Growing Indian Agriculture by Leaving Farmers Alone

The Economic Times of India published an article titled “Need policies to ensure farmers get better prices: Arvind Panagariya” last week. Though the article itself is relatively short, it fits into a broader policy debate that is being held in the Indian government. Namely, there has been a lot of discussion recently about whether to tax rural farmers, and if so, how much. The Indian central government does not have the constitutional authority to levy taxes on agriculture, so the debate is focused on a state by state basis, where taxing agriculture is allowed.

The Context

The National Institution for Transforming India (NITI Aayog), the leading think-tank in India, and India’s prime minister, Narendra Modi, have stated a goal of doubling agriculture income in India by 2022. Supporting technology adoption and ensuring competitive prices domestically and internationally are the main intended methods to achieve this growth. While India’s economy has developed significantly in recent years, its poorest citizens are still largely living in an undeveloped economy. According to Arvind Panagariya, “80% of the poor… in rural areas are dependent on farming.” In addition, it appears that most farmers in India rely on agriculture for subsistence.

NITI Aayog leaders and the prime minister have been asked about taxation of agriculture income in India. Almost unanimously, policy leaders have stated that there is not even a question of taxing agriculture income. However, most reports do not give a complete picture to the phrasing of “no question” when it comes to taxing farmers. Do they mean that it is obvious a provision will be included to tax farmers in the future, or that it is obviously a bad idea to tax subsistence farmers and the rural poor.

In a separate report, the Chief Economic Advisor, Arvind Subramanian, suggested that taxation of agriculture income is possible. He added, policymakers must make a distinction between rich and poor farmers.

The Model

Luckily, there are clear models that address production and wealth gains over time. Depending on the structure of the tax, a farmer would consider it as a fixed cost or a variable cost in their production function. In a developing economy, we must also realize that any money a farmer has to pay to the government cannot be used to re-invest in their farm, either as better inputs or durable goods. When analyzing this issue, it is important to make the same distinction that Mr. Subramanian did. Wealthier farmers, or those who have commercialized and see yearly profits, have much more flexibility to be taxed.

Unless state governments face strains, taxing all farmers would make the poorest and subsistence farmers much worse off, since their year on year gains in wealth and potential reinvestment would be undercut. In general, taxing farmers as their income increases from subsistence to commercial would reduce productivity and would be counterproductive to alleviating poverty.

On the most basic level, taxing subsistence farming would push the poorest farmers into a worse position, and would not encourage adoption of new inputs and technologies. In order to commercialize and take advantage of the government’s push to raise prices for agriculture products, poor farmers need access to new technology. We discussed the incentives farmers face when adopting new technologies; they must be educated and the benefits must outweigh the costs.

These policymakers are correct in their belief that taxing agriculture should be out of the question. By taxing agriculture, subsistence and poor farmers face a greater cost or diminished benefit to their yearly yields. In the face of uncertainty, they will be less likely to experiment with new technologies and will not have the resources to try new crops and inputs. The agricultural technology adoption model shows farmers each running experiments over time is the best way to increase their output. By limiting the resources for experimentation, agricultural growth will be significantly slowed, and this effect will compound over time.

Another factor of the agricultural technology adoption model at play in this decision is “information neighbors”. The policymakers aim to increase the prices of crops. In order for their ultimate goal to be achieved, doubling rural income by 2022, the first phase must be giving farmers the means to adopt new technologies. However, the real gains in production are compounded over time as farmers experiment and communicate with their neighbors.


If India’s policymakers are serious about increasing agriculture productivity and income, then taxation is absolutely “no question”. In a country like the United States, where industrial agriculture is the norm, taxation is possible because of the surplus that farmers face. However, in India’s case most farmers need to be nudged into commercialized agriculture and educated about the new technologies available. In order to achieve this, the whole system should be tailored toward the goal. Also, based on NITI Aayog’s statistics, increasing rural income can benefit a huge portion of the impoverished population in India as well. Based on these facts, Indian policymakers have made the right decision for ensuring growth of agriculture output.



Sources Cited:




Remittances in South Asia and Development Economics

Using a recent report on remittances to motivate a discussion on how remittances play into Developmental Economics

Nafee H. Ahmed


Migrant Workers from South Asia working in Qatar (picture by European Pressphoto Agency)

On April 21 2017, The Times of India published an article which summarized the findings of a recent World Bank Report on remittances. The article referenced a few interesting facts which can motivate a discussion on how remittances relate to development economics. After discussing remittances in depth one can revisit the article to see how well developmental economic theory reflects real world events.

The article highlights that India received more money in remittances than any other country in 2016; Indian workers sent home 62.7 billion American dollars in total. The article also notes that the total amount of money in remittances to India fell by 8.9 percent in 2016 and contextualizes that drop by explaining that the total amount of money in remittances sent to all developing countries fell by 2.4 percent in 2016. The article later reveals that money in remittances sent to South Asia fell by 6.4 percent.

This article claims that the primary cause of migrant workers sending less money home is related to lower oil prices and lower economic growth among countries in the Arabian peninsula; many Indian migrant workers work in these countries lower economic growth in these countries can decrease the amount of money migrants can send home.

The article provides a useful list of the countries which received the most money in remittances. In terms of absolute dollars those countries are India, The Philippines, China, Mexico and Pakistan; however, the countries which take the most money in remittances as a percentage of that country’s GDP are Kyrgyz Republic, Nepal, Liberia, Haiti, and Tonga.

The Importance of Remittances

Remittances are an important topic for two main reasons. Firstly, remittances are major component of the economy in many developing countries. The Times of India article referenced above divulged that remittances account for 6.0 percent of Bangladesh’s GDP, 6.9 percent of Pakistan’s GDP and 2.9 percent of India’s GDP.


Secondly, there is evidence that remittances decrease poverty in developing countries. In 2005, an article in the journal, World Development, by Richard Adams and John Page found a relationship between remittances and poverty. Adams and Page claimed, “both international migration and remittances have a strong, statistically significant impact on reducing poverty in the developing world … After instrumenting for the possible endogeneity of international remittances, a similar 10 percent increase in per capita official international remittances will lead, on average, to a 3.5 percent decline in the share of people living in poverty.” (Adams and Page  1660).


Remittances and Growth


Katushi Imai et al.’s article, “Remittances, Growth and Poverty: New Evidence from Asian Countries,” provides a strong claim that that remittances have a positive relationship with GDP growth. The authors’ model found that, on average, a 10 percent increase in a country’s remittance payments as a share of that country’s GDP increased that country’s rate of growth in GDP per capita. The authors also provide an intuitive explanation for why higher levels of remittances are related to higher economic growth, stating “The existing literature (for example, Barajas et al., 2009) identifies various channels through which remittances enhance growth, including the boosting of capital accumulation, labour force growth, and total factor productivity …” (Imai et al. 530-531).


A link to the article “Remittances, Growth and Poverty: New Evidence from Asian Countries” can be found here:


Applying Remittances to a Growth Model in Development Economics


If one accepts that remittances positively influence GDP growth in developing countries, then one can also look at traditional growth models in Development Economics and apply the value of remittances as an additional variable. Many models in Developmental Economics relate economic growth to other variables. If economic growth has a positive relationship with one variable, then the value of remittances a country receives should also have a positive relationship with that same variable.


Consider two examples with a common model in Development Economics, The Solow Model:
The Solow Model is a common model of economic growth which relates several variables to economic growth. In summary, The Solow model describes economic output as an equation determined by physical capital, labor, the depreciation of physical capital, and the savings rate of a population. More Modern versions of the model add even more variables into this equation including the education level of a workforce and the level of population growth.

An intuitive video series by explaining the Solow Model can be found here:


One can use the Solow model to find how remittances are related to other economic variables. I created the following two examples trying to fit remittances into the logic of the Solow Model



The Solow model implies that the levels of economic growth in a country decreases with respect to time when holding all other variables constant. Assuming positive relationship between remittances and economic growth then allows the Solow model to imply that the value of remittances a country receives will also decrease with respect to time holding all other variables constant.



The Solow model implies that smaller economies experience higher levels of economic growth than large economies when holding all other variables constant. Assuming the same positive relationship between remittances and growth then allows the Solow model to imply that smaller economies will receive more in remittances than large economies holding all other variables constant.

Back to the Original Article


Looking back to the original Times of India article, we can check if our ideas about remittances derived from growth models match data from the real world. When applying remittances to the Solow model one can predict that over time, countries will receive less money in remittances as a share of that country’s GDP. While the original article does note a decrease in remittances compared to previous years, this is because of factors not related to the countries receiving remittances but rather problems in the countries from which migrant workers send remittances. The original Times of India article also claims that World Bank projections show that South Asian countries will not see significant growth in remittances in the near future. This projection is not necessarily proof that applying remittances to the Solow growth model is correct, especially since the projection was based on factors outside of South Asia, but the World Bank’s projection does not contradict the idea that a developing country might receive less money in remittances over time.


Applying remittances to the Solow model also allows one to predict that the countries which receive the most remittances as a share of the country’s GDP should also be very small economies. The Times of India article confirms this prediction; data from the World Bank database reveals that none of the countries which receive the most money in remittances as a share of the country’s GDP have a GDP per capita higher than 5,000 dollars.


The original Times of India article predicts a decreased level of remittances in South Asia for next year. Given the evidence examined in this blog post, this may have a potential negative impact on economic growth, something which businesses, policy makers, economists, and other observers should note in the coming years.

Works Cited


Academic Articles and Textbooks:

Adams, Jeffrey and John Page. “Do international migration and remittances reduce poverty in developing countries?” World Development, vol. 33, no. 10, Oct. 2005, pp. 1645-1669. Science Direct.


Imai, Katsushi et al. “Remittances Growth and Poverty: New Evidence from Asian Countries.” Journal of Policy Modeling, vol 36, no. 3, June 2014, pp. 524-528. Science Direct.


Mankiw, Gregory. Macroeconomics. 8th ed., Worth Publishers. 2012.


Photographs/ Videos:

Construction workers queue for buses back to their accommodation camp in Doha, Qatar. 19 Nov. 2013. European Pressphoto Agency, Frankfurt.


“The Solow Model of Economic Growth.” Youtube Playlist, uploaded by Marginal Revolution University. 28 March 2016.


“India tops global remittances at $62.7 billion in 2016: World Bank.” Times of India, 21 April 2017.


“DataBank World Development Indicators.” The World Bank, 1 May 2017,

Structural Labor Changes in Sub Saharan Africa Could be the Key to Creating Positive Economic Growth

Empirical analysis of recent data suggests that Sub-Saharan Africa is on track to follow the economic growth paths of developed countries, through a structural shift away from the agricultural sector of labor and a diminishing productivity gap. By: Ben Whitacre


The region known as Sub Saharan Africa (SSA) contains some of the poorest countries in the world, known for its economic failure and astounding poverty rates. In recent years, the dynamic of the economy, particularly in the labor force, has generated the first ever recorded positive economic growth rates in this area. The analysis, “The Changing Structure of Africa’s Economies”, performed by Xinshen Diao, Kenneth Harttgen, and Margaret McMillan, seeks to provide evidence that Sub Saharan Africa is beginning to indicate a shift towards the development track that many successful economic countries followed on their way to prosperity. The authors claim that most of the economic progress comes from a structural change of the labor force: a shift from the agricultural sector, to the manufacturing and service sectors. This shift contributed to overall labor productivity growth, and allowed Africa to experience its “strongest growth in four decades” (Diao et al 20).

Basis and Assumptions:

To adequately analyze the economic trends of rural Africa, the authors chose to utilize two data sets: the Groningen Growth and Development Center (GGDC), and the Demographic and Health Surveys (DHS). Between these, there are varying numbers of observed countries, but the eight overlapping countries are specifically targeted for the data analysis. These countries include some of the lowest income African countries such as Ethiopia, Nigeria, and Tanzania. These are compared to some of the highest income African countries such as Botswana, South Africa, and Mauritius, as well as data groups from Latin American, Asian, and highly developed countries (such as the United States). The purpose behind using developed countries’ data was “to study the evolution of the distribution of employment between sectors across levels of income experienced in Africa and how it compares with the patterns seen historically in other regions over the course of development” (Diao et al 12).

For decades, poverty-stricken areas of Africa have largely focused simply on agricultural labor: providing the basic food for the people to survive. The authors believe that a shift in the labor sectors is leading to a decrease in the magnitude of the labor productivity gap, and an increased prosperity level overall. The heart of the paper focuses on the changes in the “level of employment shares” in each labor sector, corresponding to the “levels of income” (Diao et al 6). The logic behind comparing the levels of the structural sections of labor provides the observer the ability to map out trends of shifts based on previously developed nations.

Several assumptions related to the accuracy and availability of the data collected by both the GGDC and the DHS surveys must be made to observe and compare the economic growth in Africa. The authors point out that employment data, informal labor sector knowledge, and measurements of human capital (well-being, education, etc.) are all taken at the level of detail and availability that the surveys provide. Unfortunately, as seen with many studies throughout research, data which comes from poverty stricken countries is not always reliable, accurate/without error. There are instances where the authors of this NBER analysis exclude an entire group of data, justifying their actions as if generalizing groups will “avoid confounding the results” of the data (Diao et al 28). However, the assumptions in the analysis are justified, as the authors took care to check each set of data, inquire the survey agencies regarding errors they found, and base their analysis of trends on data in which two or more benchmark surveys were always provided.


“The Changing Structure of Africa’s Economies” is based on the hypothesis that structural change in the labor sector distribution has a positive effect on economic growth. The author’s stand by their premise by stating that in developed/prosperous countries, there are very few people who are involved in the agricultural labor sector. Reallocating labor in rural areas into sectors such as manufacturing can have a huge increase in labor productivity, thus “allowing aggregate productivity to catch up…[causing] rapid growth rates” (Diao et al 11). In fact, empirical evidence suggests that according to a “GGDC sample, annual labor productivity grew by an (unweighted) average of 2.82 percent, and structural change contributed an (unweighted) average of 1.13 percentage points to overall labor productivity growth. Put differently, from 2000 to 2010, structural change accounted for 40 percent of Africa’s annual labor productivity growth” (Diao et al 24).As observed in Table 1, for the majority of African countries the labor sector with the lowest productivity is agriculture: with a maximum value of 4.37.

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Table 1: Diao, X., Harttgen, K., & McMillan, M. (2017, January). The Changing Structure of Africa’s Economies

By improving the labor productivity growth, the overall growth rate of the economy increases, poverty rates decrease, and human capital increases by creating skilled workers. The manufacturing sector in Africa may never compare to the manufacturing industry in a previously developed country, but it has been shown that African areas who devote their resources into building human capital to provide skilled manufacturing works generate higher levels of income to raise the poverty headcount ratio at drastic rates. Although some of these labor sectors are specialized and do not have the capacity to bear all structural change, there is a positive observable correlation due to structural transformation ASSUMING the transfer of labor ends up in a more profitable sector. Transfer of labor to a less profitable sector can lead to recession of economic growth. Because of this risk, it is often profitable to look within sectors to make infrastructure changes. Related articles, such as a USDA Economic Research Service report written by Keith Fuglie and Nicholas Rada, indicates that although a transfer in internal-sector productivity may be useful, doubling agricultural research can also boost Total Factor Productivity (which compares total outputs to total inputs in a country) growth rates by over 4%. According to the GGDC data, the initial benchmark revealed low-income countries exhibited an approximate 70% of their labor force was dedicated to agriculture, a number with declined by 9.3% by the time the most recent data was observed (Diao et al 24). This led to an increase of over one and a half percentage points in labor productivity and in some cases, positive country economic growth rates.  Studies in the GGDC data, and the DHS data (categorized by gender, education, age, etc.) showed similar improving results with the decrease in agricultural labor – with the greatest difference being observed with females in rural areas. Figure 1 demonstrates the decline in the GDP level per capita of Agriculture, while contrasting with the increase of GDP wealth per capita in other labor sectors.

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Figure 1: Employment shares by labor sector, increasing/decreasing with income: Diao, X., Harttgen, K., & McMillan, M. (2017, January). The Changing Structure of Africa’s Economies


Daio, Harttgen, and McMillan chose to analyze two data sets to expand research on Africa’s upcoming economy that had previously never been approached. What they found was that distinct patterns were found in the structural trends that can be compared and adjusted by observing the development paths of previously poverty stricken and underdeveloped nations. In fact, at “lower levels of income, countries that pull themselves out of poverty also exhibit positive structural change” as a critical part of their economic development. Now, not every sector of expansion that prospered in a different country (ex. Latin America) will work in rural Africa, but similar growth concepts, such as the “importance of investing in human capital and infrastructure…can raise productivity levels” and improve a country’s overall state of well-being (Diao et al 32). The authors summarize their findings with 5 stylized facts listed below, which begin to outline growth patterns based on structural shift. The largest implication of this paper however, is to open the door for more empirical analysis and research in sub Saharan Africa, as the authors state that until this point, “economic data to undertake such analysis has been largely unreliable or nonexistent for most African countries” (Diao et al 12).

Stylized Facts:

  • “First, when the patterns of employment in Africa are compared to the patterns observed in other regions across levels of development, the pattern among our sample follows that seen in other regions for agriculture and services—that is, the agricultural employment share is decreasing in income, while the services employment share is increasing in income.”
  • “Second, when the levels of employment shares are compared to the levels observed in other countries, the levels of employment shares in agriculture and services approximate the levels observed in other countries at similar levels of income.”
  • “Third, all of this holds for industry and manufacturing in the eight low-income African countries.”
  • “Fourth, in Botswana, Mauritius, and South Africa, the patterns in industry are similar but the levels differ, and in the case of manufacturing, the relationship between income and employment shares follows more of an upward sloping line than an inverted U-shape.”
  • “Fifth, Africa is still, by far, one of the poorest regions of the world.”
  • “Finally, structural change continues to remain a potent source of labor productivity growth in much of SSA.”

Stylized Facts Courtesy of Daio, Harttgen, and McMillan, “The Changing Structure of Africa’s Economies”.

Works Cited

Diao, X., Harttgen, K., & McMillan, M. (2017, January). The Changing Structure of Africa’s Economies (Working paper No. 23021). Retrieved April 19, 2017, from National Bureau of Economic Research website:

JEL No. O11,O4,O55

Fuglie, K., & Rada, N. (2013, May 6). Research Raises Agricultural Productivity in Sub-Saharan Africa. Retrieved April 24, 2017, from

The Bolsa Familia Welfare Program in Brazil and its Impact on Poverty and Inequality

An analysis of the Bolsa Familia program compared to models and theory from 416 – by Erica Ryan

My dear friend and colleague Rafael is from Brazil. For a long time I didn’t quite understand why he was here at the University of Maryland. Not only is Brazil the largest country in South America, it also has the largest population, largest economy, and is home to a vast amount of the natural resources in South America.[1] Even so, the socioeconomic climate in Brazil is far from perfect. For this blog post I am going to focus on two articles that appeared in the Rio Times in early 2017, one titled “Economic Crisis to Push 3.6 Million Brazilians Into Poverty” and the other “Forty Percent of Children Under 14 Live in Poverty in Brazil.”

The first article talks about how the current economic hardships could push up to 3.6 million more Brazilians into poverty by the end of 2017, with most of the new poor living in urban areas. There is a welfare program in Brazil called Bolsa Familia that provides cash transfers to low income families, and the current crisis could add up to 1.16 million families to the program. Currently, there are no plans to increase funding for Bolsa Familia.

The second article talks about how seventeen million children under 14 are currently living in low income households, which is approximately 40% of the population in this age group. The North and Northeastern regions of Brazil are in a more critical state and have much higher percentages than the average (approximately 60% and 54% respectively). Around 5.8 million of these children are living with a per capita income of less than 25% of the minimum wage.

Income inequality and poverty are really big problems facing Brazil today. According to World Bank Data, the top 10% of the population holds 40.7% of the total income while the lowest 20% only holds 3.6% of the total income as of 2014.[2] Since 2014, the economy has continued to shrink, many political leaders have been accused of corruption, and the president of Brazil was impeached, leading to the poverty and suffering discussed in the two articles.

The Bolsa Familia program is very important for improving conditions for the poor people in Brazil, particularly for poor children. The cash transfers it provides are conditional on the children of the family going to school and receiving regular health checkups.[3] The cash transfer it provides should assist in reducing poverty simply because the poor now have more money, but it is important to look at both the income and the welfare of the poor when determining the effectiveness of the program. The conditionality of the transfer will do more in the long run to further reduce poverty and inequality and increase welfare.

The cash transfers make it easier for kids to attend school longer. When the decision of whether or not a child should continue attending school is being made, the expected utility of the education has to be compared to the opportunity cost (the wage they could earn if they worked instead) of that time spent in school. Because of time inconsistent preferences, people tend to discount the future benefit of education, which can lead to under consumption of education. For poor families this can be especially true because they need the income now, rather than later. So without the transfer, many children may end up working instead of going to school. With the transfer, the utility of education is increased, making the benefit of sending children to school higher.

The 1992 paper by Mankiw, Romer, and Weil shows that the accumulation of human capital is an important factor for economic growth in a country in the Solow model.[4]  The current recession in Brazil is caused by a variety of factors unrelated to the Solow model; however, in order for Brazil to move past their recession and work towards positive economic growth, accumulation of human capital is vital. Because Bolsa Familia requires that children attend school in order for the family to receive the transfer, this program is promoting education which, according to theory, should contribute to rising economic growth. Studies have also shown that increased education leads to higher wage profiles later in life.[5] Requiring that these poor children continue their education could lead to a reduction in inequality if, because of their education, they are able to obtain higher paying jobs as adults and break the trend of generational poverty.

The health checkup requirement of the transfer can also lead to higher human capital and increased productivity. Being healthy means that kids don’t have to miss as much school and will be able to learn more. Regular health checkups can also help to identify and correct health issues that might otherwise become major problems later on in their lives, limiting their future productivity. Studies of the Bolsa Familia program have shown that most of the transfer is used to purchase food, clothing, and school supplies for the children.[6] The nutritional component of food consists of quality and quantity, and malnutrition is linked to higher rates of mortality and stunting. The transfer would effectively relax the budget constraint. This could help families satisfy their nutritional needs while also allowing them to re-optimize and select foods with greater variety and nutritional content, which should have positive effects on welfare.

The current sociopolitical climate in Brazil is certainly less than optimal, and it is absolutely terrible that so many more people, including many children, are going to be pushed into poverty as a result of this recession. The Bolsa Familia program will be vital not only in the current alleviation of poverty, but also for sustained reduction of poverty. The investments the program makes in children will help to improve the health and welfare of poor children, while also allowing them to increase their human capital. According to the Solow model augmented with human capital, this should lead to higher growth rates, which should help Brazil to escape their recession. The increase in human capital should also help poor children to escape generational poverty, which will lead to a reduction in inequality. The Bolsa Familia program is important not only for improving the lives of the poor, but also for moving Brazil as a whole towards a better socioeconomic climate.








The two articles I analyzed are as follows:

Economic Crisis to Push 3.6 Million Brazilians Into Poverty

Forty Percent of Children Under 14 Live in Poverty in Brazil


Insurance – How Uganda Will Quadruple Its Coffee Industry

An analysis explaining how an innovative style of insurance policy can lead to farmers’ confidence in coffee to rise.


The government of Uganda is promoting growth of its coffee industry by nearly 400%. Aiming to increase production of coffee from four million bags to twenty million bags, the government is investing in irrigation and subsidizing coffee seedlings to increase interest in growing coffee, a crop often seen risky by farmers because of the unpredictable nature of rainfall in Uganda. NUCAFE, the National Union of Coffee Agribusiness and Farm Enterprises, is promoting crop insurance as a tool to increase interest in growing coffee. Justus Lyatuu of The Observer, writes of NUCAFE’s foray into crop insurance.

Coffee is extremely reliant on moisture and rainfall to successfully grow and mature to a  crop fit for harvest. A slight decrease in rainfall could cause mass coffee crop failure, leading farmers to stray from growing the risky crop. Nearly 65% of crop losses in Uganda are due to drought, and the farmers’ inability to accurately predict weather and effectively mitigate the risks associated with weather leads farmers to devote their resources to growing less risky and less valuable crops.

NUCAFE is encouraging farmers to grow coffee through the offering of crop insurance, which will function to reduce the risk carried by farmers from investing in the production of coffee. Farmers will pay 5% of their expected yield of harvest in the beginning of the grow, and in the event of crop failure due to weather events such as drought, the insurance policies will pay out to farmers near the expected yield of the harvest. Not only does this promote the growth of coffee by mitigating many of the risks of doing so, NUCAFE also will offer education and access to weather information from NASA to allow farmers to more accurately predict weather and mitigate losses from drought.

Index Insurance, How Can It Promote Increased Confidence in Risky Crops?

Index insurance is an emerging form of insurance beginning to become available to those in the agriculture industry, that offers policies to farmers based on weather indexes. Farmers will pay premiums to the insurer, who will in turn, pay out to the farmer in the event of weather conditions suitable for crop failure are met. For example, if the agreed upon conditions for the weather index insurance policy state that if below 15 inches of rain falls in the grow period, then the insurance policy will pay out to the farmer.

Pre-existing forms of crop insurance were structured so the farmer pays premiums to the insurer, and if the crop fails, then the insurance policy pays out near equal to the crop loss.

Index insurance has many advantages over standard crop insurance policies. Because index insurance uses publicly available data to determine if conditions for crop failure are met, transaction costs for index insurance are significantly lower than standard insurance, where claims often result in the insurer needing to inspect the farm themselves, increasing transaction costs. Lowered transaction costs are essential for financial products, and create suitable conditions for private insurers to exist in the marketplace as well as allowing small farmers to afford insurance. When transaction costs are minimized, the cost associated with the financial product is as close as possible to the cost to the insurer of paying out to policyholders. Not only does this increase potential profit margins for insurers, it keeps the cost of insurance low for farmers. Index insurance’s low transaction costs mean the product’s adoption might be possible without governmental and NGO financial support, which otherwise would be required to supplement insurers operating at a loss.

Index insurance protects insurers from moral hazard. With standard crop insurance, the policy may provide a better outcome to the farmer if the crop fails, tempting the farmer to intentionally sabotage their crop. They may have a policy that pays out more than the expected yield of their harvest, or they may be able to make the same amount of money with a failed harvest without having to put in effort to grow the crops. Because index insurance pays out when uncontrollable weather conditions are met, farmers don’t benefit from a failed harvest, it actually still serves the farmers best when they always strive for a successful harvest, since payouts aren’t determined with the outcome of the crop, but instead based on growing conditions.

Because index insurance determines if payout conditions are met based on weather data, it isn’t always effective in protecting the farmer from risk. If the farmer’s crop fails even when there has been 15 inches of rainfall in the grow season, the farmer has a failed crop and no payout from his insurance policy. If somehow the farmer’s crop succeeds when there has been less than 15 inches of rainfall in the grow season, he receives a payout even when his crop succeeded. So, while index insurance protects insurers from moral hazard, it often can result in ineffective risk mitigation for the farmers.

Index Insurance in Uganda

With the implementation of weather index insurance in Uganda for coffee farmers, coffee farmers can invest their resources to growing coffee without having to bear the risk of crop failure. Policies aimed to protect farmers from drought would pay out to farmers when drought conditions have been met. Since drought is the leading cause of crop loss in coffee agriculture, insurance policies that pay out when drought conditions occur mitigates the risk of low rainfall to coffee farmers, the largest drawback to growing coffee instead of safer crops.

Works Cited

Lyatuu, Justus. “Coffee Farmers Urged to Embrace Insurance.” The Observer. N.p., 10 Mar. 2017. Web. 11 Apr. 2017. <;.

Hellmuth M.E., Osgood D.E., Hess U., Moorhead A. and Bhojwani H. (eds) 2009. Index insurance and climate risk: Prospects for development and disaster management. Climate and Society No. 2. International Research. Institute for Climate and Society (IRI), Columbia University, New York, USA.

Leiva, Oscar. Hands of María Del Socorro López López. Digital image. Coffeelands. Catholic Relief Services, 9 Nov. 2015. Web. 17 Apr. 2017.


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







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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