Author: Sujan (page 1 of 3)

India’s education quandary: Learning from learning outcomes

[The article appeared in Ideas for India on 10th December 2018, and has been written with my colleague, Anmol Agarwal. To read the article in Ideas for India, please click here]

 

The World Bank’s Human Capital Index, 2018 ranks India at 115 out of 157 nations. While there has been improvement in universalisation of elementary education and gains in broad indicators such as enrolment rates and student-teacher ratios, the quality of education continues to remain a black box. In this post, Bandyopadhyay and Agarwal discuss the key factors behind the deteriorating learning outcomes, with an emphasis on per-pupil expenditure, school infrastructure, and teaching.

 

The recently released Human Capital Index of the World Bank ranks India at 115 out of 157 nations under study. An interesting component of the Index is the ‘Harmonized Test Scores’ where India scores 355, well below the world average of 431. While the introduction of Sarva Shiksha Abhiyan1 (SSA) and Right to Education (RTE) Act, 2009 have contributed immensely to universalisation of elementary education, improvements in quality continue to remain a black box. With gains in broad indicators like enrolment rates and student-teacher ratios being well documented, we discuss the key factors behind the deteriorating learning outcomes, with an emphasis on per-pupil expenditure (PPE), school infrastructure, and teaching2.

According to the Annual Status of Education Report (ASER) (2015), learning outcomes – as measured by grade 3 and 5 reading- and mathematics abilities – have deteriorated for most states for over a decade until 2014. The possible underlying causes behind this worrisome phenomenon deserve fair attention.

State-level expenditure on education

Firstly, we look at the spending on education. Most of the expenditure on school education in India is undertaken by state governments. In Figure 1, using data from Reserve Bank of India’s Database on Indian Economy (DBIE), we compare state-level expenditure on education as a share of the state’s total expenditure in 2000-01 and 2016-17. Despite India’s growing young population, it is evident that, barring a few, the share for most states fall below the 45-degree line reflecting a reduction in education expenditure.

Figure 1. State-wise share of education expenditure in total expenditure in 2000 and 2016

Source: DBIE.

Note: Key to abbreviations is as follows. AP – Andhra Pradesh, AS – Assam, BR – Bihar, CG – Chhattisgarh, GJ – Gujarat, HR – Haryana, KA – Karnataka, KL – Kerala , MH – Maharashtra, MP – Madhya Pradesh, OD – Odisha, PB – Punjab, RJ – Rajasthan, TN – Tamil Nadu, UP – Uttar Pradesh, WB – West Bengal.   

More importantly, we calculate the real per-pupil expenditure (RPPE) on education across states, using state-level CPI (consumer price index) data, available from 2010-11 to 2015-16 from the Ministry of Statistics and Programme ImplementationWe deflate the nominal expenditure on education to get the real expenditures (base year  2010) and divide by the state-level enrolment in government primary schools available from DISE (District Information System for Education) state report cards 2010-2015. The average RPPE over the six-year period demonstrates considerable variation across states as shown in Figure 2: The figure for Kerala is more than 17 times that of Bihar. 

Figure 2. Variation across states in average real per-pupil expenditure (2010–2015)

Source: DBIE, DISE. 

Such significant variation across states begs the question: what channels are driving these results? In Figure 3, we look at the components of RPPE separately. We take the best-performing state – Kerala – as the base against which we calculate the ratio of average real expenditure and average enrolment for all the other states. It is clear that while the enrolment rate in other states is, on average, 6.5 times that of Kerala, it is not compensated by a higher real expenditure, which is on average, merely 1.2 times that of Kerala. The situation is rather harrowing for the worst-performing state, Bihar, where enrolment is 16 times that of Kerala but real expenditure only 1.2 times. This suggests that the harmony between real expenditure and enrolment rates is lacking in the low RPPE states.

Figure 3. Comparing enrolment and real expenditure of all states with Kerala

Source: DBIE, ASER.

What is crucial, though, is whether higher RPPE translates into better learning outcomes. From Figure 4, it seems certainly the case. We find that for grade 3 students (same trend holds for grade 5) the average mathematics- and reading ability scores show a positive correlation with average RPPE over 2010-2014. The positive correlation also holds if we look at average changes in learning outcomes. It suggests that higher the RPPE on education in a state, the better the prospects for the state to improve its student learning outcomes.

Figure 4. Positive correlation between real per-pupil expenditure and learning outcomes

Note: Figures indicate grade 3 average reading and mathematics abilities (levels and changes).

Source: ASER, DBIE, DISE. 

Another important aspect to consider is the time trend of RPPE, which we note in Figure 5. The figure plots the difference in average RPPE of the top-five and bottom-five states (from Figure 2) across years. The difference has increased unequivocally, in fact, doubling from below Rs. 23,000 in 2010-11 to over Rs. 46,000 in 2015-16. The increase in this gap shows that the worst-performing states are not making amends on the expenditure side, which presents a glum outlook for any future improvements in learning outcomes.

Figure 5. Difference in real per-pupil expenditure between top-five and bottom-five states across time

Source: DBIE, ASER. 

Status of school infrastructure and learning outcomes

Next we investigate school infrastructure as a possible determinant of learning outcomes. Figure 6 shows the nation-wide performance of schools in 2016 in terms of several key amenities, with state populations used as weights, using data from DISE state report cards. The data show mixed results with facilities like girls’ toilets, mid-day meals, and drinking water reaching near-universal levels, but a large scope for improvement in some of the others. More than a third of the schools still lack access to electricity and less than a third have access to a computer. Similarly, more than a third of the schools do not have a playground or boundary walls. Additionally, these national-level statistics mask wide differences across states.

Figure 6. Status of school infrastructure in India, 2016  

Note: Based on population-weighted averages.

Source: DISE.

For instance, in terms of electricity in schools, six states including Punjab, Gujarat, and Kerala have near-universal coverage. On the other hand, there are six states in which less than half of schools have electricity, with Jharkhand being the worst performer with 15% of schools having electricity. Similarly, in terms of schools with computer facility, Kerala leads the pack with 94% coverage, while there are eight states with less than 15% coverage. We test if the status of infrastructure is correlated with learning outcomes in Figure 7, specifically focusing on percentage of schools with access to electricity. We find a clear positive correlation between grade 3 students’ average mathematics- and reading ability scores (levels and changes) and the availability of electricity (same trends hold for grade 5, and computer coverage).

Figure 7. Positive correlation between percentage of schools with access to electricity and learning outcomes

Note: Figures indicate grade 3 average reading- and mathematics abilities (levels and changes).Source: ASER, DBIE, DISE.

Teaching capacity

Coming to the teaching side, there are several aspects that deserve attention. India is clearly lacking in terms of teacher specialisation with over 100,000 schools being single-teacher schools, according to DISE. Furthermore, ex-ante, under-qualified teachers are not subjected to rigorous training during their initial tenure. The deadline to train teachers under the RTE Act, 2009 has already been extended from 2015 to 2019 due to the presence of 1.11 million teachers who are still untrained.

The high incidence of teacher absenteeism in India is a serious issue as shown by a Das et al. 2016. This World Bank working paper suggests that between 2003 and 2010 teacher absenteeism has fallen only marginally from 26.3% to 23.6%. In the worst-performing state, Jharkhand, teachers are absent nearly 46% of the times. The paper estimates the fiscal burden of absenteeism at around Rs. 81–93 billion.

The problems are worse for the public schools in rural areas. The system of checks and balances is often lax, and the teachers are often paid better than their private-school counterpart, sometimes even resulting in socioeconomic divide and biases between teachers and the poor students. 

Concluding remarks

As the positive impact of higher expenditure and better infrastructure on learning is evident, the Central and state governments can work together to ramp up spending on education, which is currently hovering around the 3% of GDP (gross domestic product) mark. India is the worst-performing country in this regard amongst the BRICS (Brazil, Russia, India, China, and South Africa) nations (BRICS Statistics, 2017). Some Indian states – most noticeably Delhi – have taken steps by almost doubling the budget on education and infrastructure spending in schools.

In terms of teacher training, India has a long way to go. It can learn from countries like Singapore  – global leader in education outcomes  – which requires teachers to undergo 100 hours of compulsory training annually to stay updated with the latest technology and curriculum.

Furthermore, targeting student performance at the entry level is also crucial. Humans are prone to forming internal benchmarks while performing a new activity. When students performs well in their first few exams, they subconsciously create benchmarks against which future performance is self-evaluated. The fear of falling short often motivates students to perform to their full potential.

However, we need to remember that changing the entire landscape of education in the country will surely take time. There is no magic wand that will improve education outcomes overnight.

Notes:

  1. Sarva Shiksha Abhiyan (SSA) is Government of India’s flagship programme for achievement of universal elementary education as mandated by the 86thamendment to the Constitution of India – making free and compulsory education to the children of 6-14 years age group, a fundamental right.
  2. We have analysed data on the 17 largest Indian states, by population. Primary data sources include Annual Status of Education Reports (ASER), District Information System for Education (DISE) state report cards, Ministry of Statistics and Programme Implementation (MOSPI), and RBI’s Database of Indian Economy (DBIE). Based on availability, we have used data between 2000 and 2016.

Further Reading

The role of software in manufacturing in India

[This article was published by Mint on 6th June 2018. It was written in collaboration with Anmol Agarwal, a colleague at CAFRAL. To read the article on the Mint website, please click here]

Bill Gates, the co-founder of Microsoft, had once said: “Software is a great combination between artistry and engineering.” Today this combination of art and science is ubiquitous, used in a variety of everyday products. However, has software really affected the production process in traditional manufacturing industries, like automobiles and aerospace?

A recent working paper, “Get With the Program: Software-driven Innovation in Traditional Manufacturing” by Lee G. Bransetter and Namho Kwan (Carnegie Melon University), and Matej Drev (Georgia Institute of Technology), has documented the increasing prevalence of software in traditional manufacturing industries, at the cost of traditional processes like mechanical and chemical engineering, to develop and innovate products.

The importance of software in the innovation process has been measured by the patents citing software-based technologies in these industries. These have seen a large uptick over the last few decades. In the US, for example, the share of software patents has increased threefold over a 20-year period, from only 5% of all patents in the 1980s to around 15% in 2005. More importantly, the share of patents citing previous software patents has also doubled over this period. These are patents in non-software industries like automobiles, and these are an important measure of software intensity in traditional manufacturing.

Does this mean that traditional manufacturing industries are increasingly using software? The researchers use some insightful anecdotes to give the readers an idea. Up to 40% of the cost of a new car is determined by electronics and software content, and most premium cars are equipped with 70-80 microprocessors. The Boeing 777 contains no less than 1,280 on-board processors that use more than four million lines of computer code. More than 50% of medical devices contain software, with a modern pacemaker containing up to 80,000 lines of computer code.

The researchers find that firms resistant to adopting software-based techniques are being outperformed by their peers. As a result, the researchers observe an “R&D (research and development) productivity gap” in the traditional manufacturing sector where highly software-intensive firms produce more patents for each dollar invested in R&D than less software-intensive firms. They also find evidence that equity markets tend to value software-intensive firms more than others.

These findings beg the question: If there are so many benefits of being software-intensive, why aren’t all firms using more software? Why are some firms lagging behind? Armed with the fact that US firms tend to be much more software-intensive than European and Japanese firms, the researchers argue that availability of talented human resources is a crucial reason why this phenomenon exists. In fact, they argue, the availability of talented and inexpensive software engineers from India is one of the key reasons the US has a competitive advantage over these other nations.

Implications for Manufacturing in India

This research provides important cues for Indian firms and policymakers. First, it is important to check whether the results hold for India. One of the key claims of the paper is that more software- intensive firms generate more patents as compared to their less software-intensive counterparts. India has rapidly expanded its software capabilities, with regions like Bengaluru, Pune, Mumbai, Chennai, Delhi and Hyderabad leading the charge. The five states containing these cities, unsurprisingly, accounted for 69% of the total patent applications by Indian residents in the year 2016-17, in line with the research findings.

A focus on software intensity in a country with surplus labour like India may raise a few eyebrows. But, a look at some recent trends highlights the importance of patents. Manufacturing growth and patent filing growth by Indian residents has shown strong positive correlation over the last decade, with a correlation coefficient of 0.62. Both of them plummeted to the 2% mark in 2008-09, followed by a period of resurgence, where patent and manufacturing growth increased to 17% and 8.5%, respectively, in 2010-11. Both shrank sharply in 2011-12, and since then have stabilised and continued to move in tandem.

The relationship between growth of patent filing by Indian citizens and growth in gross domestic product shows a similar picture, with a positive correlation coefficient of 0.53. Further, the growth rates almost converged in 2015-16, indicating a crucial role played by software and patents in assessing the health of an economy.

Software, as an input to production, deserves more attention. With 45,444 applications in 2016-17, India was ranked seventh in the world in terms of patent applications filed. Worryingly, the number declined by 3% from 2015-16, whereas it has grown considerably over the past few years in China and the US, which are already ranked higher than India. The patent-grant rate, hovering around 21% in India, has also been comparatively low.

India’s quest to become a manufacturing powerhouse will, to a large extent, depend on how it embraces software and technology. India is already losing its low-cost advantage in employment generating sectors, like textiles and electronic equipment, to Bangladesh and Vietnam, respectively. But it can certainly take the lead in software engineers’ labour market. Information technology (IT) and software professionals from India are regarded among the best in the world. But hardly any of the most skilled professionals stay back in India. The US has earned great dividends by attracting and retaining the top software talent from India, and around the world, through its prestigious universities and attractive STEM (science, technology, engineering and mathematics) visa programmes.

It’s about time this drift of our software professionals to the West was curtailed through the creation of world-class research facilities and remunerative opportunities. Software can allow India to differentiate its products from the low-end products of its competitors and enjoy a lasting manufacturing boom.

Anmol Agarwal and Sujan Bandyopadhyay are research associates at CAFRAL, Reserve Bank of India. These are their personal views.

Bootstrapping: An Introduction

“On first reading the bootstrap may seem a little like magic. But really it is not.” 

I have come across countless such quotes on the internet. I was confused myself, when I came across the concept for the very first time.  Bootstrapping is a concept that is widely used in Statistics, and Econometrics. The very basic and simple idea behind the bootstrapping methodology can be a bit confusing, and thus, I have created a small presentation on the topic.

To refer to the presentation, click here

A Closer Look at Statistics on Sexual Violence in India

[This article was published by TheWire.in on 5th May 2018. To read the article on TheWire.in, please click here]

 

Recent incidents of rape have stirred the conscience of the nation. Even as India reels from the shock of the cases in Kathua (Jammu and Kashmir) and Unnao (Uttar Pradesh), there are more such incidents being reported almost on an everyday basis, such as the ones in Surat (Gujarat) and Nadia (West Bengal).

These barbaric incidents at various parts of the country have once again put the spotlight on India’s poor track record in protecting its women, almost five years after the brutal Nirbhaya case, in which a young medical intern was gang-raped and tortured in a moving bus in South Delhi.

This case had led to changes in India’s legal system, including the passing of stricter sexual assault laws, and the creation of fast-track courts for prosecution of rapes. Recent cases have also led to legislative changes. At least four states – Rajasthan, Jammu and Kashmir, Haryana, and Arunachal Pradesh – have introduced the death penalty for rapes of minors, defined as below 12 years of age. According to news reports, the Centre is also contemplating amending the Protection of Children from Sexual Offences (POCSO) Act to introduce the provision of the death penalty for raping minors aged below 12 years.

Understanding the issues concerning violence and crimes against women are critical to generating sustainable solutions to the problem. The National Crime Records Bureau (NCRB), a government of India agency, collects statistics and information on crimes. These crime statistics, especially those on sexual violence, tend to suffer from under-reporting. In fact, some studies have found that reported crime rates and actual crime rate could have a negative correlation, due to other issues like education, legal infrastructure etc.

Keeping this in mind, we take a closer look at the data.

Rape accounts for about 12% of all crimes against women. The distribution of reported cases (shown below) is quite uneven across the nation.

Heat map of reported cases of rape (per 100,000 of the population) in India (2016). Source: NCRB

Heat map of reported cases of rape (per 100,000 of the population) in India (2016). Source: NCRB

India’s average rate of reported rape cases is about 6.3 per 100,000 of the population. However, this masks vast geographical differences with places like Sikkim and Delhi having rates of 30.3 and 22.5, respectively, while Tamil Nadu has a rate of less than one. Of course, one must be careful in interpreting these state-wise differences as these are ‘reported’ cases and could suffer from under-reporting.

Even India’s average rate of 6.3, which is not very high when compared with the rest of the world, suffers from under-reporting. According to a recent report by the Livemint, about 99% of cases of sexual violence go unreported. If true, this would put India among the nations with highest levels of crimes against women.

Region-wise break up of reported cases of rape over 2001-2015. Source: NCRB

Region-wise break up of reported cases of rape over 2001-2015. Source: NCRB

 

India’s cases of reported rape have seen a massive jump in the last few years, mainly owing to the outrage and awareness created out of the unfortunate Nirbhaya case. Reported cases jumped by a massive 26% in 2013, the highest in the last 15 years, mainly driven by an increase of reports in the states of Northern India, like Rajasthan, Delhi, and Uttar Pradesh. The trend is also mirrored for all crimes against women, and not just rape, which also saw an increase of 26% in 2016.

What could possibly account for these differential rates? Looking at state-wise differences in legal institutions, economic indicators, and social indicators could provide possible answers.

Legal institutions

Good proxies for the state of the legal system are the conviction rates for crimes against women, and the amount of time taken to investigate cases.

Heat map of conviction rates for crimes against women in India (2016). Source: NCRB

Heat map of conviction rates for crimes against women in India (2016). Source: NCRB

While the conviction rate for all crimes against women stands at a measly 19% across India (compared with an average conviction rate of 47% for all crimes), again, there are wide differences across different states. Northeastern states, quite notably, have relatively high conviction rates ranging from 25% to 70% (with the exception of Assam). On the other hand, states like West Bengal, Gujarat, and Karnataka have rates of less than 5%. Data on conviction rates for rapes as well follow the same trend, with an all India rate of about 25% in 2016.

Scatter plot of conviction rate and reported cases of rapes per 100,000 of the population (2016). Source: NCRB

While it is difficult to generalise results for the entirety of India, a scatter plot of conviction rates and reported cases of rapes per 100,000 of the population indicates a slight positive correlation, alluding to the fact that better chances of conviction encourage the reporting of this crime. Of course, higher conviction rates should also act as a strong deterrent against committing crimes, and this could be a reason why the correlation is not very pronounced.

A troubling observation is that while cases being reported have increased over the last five to six years, conviction rates, unfortunately, have remained stagnant to slightly falling. It is important to note that conviction rates refer to only cases which have completed court proceedings in the current year. They do not include cases that carry forward, which incidentally are a large number. At the beginning of 2016, over 118,537 cases of rape were pending at the courts. At the end of the year, the pending cases went up to 133,813, an increase of 12.5%. For crimes against women overall, pending cases increased from 1,081,756 to 1,204,786. Of course, this is a consequence of the larger inefficiency in the judicial system which had pending cases increase from 9,012,476 to 9,703,482a truly staggering number.

Comparison between Conviction Rates and Rape Cases Reported (2010-15). Source: NCRB

Comparison between Conviction Rates and Rape Cases Reported (2010-15). Source: NCRB

What’s more, it is not just slow court proceedings that are an issue. Over 16,500 cases, or just under a third of reported rape cases in 2016 were pending investigation by the police at the end of that year. For all crimes against women, the number stood at 164,181, or exactly a third of the cases. The highest number of cases pending police investigation as a percentage of all cases was the highest in Manipur at 84% and Delhi at 62%, and lowest in Rajasthan at seven and Haryana at 15%.

Economic indicators

There are wide-varying differences in GDP per capita in Indian states, with Goa, the richest, having a GDP per capita of Rs 260,000, over ten times that of Bihar, the poorest, of only Rs 24,572.

The scatter plot of GDP per capita and reported rapes per 100,000 of the population has a positive correlation, indicating that higher levels of income and associated benefits have a positive impact on reporting of rapes. Again, one must keep in mind that there may be two effects at play here – firstly, higher income may encourage women who have faced this crime to report it more openly, but at the same time it may also act as a deterrent against the violent sexual behavior.

Comparison of State GDP per capital (2014-15) and reported cases rapes per 100,000 of the population (2016). Source: Reserve Bank of India, NCRB

Comparison of State GDP per capital (2014-15) and reported cases rapes per 100,000 of the population (2016). Source: Reserve Bank of India, NCRB

Similarly, the correlation between the poverty rate of a state and reported cases of rapes per 100,000 of the population is negative, indicating that states with lower poverty rates tend to have a higher reporting of rapes per 100,000 of the population.

Social indicators

Sex ratio, or the proportion of females per 1,000 males, is an important indicator for the position of women in society. Due to the legacy of female infanticide, several states in India have very low sex ratios.

Comparison of Sex Ratio (2011 census) and reported cases rapes per 100,000 of the population (2016). Source: Reserve Bank of India, NCRB

Comparison of Sex Ratio (2011 census) and reported cases rapes per 100,000 of the population (2016). Source: Reserve Bank of India, NCRB

Here, we observe a clear negative correlation between sex ratio and reported cases of rape. It seems likely that state with higher sex ratios have lower reported cases because the actual number of cases is lower.

Unlike the relationship with sex ratio, there exists a positive correlation between the female literacy rate of a state and reported cases of rapes per 100,000, indicative that women are more likely to report crimes with higher levels of education.

Concluding Remarks

Interestingly, better levels of social indicators, economic indicators, and legal institutions tend to be correlated with higher levels of reported crimes for most indicators; indicative of the underreporting of sexual violence crimes in India. While stricter laws are a welcome step in dealing with the problem, they are not enough. As observed, stricter laws in the aftermath of the 2012 Nirbhaya case have led to higher levels of reporting but not necessarily to higher conviction rates or quicker investigations. Thus, what is required is an overhaul of the current legal infrastructure in place to deal with these cases in a quicker and more efficient manner, along with other remedies of social welfare, economic growth, awareness programmes, sex education etc.

Haryana, a state known for its dismal sex ratio, has recently reported that its ratio has improved to 914 girls to 1,000 boys. Not long ago, it used to have one of the worst indicators in the country with a ratio of only 879 girls to 1,000 boys in 2011.  While it still has a long way to go to ensure gender parity, the state has achieved  considerable progress through a combination of strong laws, their strict enforcement, and innovative awareness campaigns like ‘Beti Padhao, Beti Bachao’. It’s time to implement a similar holistic approach to deal with sexual violence across India.

The Whole Nine Months: Fertility Rates as Predictors of Economic Growth

[This article was published by TheWire.in on 9th April 2018. It was written in collaboration with Aniruddha Ghosh, a classmate from LSE. To read the article on TheWire.in, please click here ]

Featured on the Harvard Economics Review, click here ]

 

Nearly 10 years after the Great Recession of 2007-09 that brought monetary and real disturbances across the world economy, researchers have stumbled upon a new and a rather ingenious business cycle fact that may aid in predicting upturns and downturns in an economy: the growth rate of conceptions as predictors of economic growth.

While economists have uncovered such a relationship for the United States, a preliminary reading of Indian data also suggests similar patterns. Of course, a more in-depth and rigorous analysis is needed to unmask the trends. Such trends could be an aid in augmenting the much needed high-frequency indicators to aid policy making for the Indian economy.

It is a well-known fact that modern developed economies, as documented by most recent studies, have tended to show a positive association between fertility rates and economic growth: procyclical fertility. Patrick Galloway in his 1988 study on pre-industrial Europe highlighted the sharp responsiveness of fertility rates to the price of food grains. A higher price of food grains meant lower economic well-being, in turn negatively influencing fertility rates.

Moreover, the magnitude of positive association between fertility rates and economic growth was consistent across most of the pre-industrial European economies. While the fact remains that cross-country studies report a negative relationship between per-capita income and fertility levels, fertility rates indeed tend to be procyclical with economic movements. Indeed, statistics from the US’s National Center for Health Statistics show how the number of births began to decline in 2008 as the fallout of the Great Recession became prominent, falling by over 15% after hitting a 50-year high in 2007.
In addition to these facts, economists have deciphered a new fact about the movement of fertility rates. According to a recently published working paper at the National Bureau of Economic Research (NBER) – a leading economic research organisation in the US – fertility data not only moves in tandem with economic cycles but can even help predict the onset of a recession or an upturn. Studying quarterly economic and demographic data for the last three decades (1988-2015) , researchers Kansey Buckles and Daniel Hungerman from University of Notre Dame, and Steven Lugauer from University of Kentucky find that the growth rate of conceptions falls sharply during a recession, and importantly, this fall actually begins a few quarters before the onset of a recession.

In the jargon of macro-economics, the growth rate of conception is a leading indicator of business cycle, having predictive power in foretelling an impending downturn. Technically, conception is a measure based on births, therefore, it has a built-in nine month lag attached to it.

Figure 1: Growth Rate of Conceptions fall sharply during recessions, and this fall typically begins a few quarters before the onset of a recession (US 1988 Q1:2015 Q4). Source: Buckles, Hungerman, and Lugauer (2017)

This anticipatory nature of the growth rate of conceptions has been observed for all the major recessions in the last three decades, that of 1990, 2001, and 2008, and has been more pronounced in the recent recession. This high-frequency conception data allows authors to state a rather unconventional business cycle fact and we quote, “the growth rate of fertility declines prior to economic downturns and the decline occurs several quarters before recessions begin.” It is important to keep in mind that the fertility decline is solely driven by fall in conceptions for the US data rather than abortions or foetal deaths. The extent of fall in conceptions is large, growth rate of conceptions fell nearly 5 percentage points at the onset of Great Recession of 2007-08.

As mentioned above, the findings show that the growth rate of conceptions typically starts falling a few quarters before a recession, and since its is measure based on births, has an implicit 9 month lag built in to it. So what explains these possible associations? The hypothesis that researchers put forward is a straightforward one: Couples make forward looking decisions on having children based on economic outlook. If this hypothesis is true, then fertility behaviour in the US over the last three decades has been forward-looking and quick to respond to changes in the state of the economy. The explanation does depend on the assumption that almost all the conceptions are ‘planned’, i.e. couples are making conscious choices about bearing children, as opposed to unplanned, i.e. children born due to lack of contraception, out of wedlock, etc. With unplanned pregnancies being quite high in the US (estimates put it between 40-50% of all pregnancies), there is a legitimate question as to whether there are other factors in play which are causing this predictive relationship between growth rate of conceptions and economic growth.

Nevertheless, if future economic conditions matter for current conception decisions, and if expectations are at least rational or future incorporating, movements in current conceptions may likely be harbingers of future conditions.

Figure 2: Growth Rate of Conceptions and economic growth during the 2007-09 Great Recession. Source: Buckles, Hungerman, and Lugauer (2017)

That’s one part of the story. Interestingly, the researchers also find that the growth rate of conceptions tends to moves together with common economic indicators like consumer confidence and durables purchases, If you look at Figure 3, the fall in growth rate of conceptions coincides with or even precedes falls in consumer confidence and durable purchases. Consumer Confidence Index, Durable purchases are often looked upon as high frequency indicators that can foretell the prospects of the economy in the near term. In India, the Reserve Bank of India (RBI), Centre for Monitoring Indian Economy (CMIE) track independently consumer confidence which are useful aids in capturing expectations for the near term future of the economic situation.

Figure 3: Fall in conventional economic indicators like consumer confidence and purchase of durables, and the growth rate of conceptions tend to be coincide during recessions. Source: Buckles, Hungerman, and Lugauer (2017)

What does the trend seem like for India? We do a very preliminary exercise here and graph the growth of birth rate and GDP series for India. The graph points to some predictive behaviour but of course, this has to be subjected to more stringent and robust econometric evaluations. Also, we use birth rates, a measure different from conception rates that Buckles et.al have used. However, as a first pass, this must interest practitioners to look carefully between the associations. Although, we think that the hypothesis that Buckles et.al put forward may not hold tightly for India. With a low per capita income, a gloomy economic situation might step up conceptions as one needs more hands to feed. Again, all these are possible speculations and a careful analysis of the data will only uncover the mechanisms in place.

See Figure 4 here.

While whether conception rates or birth rates should be the variable of interest can be discussed, a common challenge with using both these measures is that they are hard to measure in real time, even when compared to other measures like consumer purchases. However, with the coming of sophisticated data analytics and statistical techniques, it is quite possible to get predictions of these measures on a real time basis. One possible way to track conceptions is to use purchases of goods that are especially likely to be bought by those who are attempting to conceive or who are newly pregnant. This data is usually tracked real time by retail firms using scanner technology. In fact, this data is already being used by retail firms for targeted marketing and advertising.

This New York Times piece in the year 2012 outlined how Target Corporation could observe, track and study consumer purchases and predict if a customer is pregnant. Most of the online portals place cookies on your devices to track your shopping habits. Therefore, there are tools that are available to harness real time data on conceptions. Of course, such tracking has to respect the contours of privacy since these are very private issues Buckles et. al explores few choices on tracking conceptions and interested readers are suggested to have a look at the appropriate sections.

Yogi Berra (the legendary Yankees Baseball player), famous for his Yogi-isms, had once quipped, “It’s tough to make predictions, especially about the future.” It’s encouraging for policy practitioners to have such an evidence of conception rates as predictors of economic cycles. To our knowledge, this study is one of its kind and should make researchers across the globe investigate for such associations in their respective geographical spaces. Policy institutions in India often cite the lack of high frequency indicators that can be an aid in the policy formulation process. Buckles et.al should encourage them to hunt for innovative indicators and develop tools that can be usefully utilised. With so much jargon and tools being thrown around Big Data, it will be useful if researchers look at such meaningful constructs like Buckles et.al do.

 

 

 

 

How to Split the Pie and Eat it Too! – A Game Theory Exercise

Bargaining is a very important concept in economics, most notably in game theoretical frameworks. A common problem which one comes across is how to split the pie between two players.  The question is as follows:

There are two players; A, and B. They are given by a pie which they must split between themselves. In Period 1, Player A proposes a division, and Player B accepts or rejects this division. If Player B accepts the division proposed by Player A, then the game ends at that time itself and both players walk away with their share of the pie. If Player B rejects, then Player B proposes the division in Period 2. Now Player A has the option of accepting or rejecting the division. Again the same conditions apply, and the game can continue or end in Period 2. However, for each Period the game goes on for, the size of the pie keeps diminishing by a certain amount. There are a certain number of periods the game can go on for, before the value of the pie becomes zero. 

So how should the pie be divided?

This problem is important, both as an introduction to bargaining games, as well as a introduction to thinking about ‘dynamic’ issues in economics — where agents think across time periods (as opposed to static frameworks where agents are only thinking about the current period of time).

 

I have made a small presentation about this problem and how to resolve it. To check it out, click here!

Price Discount Calculator

Most firms offer consumers one of two things during marketing campaigns and sales to boost sales -: i) Price discounts, like a 50% off on prices; ii) Extra Volume, like 50% extra volume for the same price. But, is a 50% increase in quantity equal to a 50% discount on prices? Actually, no! A 50% increase in quantity is only equal to about a 1/3rd or 33.333% discount on prices.

But according to an article in The Economist (2012) (Something Doesn’t Add Up), and research by behavioural scientists (Chen et al (2012)), people fail to realise the difference between the two and often end up viewing them as the same.

One of the researchers, Akshay Rao of the University of Minnesota’s Carlson School of Management, offered his undergraduate students a choice between two a deals when buying loose coffee beans – 33% extra for free, or 33% off on the price. While the discount on the price is the clearly better deal, the students viewed both deals as the same!

For equivalent deals (like a 33.33% price discount vs 50% extra quantity), researchers found that consumers mostly went for the extra quantity (about three quarters of them), even though the deals were numerically equivalent.

This tool helps you to not fall into this numerical blindspot by calculating what price discount (%) corresponds to extra quantity (%) and let’s you make informed decisions on purchases.

Please find below the link for the app:

https://sujanbandyo.shinyapps.io/price_discount_calculator/

Generating OLS Results Manually via R

Statistical softwares and packages have made it extremely easy for people to run regression analyses. Packages like lm in R or the reg command on STATA give quick and well compiled results. With this ease, however, people often don’t know or forget how to actually conduct these analyses manually. In this article, we manually recreate regression results created via the lm package in R.

Please click here to read the article!

ASER 2017 Shows India’s Secondary Education Sector Is Failing to Impart Basic Skills

[This article was published by TheWire.in on 20th January 2018. It was written in collaboration with Aniruddha Ghosh, a classmate from LSE. To read the article on TheWire.in, please click here .]

 

[This article was cited in an article in the Economic and Political Weekly (EPW) journal –
Nawani Disha (2018), Assesing ASER 2017 Reading Between the Lines, Economic & Political Weekly, Vol 53, No 8.]

 

Merely increasing enrolment will not lead to the development of elementary skills that education is supposed to provide.

ASER’s statistics have shown how the ability of grade 8 students has been consistently falling over the years, coinciding with the increase in enrolment rates. Credit: TESS India/Flickr (CC BY-SA 2.0)

ASER’s statistics have shown how the ability of grade 8 students has been consistently falling over the years, coinciding with the increase in enrolment rates. Credit: TESS India/Flickr (CC BY-SA 2.0)

The Annual Status of Education Report (ASER) 2017 ‘Beyond Basics’ was recently released and is unfortunately on expected lines. The report highlights the sorry state of education when it comes to India’s 14-18-year-olds and asks rather uncomfortable questions that policy practitioners must find answers to. In an earlier article, we had outlined some issues that have plagued India’s primary education sector and some anticipations that we had from the ASER 2017 report. Not surprisingly, we see a repetition of the same issues when it comes to India’s secondary education space.

Previous ASER reports observed that despite high enrolment ratios of over 96% in the last eight years in the primary education sector, improvement in reading outcomes and arithmetic ability continues to be low. Moreover, a large proportion of students in both government and private schools continue to be below the ‘Grade level’. Grade level means that a student can deal with what is expected of her in that grade.

The ASER 2017 is targeted to look ‘beyond basics’: the age group between 14-18, primarily those outside the Right to Education ambit and on the verge of entering adulthood. The government’s flagship Rashtriya Madhyamik Shiksha Abhiyan (RMSA) launched in 2009 and re-booted in 2013 as RMSA-Integrated has not been much of a success in India’s secondary education scene.

‘Aspirations’ of young India

What we find as a salient feature of this report is the coverage on the aspirations of young India. Capturing ‘aspirations’ by well-defined metrics is a tough ask, very few datasets across the world capture them with some rigour. The Young Lives Survey in Peru is one example that comes to our mind that captures subjective well-being and is actively used by researchers to gauge aspiration-achievement shortfall and their reasons. While successive governments pride over India’s sizable ‘demographic dividend’, the ASER 2017 points starkly to the basic skill gaps that plague our young population.

Before documenting some necessary takeaways from the ASER 2017 report, it is prudent to mention that the complexity of the data collected makes the national estimates a summation of estimates generated at the district level (24 states, 26 districts, 23,868 households and 28,323 youths).

Like the primary education sector, enrolment rates have also been high and increasing in the secondary education space. The RTE Act covers mandatory and free schooling up until the age of 14, or roughly grade 8. ASER surveys show that enrolment in grade 8 has been steadily increasing from less than 50% in 2005-06 to close to 90% in 2014-15. However, the quality of education remains a concern. ASER’s statistics have shown how the ability of grade 8 students has been consistently falling over the years, coinciding with the increase in enrolment rates.

While grade 8 enrolment has nearly doubled over the last ten years, the proportion of students acquiring base skills has been reducing. In 2014-15 only 44% of grade 8 students could solve a grade 4 level division problem, down from 72% in 2007-08. Similarly, the ability to read grade 2 level texts has fallen from 87% to 75% over the same period. Source: ASER 2017

While grade 8 enrolment has nearly doubled over the last ten years, the proportion of students acquiring base skills has been reducing. In 2014-15 only 44% of grade 8 students could solve a grade 4 level division problem, down from 72% in 2007-08. Similarly, the ability to read grade 2 level texts has fallen from 87% to 75% over the same period. Source: ASER 2017

Enrolment rates after grade 8

Another important issue is analysing how enrolment rates develop after grade 8, or once students are no longer under the purview of the RTE Act. Looking at the 2011-12 grade 8 cohort, the findings show about a one-third decline until grade 12, indicative of a trend of increasing dropout rates after grade 8. The same trend is reflected when enrolment rates are analysed by age, showing a steady increase from age 14-18.

Once students leave the purview of compulsory and free education at age 14 (around grade 8), enrolment rates for further education drop. Source: ASER 2017

The reasons for discontinuing studies vary. Around 25% of the youth who dropped out after grade 8 said they did so due to financial reasons. Worryingly, a large number of students (34% of boys and 19% of girls) said they dropped out due to lack of interest, pointing to deficiencies in the curriculum and teaching infrastructure. One-third of girl students said they dropped out due to ‘family constraints’.


Also read: Enrolment Rates Are Climbing. So What Explains the Sorry State of India’s Education Sector?


Another startling fact is that about 17% of students dropped out because they failed in their studies. Current government policy doesn’t allow schools to fail students until grade 8. As the ASER report points out, while the intention of the policy is commendable, there need to be measures in place to identify and focus on students who have fallen behind in the earlier grades. Currently, it would seem that the policy of not failing students has led to an adverse consequence where students left behind are not identified until they end up failing exams after grade 8.

Despite the fall in enrolment rates, over 86% of youth in the 14-18 range continue to be within the formal education system. Only about 5% are taking some type of vocational training, the majority of which are less than three months long.

Interestingly, a substantial proportion of youth in this age group is employed, irrespective of whether they are engaged in formal education or not. Overall, 42% of the youth is employed, including 39% of students engaged in formal education and 60% of students who have dropped out. Most of the work – over 70% – is on their own family’s farm. It is instructive to keep in mind here that ASER is a rural survey and urban deficiencies are still a black box when it comes to concrete data.

However, after accounting for work and enrolment in vocational courses, over one-third of the youth who have dropped out of education are not engaged in any kind of activity, i.e. neither studying, preparing for exams or employed – with nearly 75% of them being girls.

As shown in Figure 1, youth in the 14-18 age bracket didn’t have the skills expected of them from an elementary education and worryingly, there has been a declining trend in these skills. Apart from basic reading and arithmetic, ASER also conducted some testing on common everyday skills like counting money, reading maps, measuring length, calculating time, etc. The performance of the youth in these tasks was noticeably better.

The surveys use some specific metrics like access to mobile phones and bank accounts as proxies of youths’ exposure to the outside world. The findings are mixed: Approximately three-fourths of the youth surveyed had access to mobile phones and had their own bank account. But only about one-fourth had access to the internet and a computer, and only 16% had ever used an ATM.

In terms of aspirations, over 60% of youth in the age of 14-18 had aspirations to study beyond grade 12. Professional aspirations varied from military service and police for boys, to nursing and teaching for girls.

Policy interventions

After acknowledging the importance of early childhood education as well secondary education after age 14, the government is considering increasing the coverage of the RTE Act from 6-14 years to 3-16 years. However, as the current and previous ASER reports have demonstrated, merely increasing enrolment will not lead to the development of elementary skills that education is supposed to provide. If anything, the increase in enrolment rates over the last decade has coincided with the fall in the ability of students. Thus, stakeholders must take additional steps to ensure that the quality of education being imparted is not affected.

In that regard, two interesting policy interventions that are in the process of being rolled out hold some promise: same language subtitling (SLS) and outcomes fund for the education sector. Drop-out cases involving ‘loss of interest’ and ‘inadequate funds’ are problems that can be innovatively tackled. It is high time India develops its own indigenous Escuela Nueva model for the primary and secondary education space incorporating experiences from various cross-country models.

Conceived by Professor Brij Kothari of IIM Ahmedabad, SLS is the concept of subtitling existing Bollywood film songs on television. Kothari’s research estimates a 9% increase in the number of functional readers who watch TV programmes with SLS within a period of two years.

Similarly, a large outcome-based fund for education is all set to launch in India in early 2018. Touted as one of the first and largest funds for social enterprises, the fund would invest in education providers to work with government-run schools to deliver outcomes. There could be a variety of outcomes like early childhood interventions, retention of girl students, learning in primary schools and employability of students after high school. The fund is being launched by the Global Social Impact Investment Steering Group, an organisation comprising 13 member countries (including India), with a focus on channelling global social impact investment. Outcome fund based models are actively being employed by nations across the globe to fund social projects and have the potential to deliver the necessary outcomes.

While the statistics borne out by ASER 2017 do reveal a gamut of concerns regarding our secondary education sector, let’s take this as an opportunity to set up our house in order. While we may be gung-ho about increasing GDP growth rates or surpassing major economies by 2030, let’s also focus on translating these high numbers to meet the aspirations of India’s young and providing them with an education system that is innovative, proactive and prescient and yet deeply invests in foundational skills. The ‘dividend’ that we enjoy must not end up becoming a recipe for ‘disaster’.

Standard Deviation vs. Standard Error

Standard deviation and Standard Errors have been concepts that I have often erroneously mixed up and have struggled to differentiate between. As a result, I made a small presentation clarifying the difference between the two concepts.

To read the presentation, please click here

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