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 .]

 

 

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

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

[This article was published by TheWire.in on 21st December 2017. It was written in collaboration with Aniruddha Ghosh, a classmate from LSE. To read the article on TheWire.in, please click here . The article has also been posted on the LSE South Asia Blog and has been featured at Qrius]

Economic research shows that interventions aimed at improving cognitive skills rather than mere enrolment rates are required to boost economic growth.

Despite high enrolment ratios of over 96% in the last eight years, improvement in reading outcomes and arithmetic ability continues to be low. Credit: World Bank/Curt Carnemark/Flickr (CC BY-NC-ND 2.0)

Despite high enrolment ratios of over 96% in the last eight years, improvement in reading outcomes and arithmetic ability continues to be low. Credit: World Bank/Curt Carnemark/Flickr (CC BY-NC-ND 2.0)

Come January 2018, the Annual Status of Education Report (ASER) 2017 will be up for discussion among policy experts. Based on household-based surveys that cover children in the age group 3-16 across almost all rural districts of India, ASER provides estimates of children’s schooling status and their ability to do basic reading and arithmetic tasks.

ASER’s 2016 report observed that despite high enrolment ratios of over 96% in the last eight years, 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 ‘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. There is a significant dearth of information in this regard and therefore, ASER 2017 will be a critical information asset to assess India’s madhyamik shiksha scenario. 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. In light of these observations, it is likely that ASER 2017 will throw up systematic issues that have continued to plague our secondary education.

ASER 2016 and other previous reports

Let’s take a step back and illustrate how our performance has been in the age group 3-16. Here is a snapshot of ASER’s previous reports on the state of India’s early grade development.

Figure 1: Percentage of children not enrolled in school. The trend for enrollment has been on the rise. Source: ASER 2016 Report

Figure 1: Percentage of children not enrolled in school. The trend for enrolment has been on the rise. Source: ASER 2016

Figure 2: Children in class III who are at ‘Grade Level’ 2008-2016. The lack of commensurate ability with grade level is evident. Source: ASER 2016

Figure 2: Children in class III who are at ‘Grade Level’ 2008-2016. The lack of commensurate ability with grade level is evident. Source: ASER 2016

Fig 3: Percentage of students who can at least do subtraction in Grade III. Large variation across states. In 2016, the number of children at grade level ranged from 50% in Himachal Pradesh to 10% in Uttar Pradesh. Source: ASER 2016 Report

Enrolment levels have been high for the 6-14 age group, and around 96% since 2009 onwards.

The percentage of children in grade 3 who are able to read at least a grade 1 level text has improved marginally – from 40% in 2014 to 42.5% in 2016.

Percentage of grade 3 children who could do two digit subtraction has marginally improved from 25% in 2014 to 27% in 2016. This has been the first year since 2010 when there has been an upward trend observed in arithmetic ability.

However, trends over time point to a dismal outlook. Figure 4 demonstrates the ability of grade 4 children in successive cohorts to read and do basic arithmetic. One can see a downward trend in the ability of successive cohorts.

Figure 4: The graphs show the performance of three cohorts from class IV to class VIII. The graph on the left shows the percentage of students who can do division; the graph on the right shows the percentage of students who can read a class II 2 level text. Source: ASER 2016

Figure 4: The graphs show the performance of three cohorts from class IV to class VIII. The graph on the left shows the percentage of students who can do division; the graph on the right shows the percentage of students who can read a class II 2 level text. Source: ASER 2016

Economics, enrolment rates and cognitive ability

One of the most influential studies to look at the relationship between education and economic growth was professor Lance Pritchett’s  Where has all the Education Gone? His 2001 study found no significant positive relationship between educational attainment and economic growth.

He wrote:

“In the decades since 1960, nearly all developing economies have already seen education attainment grow rapidly. The cross-national data show, however, that on average, education attainment contributed much less to growth than would have been expected in the standard augmented Solow model.”

He proposed several possible explanations for this phenomenon, the most important of which being the classic quantity-quality argument: Quality of educational attainment was so low that despite high educational attainment there wasn’t a significant increase in cognitive skills and human capital.

As outlined above, the very idea that improved schooling (through the lens of school attainment rates) – which has often been a cornerstone of most interventionist strategies – will raise economic well-being has often been discounted by economists. Eric Hanushek and Ludger Woessmann in their authoritative paper, ‘The Role of Cognitive Skills in Economic Development (2008)‘ find strong evidence that the cognitive skills of the population – rather than school attainment rates – are powerful determinants of earnings, economic growth and income distribution.

Their work premises itself on a rather simple question: whether education is the steering force or merely one of the several factors that are correlated with more fundamental development forces, say cognitive skills when it comes to economic growth. Hanushek-Woessmann armed with strong econometric evidence argue that cognitive skills overwhelmingly outnumbers schooling attainment rates when it comes to influencing economic growth. Moreover, the effect of cognitive skills on economic growth is larger in developing countries than in the developed ones.

 

Building on the works of Pritchett, and Hanushek and Woessmann, a recent research paper titled Does One Size Fit? The Impact of Cognitive Skills of Economic Growth by professor Nadir Altinok of the University of Lorraine and professor Abdurrahman Aydemir of Sabanci University studies the difference in the impact of cognitive skills on economic growth between developing and developed economies.

In addition to the findings mentioned above, the heterogeneous effects of cognitive skills vis-a-vis the income levels of the economies is rather stark: the magnitude of the effect of cognitive skills is about 60% higher for low-income countries compared to high-income countries, and this more than doubles when low total-factor productivity (TFP) countries are compared to high TFP countries. From a policy perspective, this encourages the view that the promotion of education policies that focus on the quality of education has especially larger payoffs in the least developed regions through the productivity channel. To sum it up, economic research overwhelmingly supports the idea of interventions aimed at improving cognitive skills rather than mere enrolment rates.

To cite a success story, Colombia has made impressive progress towards universal enrolment in basic education and at the same time has raised learning outcomes. A lot of this accrues to the flexible ‘new school’ model, commonly known as Escuela Nueva.

Escuela Nueva accepts multigrade teaching as an unavoidable condition in small schools of rural areas. It encourages to develop special materials and teaching methods for multigrade teaching. The academic achievement of the students in Escuela Nueva has been consistently higher than in urban schools. There are plenty of cross-country experiences to learn from and we should actively explore and replicate them here.

India’s scenario

Sarva Shiksha Abhiyan (SSA) was launched in 2000 to spread the availability of universal elementary education across India. Under SSA, commendable progress has been made in increasing enrolment rates; as well as providing basic infrastructures such as classrooms, water, toilets and boundary walls to all schools. Yet, what is the scenario when it comes to learning outcomes?

India made its debut in the Programme for International Student Aptitude (PISA) test in 2009 with 16,000 students from 400 schools across Himachal Pradesh and Tamil Nadu. While China – also a first-timer in 2009 – stormed into the number one position with Shanghai schools topping in math and science, India was at a paltry 72nd among the 74 participating countries. Since then, India has boycotted the PISA rankings citing ‘methodological differences’ but it plans to return to the rankings fold in 2021. Coupled with ASER’s findings over the years, this paints a sorry state of India’s primary education sector.

Now that enrolment rates are high, we would need to look for innovative interventions to improve learning-based outcomes in India. One of the recommendations of the ASER report, which the government has been focussing on in the last few years, has been early-childhood care and nurture, especially for children in the 0-3 age group. In fact, ASER’s study on three states (Andhra Pradesh, Assam and Rajasthan) found a positive and significant relationship between early childhood care and nurture and early grade learning outcomes. Although issues do remain in implementation, this avenue holds promise to further improve outcomes in India.

Additionally, in India, there are two interesting policy interventions that are in the process of being rolled out: same language subtitling (SLS) and outcomes fund for the education sector.

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 channeling global social impact investment. Outcome fund based models are actively being employed by nations across the globe to fund social projects and has the potential to deliver the necessary outcomes.

While we wait for ASER’s 2017 findings, much of the theory and evidence that we have strongly suggests that raising enrolment rates hasn’t been enough to push our growth frontiers. The hope is that the required stakeholders will aim, plan and push for innovative interventions that encourage student achievements.

Finite Populations: How do we think about them?

Consider how a normal regression output on STATA looks.

Sample Regression Output

 

We find that along with the coefficients, we get other values like standard errors, p values, confidence intervals, etc. So what is the purpose of these stats?

The standard assumption in stat-econ literature is as follows: i)the observed sample is a random representative sample from the entire population, ii) we are interested in the true population parameters.

What does this mean? We assume that we pick a random representative sample from a population. After running a regression, we get a particular coefficient on all the x’s. We assume that in repeated sampling, we would get a distribution of coefficients for the x’s. In fact, one of the fundamental properties of a good estimator is unbiasedness or that the Expected Value of the coefficient from the sampling distribution is equal to the true value, i.e. the true coefficient is equal to the expected value of the coefficient in repeated sampling. Although we rarely do repeated sampling in practice, we do check for the significance of the coefficients with a null hypothesis that they are equal to zero. Thus, in a normal regression framework, the population has the true coefficients and is quite distinct from the samples which have a distribution of coefficients.

So what happens when we do have the entire population?

This is a tricky question, as we usually always assume that the population is infinite and we can never have the entire population. Thus, in cases of finite population, like the 28 states of India, it becomes a little bit tricky as to how one should interpret the regression output. I will attempt to summarise two popular views in the stat-econ literature in this regard.

Irrelevance

One view says that if you have the entire population, the p values and t statistics are irrelevant.  The finite population is considered to be a fixed set of elements. The coefficients derived are the true relationship since you have the entire population and not a sample of the population. Thus, the concepts of hypothesis testing and significance become meaningless for an entire population. This is because the stats and tests are only relevant for a sample, and not the entire population. The caveat here is that the model has to be correctly specified.

Super Population / Underlying Process

This view states that the observations in the population are simply a sample from an infinite population. For example, if we’re looking at the 28 states in India, we can interpret the set of observations at one time period as a sample; and the set of observations across all time as the infinite super population.

This becomes important, especially if you want to make inferences not just about relationships today but also if you want the relationships to hold for similar groups in the future.

Another related but slightly different view would be that the observed outcomes in the population are the products of an underlying process. In that regard, what standard errors and other test stats capture become relevant. According to Abadie et al (2014) “there are for each unit missing potential outcomes for the treatment levels the unit was not exposed to.”

On a similar vein Wallis and Robers (1956) claim, “the totality of numbers that would result from indefinitely many repetitions of the same process of selecting objects, measuring or classifying them, and recording results.”

In these regard, test stats become important in understanding whether the coefficients generated are true or have been generated as a result of a chance outcome.

 

References

  1. Abadie A, Athey S, Imbens G, Wooldridge J (2014), Finite Population and Causal Standard Errors, NBER Working Paper Series.
    Available at: http://www.nber.org/papers/w20325.pdf
  2. Asali M (2012), Can I make a regression model with the whole population? [Msg 1], ResearchGate.
    Message posted: https://www.researchgate.net/post/Can_I_make_a_regression_model_with_the_whole_population
  3. Frick R (1998), Interpreting statistical testing: Process propensity, not population and random sampling, Behavioral Research Methods, Instruments, & Computers. Vol 30 (3), pp: 527-535
  4. Hartley H, Sielker Jr R (1975), A “Super Population-Viewpoint” for finite population sampling, Biometrics, Vol. 31 (2) , pp: 411-422
  5. Hidiroglou, Michael Arsene (1974), Estimation of regression parameters for finite populations , Iowa State University Digital Repository.
    Available at: http://lib.dr.iastate.edu/rtd/5146
  6. January (2013), How to report data for an entire population? [Msg 1], CrossValidated.
    Message posted to: https://stats.stackexchange.com/questions/70296/how-to-report-data-for-an-entire-population

 

 

Simple Guide to Getting Orthogonalized Impulse Response Functions

Vector Auto-Regressions or VARs are used in time series analyses when there may be inter-dependencies or relationships among multiple time series.  For example, we may want to understand the relationship between GDP, current account balance, and inflation rates. Running a normal OLS regression would be inappropriate as each variable affects the other variables; OLS estimates would have an endogeneity problem or the estimates would be biased. In such scenarios, we use VAR methods.

Recently, I was working on some time series data that had the issues of reverse causality. As a result, I had to use a VAR model to get orthogonalized impulse response functions (OIRFs) in order to understand the relationship between variables. In the attached presentation, I describe the theory behind this orthogonalization as well as the steps to generate OIRFs on STATA.

To check the presentation, please click on this link

India’s Growth Slump: No Easy Answers

[This article was published by the Business Standard  on Tuesday, Oct 31, 2017. The article was written in collaboration with Anmol Agarwal, a classmate from LSE. To read the article on Business Standard, click here]

 

“We should be very careful lest fiscal actions undercut stability,” said Reserve Bank of (RBI) Governor, Urjit Patel, in response to a journalist’s query on fiscal stimulus packages during the monetary policy conference on October 2017. The Prime Minister’s Economic Advisory Council (PMEAC) has also recently expressed its reservations about a mid-term fiscal stimulus package by the government to revive India’s economic growth.

 

While critics of a fiscal stimulus cite stability — most notably upside risks — as a key reason against a fiscal stimulus, advocates routinely talk about the famed fiscal multiplier and how it would spur a much-needed economic revival.

 

Fiscal multipliers were first introduced to the world by John Maynard Keynes during the Great Depression of the 20th century. Keynes had argued that a recession could be curtailed by an increase in government expenditure, fuelling savings and capital formation. For instance, a rise in the government expenditure of $100 would raise the real GDP or gross domestic product of a country by more than $100 and bring it back on the path of economic growth.

 

Keynes and his policies began to be followed by policymakers all over the world until the advent of Milton Friedman, one of the most influential economic thinkers of the 20th century. Friedman challenged ‘naive Keynesianism’ (as he put it) and argued that a fiscal expansion is highly inflationary even as the neoclassical school argued that fiscal deficits brought about by an expansionary fiscal policy would result in rising interest rates, and a subsequent crowding out of private investment.

 

These ‘non-Keynesian’ effects of government spending were fist empirically documented in the 1990s in a series of researches published by the National Bureau of Economic Research (NBER), a leading economic research organisation in the US. The authors – Francesco Giavazzi of the NBER and Marco Pagano of the University of Naples Frederico II – studiedthe impact of fiscal contractions and expansions in Organisation for Economic Cooperation and Development (OECD) countries, and analysed their impact of private investment, consumption, and economic growth. The OECD is an intergovernmental economic organisation with 35 member countries, most of whom are high income and can be considered as being developed.

 

Interestingly, they found that spending cuts in Denmark (1983-86) and Ireland (1987-89) actually lead to an increase in aggregate demand and private consumption, stimulating economic growth. On the other hand, the Swedish fiscal expansion — where Swedish Government Debt to GDP jumped from 25 per cent in 1990 to 67.8 per cent by 1994 — counterproductively led to a fall in private consumption and investment. The authors called the events in Denmark and Ireland as ‘expansionary fiscal contractions’, while the events in Sweden as ‘contractionary budget expansions’.

 

Simply put, the impact of the fiscal multiplier in these cases was negative. These events were not anomalies as further studies have gone on to show several such outcomes from budgetary changes.

 

In India, there have always been divergent views about the effectiveness of a fiscal stimulus. An important Keynesian argument to illustrate the effectiveness of the multiplier is that a fiscal stimulus should increase income and eventually spur private savings and investment. Does this hold good for Indian? A look at the chart below suggests otherwise. India’s fiscal deficit as percentage of GDP declined continuously from 5.98 per cent in 2001-02 to 2.54  per cent in 2007-08. But, contradictory to the Keynesian view, domestic savings as a percentage of GDP show a continuous rise, peaking at around 38 per cent in 2007-08 when the deficit was the lowest.

 

Subsequently, there was a sharp decline in savings in 2008-09 due to the onset of the financial crisis in a situation economists commonly refer to as ‘savings paradox’ — where individuals desire to save more due to increasing uncertainty in the economy, but end up saving less due to a decline in their incomes as brought about by a crisis. Focusing on years after the crisis, fiscal deficit rose continuously from 2010-11 until 2014-15, but savings have been on a downward trajectory, clearly suggesting an absence of a Keynes style deficit–income-savings correlation in 

 

In the Study of State Finances report of 2016-17, the RBI expressed concerns about how increased market borrowings by the states could lead to higher bond yields and costs associated with borrowing. Even a significant part of the central government’s borrowing requirement is taken care of by market borrowings – based on budget estimates net market borrowings for the year 2017-18 stand at Rs 3.48 trillion, or about 64% of the gross fiscal deficit. Since an increased fiscal deficit is likely to be financed with market borrowings, it is likely that bond yields would rise. Theoretically, this can crowd out private investment and have a detrimental effect on the economy, especially at a time when banks are not willing to lend fearing rise in bad debts and many companies have been raising money from the corporate bond market. There have been several studies, which corroborate the relationship between a fiscal stimulus and higher cost of borrowing, including a 2004 study published by Economic & Political Weekly, where an RBI Economist Rajan Goyal, established the relationship for 

 

Graph

After a sharp fall at the onset of the 2008 financial crisis, India’s benchmark 10-year bond yield had an upward trend until 2014-15 (Source: Authors’ Calculations and Bloomberg)

Even those who advocate a fiscal stimulus acknowledge that fiscal multipliers only lead to economic growth when the increased government expenditure is spent productively. A study by the National Institute of Public Finance and Policy, a New Delhi-based economic policy think tank, in 2012 had found that a capital expenditure multiplier was 2.45, while other revenue expenditure multipliers were less than one. However, if one looks at India’s government capital expenditure, the trend is puzzling. In the years when the fiscal deficit was higher, there was a drop in the government’s capital expenditure. This clearly suggests that the quality of expenditure in a fiscal stimulus may not necessarily lead to an economic revival.

 

A fiscal stimulus will also have a bearing on India’s sovereign rating. It has been stuck at a low level, being upgraded only once in the past 25 years. On 2nd November 2016, the credit rating agency S&P Global Ratings kept the credit rating for unchanged at the lowest investment grade (BBB-), only 1 grade higher than a junk bond rating, with a stable outlook, citing India’s low per capita income and weak public finances as the major reasons. Moody’s and Fitch Ratings followed the suit, expressing scepticism regarding upgrading India’s rating in the near future.

 

The issue of consistently low ratings baffles Indian economists. India’s chief economic advisor Arvind Subramanian blamed the agencies for their ‘poor standards’, while India’s Economic Affairs Secretary, Shaktikanta Das, had said that rating agencies were out of touch with India’s reality. Even the OECD threw its weight behind India, suggesting that deserves a credit rating upgrade.

 

performance, growth prospects, debt position and the state of public finances are some of the key criteria used by rating agencies. With the growth rate sagging, India’s only hope of expecting a better rating in the future is for the government to be fiscally prudent. An untimely fiscal stimulus will lead the government missing the 3.2 per cent fiscal deficit target in fiscal year 2018, dent credibility of the government and ruin chances of upgradation in our sovereign rating. The investment climate continues to be weak – gross fixed capital formation as a percentage of GDP has steadily declined from 34.3 per cent in 2011-12 to 29.5 per cent in 2016-17. In such a scenario, missing the fiscal deficit target may dent the confidence of investors, which in turn, could end up threatening capital inflows.

 

When a patient is sick, the doctor will always suggest medicines but some of the medicines have side effects and taking too much of them may end up causing more harm than good to the patient. It’s time India’s policymakers prescribed the right remedy for the ills that have been plaguing the economy. Fiscal stimulus is not the panacea.

References:
1. Francesco Giavazzi and Marco Pagano (1990), National Bureau of Economic Research, Can Severe Fiscal Contractions be Expansionary? Tales of Two Small European Countries (http://www.nber.org/chapters/c10973.pdf)
2. Francesco Giavazzi and Marco Pagano (1995), National Bureau of Economic Research, Non Keynesian Effects of Fiscal Policy Changes: International Evidence (http://www.nber.org/papers/w5332)
3. Reserve Bank of (2017), State Finances: A Study of Budgets 2016-17 (https://rbidocs.rbi.org.in/rdocs/Publications/PDFs/0SF2016_12051728F3E926CFFB4520A027AC753ACF469A.PDF)
4. Rajan Goyal (2004), Economic and Political Weekly, Does Higher Fiscal Deficit Lead to Rise in Interest Rates
(http://www.epw.in/journal/2004/21/special-articles/does-higher-fiscal-deficit-lead-rise-interest-rates.html)

 

Clues for India: Looking at the puzzle of Total Factor Productivity and Capital Flows

[This article was published by the the London School of Economics South Asia Centre Blog (South Asia @ LSE)  on Monday, Oct 30, 2017. The article was written in collaboration with Aniruddha Ghosh, a classmate from LSE. To read the article on South Asia @ LSE, click here. The article was also posted by Oxford India Policy Blog, you can read it here]

 

While examining previous trends and research on capital flows and total factor productivity, Aniruddha Ghosh and Sujan Bandyopadhyay write that India must be careful to continue to maintain the positive correlation between growth and net foreign capital flows.

“Among all the means of power subordinate to the regulation of the State, the power of money is the most reliable, and thus the States find themselves driven to further the noble interest of peace, although not directly from motives of morality”— Immanuel Kant, “Perpetual Peace: A Philosophical Sketch”,1795

While the Kantian view on increasing economic integration was primarily driven by considerations of war and peace, international capital flows and their management have become pivotal to the macroeconomic growth and stability of modern economies. Over the course of the last two decades, India’s net capital flows have surged from little over the US $500 million in the first quarter of 1990-91 to the US $25.38 billion in the first quarter of 2017-18.

In This Time is Different (2011), Carmen Reinhart and Kenneth Rogoff flag an important historical insight: Periods of high international capital mobility have repeatedly produced international banking crises, not only famously as they did in the 1990s, but historically.

Figure 1: Periods of high capital mobility have often been associated with higher incidence of banking crises Source: Bordo et.al (2001), Caprio et.al (2005), Kaminsky and Reinhart (1999), Obstfeld and Taylor (2004), Reinhart and Rogoff (2008) 

The traditional neoclassical technology models tell us that net financial capital flows should move from richer to poorer countries. That is, it should flow from countries that are capital-abundant, and thus lower returns, to those that are capital-scarce (higher returns) and have greater investment opportunities.

Robert Lucas Jr . has pointed out an empirical paradox using the 1988 data: If the traditional neoclassical model were true, the rate of return on a unit of capital investment in India would be nearly 58 times more than the return one would get in the US, yet the level of capital flows to India from the US were modest and nowhere near the levels the traditional theory predicted. Hence, the traditional neoclassical theory fails to imbibe the assumptions of cross-country differences in productivity and capital market imperfections. After accounting for cross-country differences in the fundamentals and capital market frictions, the risk-adjusted returns to investment should govern capital flows and therefore, should resolve the paradox. However, the paradox remains as relevant today given that the poorer countries of the world tend to run current-account surpluses (thus exporting capital) and the richer ones (most notably the US) tend to run current-account deficits (thus importing capital).

The paradox gets rather perverse when we move from absolute levels of income to income growth. In 2006,  “Foreign Capital and Economic Growth,” written by Eswar Prasad, Raghuram Rajan and Arvind Subramanian and published by the International Monetary Fund, took the question of capital flows one step forward. The paper presented evidence to support the intensification of the Lucas Paradox, while observing that within developing countries, growth and foreign capital inflows were, in fact, negatively correlated. This meant that poorer countries which had higher amounts of net foreign capital inflows had lower economic growth than those that didn’t. Additionally, within the group of poorer nations, capital flows out of countries that grow faster. Interestingly, this relationship breaks down for developed nations, i.e. developed nations have a positive correlation between growth and foreign flows.

The authors of the IMF paper argue that there could be a number of reasons why we see this anomaly between growth rates of developing countries and their foreign capital flows.

Firstly, successful developing countries may have a limited ability to absorb foreign capital flows due to structural impediments in their financial sector. Secondly, it is possible that developing nations actively make the choice to avoid excess foreign flows to prevent overvaluation of assets. And the final conclusion is that nations develop, structural impediments in their financial sectors reduce and their ability to absorb foreign flows increases – such that it can become a driver of growth, like industrialized nations, in the long run.

Figure 2: The composition of capital exporting countries has changed from higher to lower income countries and the Lucas Paradox seems to reinforce over time. Source:  Prasad, Rajan and Subramanian (2006)

Figure 3: Countries with higher growth have attracted less net foreign capital than medium- and low-growth groups. Particularly, China and India have been exporters of capital despite high economic growth. Source:  Prasad, Rajan and Subramanian (2006)  

To explain these rather perverse findings presented in the IMF paper , Francisco J.Buera and Yongseok Shin in their 2017 paper, “Productivity Growth and Capital Flows: The Dynamics of Reforms”, focus on the relationship between growth accelerations in total factor productivity (TFP) and capital flows. They attribute the observational findings to the disparate dynamics of aggregate savings and investment behavior. For the readers, TFP accounts for the growth in output not accounted for by the growth in inputs used for its production and is often synonymous with improvements in the technological state of an economy. A rising TFP is necessary for a higher economic growth rate and therefore, investment in R&D is essential for sustained economic growth.

Figure 4: Panels A and C show, respectively, the average of saving minus investment rates and TFP over the 33 episodes of sustained accelerations before 1980. The pre-1980’s negligible savings and investment gap confirms our understanding of limited capital flows.  Panels B and D show the average over 22 such episodes after 1980. Source: Francisco J. Buera and Yongseok Shin (2017)

Buera and Shin present the following observations using data for 22 sustained growth acceleration episodes post-1980. First, in contradiction to the predictions of standard neoclassical models, capital flows out of countries experiencing fast growth in output and TFP. Second, this pattern is a lot more prominent in the early stages of these growth accelerations, where many of these nations undergo economic reforms. These first generation reforms are primarily concerned with the removal of idiosyncratic distortions (tax-cuts) in their economies. Finally, capital outflows reflect a surge in aggregate savings and a delayed rise in aggregate investment at the onset of sustained growth accelerations. Interestingly, when the reform is a far-reaching one – it removes goods market distortions as well as improves the health of financial institutions (second generation reforms) – capital flows into the countries experiencing faster TFP growth.

Figure 5: Net Capital Flows & Net Capital Flows as a percentage of GDP (RHS) for India since 1990. Note the peak of capital flows coincides with the Great Recession as ‘plata dulce’  moved around the world easily. Source: Authors’ Calculations and Bloomberg

Figure 6: Total Factor Productivity level growth in India, China, and the US. India has been on a rising TFP path. Source: Authors’ Calculations and The Conference Board

India has had a long and chequered history with foreign capital flows. The first few decades after independence were characterised by import substitution policies which placed severe restrictions on the flow of foreign goods and capital, and it is only in the last few decades that foreign flows to India have really picked up. In the last two decades, both net foreign capital flows and GDP have grown substantially indicating a positive correlation, though this positive correlation is not as strong as the traditional neoclassical models predict. In light of the fact that there have been rising TFP levels in recent years as well, India must be careful to continue to maintain this positive correlation between growth and net foreign capital flows. Rakesh Mohan, in a speech in 2008 in his capacity as the Deputy Governor of the Reserve Bank of India said, “A large surge in capital [in]flows over a short span of time in excess of domestic absorptive capacity of the economy can lead to upward pressure on the exchange rate, possible overheating of the economy and asset price bubbles. They can also pose the risk of an abrupt reversal, which may have potential negative real economic effects.” With far-reaching reforms in the goods and capital markets, it becomes imperative for the NDA government to keep these historical and policy insights in mind.

Nudging People the Right Way: A Couple of Personal Anecdotes

Richard Thaler, the 2017 winner of the Sveriges Riksbank Prize in Economic Sciences, lists out several fascinating insights into the human psyche in his book – Nudge : Improving Decisions about Health, Wealth and Happiness.

The two systems of thinkings is perhaps the most significant contribution that behavioral scientists like Thaler and Kanheman. The simple concept elegantly explains how the human mind thinks. We have an automatic system which is instinctive and quick, and a reflective system that is that is slow and self-conscious. I would encourage everyone to read Thaler’s Nudge and Kanheman’s Thinking Fast and Slow, to better understand this concept.

But moving back to Nudges. Thaler and his co-author Cass Sunstein advocate a philosophy of ‘libertarian paternalism’ throughout the book. To them, freedom of choice is sacrosanct and they don’t want to impede on the liberty of people under any circumstance. Yet, they believe that ‘choice architects’ can drive people’s actions in subtle smart ways, through nudges, to improve outcomes. The authors define a ‘nudge’ as an activity that would alter people’s behaviour in a predictable way, while ensuring that people have the option of not altering their behaviour at little or no cost, if they so desire. An example used in the book is that if moving the arrangement of food in a school cafetaria encourages students to eat healthier, that would count as a nudge. However, banning the sale of unhealthy food items would not count as one.

Nudges can be especially useful in less developed nations. With states not having resources to impose mandates or bans, altering peoples’ behaviour through nudges can be an efficient way of achieving optimal outcomes.

Mumbai’s suburban railways experimented with nudges to prevent fatalities on its network. Mumbai’s rail network sees several deaths every day as trains and rail facilities are packed to the brim during rush hour and infrastructure is not enough to deal with such heavy crowds. Yet despite providing over-bridges to cross tracks people often resort to crossing the tracks of foot to save time or due to laziness. Despite being considered an illegal activity that attracts fines and a possible prison sentence, crossing tracks is a widely prevalent phenomenon.

In 2010, the rail administration decided to try and use nudge theory to prevent deaths by crossing tracks. Along with a behavioural economics think tank, they designed a set of posters that showed a person being a mowed down by a train with an emphasis on the person’s facial expressions of fear and shock. These posters were placed at locations which were prone to crossing in the eye-line of people who may contemplate crossing the tracks. Previous campaigns had been restricted to announcements, or written signs that contained information about fined people had to pay in case they were caught crossing the tracks.

This was a unique effort where the state was actually trying to impose a ban via a nudge. While preliminary results did show that the efforts had reduced fatalities, there has been no comprehensive report on the effect of the campaign. Yet, this exercise does show that in cases where governments are unable to impose their mandates, nudge theory may help them along the way.