More Females but Richer Households

Basan Shrestha, Research, Monitoring and Evaluation Expert

basan_shrestha@yahoo.com, basanshrestha70@gmail.com

Female population increased and poverty rate decreased in Nepal. Sex ratio of males per 100 females decreased from 99.8 in 2001 population census to 94.2 in 2011. Nepal Living Standards Survey (NLSS) 2011 disclosed that poverty rate decreased from 30.8 percent in 2004 to 25.2 percent in 2011.  It indicated that households with more females were richer. Relationship between sex ratio and poverty rate was evident more in mid-west than in other regions of Nepal. Likewise, relationship was evident in rural areas.

NLSS 2010/11 estimated poverty line at Nepalese rupees 19,261 per capita annual consumption. Small Area Estimation of Poverty report published in 2013, using data from NLSS 2011 and Population Census 2011 estimated poverty rates of 976 sub-districts (Ilaka) which constituted 3,968 Village Development Committees (VDCs) and municipalities (MPs) located in 75 districts of all five regions including – 1,215 VDCs/ MPs of 19 districts in centre, 907 of 16 districts in east, 876 of 16 districts in west, 581 of 15 districts in mid-west and 389 of nine districts in far-west. Sex ratios for VDCs/ MPs in those Ilakas were taken from census report 2011 and compared with poverty rates to establish overall relationship, regional difference and rural-urban difference in predictability of poverty rate based on sex ratio.

In nutshell, households with more females were richer although relation between sex ratio and poverty rate was not very strong. It could be because households with more females might have male members away for employment. However, there could be many other factors that explained variation in poverty rates. Thus, sex ratio is important but not sufficient to predict poverty rate.

Overall Relation

In a regression analysis between sex ratio as explanatory variable and poverty rate as response variable regression coefficient was statistically significant indicating with 95 percent confidence that for each reduction in sex ratio, poverty rate will decrease by 0.28 percent. It indicated that bigger number of females in a family higher chances of its being rich. However, sex ratio poorly predicted, only 4 percent of variation in poverty rate as indicated by coefficient of determination.

Poor predictability of poverty rate could be because 64 percent VDCs/ MPs had more females as their sex ratios were less than national average ratio. Unlike, 46 percent VDCs/ MPs were rich as they had poverty rates less than national average rate, which was significantly lower than 53 percent of VDCs/ MPs that had sex ratios less than national average ratio had also poverty rates less than national average revealing that families with more females are likely to be rich. Among all VDCs/ MPs, average sex ratio ranged from 145.1 males per 100 females (Manang VDC of Manang district in west) to 64. 5 (Sari VDC of Pyuthan district in mid-west). Average poverty rate ranged from 72.8 percent (Kankada and Raksirang VDCs of Makwanpur district in centre) to 0.5 percent (Imadol VDC, Lalitpur district in centre).

Regional Difference

In a regression analysis, coefficients were significant indicating with 95 confidence that for each reduction in sex ratio, poverty rate decreased by 0.70, 0.50, 0.46 and 0.23 percentage points respectively in mid-west, east, west and centre. More females in a family had higher chances of being rich. Coefficient in far-west (0.02) was not significant. Sex ratio determined moderately to poorly variation in poverty rate by 26, 15, 7 and 3 percent respectively in mid-west, west, east and centre. Sex ratio hardly determined poverty rate in far-west indicating that far-western households had almost similar well-being status whether households had more or less number of females.

West had highest number of females as 83 percent VDCs/ MPs had sex ratios lesser than national average ratio, followed by far-west (78 percent), east (72 percent), mid-west (59 percent) and centre (42 percent). West had highest number of rich households as 61 percent western VDCs/ MPs were richer as they had  poverty rates less than  national average rate, followed by centre (55 percent), east (54 percent), mid-west (23 percent) and far-west (1 percent). Those proportions of VDCs/ MPs were significantly lower than proportion of VDCs/ MPs that had sex ratios less than national average ratio had also poverty rates less than national average in four regions (mid-west, centre, west and east) except in far-west. It revealed that in VDCs/ MPs of those reasons families with more females were likely to be rich in those four regions.

Rural-Urban Difference

Government designates VDCs and municipalities as rural and urban areas respectively. In a regression analysis, coefficient was significant in rural areas indicating with 95 percent confidence that with a unit decrease in sex ratio poverty rate will decrease by 0.28 percent. Thus, more females in a family increased their well-being status. In urban areas,  coefficient was almost nil and statistically insignificant indicating with less than 95 percent confidence that with unit decrease in  sex ratio  poverty rate will hardly decrease by 0.1 percent. Irrespective of decrease in sex ratio poverty rate will almost remain stagnant. Urban households had almost similar well-being status whether households had more or less number of females.  

Rural areas had more females than in urban areas, as 64 percent VDCs and 47 percent MPs had their sex ratios were less than  national average ratio. Likewise, rural areas were poorer than urban areas, as 46 percent VDCs and 81 percent MPs had poverty rates less than national average rate. Rural areas that had significantly higher number of females were richer as well as 52 percent VDCs that had sex ratios less than national average ratio had also poverty rates less than national average. Unlike, urban areas that higher number of females were not significantly richer as 81 percent MPs that had  sex ratios less than  national average ratio had also poverty rates less than  national average.

Conclusively, more females in a household meant household was rich. Those households might have their male members out for employment. However, relationship between sex ratio and poverty rate was not very clearly seen as there could be many factors determining poverty rates. 

Predictability of Poverty Rates in 2011

Basan Shrestha, Research, Monitoring and Evaluation Expert

basan_shrestha@yahoo.com, basanshrestha70@gmail.com

Nepal Living Standards Survey (NLSS) 2011 disclosed that on average 25.2 percent people lived below the national poverty line. It was an improvement from the average rate of 30.8 percent in 2004. However, there were variations that some Village Development Committees (VDCs) and municipalities (MPs) decreased the rates and others increased. Given the poverty rates in 2004, how predictable were the rates in 2011? The answer to this question could give some insights for targeting the rates.

The NLSS 2011 defines the poverty rate as the percent of people below the national line of Nepalese rupees 19,261 per capita annual consumption. The Small Area Estimation of Poverty reports in 2013 and 2006, using data from the NLSS 2011 and 2004 and the Population Census 2011 and 2001 estimated the rates of 976 sub-districts (Ilaka) which constituted 3,926 VDCs and MPs located in 75 districts of all five regions including – 1,215 VDCs/ MPs of 19 districts in centre, 907 of 16 districts in east, 876 of 16 districts in west, 539 of 15 districts in mid-west and 389 of nine districts in far-west. The rates of VDCs/ MPs in those Ilakas were compared to estimate the overall predictability and the regional difference in the rates of 2011 given the rates of 2004.

Overall, the poverty rates in 2004 poorly predicted the rates in 2011 indicating that the rate in 2004 alone was not a good predictor of the rate in 2011. There could be many other factors that explained variation in the poverty rates of 2011. Thus, the prediction of the poverty rates in 2011 only based on the rates in 2004 would be poor.

Overall Predictability

The poverty rates at VDC/ MP level in 2004 and 2011 were fairly normally distributed as mean and median were closer. Rates in 2004 had a moderate positive correlation with the rate in 2011 (Karl Pearson’s correlation coefficient=0.47; maximum value is 1, which means total positive correlation). The statistically significant correlation coefficient indicating with 95 percent confidence that the high rate in 2004 was likely to be high in 2011.

In a regression analysis taking the rate in 2004 as the explanatory variable and the rate in 2011 as the response variable, the positive regression coefficient of 0.47 was significant indicating that for sure each percentage point increase in the poverty rate of 2004, the rate of 2011 would increase by 0.47 percentage point. The small coefficient of determination equal to 0.22 indicated that the rate of 2004 was the poor predictor determining only 22 percent of the variation in the rate of 2011.

The poor predictability could be because in some VDCs/ MPs the rates decreased, increased or stagnated in 2011. 77 percent (3039 of 3,926) VDC/ MPs of 68 districts in all five regions decreased the rates in 2011 than in 2004. Three VDCs (Ahale, Mahabharat and Vedetar) of Dhankuta district in east had the highest decline in the rate by 50.5 percentage points from 65.7 percent in 2004 to 15.2 percent in 2011. Unlike, 23 percent VDCs/ MPs (886 of 3,926) of 36 districts in all five regions increased the rate in 2011 than in 2004. Two VDCs (Chhonhup and Lomanthang) in Mustang district of west had the highest increase in the poverty rate by 36.7 percentage points from 28.8 percent in 2004 to 65.5 percent in 2011. Hetauda MP in Makawanpur district of centre stagnated the rate of 6.6 percent in both 2004 and 2011.

Regional Difference

Centre had the highest positive correlation between the poverty rates in 2004 and 2011 (Karl Pearson’s correlation coefficient=0.67), followed by moderate to lower positive correlation coefficients of 0.52, 0.18, 0.11 and 0.08 respectively in west, east, mid-west and far-west. The correlation coefficients were statistically significant in first four regions and insignificant at the far-western region. It was pretty sure if the rate of a VDC or MP was high in 2004 and that would remain high in 2011 in centre, west, east and mid-west. But, one could not be sure about the relationship between the rates of 2004 and 2011 in far-west.

In a regression analysis, the positive regression coefficients of 0.57, 0.54, 0.14 and 0.13 respectively in west, centre, mid-west and east were significant indicating with 95 percent confidence that for each percentage point increase in the poverty rate of 2004, the rates of 2011 were likely to increase by 0.57, 0.54, 0.14 and 0.13 percentage points respectively in those regions.

Centre had the highest coefficient of determination equal to 0.45 that was significant indicating that the poverty rate of 2004 determined moderately, 45 percent of the variation in the rate of 2011. West, east, mid-west and far-west had the lower coefficients of determination equal to 0.28, 0.03, 0.1 and almost nill respectively showing low predictability. The coefficients in the first three regions were significant and that in the far-western region was insignificant.

The moderate to lower predictability was because 93, 83, 79, 73 and 38 percent VDCs/ MPs decreased the rates in 2011 respectively in west, mid-west, centre, east and far-west and the remaining proportions increased. The range, difference between maximum and minimum poverty rates, in the regions also changed from 2004 to 2011. Eastern VDCs/ MPs turned homogenous since the range decreased most from 63 percentage points in 2004 to 49.9 in 2011. Central VDCs/ MPs continued to be heterogeneous although the range slipped from 80.9 to 72.3 percentage points. Western VDCs/ MPs also continued to be heterogeneous as the range slipped slightly from 70.8 to 70.3 percentage points. Far-western VDCs/ MPs became heterogeneous as the range increased heavily from 37.4 to 51.1 percentage points. Mid-western VDCs/ MPs also turned heterogeneous as the range increased from 54 to 62.3 percentage points.

Conclusively, the prediction of the poverty rates in 2011 only using the rates in 2004 would not suffice. There could be many factors contributing to the rates. This could give insight to targeting the Sustainable Development Goal to eradicate poverty across all corners in coming 15 years commencing 2016.

Poverty Rates in 2004 and 2011 at Village Development Committees and Municipalities

Basan Shrestha, Research, Monitoring and Evaluation Expert

basan_shrestha@yahoo.com, basanshrestha70@gmail.com

The Millennium Development Goal (MDG) targeted to halve the poverty rate from 42 percent in 1990 to 21 percent by 2015. Nepal made a remarkable progress in reducing poverty. The Nepal Living Standards Survey (NLSS) 2011 revealed that on average 25.2 percent people lived below the national poverty line that decreased from 30.8 percent in 2004. However, the poverty rates did not drop in all Village Development Committees (VDCs) and municipalities (MPs). Thus, the policy makers, planners and development practitioners are contested that people from all corners feel improved well-being status.

The NLSS 2011 defines the poverty rate as the percent of people below the national poverty line of rupees 19,261 per capita annual consumption. The Small Area Estimation of Poverty reports 2013 and 2006, using data from the NLSS 2011 and 2004 and the Population Census 2011 and 2001 estimated the poverty rates of 976 sub-districts (Ilaka) that constituted 3,926 VDCs and MPs located in 75 districts of all five regions including – 1,215 VDCs and MPs of 19 central districts, 907 VDCs and MPs of 16 eastern districts, 876 VDCs and MPs of 16 western districts, 539 VDCs and MPs of 15 mid-western districts and 389 VDCs and  MPs of nine far-western districts. An Ilaka constituted an average of 4.3 VDCs. A MP was considered as one single Ilaka. Thus, the poverty rates of VDCs within an Ilaka and MPs in 2004 and 2011 were compared to find out the change. This analysis considers that the poverty rate at Ilaka level represents the poverty rates of VDCs therein.

The development interventions had positive impact on the poverty reduction. Centre was most heterogeneous that had both extremely well-off and impoverished VDCs in both periods. The gap between the most well-off and impoverished VDCs/ MPs narrowed in 2011 than in 2004. Gonggabu VDC in Kathmandu district of centre and Imadol VDC in Lalitpur district of centre had the lowest poverty rates of 1.2 percent and 0.5 percent respectively in 2004 and 2011. Kankada and Raksirang VDCs of Makawanpur district in centre continued to be most impoverished with the highest poverty rates of 82.1 and 72.8 percent respectively in 2004 and 2011.

The second richest VDCs were from centre in both periods- two VDCs (Fulbari and Sibanagar) of Chitwan district in 2004 and Katunje VDC of Bhaktapur and Tikathali VDC of Lalitpur districts from centre in 2011.

Seven VDCs (Balting, Banakhu Chor, Bhimkhori, Budhakhani, Foksingtar, Ghartichhap and Gokule) from Kavrepalanchowk district of centre and three VDCs (Jair, Kalika and Shree Nagar) in Humla district of mid-west were second poorest in 2004 and 2011 respectively.

Pokhara Sub-metropolitan city in Kaski district of west and three VDCs (Gothatar, Mahankal and Mulpani) in Kathmandu district of centre were third richest in 2004 and 2011 respectively. The third poorest VDCs/ MPs were from west in both periods - six VDCs (Bharatipur, Bulingtar, Dadajheri Tadi, Jaubari, Kotathar and Upallo Arkhale) of Nawalparasi district in 2004 and seven VDCs (Bihi, Chhekampar, Chumchet, Lho, Prok, Samagaun and Sirdibas) of Gorkha district in 2011.

The west had the greatest impact of development interventions. Three quarter VDCs/ MPs improved their well-being status by dropping their poverty rates from 2004 to 2011. Nine tenth VDCs/ MPs from west dropped the poverty rates, followed by VDCs/ MPs from mid-west. However, three VDCs (Ahale, Mahabharat and Vedetar) of Dhankuta district in east had the highest decline in the poverty rate by 50.5 percentage points from 65.7 percent in 2004 to 15.2 percent in 2011. The third poorest six VDCs in 2004 of Nawalparasi district from west also improved their well-being status considerably by decreasing the poverty rate by 42.7 percentage points from 72.4 percent in 2004 to 29.7 percent in 2011. Unlike, the third richest VDC/ MP in 2004 of Kaski district from west thinly improved the well-being status by decreasing the poverty rate only by 0.3 percentage point from 1.6 percent in 2004 to 1.3 percent in 2011.

The far-west was most impoverished. One quarter VDCs/ MPs were impoverished in 2011 than in 2004 by increasing their poverty rates. Three fifth VDCs/ MPs from the far-west increased the poverty rates, followed by VDCs/ MPs in the east. However, two VDCs (Chhonhup and Lomanthang) in Mustang district of west had the highest increase in the poverty rate by 36.7 percentage points from 28.8 percent in 2004 to 65.5 percent in 2011. The richest VDC in 2004 of Kathmandu district from centre also impoverished by increasing the poverty rate by 1.2 percentage points from 1.2 percent in 2004 to 2.4 percent in 2011. The second richest two VDCs in 2004 of Chitwan district from centre also increased the poverty rate by 0.9 percent from 1.5 percent in 2004 to 2.4 percent in 2011.

Hetauda MP in Makawanpur district of centre was the only MP of all VDCs/ MPs that neither improved the well-being status nor impoverished, with the equal poverty rate of 6.6 percent, lower than the national level and did not change in both periods.

The development interventions had the positive impact on the poverty reduction in another way as well. The proportion of VDCs/ MPs that had the poverty rates higher than the national average rate of 30.8 percent decreased from two third in 2004 to half in 2011 that had the national average rate of 25.2 percent. However, most of them continued to remain poor. Three fifth VDCs/ MPs that had the poverty rates higher than the national average rate in 2004 continued to have higher rates than the national average rate in 2011 as well. Far-west had lowest impact. Nine tenth VDCs/ MPs from far-west continued to have higher rates than the national average rate in 2011, followed by mid-west.

Those changes in the poverty rates of VDCs/ MPs could give some insights to the planners and policy makers such as Poor Household Support Coordination Board to give priority to some regions, districts and VDCs/ MPs than others to provide support to the poor households.

Small but rich

APR 15 - Malthus and followers argue that the increasing population adds pressure to the resource base because of the increase in demand. Poverty could be the result of the over consumption of the resources by ever increasing population. The relationship between household size and poverty rate is evident from facts on Nepal.

The Nepal Living Standards Survey (NLSS) 2010/11 reveals that on average one quarter of the country’s population lives below the national poverty line. The poverty rate decreased from 30.8 percent in 2003/04. The 2011 census reveals that the rate of population change has reduced from 2.24 percent per annum in 2001 to 1.35 percent. In the same period, the average household size decreased from 5.44 persons per household to 4.88 persons. These facts indicate that the household size is related to poverty rate (percent of people below the national poverty line).

The household size and poverty rate of 3,973 Village Development Committees (VDCs)/ municipalities (MPs) located in 75 districts of all five development regions were analysed to find the relationship between the two. Data were taken from the NLSS 2010/11 and the 2011 census.

The average household size in Nepal ranges from 8.72 persons (Abhirao VDC of Kapilvastu district) to 2.15 persons (Ghyaru VDC of Manang district). The average poverty rate ranges from 72.8 percent (Kankada and Raksirang VDCs of Makwanpur district) to 0.5 percent (Imadol VDC, Lalitpur district). Both the average household size and the average poverty rate increased from east to west.

The average household size had a moderate positive correlation with the poverty rate (Karl Pearson’s correlation coefficient=0.44; maximum value is 1, which means total positive correlation). In a regression analysis between these two variables, the household size determined 20 percent of the variation in the poverty rate. This means, the bigger the size of a family the higher the chances of its being poor.

A high positive correlation between average household size and the poverty rate was observed in the eastern development region (Karl Pearson’s correlation coefficient=0.66). In a regression analysis between these two variables, the household size determined 43 percent of the variation in the poverty rate in the region. In the east, it was clearly evident that the bigger family size increases the higher the rate of poverty incidence. The relation, however, was poor in far-western development region. The region is poor, irrespective of the household size.

There could be many factors determining the poverty rates, but the analyses indicate that the bigger households are relatively poorer. With the increasing level of awareness and family planning interventions, the household size is expected to decrease, leading to a decline in the poverty rate.

Basan Shrestha

Posted on: 2015-04-16 09:56

http://www.ekantipur.com/the-kathmandu-post/2015/04/15/oped/small-but-rich/275427.html 

Mercy Corps Nepal’s Beneficiary Data Visualization

As the Design Monitoring and Evaluation Manager, I developed a website on Mercy Corps Nepal’s Design Monitoring and Evaluation Unit to visualize data on the number of beneficiaries the projects reached from 2006 to 2013. The website was developed using the Google site.

Mercy Corps Nepal (MC-N) reached 385 Village Development Committees (VDCs)/ Municipalities (MPs) in 23 districts of all five development regions of Nepal benefiting close to nine hundred thousand people directly or indirectly through 24 projects in past seven years (2006-2013). 51 percent were women and 21 percent were youth aged 15 to 24 years. MC-N reached close to two-fifths of VDCs/ MPs in those districts and one-fifths of people in those VDCs/ MPs.

The website presents beneficiary data by districts and by different projects. The annexes are presented to provide information on the district profile, VDC/ Municipality profile, map of districts where projects were intervened, and the list of projects.

It’s an effort to compile data and visualize presenting in the viewer friendly manner considering what information will be useful to the viewers and how that information could be presented with the available technologies.

 
The link is: https://sites.google.com/a/np.mercycorps.org/mcndme/

Limiting Disaster and Impact

Studies indicate that Pokhara valley has fragile geology. Besides, the situation has been jeopardized due to fast growing urbanization leading to the pressure on the fragile geology. Quarrying of sands and stones along the rivers and beds, and over extraction of ground waters also add pressure to fragility. Thus, such disasters can be prevented if the concerned government authorities in active participation of the community people develop the urban and rural land use plans, implement and periodically review them. As a result, infrastructure building measures are strictly followed. There will be limited quarrying and people will maintain greenery that help bind soil erosion. Community awareness is another important dimension of limiting the disaster vulnerability. Communities also may know early symptoms of disaster which they can disseminate among other members. They can strengthen their early warning system; share their local expertise with government and other stakeholders to mitigate the impact of disaster. As an individual, I would be interested to strengthen the local early warning system through studying the early warnings and disseminating to the communities.


http://epaper.thehimalayantimes.com/PUBLICATIONS/THT/THT/2013/12/15/ArticleHtmls/PEOPLESPEAK-Inevitable-natural-disasters-15122013008007.shtml?Mode=1

My few learning from American Evaluation Association (AEA) conference 2013

Here, I’m sharing some points from the American Evaluation Association (AEA) Conference that was held from 16 to 20 October 2013, including the professional development workshop on the last day. The event was held at Washington DC, USA.

First of all, I’d like to thank the Mercy Corps and AEA for accepting my paper for presentation and providing financial support to attend the event. Without those and other logistic supports, the participation was not possible.

Remaining, I’ll try to briefly share my learning on the event and from some of the presentations that I attended. The AEA conference 2013 was the huge event that I’d ever seen or attended. More than 37 hundred participants presented papers that were categorized under close to nine hundred sessions. The link takes to the detailed list of sessions and the abstracts of the papers therein: http://www.americanevaluation.org/search13/allschedule.asp. The email of the presenters is also provided to facilitate the communication in case a paper is of interest to the reader. Besides, another link compiles the list of participants that also lead to the presentation of the participant: http://www.americanevaluation.org/search13/choosepresenter.asp.

I presented a paper in the session entitled ‘Around the World: Cost Experiences and Insights’. The paper is entitled as Estimating Social Return on Investment of Social Protection Programs:
Piloting an Approach in Irrigation Canal Improvement Project in Far-western Hills of Nepal. The paper concludes that irrigation scheme is a strong investment to social benefit of target communities. However, the high benefit cost ratio (BCR) poses whether irrigation scheme alone is attributed to increased production. The cost of other interventions (such as training, demonstration, seed distribution) needs to be considered for their attribution and contribution to the increased production. Participants raised their interests to know on what basis discount rate of 10% and time horizon of five years were assumed to estimate BCR. It was clarified that those were assumed based on the standard used by Asian Development Bank (1009) and other studies undertaken in disaster risk reduction in Nepal (White and Rorick 2010 and Willenbockel 2011). There were some suggestion to undertake the sensitivity analysis  by altering the discount rate and time of benefit to see at what levels the cost of intervention outweighs the net present value. The abstract of my paper that I submitted at the time of call is available at the link: http://www.americanevaluation.org/search13/session.asp?sessionid=2037&presenterid=0.

Jon Kurtz, Mercy Corps Director for Research and Learning, Lara Hilton, an Evaluation Expert (PhD scholar at Claremont Graduate University) and I presented jointly a paper entitled Connecting program and social science theory in a Nepalese microfinance program evaluation. The paper is based on the baseline evaluation of PROMISE project. The purpose of the evaluation is to examine who accesses and benefits most from VSLAs, thus answering the question of whether they constitute an effective vehicle for social protection of very vulnerable groups. Besides, it will contribute to answering the broader question of: Are the lack of formalization and capacity of existing savings and loan groups in rural Nepal major constraints to their abilities to contribute to improving their members’ livelihoods and abilities to cope with risks and shocks? During presentation, it was emphasized that Mercy Corps relies increasingly on program theory-based evaluation to increase knowledge about effectiveness of its intervention strategies. The theoretical approach to move beyond the current practice of performance monitoring evaluation was discussed. Participants were curious to know whether there was any difference between eastern and western districts. Abstract of the paper submitted to the organization is posted with a different title: What They Don't Tell You: Connecting Program and Social Science Theory, in a Nepalese Microfinance Program Evaluation at:  http://www.americanevaluation.org/search13/session.asp?sessionid=2145&presenterid=1795.

Jon Kurtz, Mercy Corps Director for Research and Learning presented a paper entitled Mercy Corps' Contingency Approach to Measuring Resilience. The paper highlighted the methods applied to generate evidence on what contributes to strengthening resilience in the arid lands of Africa. The main methods are: analysis of post-shock cross-sectional data to predict resilience; quasi-experimental impact evaluation designs to determine attributable program effects on resilience outcomes; and ex-post program evaluations to understand how target populations recover and manage subsequent shocks.

Scott Chaplowe, International Federation of Red Cross and Red Crescent Societies (IFRC) presented a paper on Counting People Reached – Reality versus Fiction. Harry Carr from Catholic Relief Services presented the paper on Tracking Beneficiary and Service Delivery - Lessons From Catholic Relief Services. Michael Wallace, Mercy Corps Senior Monitoring, Evaluation and Learning Advisor had facilitated the discussion. In the pursuit of demonstrating accountability, measuring both output and outcome level results have been the pressing need. However, measuring direct versus indirect people-reached, as well as avoiding double-counting are contested. The papers discuss the challenges in the development and roll-out of the Monitoring System. IFRC has the system of counting only once the direct recipients of services within regardless of how may services they receive. They only count indirect recipients when a credible approximation can be made. The paper suggests to count Households to determine the number of people reached, based on the credible evidence that household has received service and based on reliable statistic of the average household size. The purpose of collecting people reached data is an important organizational decision.

Jane Buckley, Cornell University and Thomas Archibald Cornell Office for Research on Evaluation facilitated the workshop on Evaluation Thinking (ET). They define ET as a cognitive process motivated by inquisitiveness and a belief in the value of evidence. The process includes: 1) identifying assumptions, 2) posing thoughtful questions, 3) pursuing deeper understanding through reflection and perspective taking, and, 4) making informed decisions in preparation for action. In the development context, the interventions are undertaken with the assumption that they produce short-run to long-run results. For e.g. irrigation canal is constructed with the assumption that it will increase access to irrigation facility to cultivate crops. With irrigation facilities, the farmers are assumed to increase the production of the crops that they have been cultivating, or the farmers are assumed to select those varieties of the same crop that are potential to bear higher production in irrigated condition or the farmers are assumed to change their cropping pattern to introduce new crops that are highly producing and fetching good market price. With increased production, the farmers are expected to increase the value of production from their crops. Besides, the farmers are assumed to meet their own food requirements and sale surplus. These sorts of assumptions will help come up with evaluation questions: does irrigation intervention increase land under irrigation? Have farmers increased their production with the crops they have been producing for years? Have farmers changed the variety of the same crop? Have farmers introduced new crop? Have farmers increased the monetary value of the crops?. The question will help establish claims: irrigation intervention increases area under irrigation, area under irrigation increases choice of crops that are suitable for irrigated conditions. There, come the parts of what approach, methodology and tools will be applied to measures the variables to establish these claims: discussion, survey, or what else. Thus, evaluative thinking is based on identifying the assumption and believing on the value of evidence to establish the claims.

Overall, AEA conference 2013 was a giant forum. My learnings are few drops of water from the ocean.