Health Care Facilities in Tribal Areas of Rajasthan

An analysis of the inaccessibility of health care in rural regions of Rajasthan, India.

By Trisha Biswas

Providing accessible health care to developing countries has become an important goal towards eradicating poverty. Countries such as India, have implemented government mandated programs that intend to provide rural communities with health care. The National Rural Health Mission (NRHM) was established in 2005 and aimed to provide quality health care that was both accessible and affordable to the most vulnerable populations in India.

A recent article published by the Hindustan Times, “Provide Accessible healthcare in Rural Areas, CAG tells Rajasthan govt.”, discusses issues on compliance by the state governments in providing proper health facilities. Following the rules and regulations brought on by the NRHM, the Comptroller and Auditor General India (CAG) requested the Rajasthan state government to comply with the Indian Public Health (IPH) standards to provide rural areas with accessible health care facilities. Based on the data collected by the CAG, fewer number of health centers were implemented in tribal areas as compared to non-tribal areas. The report provided by the CAG states that the Rajasthan state government was unable to provide basic facilities in 75.77% of rural health centers. The CAG also reported that health centers that were constructed for the tribal areas were built in “inaccessible and uninhabited locations”. Overall, this report mentioned that the requirements of Community Health Centers (CHCs), Primary Health Centers (PHCs) and sub-centers as per IPH standards in non-tribal areas were provided in excess as compared to tribal regions where the number of these health centers fell short of IPH standards. Additionally, many of the health centers that were implemented in tribal areas faced severe deficiencies in facilities and quality of care.

Two Sides to the Problem: Supply vs. Demand

 Many studies have been conducted to form concrete reasons behind the disproportionate number of health care facilities in rural/tribal areas. There are two sides to this growing issue, the supply side and the demand side. The problem from the supply side stems from recruitment and retention problems, of highly educated health care professionals. Many health care providers may not want to work in rural areas due to inadequate staffing of hospitals and health facilities. The doctors do not want to take on the burden of treating hundreds of patients alone. Therefore, they do not accept job offers in rural/tribal health facilities. In a 2008 paper, The Quality of Medical Advice in Low-Income Countries, written by Jishnu Das, Jeffrey Hammer, and Kenneth Leonard, discuss a particular story in Delhi. At this particular facility, there were only two working doctors who provided care to more than 200 patients per day. This greatly decreased the average amount of time the doctors could spend on each patient. On average, the doctors asked “3.2 questions, and [performed] an average 2.5 examinations (Das et al., 2008).” The numbers presented in this study show that a sufficient amount of time was not being spent on each patient to properly diagnose them.

Consequently, the supply side problem leads to the demand side problem. The quality of care provided to low-income countries are considered to be inferior to other developed nations. Das et al., discussed that doctors employed in health facilities in developing countries and regions have lower education levels than their counterparts. This effects the quality of care provided to the patients. However, it was found that these doctors administer an even lower quality of care than they are trained to provide. The poor quality of care can affect the decision making process of the individuals living in these rural areas. First and foremost, most of the health care facilities located in tribal areas are stationed in remote regions away from the villages. In addition, if the residents of the rural villages are aware that traveling the great distance does not guarantee proper health care, they do not have any incentive in making the extensive trip.

The incentive to travel to far located health facilities are also affected by the availability of doctors. In many instances, after patients have traveled long ways to see a doctor, they come to find out that the doctor is not present or the entire facility had closed for the day. A 2011 paper written by Pascaline Dupas, “Health Behavior in Developing Countries”, referred to a supplemental study completed by Banarjee et al. (2010) in Udaipur India, which showed that public facilities that provided free immunization for children had a very high rate of staff absences. It was found that “45% of the health staff in charge of immunizations [were] absent from work on any given day, being neither at the health center nor on their rounds in surrounding villages (Banarjee et al., 2010).” Due to these uncertainties many families do not complete the full round of immunization for their children. It was found that if health facilities properly advertised the hours of operation for immunization camps, the immunization rates drastically increased. The immunization rates increased form 49% of children completing one round of immunization shots when supply was unreliable to 78% when the supply became reliable. The consensus of the studies showed that if proper health care was supplied to the patients, there would be a high demand in health care.

Conclusions and Possible Outcomes

As previously mentioned, the low number of health care facilities provided in rural/tribal areas are due to both supply and demand side issues. However, based on the data it can be seen that if rural families are provided with reliable and quality health care, take up increases drastically. It is important for the health care facilities to be fully staffed with well qualified doctors so that residents of rural areas have access to quality health care. Spending more time with each patient will increase the likelihood of correctly diagnosing the patients and therefore increase their chances of a healthy life. In addition, correctly advertising the operating hours of the health care facility will allow the residents of the rural areas to know exactly when the correct times are to go to the facility. If individuals from tribal areas know that the health centers will be open when they arrive, their incentive to make multiple trips throughout the year will increase. Improving these different components, will increase the value of expected returns in receiving medical care for the tribal area residents. It is possible that if one individual had a decent experience at the health center they will spread the word throughout their community, therefore making other people more likely to visit the health facilities. Overall, increasing the supply of proper health care will create more incentive for people to make frequent visits. This will ensure the increase in the demand in health care in tribal regions as well.


Works Cited

Banerjee, Abhijit, Esther Duáo, Rachel Glennerster, and Dhruva Kothari (2010). Improving Immunization Coverage in Rural India: A Clustered Randomized Con-trolled Evaluation of Immunization Campaigns with and without Incentives. British Medical Journal 340:c2220.

Das, J., Hammer, J., & Leonard, K. (2008). The Quality of Medical Advice in Low Income Countries. The World Bank, 1-38. Retrieved April 16, 2017.

Dupas, P. (2011). Health Behavior in Developing Countries. Annual Review of Economics, 3, 1-39. Retrieved April 16, 2017.

Provide accessible healthcare in rural areas, CAG tells Rajasthan govt. (2017, April 01). Retrieved April 16, 2017 from

Universal Health Coverage. (n.d.), Retrieved April 16, 2017, from

Top image by Ejaz Kaiser, Ruchir Kumar and Subhendu Maiti from the Hindustan Times







Gigaba to take “tough, unpopular choices” to grow economy

By: Devin Griffiths

A summary of South Africa’s newest finance minister and the challenges he will face to lead a struggling economy.


South Africa’s newest Finance Minister, Malusi Gigaba

The economy of South Africa has been on thin ice over the recent years, and if changes aren’t made soon this whole country may find itself in deep water. This article talks about Malusi Gigaba and the challenges ahead of him as South Africa’s newest finance minister. Gigaba replaced Pravin Gordhan who was not seeing eye to eye with South African President Jacob Zuma on the country’s financial plans. Gordhan was the finance minister from 2009-2014 and returned in late 2015 after the back-to-back firing of the finance ministers who had replaced him. Though Gordhan did not have the economy flourishing, he was able to keep it stable and he strengthened the rand (South Africa’s dollar) 20% upon his return in 2015. He planned to uplift the economy by cutting government spending and raising taxes on certain products to bring in more revenue. He also had the support of many investors and big businesses in the country that believed firing him could lead to market chaos. The rand has already dropped more than 5% since he was fired, and big investment firms like Old Mutual are seeing their shares decrease. The rand is currently .075 of the U.S. dollar. Zuma’s decision to fire Gordhan didn’t go well with many citizens either, including members of the ANC (African National Congress), which is his political party that has reigned throughout South Africa since the 90s. Citizens are suffering due to the failing economy and protests have broken out throughout the country over lack of services. Farmers have also been hit with the worse drought in over a century, which doesn’t help this country’s economic situation either. Many have said they will do what they can to make sure Zuma is no longer president in the near future.

South Africa’s economy is struggling so much that it is on the verge of reaching junk status from national agencies. Junk status is the low point for credit rating and would mean South Africa could be at high risk of losing its bonding privileges and therefore increasing its debt. The Citi World Government Bond Index has already said it would no longer support South Africa if it reached junk status. The fall in global commodity prices hasn’t helped either. The article also states that economic growth has also slowed to 0.3% in 2016 from a previous 1.3% in 2015. We can use the Rule of 72 to give us a perspective of just how slow this country’s growth rate is. This method estimates how many years it would take a country’s GDP per capita to double, given it’s current growth rate. It divides the number 72 by the growth rate. In South Africa’s case, it would take 240 years for income to double (72/0.3=240). Economic growth is determined by a multitude of factors from policies and market conditions to national resources. There are many different ways to go about increasing economic growth. The Harrod-Domar Model is an economic model that states economic growth depends upon saving and capital output:

Y = s/v-&

Y represents the growth rate, s represents savings, v represents capital output and & represents depreciation. Increasing savings while holding capital output steady can increase growth, or decreasing capital output while holding savings steady. Another way is to increase savings by more than you increase output. This is one example of many growth models. Below you can see a table provided by the World Data Bank that shows you the economic growth of South Africa over the past decade.

Year GDP growth (annual %)
2007 5.360474053
2008 3.191043888
2009 -1.538089135
2010 3.039734625
2011 3.284197135
2012 2.213258978
2013 2.330342259
2014 1.628871543
2015 1.264651378

Gigaba will be taking over financial minister of a country that is failing in almost every economic aspect. South Africa is also suffering from extreme economic inequality. Below is a table provided by the World Data Bank that shows you the income shares of each income group during the past decade.

Income Share % 2008 2011
Income share held by third 20% 8.05 7.97
Income share held by fourth 20% 15.79 15.9
Income share held by highest 20% 68.68 68.94
Income share held by second 20% 4.87 4.71
Income share held by lowest 20% 2.6 2.47

South Africa has one of the most unequal distributions of wealth in the world. The table of income shares shows that the top 20% own 68% of the wealth. These top 20% also own close to 100% in the country’s economic assets. To understand just how unequal the distribution of wealth in this country is, we can measure the Kuznets Ratio:

share of income owned by the poorest X% / share of income owned by the richest Y%

In this case we would divide South Africa’s poorest 20% by its wealthiest 20%. From the table, we would divide 68.94 by 2.47 = 27.91. This number is very high, and is triple the most recent Kuznet Ratio of the United States.The margin of wealth is so wide that a middle class technically does not exist. Even more inequality exists racially, as the white population owns the majority of the wealth, which is shocking considering that the white population make up only 8% of the country. A method that can decrease inequality is known as the Principle of Transfers. If wealth or resources are removed from the rich and given to the poor, inequality will go down. Unfortunately, increasing everyone’s wealth will not change inequality at all, which is known as Scale Independence. Capitalism is often criticized as a system where the rich continue to get richer while the poor consistently suffer. Wealth is often passed down from generation to generation. South Africa is a country that saw its first democratic government in 1994, evolving from that of an Apartheid system before hand. So it may not be that shocking to see the majority of the wealth in the hands of whites, considering that whites were historically always in power in South Africa up until the last two decades. Many citizens have continued to voice the issue of economic inequality to the government, and it is something Gigaba must try to change.

The road ahead of Gigaba will not be an easy one, which is something he definitely has realized. He knows he will have to make tough decisions and implement changes along the way, which will not always be supported by the citizens. As for now, he intends to stick to the financial plans laid out by Gordhan in February, which include seeking up to $2 billion in foreign aid for the next couple of years. He also maintains that he will work to redistribute wealth as well as shifting majority of it to blacks. There are many different models and formulas that can be used as guides to help decrease the problems in this struggling economy, so Gigaba will have a lot evaluate. He seems to be confident in his position, and hopefully his work will positively impact the economy of South Africa in the near future.


Cotterill, Joseph. “Fitch cuts South Africa’s credit rating to junk.” Subscribe to read. N.p.,      7 Apr. 2017. Web. 18 Apr. 2017.

Mullen, Jethro, and Alanna Petroff. “South African rand plummets after Finance Minister Pravin Gordhan is fired.” CNNMoney. Cable News Network, 1 Apr. 2017. Web. 18 Apr. 2017.

Orthofer Economics PhD Candidate, Stellenbosch University, Anna. “South Africa needs   to fix its dangerously wide wealth gap.” The Conversation. N.p., 6 Oct. 2016. Web. 18 Apr. 2017.

“South Africa’s economy ‘in crisis'” BBC News. BBC, 24 Feb. 2016. Web. 18 Apr. 2017.

The Microfinance Industry in Mali Has Been in Crisis Mode – Will it Rebound?

An analysis of the problems plaguing the industry and insight on how the country can recover.

By: James Harris

Mali, the land-locked West African nation, is trying to revive its microfinance sector amid a crisis fueled by structural and political issues, as well as continued violence in the northern regions. A recent article in The Essor, a Malian news agency, details a presidential commitment to help end unemployment. The Youth Employment Promotion Agency (APEJ) will distribute CFAF (African Financial Community Franc) 1.3 billion to “young widows in military camps, young retailers, and young people from the diaspora” using the microfinance industry (Diabate). Microfinance institutions (MFI) seek to enhance the lives of people who cannot receive credit to fund projects, buy insurance, or smooth consumption through micro loans.

The microfinance structures in place have not been up to par because of inadequate resources and a lousy legal foundation that allowed too many MFIs to participate, reducing the overall efficiency of the system. A lack of government accountability, poor targeting, and insufficient infrastructure inhibit progress by limiting access to credit, preventing them from being successful.

The influx of funds is certainly helpful, but problems must be solved to maximize its effectiveness. A more open and transparent system with better trained staff is a good place to start, along with a better allocation of resources that will contribute to the goal of 200,000 new jobs by 2018.

The current system in place “considerably limits the access to credit for certain vulnerable groups” such as women and the younger population, according to the Minister of Employment and Vocational Training, Mahamane Baby. The problem started over a decade ago with a flawed legal framework that allowed too many underqualified institutions to enter the market. Mali, consistently ranking as one of the poorest countries in the world, received plenty of donations from the United States, Britain, and organizations like the World Bank, but the system in place was not ready for such a rapid expansion. Over 300 MFIs were given licenses to operate during an eight-year period spanning from 2002 to 2010. However, not all met the “minimal conditions for ensuring the security of savings deposits,” found in a 2015 report by the World Bank.

Furthermore, the volume of deposits and outstanding loans more than doubled, while insufficient resources were dedicated to hiring more experienced staff to oversee the projects. The national supervisory body (CCS/SFD) only conducted about 20 supervisory missions each year and if recommendations are made, rarely do follow-up missions take place (World Bank).

The poor state of infrastructure exacerbates the problem by making it hard for the CCS to conduct these audits. Some rural villages are, on average, “14 miles from the nearest paved road” (Beaman). Formal and informal institutions must try to lower supervision and transportation costs as much as they can, because the interest rate and the default rate will decrease. The default rate is the percentage of borrowers who do not pay back their loans. When borrowers are risky, their default rates are high and for lenders to carry out this loan, the interest rate will be higher as well.

Officers tasked with checking up on a project in a very rural area might have trouble physically getting there, making it extremely difficult to know if an investment will go up in smoke. Being able to supervise more projects with greater ease will result in more success for rural borrowers and a substantial improvement in their lives.

Improved infrastructure will also help reduce the effects of asymmetric information in the microfinance sector. When the borrowers know more about their immediate situation than the lenders do, the loans may be given to bad risks because they could not properly evaluate the projects. Modern technology and access to more roads will better enable the lenders to judge each investment.

The government’s targeting of funds is certainly skewed because the economy is growing, but per-capita GDP is not increasing. According to the World Bank, 19% of government spending in 2011 was on education, yet the top 10% most educated children absorbed 50% of the resources. The literacy rate in Mali is only 54%, so evenly spreading out education funds would be beneficial to MFIs, as it could decrease costs associated with explaining loan terms to borrowers and accurately communicating information. A more educated population will improve the quality of investments and might also reduce violence that has impeded progress in the past, such as the 2012 political coup that stopped the inflow of funds for MFIs.

Several issues need to be fixed if the country will achieve its goal of 200,000 new jobs by 2018 and revive the microfinance industry. The MFIs need to employ more loan officers and staff to handle the funds efficiently. More workers will enable them to supervise more projects and ensure that they are giving loans to the right targets. Similarly, the government must spend more on infrastructure to improve communication between lenders and borrowers, which will reduce costs and the default rate.

The desire for change is strong in Mali, because ironically, there is too much money and not enough people to manage it. There is potential for the microfinance sector to be successful, but it cannot happen without improvements to the current system and the infrastructure.


Diabate, F. “Promoting Youth Employment: CFAF 1, 3 BILLION TO FINANCE GENERATING INCOME ACTIVITIES.” The ESSOR. The ESSOR, 23 Mar. 2017. Web.

Mali The Microfinance Sector. Rep. World Bank, Dec. 2015. Web

The World Bank

International Labour Organization


Beaman, Lori, Dean Karlan, and Bram Thuysbaert. SAVING FOR A (NOT SO) RAINY DAY: A RANDOMIZED EVALUATION OF SAVINGS GROUPS IN MALI. Working paper no. IPR-WP-14-15. N.p.: n.p., n.d. Print.

Ray, Debraj. Development Economics. N.p.: Princeton UP, 1998. Print.

Schaffner, Julie. Development Economics. N.p.: Wiley, 2014. Print.

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Published by: La REDACTION
Date: Thursday 23 March 2017
in: Top Story , Politics
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The beneficiaries of this APEJ support in the form of credit lines are the young widows of the military camps, the “Malitel Da” displaced youths, the young retailers and the young people of the diaspora
As part of the fulfillment of the presidential commitment to create at least 200,000 jobs by 2018, the Youth Employment Promotion Agency (APEJ), in partnership with the Armed Forces Social Services Department, The collective retailers and the collective of the “Malitel Da” dismantled last Tuesday, granted three credit lines for young women widows military camps, young retailers and young people from the diaspora . The ceremony was presided over by the First Lady, Mrs. Keita Aminata Maiga, at the Military Engineering Square. It was in the presence of the Minister of Employment and Vocational Training, Mahamane Baby, the General Director of APEJ, Amadou Cissé, the representative of the Minister in charge of Defense, Mamadou Diaou,
The sum of CFAF 1.3 billion is distributed as follows: CFAF 100 million for young widows in military camps, CFAF 200 million for young people displaced by “Malitel Da” and young retailers, And 1 billion FCFA for young people in the diaspora. These lines of credit aim to facilitate the integration of young people and women into the economic fabric through the financing of income-generating activities through microfinance institutions.
In his speech, the Minister of Employment and Vocational Training will say that the inadequacy of the services offered by the financial structures considerably limits the access to credit for certain vulnerable groups with modest incomes or in certain sectors of the economy ‘economy. “Accessing credit for the vulnerable layers especially for his young fringe, is part of the journey of the combatant,” he said. Furthermore, Mahamane Baby has indicated that this amount will be housed in the accounts of the partner microfinance institutions in the form of credit lines to finance the start-up or revival of the economic activities of the various beneficiary groups.
“The granting of these credit lines will be made through loans that will be granted to beneficiaries,” he said, adding that these loans will be repaid over a period of 12 to 18 months depending on the nature of the sector Trade, agriculture, handicrafts, livestock and fisheries.
In addition, Minister Baby urged future beneficiaries to better arm themselves for the management of their various companies and to persevere in the repayment of loans in order to allow others to benefit from these funds. He was convinced that it was through these kinds of actions that Mali could win the battle against unemployment.
He also took the opportunity to thank the First Lady who, despite her busy agenda, agreed to enhance the presence of this event. “We found that these widows and young people are somehow excluded from society, so we gave them these lines of credit,” said the director general of APEJ, adding that it is these genres Activities that will effectively combat unemployment. “The APEJ has to think about these layers, that is our mission. These women and young people are really in need. The APEJ will do its utmost to bring them up to the level of consideration, “assured Amadou Cissé.
The beneficiaries’ representative thanked the President of the Republic, the First Lady and all those who took part in the implementation of this action. “It does not surprise us because solidarity is a custom in Mali,” noted Moriba Doumbia. The signing of the conventions of the three lines of credit ended the ceremony.


Targeting Poverty via Technology: The Aadhar Initiative

An article exploring the Aadhar Initiative and its impacts on targeting poverty in India, using technology.
By: Abhinav Saraogi

On the 27th of March 2017, responding to the increased uproar in the compulsory use of the Aadhaar Card for non-welfare related schemes, the Supreme Court of India announced the decision in favor of the Indian Government, granting them the power to make the Aadhaar Card mandatory for most schemes. Although, for most welfare related scheme the Government is prohibited by law to demand an Aadhaar card, this decree has enabled the Modi Administration to pursue their agenda on creating a single identification document as well as promoting ‘Digital India’. The primary focus, in an article written by Kanishka Singh,, revolves around the  different schemes where an Aadhaar Card would be compulsory and is the main talking point in the Indian Government’s plan of targeting and eradicating poverty (World Bank 2013).

With the exponential rise in the population in India, the Government has been facing a tough time in order to implement a system so as to capture and unify the citizens in a single database. ‘Digital India’ allows the government to implement and promote technology in order to benefit the society. The Unique Identification Authority of India (UIDAI) created the Aadhaar, issuing every individual with their own unique 12-digit identification number, storing each individual’s biometric as well as demographic data.

Reviewing an article by the World Bank, we observe that inspite of spending close to 2% of its GDP on social welfare programs, the Indian Government has not been fully efficient in distributing resources to the needy and poor in the country (“India’s aid schemes fail to tackle poverty: World Bank” 2011). Due to the high level of corruption existing within the government as well as the existence of multi-level distribution systems of welfare, and the inefficiencies involved with them, funds that are allocated by the central government gets lost before it reaches the household of the targeted recipients. The Aadhaar Initiative seeks to disrupt this system and aims to directly target individuals and households thereby reducing poverty through carefully constructed welfare transfer schemes, as noted by World Bank President Jim Yong Kim (PTI 2017).

The Poverty Index Framework

The Headcount Ratio describes the percentage of the population that is below the global official poverty line, additionally, the Poverty Gap Index, or the P1 Index, measures the average amount of funds that must be distributed to the poor so as to alleviate them from poverty. The primary way for the government to engage in redistribution of resources has been through ration cards as well as through various multi-level channels, following a hierarchal path from the center to the state and then further divided up into districts before finally reaching the recipient. Targeting a poor population of close to 224 million people is a herculean task (Business Today 2016). The inefficiencies of the Public Distribution System are highlighted in the literature by Vivek Kaul, which found that 48% of sugar and 15% of rice allocated for the poor, had been lost in the process of the transfer (Kaul 2016). Rice and Sugar are just a few commodities, the problem is faced in a slew of different commodities which are required by the poor in India and are subsidized, yet fail to reach the recipient causing a massive dent in the progress of social welfare programs. The Aadhaar Initiative provides us with a framework where the middle man is eliminated and the government is able to directly target the poor section, thereby taking a stride forward in reducing poverty via a direct transfer of cash that is not leaked midway.

In order to understand how the Aadhaar Smart Cards would help to target the poor section of the society, directly benefitting the poor and thereby reducing poverty, we need to assume the assumptions held by the Aadhaar Initiative to explain the model. Currently, in order for a poor person to benefit from the subsidy given by the Indian Government, he or she needs to use their ration card and go to the nearest government store where they are allocated their fair share of commodities based on the limited supply they obtain. Let us observe the sequence of events as seen in Panel A. After a worker has filed a report claiming their need of funds or subsidized commodities, indicating that they are poor, the Gram Panchayat collects all the data and sends it to the Mandal, a computer center which consolidates the data and sends it to the state government for review. Using the funds the center has allocated to the state, the state then seems to divide up commodities as well as cash transfer based on smaller districts, which is then distributed to the local level or the Gram Panchayat before finally reaching the recipient.

The numerous levels associated with Panel A leads to the inefficient targeting and thus does not benefit the poor. Looking at how Aadhaar cards would help, we see the existence of fewer channels of distribution. Once the state has all the information and the exact number of poor people, they transfer equivalent cash amounts to the bank, which in turn works with the Technology and Customer Service Provider to identify the workers and transfer the money directly to their bank accounts.

* Picture Taken from Karthik Muralidharan’s “Building State Capacity: Evidence from Biometric Smartcards in India”

* Picture Taken from Karthik Muralidharan’s “Building State Capacity: Evidence from Biometric Smartcards in India”
Blue dotted line is the control group- No access to Smart Card
Blue solid line is the treatment group- Access to Smart Card

Going a step forward, provided the model is functional, we should notice an improvement in the efficiency in the Public Distribution System. Our model seems to match with Muralidharan’s experiment.  Looking at Panel A and Panel B, we notice that the using smartcards would enable the government to be able to target the poor section and efficiently transfer funds to their accounts without further delay.

For the model to function, we assume that there would be a substantial investment in technology in rural India, since it is observed that these regions are marked by low levels of technology (Punj 2012). Given the fact that most people in rural India not having bank accounts, we further to go ahead and assume that there exists procedures where the government can incentivize the public to open bank accounts, further assume that both private and public banks would like to extend their branches to all parts of the company. As the article mentions, by making programs compulsory, the government is able to spread awareness and encourage citizens to obtain their Aadhar cards. There is a huge market for private data collection companies to benefit from this scheme. Owing to the large population, data collection firms would partner with the Government in order to obtain the biometric data. Another industry which would thrive would be the cyber security sector, which would have to be set up to monitor the vasts amount of data that is collected and stored through this plan. The government has a long way to go and must take on the task of education its citizens to adopt 21st century technology so that it can show to the world that poverty too can be tackled digitally.










Works Cited

“India’s Massive I.D. Program Exemplifies ‘Science of Delivery'” World Bank. N.p., 2 May 2013. Web. 01 Apr. 2017. <;.

“India’s aid schemes fail to tackle poverty: World Bank.” OWSA. N.p., 19 May 2011. Web. 02 Apr. 2017. <;.

Kaul, Vivek. “There Is A Very Compelling Case For India To Move To Cash Transfer Of Subsidies.” Huffington Post India. The Huffington Post, 26 Feb. 2016. Web. 02 Apr. 2017. <;.

McGivering, Jill. “India aid programme ‘beset by corruption’ – World Bank.” BBC News. BBC, 18 May 2011. Web. 02 Apr. 2017. <;.

Muralidharan, Karthik, Paul Niehaus and Sandip Sukhtankar. 2016. “Building State Capacity: Evidence from Biometric Smartcards in India.” American Economic Review, 106(10): 2895-2929.

PTI. “Aadhaar to help eradicate poverty, says World Bank chief Jim Yong Kim.” The Economic Times. Economic Times, 09 May 2013. Web. 02 Apr. 2017. <;.

PTI. “India has highest number of people living below poverty line: World Bank.” Business News – Latest Stock Market and Economy News India. Living Media India Limited, 3 Oct. 2016. Web. 02 Apr. 2017. <;.

Punj, Shweta. “Aadhaar: How the UID Project Can Transform India.” Aadhaar: How the UID Project Can Transform India. Living Media India Limited, 04 Mar. 2012. Web. 2 Apr. 2017.

Singh, Kanishka. “What is Aadhaar card and where is it mandatory?” The Indian Express. N.p., 27 Mar. 2017. Web. 01 Apr. 2017. <;.

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Inflation in India: The Same Old Story or Something More?

A summary and analysis of the current inflationary predicament India faces, and the decisions that have yet to come.
By: Perry Bloch

Developing countries often experience faster economic growth rates than more developed nations. However, rapid growth doesn’t come without significant costs, whether they be inflationary or otherwise. Drs. Laurence Ball, Prachi Mishra, and Anusha Chari in their paper “Understanding Inflation in India” dive deep into discovering the core driving factors that caused inflation in India to skyrocket from 3.7% in 2001 to an unsustainable rate of 12.1% in 2010.


At first glance, some attribute India’s inflation to the same thing as many other countries’ key inflationary driver; fluctuations in the prices of food and energy. The Reserve Bank of India’s Governor Rajan leads a pack of mass speculation that “high levels of inflation may become embedded in the expectations of price setters” which Rajan labels as an “inflationary spiral”, or as I prefer to call it, a self-fulfilling prophecy. Simply put, the idea that higher inflation rates are caused by expectations, means that the economic forces that set prices are anticipating what they believe to be the upcoming period’s inflation rate. However, their very expectations now reflected in the prices are the cause of what they initially believed the inflation would be, ensuring its realization and hence why I call it a ‘self-fulfilling prophecy.’

Core Inflation versus Headline Inflation

The authors importantly highlight the differentiation between core and headline inflation, which is important in any conversation about understanding the sources of inflation.

Core inflation refers to an “underlying trend in the inflation rate determined by inflation expectations and the level of economic activity.” Basically, the core inflation observation is inflation that is driven by natural economic forces working over time. Headline inflation, the more volatile measure of the two, is core inflation with inflationary supply shocks added in. Short run supply shocks are often measured by the relative price changes of the two main drivers of inflation in any country: Food and Energy. It is for this precise reason, that core inflation measures price variations without considering the fluctuations in both the food and energy sectors.

The overarching goal of this important differentiation is to be able to discern a long-run trend of inflation rates for any particular economy over time, without the influence of short-term price variation. In India however, the food sector possesses the largest share of the aggregate economic output. Discounting the food sector from consideration in India’s economy over time would leave a measure that does not accurately represent a long run trend. Therefore, the authors instead try to work with a weighted median inflation measure, which does not discount the important industries, but rather does strip away most of the quarter-to-quarter outlying price fluctuations, resulting in a measure similar to the core inflation. Specifically, the paper studies the rate of change in the headline Wholesale Price Index (WPI), and observes the core inflation in the WPI measured using a weighted median inflation rate. The Wholesale Price Index was chosen because beginning with data collected in 1994, it has a “relatively high level of disaggregation” of varying industries inflation rates, which was not available in the more commonly used Consumer Price Index (CPI), when the data collection began. The WPI has typically been the most common price index to measure inflation specifically in India.

The Indian Inflation Problem

Wanting to be among the “big boys” of advanced economies such as that of the U.S. and Europe, India is implementing specific monetary policy to work its economy at full power, so it no longer will be discounted in major global considerations. However, no vehicle can immediately become a high flying sports car, without progressing through the proper development. Most macroeconomic textbooks teach inflation through the classic Phillips curve model, where future inflation is dependent on expected inflation and supply shocks (which we discussed earlier), and the level of output of the economy relative to its historical output trend. As you can see, this model relies heavily on the most recent preceding inflationary data to predict the future set. It is this very model that advanced economies work to move away from as they become more developed, and it is this model that India is still unable to escape on its quest to more economic respect.

The main problem lies in something I’ve touched on but have yet to fully explain. Controlling inflation to create stable and sustainable growth is not easy, nor is it free. The authors in Understanding Inflation in India make a very cold, and fact-driven analysis on the economic conditions in India. Specifically, the concept of sacrificing output to reduce inflation can have severe consequences to the population relying on the sacrificed output. In India especially, the “output” that is so vaguely described in the paper, really refers to the food that so many men, women, and children rely on for their sustenance and their lives. I’ve mentioned that food is one of the primary drivers of the Indian Economy (and most economies for that matter). In India however, there aren’t many other significant goods and sectors, like manufacturing, for the economy to fall back on. Thus, an effort to control inflation isn’t simply a far removed monetary policy decision, but rather a directive that will immediately impact the lives of millions of Indians across the country.

The Effects and Potential Outcomes

With a decision that has the potential to affect the lives of so many Indians who already struggle to make ends meet, it is critically important to consider the impact such a decision will have on the already spiraling poverty and inequality. According to data from the World Bank, the poverty headcount ratio in India in 2009 at the $1.90/day poverty threshold, was 31.1%. With the understanding that so much of India’s economy is not only driven by, but also dependent upon the food industry, sacrificing output from that essential sector can devastate entire portions of the population further increasing the poverty headcount. Aside from being a horrific reality to consider, this would have adverse long-term economic effects as well.

Conclusion? Not Exactly

Unfortunately, the issues and dilemmas I’ve explained above, that are discussed in significantly more detail in the paper, are very real and immediate. There is no one right answer or solution to solving India’s current inflationary instability. It is certainly worth noting that as recently as early 2015, inflation has fallen to 5.2%, although there is nothing to anchor or lock in that rate. India faces economic uncertainty in the future which the Reserve Bank of India can only attempt to mitigate through various monetary policy implementations, and observing the results. The challenges lie in the fact that with such significant historical fluctuations, it is nearly impossible to discern a future trend. We can only hope that the Indian government continues to keep the best interests of its citizens at the forefront, and minimize any political turmoil that might hurt the lives of millions of Indians in the years to come.

The Effects on Demonetization on Women

An article on the effects of demonetization on Indian women is discussed and connected to the household bargaining model. By the ECON 416 TA

On November 8th, 2016, Prime Minister of India Narendra Modi announced that all existing Rs 500 and Rs 1,000 notes in circulation would be invalid within four hours. While the old notes could be exchanged at banks for currency through November 25th or deposited into bank accounts until December 30th, the prime minister’s announcement kicked off an economic shock that has affected most Indian households. This is policy shock and its effects on Indian women in poor and rural areas is the focus of Nishita Jha’s article “Note demonetisation: What of the women who hide cash to feed their children or to escape abuse?”.

The crux of the issue, as Jha describes it, is that many Indian women save their money purely in cash, often in secret, and have limited or no access to the formal banking sector. Women commonly maintain these savings in order to purchase medicine for their children or food for the family when regular income is scarce. Others maintain separate savings as a safety net when faced with abuse in the household. Keeping these cash savings secret from their husbands allows women to maintain control over how the money is spent.

Demonetization presents these women with a dilemma—either lose most or all of your cash savings, or risk revealing them in an attempt to deposit them into a bank account. With as many as 80% of Indian women outside the formal banking sector, those with hidden cash savings are attempting to recoup some of their losses by selling their defunct notes for new notes, at a huge loss (Selim et al. 2005). However as Jha notes, even setting up women with a bank account is not without hurdles. Just opening an account risks revealing the existence of the secret savings. Even getting to the bank to make a deposit is especially challenging for women in poor, rural areas, because they are discouraged from leaving the home after dark.

Household Bargaining Framework

Jha’s reporting on the effects of demonetization takes the framework of the household bargaining model as given and as the proper lens through which to understand the decision problem these women face at home. The situation she describes is one where husbands and wives have different spending priorities, and women are keeping cash secretly in order to fund their priority expenditures. Beyond the anecdotes Jha offers that support the bargaining model, there is a body of academic literature that suggests this model is a useful lens through which to understand household dynamics. These include Dulfo 2003, which found that South African pensions sent to grandmothers increased child nutrition (measured as weight-for-height) whereas those sent to grandfathers did not. Under the standard neoclassical model of the household, who received the pension should not make a difference, only the size of the transfer.

If we choose to accept the evidence in favor of the household bargaining model and accept it as a good approximation of household behavior, we can then consider what the model predicts will be the effects of India’s demonetization policy. In the bargaining model, each spouse has their own set of preferences for allocating the household’s resources. For example, the wife may want to purchase food for a child, while the father may want to use that money for alcohol. Each spouse also has a threat point—a level of utility below which the spouse will leave the household or marriage. Continuing the example, if the husband spends too much money on alcohol the wife may opt to take the children and leave the household. As shown in the graph below, the position of the threat points determines which resource allocations are possible, and the maximum level of utility each spouse could attain if they had all the bargaining power, while still keeping the household intact. Bargaining between spouses determines which allocation is ultimately chosen, and this Nash equilibrium is shown in the figure as U*1—which is somewhere between the husband and wife’s preferred points, conditional on not triggering the other’s threat point.

Demonetization plays a role in this model by removing or vastly reducing the wife’s secret cash savings. Following the terminology presented in McElroy 1990, this shock to the wife’s savings can be thought of as one type of extrahousehold environmental changes (EEP). The savings allow the wife to have some money to fall back on should her husband attempt to enforce an allocation that breaches her threat point. The cash acts as an upward shifter for the wife’s threat point, which is at Tw1 prior to demonetization. The corresponding Nash equilibrium for the household is U*1. Even with full bargaining power, the best the husband could do without violating the wife’s threat point is Uh(max)1.

However, the rollout of demonetization destroyed or greatly reduced this fallback option for the wife, due to the challenges women face changing or depositing defunct notes that Jha highlighted. This pushes the wife’s threat point from Tw1 to Tw2, and moves the Nash equilibrium to U*2. Notice that compared to the original equilibrium, the wife’s utility has decreased while the husband’s has increased. This can also be seen in the movement of the husband’s maximum attainable utility rightward to Uh(max)2. Notice too that the graph assumes that the wife’s secret savings were not included in the households shared assets and thus the decrease in these savings does not shift the household’s utility frontier inward.


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