing its overall performance.