The Irrelevance of National Strategies? Rural Poverty Dynamics in States and Regions of India, 1993–2005

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  The Irrelevance of National Strategies? Rural Poverty Dynamics in States and Regions of India, 1993–2005
  1 REVISED DRAFT: February 2010 The Irrelevance of National Strategies? Rural Poverty Dynamics in States and Regions of India, 1993-2005  Anirudh Krishna and Abusaleh Shariff Summary Examining panel data for more than 13,000 rural Indian households over the 12-year period 1993-94 – 2004-05 confirms on a large scale what grassroots studies have identified before:  Two parallel and opposite flows regularly reconfigure the national stock of poverty. Some formerly poor people have escaped poverty; concurrently, some formerly non-poor people have fallen into the pool of poverty. The simultaneous inward and outward flows are asymmetric in terms of reasons. One set of reasons is associated with the flow into poverty, but a different set of reasons is associated with the flow out of poverty. Both sets of reasons  vary considerably across and within states. Not a single factor matters consistently across all states of India. Any standardized national policy is thus largely irrelevant. Diverse threats operate and different opportunities must be identified and tackled at the sub-national level. Introduction  The stock of poverty in a country increases when people fall into poverty and decreases  when people escape poverty. Because some people fall into poverty even as other people move out of poverty, the stock of poverty is simultaneously both created and reduced. This fluidity is an essential feature of poverty dynamics. Any given change in the stock of poverty can come about in different ways. For instance, a net reduction of three percent over five years will be achieved if four percent of the population escapes poverty and one percent concurrently falls into poverty. But the same net reduction figure will also be achieved if 14 percent of the population escapes poverty and 11 percent falls into poverty. Thus, taking note only of the net change (three percent in this case) is like observing the proverbial tip of the iceberg: it gives little idea of the trends that underlie the observed result. Explaining the net change in the stock of poverty over any period of time requires understanding the separate flows that make and unmake poverty in parallel. With rare exceptions, however, analyses of poverty in India and other developing countries have not attended to the flows that simultaneously make and unmake poverty. 1 Large-scale studies of poverty in India have usually examined the aggregate effects of national policies and state-level trends. A great deal of useful knowledge has been gained from these attempts to explain the stock of poverty. For instance, it has been learned how growth in agricultural productivity, improvements in infrastructure, the rate of inflation, and different starting conditions (including, historical literacy trends, health care conditions, and irrigation coverage) can help explain some part of the difference in poverty stocks across Indian states.  2  Such examinations do not, however, help understand poverty flows: How is poverty simultaneously both created and reduced? Why does a higher rate of growth of agricultural productivity or better infrastructure in some state translate simultaneously into escapes from poverty for one set of households and descents into  poverty for another set of households? Why does poverty continue being created even when the rate of economic growth is high?  2 In order to understand these differences better – to learn how poverty is created and how it is overcome in practice – it is essential to examine poverty flows directly at the level  where these are experienced. Three steps need to be followed in order. First, those households must be identified who escaped poverty (or who fell into poverty). Second, the experiences of such households must be compared with those of others who remained poor (or who stayed out of poverty). Third, factors common to particular household experiences must be identified. What factors are common to the experience of households who escaped poverty and not common among households who remained poor? What other factors were experienced by households who fell into poverty and not by those who remained non-poor? Identifying these factors gives a better idea about the natures of reasons responsible for escape and descent which, in turn, helps formulate more effective anti-poverty policies. Grassroots investigations conducted in different parts of three Indian states have shed new light upon factors associated, respectively, with escaping poverty and falling into poverty. 3 Four main conclusions follow from this examination:  We complement and extend this analysis with the help of a nationally representative panel data set of rural households. Examined over the period from 1993-94 to 2004-05, when high-speed economic growth was being experienced in India, this data set contains information for 13,593 households randomly selected in rural areas of 16 Indian states that together constitute more than 90 percent of the Indian population. (1)   Large numbers of people have fallen into poverty over this twelve-year period, even as many others have moved out of poverty. The effects of national economic growth  were experienced very differently by people in rural India, with some among them experiencing considerable improvements in household income and others simultaneously becoming poorer than before. Overall, the stock of rural poverty has increased, but there is considerable variation across states and among regions within states. (2)   Rural poverty has fallen in states (such as Himachal Pradesh, Kerala, Rajasthan and  West Bengal) where more people moved out of poverty than fell into poverty. Over the same period rural poverty increased in a second group of states – including  Andhra Pradesh, Bihar, Gujarat, Haryana, Maharashtra, Madhya Pradesh, Orissa,  Tamil Nadu, and Uttar Pradesh – where more people fell into poverty than moved out of poverty. This group of states includes some in which per capita state domestic product increased at lower-than-average rates (Bihar, Uttar Pradesh, Orissa), but it also includes some others that experienced high rates of economic growth during the 1990s (Gujarat, Maharashtra, Tamil Nadu). 4 (3)    Analyzing the aggregate data (for all states) helps identify factors commonly associated, respectively, with escaping poverty and falling into poverty. While some factors – such as women’s media exposure, remittances, and the prevalence of telephones – are significantly associated with both escapes and descents, there is also another set of factors that matters only for escapes or only for descents. For instance, location within five kilometers of a town and the presence of an adult son in the base year (1993) were found to be significantly associated with escapes but not  with descents. Conversely, education of the household head to secondary level or higher, ownership of land and other rural assets, and engagement in rural social networks helped reduce the risk of descent into poverty – but these factors had no significant impact upon households’ prospects for escaping poverty. These  Thus, when examined at the level of states (and regions within states), the correlation between economic growth and poverty reduction is far from perfect.  3 differences in underlying reasons suggest that a single national policy will not suffice. Different policies are required for dealing with each of the two constitutive poverty flows. (4)   Further differences were revealed when both poverty flows (escape and descent)  were analyzed at the level of individual states. Reasons for escape and descent vary considerably across state boundaries. The factors that made a significant difference for escape (or descent) within one Indian state mattered little or not at all within other states and regions. Thus, designing standard national policies to combat poverty hardly represents the best use of available resources. Poverty can be reduced faster and more cost-effectively if attention is paid to diverse factors variously associated with escapes and descents in different states and regions. Data and Methods  Three caveats are in order before data in support of these arguments are presented. First, these data, derived from nationally-representative sample surveys carried out by the National Council for Applied Economic Research (NCAER) deduce estimates of poverty based on calculations of household income. 5  Second, because we have data for only two points in time, respectively, 1993-94 and 2004-05, we lack information about several important events that households experienced during the intervening period. That such household-level events and processes can make critical impacts on households’ prospects for escaping poverty (as well as for their chances of falling into poverty) has been well documented by the grassroots investigations referred to above. To some small extent, household events were captured in the NCAER data sets. For instance, the survey administered in 2004-05 inquired about loans taken by each household in the previous five years and about deaths and major illnesses occurring during the 12 months preceding the survey. However, the majority of household-level events continue to remain unknown. Our estimate for rural poverty in any state is not directly comparable, therefore, with other and more widely-used estimates derived from consumption data provided by the National Sample Survey Organization (NSSO). However, the aggregate figures that we have calculated using NCAER’s income data do fall within the range of figures derived by different analysts using diverse methodologies and adjustment techniques to calibrate the NSSO data.  The vast scope and coverage of the NCAER data set – in terms both of geographic reach and numbers of household and community characteristics examined – has to be complemented by additional sources of data that probe household event histories in greater depth and detail. We conducted such combined quantitative-and-qualitative examination using data from grassroots investigations previously undertaken by one of us. The scope of this analysis is restricted, because the extent of geographic overlap between the NCAER data and the grassroots studies is small. Such combined analyses can add greatly to the richness and robustness of the results. 6  Third, while we wish to highlight the need for decentralized and disaggregated analysis, it must be noted that the number of observations became progressively smaller as  we went from state to region to sub-region and as we separated descents from escapes. With the data at hand, we could meaningfully analyze differences in reasons for escape and descent at the level of an entire state, and we were able to categorize regions within states in terms of their relative rates of escape and descent. Additional data are required, however, for  We urge that they be taken up in future studies of poverty in India.  4 probing the natures of reasons associated with escapes and descents at the sub-state level.  We hope that others will take up where we have left off, assembling and analyzing these new data sets.  With these caveats behind us, we can begin to describe the date and the results that  were obtained. Two waves of sample surveys representative of the rural areas of 16 major states constitute the data base for our analysis. About one half of the 33,230 households surveyed in 1993-94 were selected at random for resurvey in 2004-05. It was possible to contact 13,593 households (located in 195 districts and 1,765 villages), resulting in a relatively high re-contact rate. The panel consists of 11,153 srcinal households along with 2,440 households who split from the srcinally surveyed households. 7  These multi-dimensional surveys encompass a wide range of human development and poverty-related issues. Both surveys were undertaken by NCAER, a well-known applied economics research institution in India.  8   Two survey instruments were administered to each household by a mixed-gender team of investigators. A household questionnaire was administered to the individual most knowledgeable about income and expenditures in each household, most frequently the male head of household. Separately, a questionnaire related specifically to health- and education-related items was administered to an adult woman of each surveyed household. Interviews typically took between 45 and 90 minutes. Survey instruments were translated into eleven Indian languages, and field work was undertaken by 25 agencies in diverse parts of India. Different household occupations were identified so as to assess and estimate incomes from multiple sources. All variables employed in this analysis are briefly described in Appendix 1. Poverty Dynamics: Escapes and Descents  Table 1 shows the results for separate states in terms of trends in the rural headcount ratio of income poverty. Overall, these data show that 18 percent of rural households moved out of poverty over this period, but at the same time another 22 percent of households fell into poverty. Thus, the stock of rural poverty, measured in terms of household income, grew by four percent over this 12-year period. A total of 36.1 percent of rural households were poor in 1993-94, and as many as 40 percent were poor in 2004-05. 9  -- Table 1 about here --  These numbers, especially those for 2004-05, are at variance with the official statistics, which report a considerably lower rural poverty rate (28 percent) in 2004-05. 10 But it is worth noting that a great deal of controversy has been generated by the official consumption-based statistics, and independent analysts have advanced a series of plausible reasons for why the official poverty estimates for 2004-05 (and for 1999-2000) should be adjusted upward. To some extent, these differences are to be expected: we rely upon household income, while the official statistics derive poverty estimates using consumption data; and we consider only 16 states, while the official statistics refer to the entire country. 11  One important set of adjustments has been proposed on account of changing consumption patterns. While official poverty estimates continue to be based on a bundle of goods and services srcinally selected in 1973, actual consumption patterns have changed substantially since that time, in particular, health and education expenditures have increased manifold. Making adjustments that take account of households’ increased expenditures on education and health care, Dev and Ravi (2008) report a rural  5 poverty ratio of 36.4 percent for 2004-05, which is considerably higher than the official figure of 28 percent and closer to our income-based estimate of 40 percent.  An expert group established by the national Planning Commission also re-estimated poverty for both rural and urban areas after revising the consumption basket. According to this committee’s calculations, the stock of rural poverty in 2004-5 stood at 41.8 percent, i.e., almost two percentage points higher   than the estimates derived by us (GOI 2009b). Other indications also point toward slow or no improvement in wellbeing in rural areas of India over the decade under consideration. 12  On the other hand, a separate set of calculations, based on national income accounts, provide estimates of poverty that are lower than the official poverty rate, showing that rural poverty could have fallen in the aggregate during the period under review. 13  Our income-based poverty statistics can thus be seen figuratively as the third pole of an ongoing debate. While they may not be measuring the same “poverty” that consumption-based official statistics have measured, these estimates provide an additional perspective on the thorny issue of wellbeing in rural areas in the wake of rapid economic growth. In order that these household income estimates could be viewed with greater confidence, we matched them against several other indicators of wellbeing in rural areas (Table 2). -- Table 2 about here --  These results showed that households who have remained poor or fallen into poverty had much lower monthly per capita incomes in 2004-05 (respectively, Rs. 217 and Rs. 221) compared to others who have moved out of poverty or remained non-poor (Rs. 717 and Rs. 981, respectively). Other indicators of wellbeing also clearly differentiated among these categories of households. The share of food expenditure in the household budget is much higher for households who fell into poverty or who remained poor; their average landholdings are much smaller than those of households who escaped poverty or remained non-poor; assets of different kinds are owned in much larger numbers by non-poor households; 14  Our purpose in this paper is not to defend some particular way of measuring poverty. On the contrary, we recognize that all “poverty lines unavoidably retain an element of arbitrariness and inevitably embody some implicit or explicit normative judgments” (Lanjouw 1998: 4). We agree with Blank (2008: 243, 252) who, in another context, has suggested that since “there is no ‘right’ way to develop poverty thresholds,” analysts should focus more closely on “progress (or regression) over time, and this may be more important than the precise level of poverty at any point in time.” fewer children from poor households attend schools; and larger proportions of these households are in debt compared to non-poor households. The existence of a close relationship between these different indicators shows that calculations of household wellbeing based in monthly incomes are, in fact, assessing real changes in households’ economic conditions over time. In fact, investigating changes in households’ conditions over time may be the only  way to learn about the reasons that simultaneously make and un-make poverty, thereby helping develop the most suitable policy interventions. Unfortunately, a great deal of poverty analysis in the Indian context has tended “to focus heavily, if not exclusively, on the definition of the poverty line and estimating poverty incidence and its trends. Factors underlying regional and temporal variations in these respects…have also been explored but not to the extent one would expect” (Vaidyanathan 2001). Some prior examinations based
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