Women's Employment: Joining Explanations Based on Individual Characteristics and on Contextual Factors

Cipollone, Angela; D’Ippoliti, Carlo

doi:10.1111/j.1536-7150.2011.00790.x

Cited by 12 publications

(11 citation statements)

References 48 publications

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“…Here is an example of the gamble (See Appendix for a full set of questions and definitions): Several methods have been applied to construct risk aversion from the IFLS dataset. First, risk aversion developed by order based on the risk-choice degree (Cipollone and D'Ippoliti, 2011). Second is the binary variable, which simplifies either risk-loving or risk-averse choice (Cameron and Shah, 2015).…”

Section: Methods 21 Data Variable Construction and Descriptive Statmentioning

confidence: 99%

Overtime risk preference and its determinants: Evidence from Indonesia

Purnama

Nugroho

2020

SMBR

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<p>This research aims at examining the potential determinants of overtime risk preference in Indonesia–one of the most diverse countries in the world. We used household and individual data as well, taken from the fourth and fifth wave of the Indonesia Family Life Survey (IFLS). The risk aversion of the individual was observed and elicited using hypothetical gambling questions. From the result of Ordinary Least Square (OLS) estimation, we find preliminary evidence that there is a decrease in risk-aversion among individuals over time. However, there is little evidence that catastrophe will alter risk choice as a proxy for a significant life event. Furthermore, our empirical finding also revealed that risk tolerance is motivated not only by demographic factors but also by the education level and individual’s time preference.</p><p> </p><p> </p><div id="gtx-anchor" style="position: absolute; visibility: hidden; left: 278px; top: 80px; width: 27px; height: 12px;"> </div><div class="jfk-bubble gtx-bubble" style="visibility: visible; left: -131px; top: 102px; opacity: 1;"> </div><div id="gtx-anchor" style="position: absolute; visibility: hidden; left: 278px; top: 80px; width: 27px; height: 12px;"> </div><div class="jfk-bubble gtx-bubble" style="visibility: visible; left: -131px; top: 102px; opacity: 1;"> </div>

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Section: Methods 21 Data Variable Construction and Descriptive Statmentioning

confidence: 99%

Overtime risk preference and its determinants: Evidence from Indonesia

Purnama

Nugroho

2020

SMBR

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“…Hasil ini sesuai dengan penelitian sebelumnya (Asiati, 2002;Cipollone & D'Ippoliti, 2011;Hosney, 2016;Majumder, 2012;Mehrotra & Parida, 2017;Octaviana, 2017;Schaner & Das, 2016;Setyawati, 2010;Shi, 2015;Spierings dkk., 2010;Widarti, 1998) bahwa pendidikan penting bagi perempuan terutama dalam menentukan status bekerja perempuan. Investasi bidang pendidikan menentukan partisipasi perempuan di pasar kerja.…”

Section: Pendidikanunclassified

Determinan perempuan bekerja di Jawa Barat

Damayanti¹

2021

JKI

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This study aims to determine the characteristics of female labor force, women’s main employment, and the individual and contextual factors that influence those two situations in West Java Province. The data sources used for this study are National Social-Economic Survey on March 2018 as well as publications on Gross Domestic Product (GDP) and Statistics of Education. This study applies three-levels binary logistics (individual, household, and city/regency) for data analysis. The independent variables that significantly affects women’s employment status are age, education, marital status, household per capita expenditure, number of non-productive household members, household members, sex ratio, residence status, GDP per capita, agricultural contribution to GDP, and mean years schooling. However, access to information does not significantly influence the working status of women. This paper indicates that education is the primary determinant factor in regard to women’s participation in labor force. Education is suggested to improve women’s skills and knowledge that resulted in the advancement of the quality of female labors.

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“…In choosing the specifications for the different equations that contribute to determine participation patterns, we looked at the 'usual suspects' identified in the literature (see Del Boca andWetzels, 2007, andBoeri et al, 2008 for surveys, but also see for instance Saurel-Cubizolles et al, 1999;Bratti et al, 2005;Attanasio et al, 2008;Genre et al, 2010;Cipollone and D'Ippoliti, 2011), as well as at policies. Thévenon (2011) performs a principal component analysis aimed at identifying 2.…”

Section: The Model and The Baseline Scenariomentioning

confidence: 99%