We provide causal evidence that delaying fertility leads to a decrease in the adjusted gender wage gap. To avoid possible reverse causality, we employ an instrumental variable approach. We introduce several instruments, among them a novel one: international variation in the introduction of the contraceptive pill. Our estimates are large: a one-year delay in fertility leads to a 12% fall in the gender wage gap
When hiring employees, an employer might use information from the group to these employees belong as a proxy for productivity related unobserved variables, such as the probability of becoming a parent. We put this conjecture to test by collecting data from over 50 countries and 40 years. We find that delaying fertility leads to a fall in gender inequality, a finding that is consistent with statistical discrimination.
Fertility, contraceptives and gender inequalityGRAPE
Our analysis shows that increasing the age at first birth is associated with a substantial decline in gender wage gaps: postponing first birth by a year reduces the gap by around 15%. In order to establish causality, we propose a novel instrument that exploits international variation in approval of oral contraceptives (the pill). Our estimates are consistent with a model of statistical discrimination where employers offer lower wages to women to hedge the expected costs associated with childbearing and childrearing.
Statistical discrimination is a possible, rational motive behind the persistent differences in earnings between men and women. Employers could women to bear a larger share of the burden associated with having children, and subsequently discount that on wages. We test the empirical validity of this claim using data from over 50 countries and 40 years. Using IV we find causal evidence consistent with this hypothesis. Postponing birth by one year leads to large falls in the adjusted gender wage gap.
We study the relationship between gender inequality among youth and fertility timing. We show that postponing fertility by one year leads to a substantial reduction of gender inequality. This finding is consistent with a simple statistical discrimination model, where employers offer lower wages when they anticipated costs related to fertility, and that these costs are higher for female employees.
Statistical discrimination at young age: new evidence from four decades of in...GRAPE
Statistical discrimination offers a compelling narrative on gender wage gaps during the early stages of the career. Expecting absences related to child-bearing and child-rearing, the employers discount productivity to adjust for the probable losses such as costs associated with finding substitutes, leaving customers, etc. If that is the case, lower and delayed fertility should imply lower discount in wages, and consequently reductions in the gender pay gap among entrants. We put this conjecture to test against the data. We provide a novel set of estimates of adjusted gender wage gaps among youth for 56 countries spanning four decades. We estimate that postponing childbirth by a year reduce the adjusted gap 2 percentage points (15%). We show that this estimate is consistent with statistical discrimination, but for some countries the estimates of AGWG imply that either statistical discrimination is not accurate or taste-based mechanisms are also at play.
Statistical gender discrimination: evidence from young workers across four de...GRAPE
Statistical discrimination offers a compelling narrative on gender wage gaps among younger workers. Employers could discount women's wages to adjust for probable costs linked to childbearing. Given trends towards lower and delayed fertility one should observe a lower discount in wages and a reduction in the gender wage gap among entrants. We test this conjecture using estimates of adjusted gender wage gap among young workers from 56 countries. We find that postponing childbirth by a year reduces the adjusted gap by two percentage points (15%). We further benchmark the implied gender inequality with the help of time-use data.
Delayed fertility and statistical discrimination against womenGRAPE
Statistical discrimination offers a compelling narrative on gender wage gaps during the early stages of the career. Expecting absences related to child-bearing and child-rearing, the employers discount productivity to adjust for the probable losses such as costs associated with finding substitutes, leaving customers, etc. If that is the case, lower and delayed fertility should imply lower discount in wages, and consequently reductions in the gender pay gap among entrants. We put this conjecture to test against the data. We provide a novel set of estimates of adjusted gender wage gaps among youth for 56 countries spanning four decades. We estimate that postponing childbirth by a year reduce the adjusted gap 2 percentage points (15%). We show that this estimate is consistent with statistical discrimination, but for some countries the estimates of AGWG imply that either statistical discrimination is not accurate or taste-based mechanisms are also at play.
Statistical discrimination offers a compelling story to understand gender wage gaps, at least during the early stages of the career. Employers believe that women will get pregnant with a positive probability, which leads to potential losses, eg. costs associated with finding substitutes, potential losses in customers, etc. Employers then have an incentive to offer women lower wages, in order to discount for future losses. If that is the case, lower and delayed fertility should imply lower discount in wages, and consequently reductions in the gender pay gap among entrants.
In order to test for this hypothesis, we collect individual level data from European countries dating back to the early 1990. Having compiled these data, we compute the adjusted gender wage gap for workers at the early stages of their career, that is for those aged 25 to 29. These adjuste differences are obtained using the non-parametric approach pioneered by Nopo. We then regress these measures on macro data on fertility changes. If the statistical discrimination hypothesis is correct, we should expect that the secular decline in fertility observed in Europe over the last 30 years is correlated with lower estimates of the gender wage gap. Our estimates suggest that this is indeed the case. Using the age at first birth as a proxy for fertility, we find that postponing childbirth by an additional year leads to a reduction of .18 in the adjusted gap.
One caveat with this result is that fertility can be endogeneous to wages. If women were to receive higher wages, they might choose to postpone childbirths. To address this issue, we instrument our measure of fertility with the number of years since the introduction of the pill in the country. This measures varies across countries and over time, while at the same time it is fairly exogeneous, as the introduction of the pill occurred several generations back, normally in the mid-60 and 70s. First stage regressions reveal that the instrument correlates well with mean age at first birth. Second stage estimates are still significant, though they are smaller in magnitude. We conclude that recent changes in fertility helped to reduce the gender wage gap among women entering to the labor market.
When hiring employees, an employer might use information from the group to these employees belong as a proxy for productivity related unobserved variables, such as the probability of becoming a parent. We put this conjecture to test by collecting data from over 50 countries and 40 years. We find that delaying fertility leads to a fall in gender inequality, a finding that is consistent with statistical discrimination.
Fertility, contraceptives and gender inequalityGRAPE
Our analysis shows that increasing the age at first birth is associated with a substantial decline in gender wage gaps: postponing first birth by a year reduces the gap by around 15%. In order to establish causality, we propose a novel instrument that exploits international variation in approval of oral contraceptives (the pill). Our estimates are consistent with a model of statistical discrimination where employers offer lower wages to women to hedge the expected costs associated with childbearing and childrearing.
Statistical discrimination is a possible, rational motive behind the persistent differences in earnings between men and women. Employers could women to bear a larger share of the burden associated with having children, and subsequently discount that on wages. We test the empirical validity of this claim using data from over 50 countries and 40 years. Using IV we find causal evidence consistent with this hypothesis. Postponing birth by one year leads to large falls in the adjusted gender wage gap.
We study the relationship between gender inequality among youth and fertility timing. We show that postponing fertility by one year leads to a substantial reduction of gender inequality. This finding is consistent with a simple statistical discrimination model, where employers offer lower wages when they anticipated costs related to fertility, and that these costs are higher for female employees.
Statistical discrimination at young age: new evidence from four decades of in...GRAPE
Statistical discrimination offers a compelling narrative on gender wage gaps during the early stages of the career. Expecting absences related to child-bearing and child-rearing, the employers discount productivity to adjust for the probable losses such as costs associated with finding substitutes, leaving customers, etc. If that is the case, lower and delayed fertility should imply lower discount in wages, and consequently reductions in the gender pay gap among entrants. We put this conjecture to test against the data. We provide a novel set of estimates of adjusted gender wage gaps among youth for 56 countries spanning four decades. We estimate that postponing childbirth by a year reduce the adjusted gap 2 percentage points (15%). We show that this estimate is consistent with statistical discrimination, but for some countries the estimates of AGWG imply that either statistical discrimination is not accurate or taste-based mechanisms are also at play.
Statistical gender discrimination: evidence from young workers across four de...GRAPE
Statistical discrimination offers a compelling narrative on gender wage gaps among younger workers. Employers could discount women's wages to adjust for probable costs linked to childbearing. Given trends towards lower and delayed fertility one should observe a lower discount in wages and a reduction in the gender wage gap among entrants. We test this conjecture using estimates of adjusted gender wage gap among young workers from 56 countries. We find that postponing childbirth by a year reduces the adjusted gap by two percentage points (15%). We further benchmark the implied gender inequality with the help of time-use data.
Delayed fertility and statistical discrimination against womenGRAPE
Statistical discrimination offers a compelling narrative on gender wage gaps during the early stages of the career. Expecting absences related to child-bearing and child-rearing, the employers discount productivity to adjust for the probable losses such as costs associated with finding substitutes, leaving customers, etc. If that is the case, lower and delayed fertility should imply lower discount in wages, and consequently reductions in the gender pay gap among entrants. We put this conjecture to test against the data. We provide a novel set of estimates of adjusted gender wage gaps among youth for 56 countries spanning four decades. We estimate that postponing childbirth by a year reduce the adjusted gap 2 percentage points (15%). We show that this estimate is consistent with statistical discrimination, but for some countries the estimates of AGWG imply that either statistical discrimination is not accurate or taste-based mechanisms are also at play.
Statistical discrimination offers a compelling story to understand gender wage gaps, at least during the early stages of the career. Employers believe that women will get pregnant with a positive probability, which leads to potential losses, eg. costs associated with finding substitutes, potential losses in customers, etc. Employers then have an incentive to offer women lower wages, in order to discount for future losses. If that is the case, lower and delayed fertility should imply lower discount in wages, and consequently reductions in the gender pay gap among entrants.
In order to test for this hypothesis, we collect individual level data from European countries dating back to the early 1990. Having compiled these data, we compute the adjusted gender wage gap for workers at the early stages of their career, that is for those aged 25 to 29. These adjuste differences are obtained using the non-parametric approach pioneered by Nopo. We then regress these measures on macro data on fertility changes. If the statistical discrimination hypothesis is correct, we should expect that the secular decline in fertility observed in Europe over the last 30 years is correlated with lower estimates of the gender wage gap. Our estimates suggest that this is indeed the case. Using the age at first birth as a proxy for fertility, we find that postponing childbirth by an additional year leads to a reduction of .18 in the adjusted gap.
One caveat with this result is that fertility can be endogeneous to wages. If women were to receive higher wages, they might choose to postpone childbirths. To address this issue, we instrument our measure of fertility with the number of years since the introduction of the pill in the country. This measures varies across countries and over time, while at the same time it is fairly exogeneous, as the introduction of the pill occurred several generations back, normally in the mid-60 and 70s. First stage regressions reveal that the instrument correlates well with mean age at first birth. Second stage estimates are still significant, though they are smaller in magnitude. We conclude that recent changes in fertility helped to reduce the gender wage gap among women entering to the labor market.
After couples have their first child, parents become more likely to agree with statements showing traditional gender norms. In this research I study how common this finding is across countries,and whether differences across countries can shed light on the reasons
Childbearing and attitudes towards gender normsGRAPE
The research studies whether major life events affect the perception of social norms. Specifically, I focus on how giving birth to a first child affects attitudes towards gender norms. I find that after childbirth people become more likely to agree with traditional division of household chores. Effects are contingent on country and demographic characteristics
Professor Jonathan Bradshaw. Child Well-being. CHIMAT Annual Conference: Informed Decisions and Intelligent Investment: The Future of Child and Maternal Health Services, Royal York Hotel, York, 18 March 2010.
Does childbearing makes us more conservative?GRAPE
The research shows that upon becoming parents, mothers (and fathers) embrace more traditional norms in a number of domains. They are more likely to put a higher value on family that before, and they would even conform to a male breadwinner model. The change in attitudes is more pronounced in Central and Eastern European countries, and almost negligible elsewhere. I further show that this is related to a series of characteristics of those countries. Noteworthy, changes are more frequent in countries where women receive less support during motherhood from the state, and where differences in norms across genders are more marked.
Impacts of Cash Transfers on Adolescents' & Young Women's Well-Being Globally...The Transfer Project
Tia Palermo's presentation for the joint UNICEF & Gates Foundation Tanzania Adolescent Symposium in Dar es Salaam on 7 February 2018.
Using evidence from around the world, Tia outlines what we know about cash transfers impacts on youth and young women's well-being.
Statistical discrimination at young age (the poster)GRAPE
During the last 30 years, women started giving birth at higher ages. While in 1990 the average age at first birth was 26, in 2019 this number was already 29. Given this trend, we ask whether the decision to postpone fertility helped to close the gender wage gap. Our results show that yes. Postponing fertility by a year lead to a fall in the gender wage gap by 2 percent, or approximately 12% of the average gap.
Error detection in census data age reportingcimran15
Age is an important demographic variable that must be carefully considered in all demographic survey. The objective of this study is to conduct a comprehensive assessment of the age reporting in Census data. The study gave an estimate of age misrepresentation in the Nigeria 2006 Population and Housing Census Data.
The data used in this study was obtained from the 2006 Population and Housing Census Priority Table, Volume(III)published by the National Population Commission, Abuja, Nigeria, in April
2010.
Age heaping and digit preference were measured using modified Whipple's index and Myers index. Age Sex accuracy was also measured using the United Nation's age-sex accuracy index.
The reported Whipple's index for both sexes was 251 indicating presence of age heaping and it also showed age heaping at terminal digit 0 and 5 as 268 and 233 respectively. The Myers index had an overall index of 50.9, 49 for male and 52.82 for female population.
The evaluation of Nigeria 2006 Population and Housing Census Data based on the technique applied in this study indicates that the data is of poor quality as a result of the presence of age heaping and digit preference in recorded ages. Therefore modern methods such as a systematic data management system, compulsion to register birth, and standard smoothing techniques are thereby recommended for future data collection.
Seminar: Gender Board Diversity through Ownership NetworksGRAPE
Seminar on gender diversity spillovers through ownership networks at FAME|GRAPE. Presenting novel research. Studies in economics and management using econometrics methods.
The European Unemployment Puzzle: implications from population agingGRAPE
We study the link between the evolving age structure of the working population and unemployment. We build a large new Keynesian OLG model with a realistic age structure, labor market frictions, sticky prices, and aggregate shocks. Once calibrated to the European economy, we quantify the extent to which demographic changes over the last three decades have contributed to the decline of the unemployment rate. Our findings yield important implications for the future evolution of unemployment given the anticipated further aging of the working population in Europe. We also quantify the implications for optimal monetary policy: lowering inflation volatility becomes less costly in terms of GDP and unemployment volatility, which hints that optimal monetary policy may be more hawkish in an aging society. Finally, our results also propose a partial reversal of the European-US unemployment puzzle due to the fact that the share of young workers is expected to remain robust in the US.
More Related Content
Similar to Statistical discrimination at young age
After couples have their first child, parents become more likely to agree with statements showing traditional gender norms. In this research I study how common this finding is across countries,and whether differences across countries can shed light on the reasons
Childbearing and attitudes towards gender normsGRAPE
The research studies whether major life events affect the perception of social norms. Specifically, I focus on how giving birth to a first child affects attitudes towards gender norms. I find that after childbirth people become more likely to agree with traditional division of household chores. Effects are contingent on country and demographic characteristics
Professor Jonathan Bradshaw. Child Well-being. CHIMAT Annual Conference: Informed Decisions and Intelligent Investment: The Future of Child and Maternal Health Services, Royal York Hotel, York, 18 March 2010.
Does childbearing makes us more conservative?GRAPE
The research shows that upon becoming parents, mothers (and fathers) embrace more traditional norms in a number of domains. They are more likely to put a higher value on family that before, and they would even conform to a male breadwinner model. The change in attitudes is more pronounced in Central and Eastern European countries, and almost negligible elsewhere. I further show that this is related to a series of characteristics of those countries. Noteworthy, changes are more frequent in countries where women receive less support during motherhood from the state, and where differences in norms across genders are more marked.
Impacts of Cash Transfers on Adolescents' & Young Women's Well-Being Globally...The Transfer Project
Tia Palermo's presentation for the joint UNICEF & Gates Foundation Tanzania Adolescent Symposium in Dar es Salaam on 7 February 2018.
Using evidence from around the world, Tia outlines what we know about cash transfers impacts on youth and young women's well-being.
Statistical discrimination at young age (the poster)GRAPE
During the last 30 years, women started giving birth at higher ages. While in 1990 the average age at first birth was 26, in 2019 this number was already 29. Given this trend, we ask whether the decision to postpone fertility helped to close the gender wage gap. Our results show that yes. Postponing fertility by a year lead to a fall in the gender wage gap by 2 percent, or approximately 12% of the average gap.
Error detection in census data age reportingcimran15
Age is an important demographic variable that must be carefully considered in all demographic survey. The objective of this study is to conduct a comprehensive assessment of the age reporting in Census data. The study gave an estimate of age misrepresentation in the Nigeria 2006 Population and Housing Census Data.
The data used in this study was obtained from the 2006 Population and Housing Census Priority Table, Volume(III)published by the National Population Commission, Abuja, Nigeria, in April
2010.
Age heaping and digit preference were measured using modified Whipple's index and Myers index. Age Sex accuracy was also measured using the United Nation's age-sex accuracy index.
The reported Whipple's index for both sexes was 251 indicating presence of age heaping and it also showed age heaping at terminal digit 0 and 5 as 268 and 233 respectively. The Myers index had an overall index of 50.9, 49 for male and 52.82 for female population.
The evaluation of Nigeria 2006 Population and Housing Census Data based on the technique applied in this study indicates that the data is of poor quality as a result of the presence of age heaping and digit preference in recorded ages. Therefore modern methods such as a systematic data management system, compulsion to register birth, and standard smoothing techniques are thereby recommended for future data collection.
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Seminar on gender diversity spillovers through ownership networks at FAME|GRAPE. Presenting novel research. Studies in economics and management using econometrics methods.
The European Unemployment Puzzle: implications from population agingGRAPE
We study the link between the evolving age structure of the working population and unemployment. We build a large new Keynesian OLG model with a realistic age structure, labor market frictions, sticky prices, and aggregate shocks. Once calibrated to the European economy, we quantify the extent to which demographic changes over the last three decades have contributed to the decline of the unemployment rate. Our findings yield important implications for the future evolution of unemployment given the anticipated further aging of the working population in Europe. We also quantify the implications for optimal monetary policy: lowering inflation volatility becomes less costly in terms of GDP and unemployment volatility, which hints that optimal monetary policy may be more hawkish in an aging society. Finally, our results also propose a partial reversal of the European-US unemployment puzzle due to the fact that the share of young workers is expected to remain robust in the US.
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Cel: oszacować wpływ inkluzywności władz spółek na ich wyniki.
Co wiemy?
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• Większość firm nie ma w ogóle kobiet we władzach
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Even tho Pi network is not listed on any exchange yet.
Buying/Selling or investing in pi network coins is highly possible through the help of vendors. You can buy from vendors[ buy directly from the pi network miners and resell it]. I will leave the telegram contact of my personal vendor.
@Pi_vendor_247
The secret way to sell pi coins effortlessly.DOT TECH
Well as we all know pi isn't launched yet. But you can still sell your pi coins effortlessly because some whales in China are interested in holding massive pi coins. And they are willing to pay good money for it. If you are interested in selling I will leave a contact for you. Just telegram this number below. I sold about 3000 pi coins to him and he paid me immediately.
Telegram: @Pi_vendor_247
how to sell pi coins at high rate quickly.DOT TECH
Where can I sell my pi coins at a high rate.
Pi is not launched yet on any exchange. But one can easily sell his or her pi coins to investors who want to hold pi till mainnet launch.
This means crypto whales want to hold pi. And you can get a good rate for selling pi to them. I will leave the telegram contact of my personal pi vendor below.
A vendor is someone who buys from a miner and resell it to a holder or crypto whale.
Here is the telegram contact of my vendor:
@Pi_vendor_247
how to sell pi coins effectively (from 50 - 100k pi)DOT TECH
Anywhere in the world, including Africa, America, and Europe, you can sell Pi Network Coins online and receive cash through online payment options.
Pi has not yet been launched on any exchange because we are currently using the confined Mainnet. The planned launch date for Pi is June 28, 2026.
Reselling to investors who want to hold until the mainnet launch in 2026 is currently the sole way to sell.
Consequently, right now. All you need to do is select the right pi network provider.
Who is a pi merchant?
An individual who buys coins from miners on the pi network and resells them to investors hoping to hang onto them until the mainnet is launched is known as a pi merchant.
debuts.
I'll provide you the Telegram username
@Pi_vendor_247
how to sell pi coins in all Africa Countries.DOT TECH
Yes. You can sell your pi network for other cryptocurrencies like Bitcoin, usdt , Ethereum and other currencies And this is done easily with the help from a pi merchant.
What is a pi merchant ?
Since pi is not launched yet in any exchange. The only way you can sell right now is through merchants.
A verified Pi merchant is someone who buys pi network coins from miners and resell them to investors looking forward to hold massive quantities of pi coins before mainnet launch in 2026.
I will leave the telegram contact of my personal pi merchant to trade with.
@Pi_vendor_247
Turin Startup Ecosystem 2024 - Ricerca sulle Startup e il Sistema dell'Innov...Quotidiano Piemontese
Turin Startup Ecosystem 2024
Una ricerca de il Club degli Investitori, in collaborazione con ToTeM Torino Tech Map e con il supporto della ESCP Business School e di Growth Capital
how to swap pi coins to foreign currency withdrawable.DOT TECH
As of my last update, Pi is still in the testing phase and is not tradable on any exchanges.
However, Pi Network has announced plans to launch its Testnet and Mainnet in the future, which may include listing Pi on exchanges.
The current method for selling pi coins involves exchanging them with a pi vendor who purchases pi coins for investment reasons.
If you want to sell your pi coins, reach out to a pi vendor and sell them to anyone looking to sell pi coins from any country around the globe.
Below is the contact information for my personal pi vendor.
Telegram: @Pi_vendor_247
US Economic Outlook - Being Decided - M Capital Group August 2021.pdfpchutichetpong
The U.S. economy is continuing its impressive recovery from the COVID-19 pandemic and not slowing down despite re-occurring bumps. The U.S. savings rate reached its highest ever recorded level at 34% in April 2020 and Americans seem ready to spend. The sectors that had been hurt the most by the pandemic specifically reduced consumer spending, like retail, leisure, hospitality, and travel, are now experiencing massive growth in revenue and job openings.
Could this growth lead to a “Roaring Twenties”? As quickly as the U.S. economy contracted, experiencing a 9.1% drop in economic output relative to the business cycle in Q2 2020, the largest in recorded history, it has rebounded beyond expectations. This surprising growth seems to be fueled by the U.S. government’s aggressive fiscal and monetary policies, and an increase in consumer spending as mobility restrictions are lifted. Unemployment rates between June 2020 and June 2021 decreased by 5.2%, while the demand for labor is increasing, coupled with increasing wages to incentivize Americans to rejoin the labor force. Schools and businesses are expected to fully reopen soon. In parallel, vaccination rates across the country and the world continue to rise, with full vaccination rates of 50% and 14.8% respectively.
However, it is not completely smooth sailing from here. According to M Capital Group, the main risks that threaten the continued growth of the U.S. economy are inflation, unsettled trade relations, and another wave of Covid-19 mutations that could shut down the world again. Have we learned from the past year of COVID-19 and adapted our economy accordingly?
“In order for the U.S. economy to continue growing, whether there is another wave or not, the U.S. needs to focus on diversifying supply chains, supporting business investment, and maintaining consumer spending,” says Grace Feeley, a research analyst at M Capital Group.
While the economic indicators are positive, the risks are coming closer to manifesting and threatening such growth. The new variants spreading throughout the world, Delta, Lambda, and Gamma, are vaccine-resistant and muddy the predictions made about the economy and health of the country. These variants bring back the feeling of uncertainty that has wreaked havoc not only on the stock market but the mindset of people around the world. MCG provides unique insight on how to mitigate these risks to possibly ensure a bright economic future.
where can I find a legit pi merchant onlineDOT TECH
Yes. This is very easy what you need is a recommendation from someone who has successfully traded pi coins before with a merchant.
Who is a pi merchant?
A pi merchant is someone who buys pi network coins and resell them to Investors looking forward to hold thousands of pi coins before the open mainnet.
I will leave the telegram contact of my personal pi merchant to trade with
@Pi_vendor_247
What website can I sell pi coins securely.DOT TECH
Currently there are no website or exchange that allow buying or selling of pi coins..
But you can still easily sell pi coins, by reselling it to exchanges/crypto whales interested in holding thousands of pi coins before the mainnet launch.
Who is a pi merchant?
A pi merchant is someone who buys pi coins from miners and resell to these crypto whales and holders of pi..
This is because pi network is not doing any pre-sale. The only way exchanges can get pi is by buying from miners and pi merchants stands in between the miners and the exchanges.
How can I sell my pi coins?
Selling pi coins is really easy, but first you need to migrate to mainnet wallet before you can do that. I will leave the telegram contact of my personal pi merchant to trade with.
Tele-gram.
@Pi_vendor_247
how to sell pi coins in South Korea profitably.DOT TECH
Yes. You can sell your pi network coins in South Korea or any other country, by finding a verified pi merchant
What is a verified pi merchant?
Since pi network is not launched yet on any exchange, the only way you can sell pi coins is by selling to a verified pi merchant, and this is because pi network is not launched yet on any exchange and no pre-sale or ico offerings Is done on pi.
Since there is no pre-sale, the only way exchanges can get pi is by buying from miners. So a pi merchant facilitates these transactions by acting as a bridge for both transactions.
How can i find a pi vendor/merchant?
Well for those who haven't traded with a pi merchant or who don't already have one. I will leave the telegram id of my personal pi merchant who i trade pi with.
Tele gram: @Pi_vendor_247
#pi #sell #nigeria #pinetwork #picoins #sellpi #Nigerian #tradepi #pinetworkcoins #sellmypi
1. Statistical discrimination at young age:
Statistical discrimination at young age:
evidence from young workers across four decades and 56 countries
Joanna Tyrowicz [FAME|GRAPE, University of Warsaw & IZA ]
Lucas van der Velde [FAME|GRAPE & Warsaw School of Economics]
Congress of the European Economic Association
August 2022
2. Statistical discrimination at young age:
Motivation
Motivation – textbook case for statistical discrimination
Fertility (-related absences) as premise for gender inequality
fertility plans → hiring decisions
(Becker et al., 2019)
child bearing → wage loss among mothers (not fathers)
(Landais & Kleven, 2019; Cukrowska-Torzewska & Matysiak, 2017; Pertold-Gebicka, 2014)
3. Statistical discrimination at young age:
Motivation
Motivation – textbook case for statistical discrimination
Fertility (-related absences) as premise for gender inequality
fertility plans → hiring decisions
(Becker et al., 2019)
child bearing → wage loss among mothers (not fathers)
(Landais & Kleven, 2019; Cukrowska-Torzewska & Matysiak, 2017; Pertold-Gebicka, 2014)
Demographic trends: ↑ age at first birth and ↓ # of births
⇒ less reasons for statistical discrimination
4. Statistical discrimination at young age:
Motivation
Motivation – textbook case for statistical discrimination
Fertility (-related absences) as premise for gender inequality
fertility plans → hiring decisions
(Becker et al., 2019)
child bearing → wage loss among mothers (not fathers)
(Landais & Kleven, 2019; Cukrowska-Torzewska & Matysiak, 2017; Pertold-Gebicka, 2014)
Demographic trends: ↑ age at first birth and ↓ # of births
⇒ less reasons for statistical discrimination
What we do
study gender wage gaps among labor market entrants
explore the role of delayed fertility
5. Statistical discrimination at young age:
Motivation
Motivation – textbook case for statistical discrimination
Fertility (-related absences) as premise for gender inequality
fertility plans → hiring decisions
(Becker et al., 2019)
child bearing → wage loss among mothers (not fathers)
(Landais & Kleven, 2019; Cukrowska-Torzewska & Matysiak, 2017; Pertold-Gebicka, 2014)
Demographic trends: ↑ age at first birth and ↓ # of births
⇒ less reasons for statistical discrimination
What we do
study gender wage gaps among labor market entrants
explore the role of delayed fertility → implicit test of statistical discrimination
6. Statistical discrimination at young age:
Motivation
Our contribution
We uncover a link from timing of fertility to (adjusted) gender wage gaps
7. Statistical discrimination at young age:
Motivation
Our contribution
We uncover a link from timing of fertility to (adjusted) gender wage gaps
Comparable measures of AGWG (across c & t) for entrants
8. Statistical discrimination at young age:
Motivation
Our contribution
We uncover a link from timing of fertility to (adjusted) gender wage gaps
Comparable measures of AGWG (across c & t) for entrants
Causal evidence: several instruments
Duration of compulsory education (multiple reforms)
9. Statistical discrimination at young age:
Motivation
Our contribution
We uncover a link from timing of fertility to (adjusted) gender wage gaps
Comparable measures of AGWG (across c & t) for entrants
Causal evidence: several instruments
Duration of compulsory education (multiple reforms)
Military conscription (many changes)
10. Statistical discrimination at young age:
Motivation
Our contribution
We uncover a link from timing of fertility to (adjusted) gender wage gaps
Comparable measures of AGWG (across c & t) for entrants
Causal evidence: several instruments
Duration of compulsory education (multiple reforms)
Military conscription (many changes)
New IV: international variation in “pill” admission
(in the US: Goldin & Katz, 2002; Bailey, 2006; Oltmans-Ananat & Hungerman, 2012)
11. Statistical discrimination at young age:
Data & method
What we would like to do
We would like to estimate the following regression
AGWGc,t = βi + β × Fertilityc,t + γXc,t + ϵc,t
12. Statistical discrimination at young age:
Data & method
What we would like to do
We would like to estimate the following regression
AGWGc,t = βi + β × Fertilityc,t + γXc,t + ϵc,t
Fertility: use mean age at first birth
TFR is noisy → we want the “risk” by employers at 20 < age < 30
13. Statistical discrimination at young age:
Data & method
What we would like to do
We would like to estimate the following regression
AGWGc,t = βi + β × Fertilityc,t + γXc,t + ϵc,t
Fertility: use mean age at first birth
TFR is noisy → we want the “risk” by employers at 20 < age < 30
AGWG: obtain own estimates
→ adjust raw GWG for 20 < age < 30
But: fertility decisions endogenous to labor force participation & AGWG
14. Statistical discrimination at young age:
Data & method
What we would like to do
We would like to estimate the following regression
AGWGc,t = βi + β × Fertilityc,t + γXc,t + ϵc,t
Fertility: use mean age at first birth
TFR is noisy → we want the “risk” by employers at 20 < age < 30
AGWG: obtain own estimates
→ adjust raw GWG for 20 < age < 30
But: fertility decisions endogenous to labor force participation & AGWG → need to instrument
15. Statistical discrimination at young age:
Data & method
(I) Fertility data
We use mean age at first birth (MAB) as a measure of fertility
Direct link to probability of becoming a parent
Less noisy than alternatives
Total fertility rate, age specific fertility, childlessness
Data collected from a variety of sources
Eurostat, UNECE, OECD, Human Fertility Database + bureaus of statistics + papers
16. Statistical discrimination at young age:
Data & method
(II) Measuring the adjusted gender wage gap
Nopo decomposition
A flexible non-parametric approach based on exact matching
Reliable even when when small set of covariates
Reliable even when cannot correct for selection bias
AGWG within common support
17. Statistical discrimination at young age:
Data & method
(II) Measuring the adjusted gender wage gap
Nopo decomposition
A flexible non-parametric approach based on exact matching
Reliable even when when small set of covariates
Reliable even when cannot correct for selection bias
AGWG within common support
We need individual level data
18. Statistical discrimination at young age:
Data & method
(II) Measuring the adjusted gender wage gap
Collecting individual level data
1 Harmonized data sources:
IPUMS + LISSY + EU (SILC, SES, ECHP)
2 Longitudinal data
Canada, Germany, Korea, Russia, Sweden, the UK, Ukraine and the US
3 Labor Force Surveys and Household Budget Surveys:
Albania, Argentina, Armenia, Belarus, Chile, Croatia, France, Hungary, Italy, Poland,
Serbia, the UK and Uruguay
4 LSMS (The World Bank):
Albania, B& H, Bulgaria, Kazakhstan, Kyrgistan, Serbia and Tajikistan
19. Statistical discrimination at young age:
Data & method
(II) Measuring the adjusted gender wage gap
Collecting individual level data
1 Harmonized data sources:
2 Longitudinal data
3 Labor Force Surveys and Household Budget Surveys:
4 LSMS (The World Bank):
In total:
– unbalanced panel 56 countries from early 1980s onwards
– ∼ 1258 measures of the Adjusted GWG
details
20. Statistical discrimination at young age:
Data & method
(III) Instruments
Compulsory schooling ⇒ fertility (Black et al. 2008, Cygan-Rehm and Maeder 2013)
Source: Compulsory schooling: UNESCO + papers for earlier years
21. Statistical discrimination at young age:
Data & method
(III) Instruments
Compulsory schooling ⇒ fertility (Black et al. 2008, Cygan-Rehm and Maeder 2013)
Source: Compulsory schooling: UNESCO + papers for earlier years
Military conscription ⇒ the timing of family formation
Source: Mulligan and Shleifer (2005) + Military Balance
22. Statistical discrimination at young age:
Data & method
(III) Instruments
Compulsory schooling ⇒ fertility (Black et al. 2008, Cygan-Rehm and Maeder 2013)
Source: Compulsory schooling: UNESCO + papers for earlier years
Military conscription ⇒ the timing of family formation
Source: Mulligan and Shleifer (2005) + Military Balance
Authorization of contraceptive pills ⇒ female education, family and labor supply
(US: Goldin and Katz 2002, Bailey 2006, Ananat and Hungerman 2012)
Source: Finlay, Canning and Po (2012)
23. Statistical discrimination at young age:
Data & method
(III) Instruments - a small bit of history
The pill first invented in 1940s in the UK, the first approved patent in the US in 1960
24. Statistical discrimination at young age:
Data & method
(III) Instruments - a small bit of history
The pill first invented in 1940s in the UK, the first approved patent in the US in 1960
Adoption timing varied a lot, even in Europe
25. Statistical discrimination at young age:
Data & method
(III) Instruments - a small bit of history
The pill first invented in 1940s in the UK, the first approved patent in the US in 1960
Adoption timing varied a lot, even in Europe
Eastern European countries were forerunners
Portugal and Spain lagged behind (late 60’s and 70’s)
The latest: Norway
26. Statistical discrimination at young age:
Data & method
(III) Instruments - a small bit of history
The pill first invented in 1940s in the UK, the first approved patent in the US in 1960
Adoption timing varied a lot, even in Europe
Admission ̸= access (→ timing)
27. Statistical discrimination at young age:
Data & method
(III) Instruments - a small bit of history
The pill first invented in 1940s in the UK, the first approved patent in the US in 1960
Adoption timing varied a lot, even in Europe
Admission ̸= access (→ timing)
E.g. former socialist countries: admitted but unavailable
Prescriptions vs otc
The UK originally admitted it only for married women
28. Statistical discrimination at young age:
Data & method
(III) Instruments - a small bit of history
The pill first invented in 1940s in the UK, the first approved patent in the US in 1960
Adoption timing varied a lot, even in Europe
Admission ̸= access (→ timing)
Until today persistent differences in use as contraceptive
∼ 38% in W. Europe; ∼ 14% E. Europe but 48% (!) in Czech Republic
29. Statistical discrimination at young age:
Data & method
Estimation procedure
AGWGi,s,t = α + β × time + γ [
MABi,t + ξs + ϵi,s,t
MABi,t = ϕ + θPILLi,t + ϱEDUi,t + µCONSCRi,t + ςM FERTi,t + εi,t
Variation in pill authorizaton: one data-point for each country
We use 2SLS for panel data as in Baltagi and coauthors (1981, 1992, 2000)
It is a random effects model (FGLS)
but... instrumentation is different
Additional instruments are redundant in White sense
→ standard errors adjusted to unbalanced panels
30. Statistical discrimination at young age:
Results
Raw correlation between MAB and AGWG
AGWGc,t = 0.88 − 0.028 MABc,t + ϵc,t
(0.046) (0.001)
More descriptives
31. Statistical discrimination at young age:
Results
The effect of delayed fertility on AGWG - IVs
AGWG estimates βIV
β
(1) (2) (3) (4)
Fertility timing -0.025*** -0.033*** -0.025*** -0.019**
(0.0062) (0.0093) (0.0065) (0.0097)
Observations 1106 1161 1114 1170
R-squared 0.28 0.28 0.28 0.76
F-statistic 25286.6 461.1 15667.1
More demanding AGWG
32. Statistical discrimination at young age:
Results
The effect of delayed fertility on AGWG - Robustness checks
HDFE Quantile Regression Heterogeneous fertility
(1) (2) (3) (4) (5) (6)
Q25 Q50 Q75 Intercepts Slopes
MAB -0.012 *** -0.023 *** -0.022 *** -0.032 ***
[-0.02,-0.00] [-0.03,-0.01] [-0.03,-0.01] [-0.04,-0.02]
MAB< Q25 0.133 *** -0.018
[0.07,0.20] [-0.05,0.02]
MAB ∈ [Q25, Q75] 0.027 -0.019
[-0.03,0.08] [-0.05,0.01]
MAB> Q75 -0.019
[-0.05,0.01]
33. Statistical discrimination at young age:
Results
Finding a benchmark
How do our estimates compare to differences in costs faced by employers?
34. Statistical discrimination at young age:
Results
Finding a benchmark
How do our estimates compare to differences in costs faced by employers?
We need information on
Probability of becoming a parent
→ Age-specific fertility rates
Differences in cost of childbearing / rearing
→ Time-use surveys / ISSP
→ Diff-in-Diff : men vs women, parents vs childless
37. Statistical discrimination at young age:
Summary
Summary
Do employers discriminate statistically? Tentatively yes
Delayed fertility among youth → GWG ↓
38. Statistical discrimination at young age:
Summary
Summary
Do employers discriminate statistically? Tentatively yes
Delayed fertility among youth → GWG ↓
IV estimates ∼ −0.03 (out of AGWG ∼ 0.12 on average)
Estimates stable and robust across model specifications
39. Statistical discrimination at young age:
Summary
Summary
Do employers discriminate statistically? Tentatively yes
Delayed fertility among youth → GWG ↓
IV estimates ∼ −0.03 (out of AGWG ∼ 0.12 on average)
Estimates stable and robust across model specifications
IV and OLS similar, but F-statistics strong
40. Statistical discrimination at young age:
Summary
Summary
Do employers discriminate statistically? Tentatively yes
Delayed fertility among youth → GWG ↓
IV estimates ∼ −0.03 (out of AGWG ∼ 0.12 on average)
Estimates stable and robust across model specifications
IV and OLS similar, but F-statistics strong
Benchmarking: ∆c × π “explains away” AGWG sometimes
→ employers may receive signals correctly, but rarely do
41. Statistical discrimination at young age:
Summary
Questions or suggestions?
Thank you!
w: grape.org.pl
t: grape org
f: grape.org
e: lvandervelde[at]grape.org.pl
44. Statistical discrimination at young age:
Summary
Trends in gender wage gaps
All age groups Youth
Raw GWG Adjusted GWG Raw GWG Adjusted GWG
(1) (2) (3) (4)
Year -0.160 -0.0308 -0.164** -0.158**
(0.101) (0.0662) (0.0773) (0.0705)
Observations 1,151 1,151 1,128 1,128
R-squared 0.204 0.117 0.105 0.108
Mean value 16.28 17.60 7.93 12.23
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46. Statistical discrimination at young age:
Summary
Robustness check: only AGWG estimates with ind. & occ.
AGWG estimates βIV
β
(1) (2) (3) (4)
Fertility timing -0.026*** -0.032*** -0.025*** -0.020***
(0.0051) (0.0069) (0.0053) (0.0060)
Observations 825 864 834 873
R-squared 0.28 0.28 0.28 0.62
F-statistic 7058.1 379.5 3894.7
Clustering SE Yes Yes Yes Yes
Time trends Yes Yes Yes Yes
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47. Statistical discrimination at young age:
Summary
Robustness check: Fertility levels
Adjusted estimates Raw estimates
Youth All Youth All
Fertility level -0.016 0.013 0.0026 0.019
(0.026) (0.022) (0.036) (0.033)
Observations 1241 1301 1241 1301
R-squared 0.74 0.83 0.62 0.80
Clustering SE Yes Yes Yes Yes
Time trends Yes Yes Yes Yes
Notes: Fertility levels are measured using Total Fertility Rate in the country.
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