rafmacalaba/datause-encoder-data huggingface dataset:
# **1. Introduction**

Measures of per capita income and consumption are among the most frequently cited indicators of economic development. They are widely used in assessments of living standards, economic growth, poverty, and inequality, both within and across countries. Despite their prominence, the two most common data sources for such measures – national accounts systems (NAS) and household surveys (HHS) – often have large gaps between them and offer differing portrayals of living standards and economic growth. For example, for Pakistan in 2015, national accounts data suggest that average household consumption expenditure per capita was $9.3 per day at 2011 PPP, while the household survey indicates it was just a bit more than half of that, $4.9 per day at 2011 PPP.<sup>1</sup> In Botswana, the two recent household surveys suggest that per capita consumption contracted at an annualized rate of -3.3 percent between 2009 and 2015, while the most closely aligned measure from the national accounts system, per capita household final consumption expenditures (HFCE), indicated a robust expansion of household consumption at an annualized rate of 3.7 percent over the same period, as did gross domestic product (GDP).<sup>2</sup> That national accounts data and survey data can lead to such diverging measures of the levels and changes in living standards is a recurring phenomenon across a wide range of countries and statistical systems. A frequently cited case is India, where large discrepancies in measures of household consumption expenditures across the national accounts and the National Sample Survey (NSS) have fueled a vigorous debate about the evolution of poverty and its relationship to economic growth (see for example Deaton and Kozel, 2005; Subramanian and Jayaraj, 2015; Sundaram and Tendulkar, 2003). The issue of diverging estimates from national accounts and household surveys is not limited to less wealthy countries. In the United States, per capita income from the two large national surveys, the Current Population Survey and the Consumer Expenditure Survey, are known to diverge from the national accounts (see for example the recent assessment by Schündeln, 2018). Nolan et al. (2019) recently reviewed gaps

> 1 Survey data for this example come from World Bank, Global Database of Shared Prosperity (GDSP) circa 2013 - 2018 (www.worldbank.org/en/topic/poverty/brief/global-database-of-shared-prosperity) and national accounts data come from World Development Indicators, from latest data using the 2011 PPPs. 2 Estimates are from the 2009 to 2015 spell for Botswana in the Global Database of Shared Prosperity (GDSP) and growth is estimated in constant terms. The survey years are decimal years

> (2009.25 and 2015.85) and national accounts growth is estimated for the same period using weighted annual data.

2
related to the estimation of the empirical relationship of interest. Section 6 presents the empirical results and Section 7 offers some concluding remarks.

# **1. Literature Review**

There have been a variety of theoretical models adduced to explain the motives underlying remittance behavior, including altruism, exchange or self-interest, and insurance. The altruistic behavior is modeled by allowing the utility of a remitter to be derived from the well-being or consumption level of those recipients left behind (Becker, 1974). This basically implies a negative relationship between the income of the recipient and the amount of remittances. Aggarwal and Horowitz (2002), on the other hand, examine the effect of multiple migrants (as opposed to a single migrant) on the level of remittances. They argue that under pure insurance (or self-interest) motives, the number of other migrants in the family should not affect the amount of per-migrant remittances. However, under altruism, the presence of other remitting migrants will reduce the average size of remittances. Using data for Guyana, Aggarwal and Horowitz (2002) find some support for the presence of altruism.

The exchange motive implies a complex relationship between the recipient’s income and the size of remittances. Cox (1987) formalizes a model where private transfers represent payments for services rendered. Under this model, an increase in the remitter’s income will be associated with a higher probability of transfers as well as a larger amount because the remitter is willing to pay more for the services provided by the recipient. On the other hand, if the recipient’s income rises, the opportunity cost of providing the service will rise, and 6
-3-

Contrary to the predictions of the standard neo,lassical growth models<sup>3</sup> , empirical evidence **have,** shown that growth is significantly related to economic distortions (e.g. Easterly and Wetzel (1989)).4 Formal models of endogenous growthS so<sup>1</sup> ve for the rate of grov.th as dependent (negatively) on economic distortions along with other more conventional growth furdamentals such as the stocks of physical and human capital. The model predicts .hat small changes in distortions do not affect growth much if the initial conditions are ones of very high or very low distortions, but have a strong effect in between those extremes Optimal saving behavior would make growth sensitive even to low distortions, however, Easterly (1990). Using data from the Sudan - an SSA country with a sizable agricultural economy, we test the implications of the endogenous growth model in this paper. Other factors relevant to economic growth in SSA that are also considered in the empirical model of this paper are the impacts on agricultural growth of exogenous worsening of terms of trade and import compression induced declines in capacity utilization, Ndulu (1990). These two factors which reflect the pote-.;al impact on economic performance in SSA of unfavorable external environment, provide some balance to the mainly 'intemalistic' view of the causes of economic crisis which centered around domestic policy-induced real overvaluation.<sup>6</sup>

> 3 Given the existence of non-reproducible factors, constant retums to scale, and diminishing returns to each factor, steady-state growth-as predicted by these models - can only take place through exogenous technological change.

'Also these models fail to eAplain the observed nonconverging per capita income growth rates across regions (Easterly (1990)).

se.g. Easterly (1990) which also contains a list of recent works in this literature.

6For a discussion of the "interiialist versus externalist" interpretation of the source of economic crisis in Africa see Mkandawire (1989) and references cited therein.
# **5 Conclusion**

This paper provides further evidence that combining GPS-based plot areas measurements with farmerreported plots areas in a Multiple Imputation (MI) application can result in reliable simulations of missing GPS-based plot areas. The analysis extends previous research by using survey data from Malawi and Ethiopia that feature negligible levels of missing GPS-based area measures. By artificially simulating missingness among distant plots in otherwise assumed-to-be-complete data, we compare the MI-based predictions to the true, observed values and gauge the levels of missingness in GPS-based land area measurements that can be handled with MI without compromising the robustness of key land area related statistics.

Since the microdata on land areas inform a wide range of research applications on smallholder agriculture, agricultural and development economists that rely heavily on public use datasets are therefore encouraged to think more critically about the use of model-based approaches to address missingness not only in GPSbased land areas but also in other variables with known missing information mechanisms that can be captured with confidence as part of the imputation model specification.

Among the outcome variables of interest, plot-level agricultural productivity, as measured by maize yield in Malawi and total harvest value per land area in Ethiopia, is found to be more sensitive to missingness. Still, we show that in Malawi, by obtaining GPS-based area measures for only 18 percent of the distant plots in Malawi that are further than 1 kilometer with respect to the dwelling location and that would be selected at random, and by multiply-imputing the remaining missing GPS-based plot areas, we can derive comparable means and distributions with respect to the true data. In the case of Ethiopia, we need a randomly selected sample of at least 44 percent of the distant plots based on the same distance threshold to achieve the same objective.

27
prepared for the Department of Agricultural Economics, Michigan State Uni‐ . versity, and the West African Cotton Improvement Program

- Blasi, Dale A., and Jim Drouillard (2002). “Composition and Feeding Value of Cot‐ tonseed Feed Products for Beef Cattle.” Kansas State University, Agricultural Experiment Station and Cooperative Extension Service.

- Dickey, David, and Wayne A. Fuller (1979). “Distribution of the Estimators for Time Series Regressions with Unit Roots,” _Journal of the American Statistical Association_ 74: 427‐431.

- Falck‐Zepeda, Jose, Daniela Horna, and Melinda Smale (2007). “The Economic Impact and the Distribution of Benefits and Risks from the Adoption of Insect Resistant ( _Bt_ ) Cotton in West Africa.” _Discussion Paper No. 718_ . Washington, DC: International Food Policy Research Institute,.

- Food and Agriculture Organization (FAO) (2008). _The State of Food and Agricul‐ ture—Biofuels: Prospects, Risks, and Opportunities._ Rome: FAO.

- ________ (2004). _The State of Food and Agriculture—Agricultural Biotechnology: Meeting the Needs of the Poor?_ Rome: FAO.

- Gergely, Nicolas (2009). “The Cotton Sector of Benin.” _Africa Region Working Pa‐ per Series No. 125._ Washington, DC: World Bank.

- Gergely, Nicolas, and Colin Poulton (2009). “Valorization of Cotton By‐ Products.” In D. Tschirley, C. Poulton, and P. Labaste, eds., _Organization and Performance of Cotton Sectors in Africa: Learning from Reform Experience_ . Wash‐ ington, DC: World Bank.

- Gilbert, Christopher (2009). “How to Understand High Food Prices.” CIFREM and Department of Economics, University of Trento, Italy.

- Hinkle, Lawrence E., and Peter J. Montiel (1999). _Exchange Rate Misalignment; Concepts and Measurements for Developing Countries._ Oxford: Oxford University Press for the World Bank.

- Houthakker, Hendrik S. (1975). “Comments and Discussion on ‘The 1972‐75 Commodity Boom’ by Richard N. Cooper and Robert Z. Lawrence,” _Brookings Papers on Economic Activity_ 3: 718‐720.

- Jarvis, Michael (2009). “Faire Tache d’Huile: Cooking Oil Fortification in West Africa.” Case Study prepared for _Business Innovation to Combat Malnutrition_ . World Bank Institute. Washington, DC: World Bank.

- International Cotton Advisory Committee (ICAC) (2010). “Gossypol in Cotton Seed,” _ICAC Recorder_ , XXVIII: 3‐8.

- ________ (2000). “The Cotton Seed,” _ICAC Recorder_ , XVII: 3‐8.

- Kelly, Valerie, Sonja Perakis, Boubacar Diallo, and Niama Nango Dembéle

**_Page 25_**
Cole, Matthew A., and Anthony J. Rayner. 2000. ―The Uruguay Round and Air Pollution: Estimating the Composition, Scale, and Technique Effects of Trade Liberalization.‖ _Journal of International Trade and Economic Development_ 9 (3, September): 339–54.

Copeland, Brian R., and M. Scott Taylor. 1994. ―North-South Trade and the Environment.‖ _Quarterly Journal of Economics_ 109 (3, August): 755–87.

———.2003. _International Trade and Environment: Theory and Practice_ . Princeton, NJ: Princeton University Press.

———. 2004. ―Trade, Growth, and the Environment.‖ _Journal of Economic Literature_ 42 (1, March): 7– 71.

Dean, Judith M. 2002. ―Does Trade Liberalization Harm the Environment? A New Test.‖ _Canadian Journal of Economics_ 35 (4, November): 819–42.

de Bruyn, S. M., J. C. J. M. van den Bergh, and J. B. Opschoor. 1998 _._ ―Economic Growth and Emissions: Reconsidering the Empirical Basis of Environmental Kuznets Curves.‖ _Ecological Economics_ 25 (2, May): 161–75.

Di Maria, Corrado, and Sjak A. Smulders. 2004. ―Trade Pessimists vs. Technology Optimists: Induced Technical Change and Pollution Havens.‖ Open Access Publication urn:hdl:10197/845, University College Dublin.

Dijkgraaf, Elbert, and Herman Vollebergh. 2004. ―A Note on Testing for Environmental Kuznets Curves with Panel Data.‖ Others 0409001, EconWPA.Washington University, St. Louis.

Egli, Hannes. 2004. ―The Environmental Kuznets Curve: Evidence from Time Series Data for Germany,‖ CER-ETH Economics Working Paper 03/28, Center of Economic Research, Swiss Federal Institute of Technology (ETH), Zurich.

Esty, D., and M. Porter. 2005. ―National Environmental Performance: An Empirical Analysis of Policy Results and Determinants.‖ _Environment and Development Economics_ 10 (4): 391–434 Gale, Lewis R. and Mendez, Jose A., 1998. "The empirical relationship between trade, growth and the environment," _International Review of Economics & Finance_ , Elsevier, vol. 7(1), pages 53-61.

Gamper-Rabindran, Shanti, and Shreyasi Jha. 2004. ―Environmental Impact of India’s Trade Liberalization.‖Working Paper, University of North Carolina at Chapel Hill, Department of Public Policy .

Grether, Jean-Marie, Nicole A. Mathys, and Jaime de Melo. 2007. ―Trade, Technique, and Composition Effects: What Is Behind the Fall in World-Wide SO2 Emissions 1990–2000?‖Working Paper 2007.93, Fondazione Eni Enrico Mattei, Milan.

21
notice that even by this approach to measuring poverty, the precise location of the “$1 a day” line is of relevance as a lower bound to the relative lines.

This paper revisits the problem of setting an international poverty line in the light of a great deal of new data. There are two important sources of that data. The first is the new set of PPP’s produced from the 2005 round of the _International Comparison Program_ (ICP) (World Bank, 2008a). This is the most ambitious round to date of the ICP (which began in 1968), and it is expected to entail a substantial improvement in data quality. The second source is a new data base of national poverty lines across developing countries (compiled for the purpose of this paper). Past work has been based on the compilation of poverty lines for the 1980s used by RDV. Our new data set covers the period since 1990, drawing on the large number of new country-specific poverty studies for developing countries that have been done since 1990 under the World Bank’s program of country _Poverty Assessments_ .

We begin with a simple model of a socially-specific poverty line, which helps motivate and interpret our empirical analysis. We present in section 3 our new compilation of national poverty lines across countries. The pattern predicted by our theoretical analysis is in evidence: the national poverty line rises with mean consumption, although with a low elasticity at low consumption. Based on these empirical results, our proposed new poverty lines are discussed in section 4, including both absolute and relative lines. Section 5 compares our proposed new international poverty lines to past lines. Section 6 discusses how our main results change with alternative PPP’s. Section 7 concludes.

# **2. The social subjective poverty line**

An important strand of the literature on welfare and poverty measurement has formalized the idea of what can be termed a “social subjective poverty line” (SSPL): the point in the income space above which people tend to think they are not poor in a given society, and below which they tend to think they are poor.<sup>8</sup> Underlying the idea of a SSPL is the premise that an individual’s own idea of what it means to be “poor” depends on that individual’s own level of living. There is now a large body of evidence consistent with that premise.<sup>9</sup>

> 8 Contributions include Groedhart, Halberstadt, Kapteyn and van Praag (1977), Hagenaars and van Praag (1985), Kapteyn, Kooreman and Willemse (1988) and Pradhan and Ravallion (2000).

> 9 See the previous footnote for examples.

4
Table 1: Alternative strategies for developing countries: estimated impact

||**Jo**<br>GDP|**in the tra**<br>Exports|**de war**<br>Expto US|GDP|**Do noth**<br>Exports|**ing**<br>Expto US|GDP|**Non-US R**<br>Exports|**TAs**<br>Expto US|<br>GDP|**Turn other**<br>Exports|**cheek**<br>Expto US|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Involved in dispute|||||||||||||
|United States|-0.3|-11.7||-0.3|-10.8||-0.3|-11.1||-0.3|-10.9||
|China|-0.1|-0.5|-10.9|-0.1|-0.4|-8.9|-0.1|0.9|-10.7|-0.1|0.9|-10.2|
|EU-28|0.0|-0.4|-8.0|0.0|-0.4|-6.0|0.0|-0.1|-6.8|0.0|-0.1|-6.3|
|Other Europe|0.0|-0.3|-4.8|0.0|-0.3|-2.6|0.2|0.3|-3.6|0.2|0.3|-3.0|
|<br>Mexico|-0.4|-7.0|-12.3|-0.4|-6.7|-11.3|-0.4|-5.3|-9.9|-0.4|-5.2|-9.6|
|Canada|-0.4|-5.4|-12.4|-0.4|-5.1|-11.2|-0.4|-4.6|-11.4|-0.4|-4.5|-11.1|
|High Income Asia|0.0|-0.9|-12.1|0.0|-0.8|-10.1|0.1|0.1|-12.0|0.1|0.2|-11.5|
|Total|-0.1|-2.2|-10.6|-0.1|-2.1|-8.8|-0.1|-1.6|-9.7|-0.1|-1.5|-9.2|
|Developing countries*|||||||||||||
|<br>Brazil|0.0|-0.5|-8.6|0.0|0.2|-5.2|0.4|2.1|-4.8|0.4|2.5|-3.9|
|Rest of Latin America|-0.5|0.5|-17.6|-0.2|1.6|-15.2|0.0|2.4|-15.1|0.1|2.7|-14.5|
|Central Amer. and Carib.|-1.4|-4.6|-15.5|-0.6|-1.5|-9.9|0.4|1.1|-8.4|0.6|2.1|-6.7|
|MENA|-0.2|0.1|-1.2|-0.1|0.2|0.7|0.3|1.0|0.7|0.3|1.1|1.2|
|Nigeria|0.3|-0.2|5.2|0.5|-0.1|6.1|0.6|0.1|5.8|0.7|0.1|6.0|
|SACU|-0.2|-0.6|-10.5|-0.1|0.0|-7.9|0.4|1.2|-8.8|0.4|1.2|-8.2|
|Rest of Africa|0.0|-0.1|1.7|0.1|0.0|3.3|1.2|2.4|4.4|1.3|2.5|4.8|
|Former Soviet Union|-0.2|0.4|-3.8|-0.1|0.5|-2.0|0.1|1.2|-2.1|0.1|1.3|-1.6|
|India|0.1|-0.8|-7.9|0.1|-0.6|-5.5|0.8|2.4|-4.4|0.8|2.5|-3.7|
|Rest of South Asia|-0.1|-1.6|-12.7|0.0|-1.3|-10.6|0.7|4.1|-7.0|0.7|4.2|-6.4|
|Rest of Asia|-0.3|-0.8|-13.4|-0.1|-0.1|-10.7|0.7|2.2|-9.8|0.7|2.4|-9.0|
|Total|-0.2|-0.3|-7.5|-0.1|0.2|-5.1|0.4|1.7|-4.6|0.4|1.8|-4.0|
|Global|-0.1|-1.8|-9.8|-0.1|-1.5|-7.8|0.0|-0.8|-8.3|0.0|-0.7|-7.8|

Source: Authors’ simulations.

Note. Comparative static percent deviation from the baseline in real GDP and exports. All scenarios assume an escalated trade dispute between the United States on the one hand, and China, Mexico and other high-income countries (regions involved in trade dispute), on the other. *Developing countries other than China and Mexico.
electronics and machinery industry in Penang, one would expect to see little collaboration with universities.

**Table 14: Distribution of US Patents Granted to Universities by Technology Class, 1969-2005**

|Class|Class Title|Total|%|
|---|---|---|---|
|424|Drug, Bio-Affecting and Body Treating Compositions (includes Class 514)|8,123|16.7%|
|435|Chemistry: Molecular Biology and Microbiology|6,991|14.4%|
|532|Organic Compounds (includes Classes 532-570)|3,331|6.9%|
|128|Surgery (includes Class 600)|1,692|3.5%|
||Chemistry: Natural Resins or Derivatives; Peptides or Proteins; Lignins or|||
|530|Reaction Products Thereof|1,361|2.8%|
|520|Synthetic Resins or Natural Rubbers (includes Classes 520-528)|1,275|2.6%|
|250|Radiant Energy|1,060|2.2%|
|73|Measuring and Testing|886|1.8%|
|324|Electricity: Measuring and Testing<br>Multicellular Living Organisms and Unmodified Parts Thereof and Related|740|1.5%|
|800|Processes|730|1.5%|

Source: Authors’ calculations To summarize, under the circumstances, USM can assist over the medium term by:

- Improving the quality of education particularly in science and engineering disciplines, by enhancing soft skills and enlarging the supply of technical workers. Communication, team working, and analytic skills are the ones which many employers feel students lack.<sup>31</sup>

-

-

-

- Initiating post doctoral programs in key areas so as to deepen the culture of research and to position the university to generate knowledge in new areas some of which could have commercial potential.

- Encouraging entrepreneurship with the help of training and specialized services provided through incubators and a science park.<sup>32</sup>

- Engaging in exploratory research and internship programs with local companies with government funding serving as a catalyst.

> 31 Over 60 percent of the 167 electric-electronics firms surveyed in Penang and Johor in 2005 reported that university curricula in Malaysia lack interface with firms activities thereby forcing them to invest in longer training periods for new recruits (Rasiah 2008)

> 32 The National University of Singapore has initiated some programs that offer useful pointers (P.-K. Wong 2008).

33
of these two types of programs could be driven by a variety of factors – incentives, program participation requirements, geographic rollout strategies, etc. The participation of specific jatis in each program is however, quite striking and an important topic for further investigation.

These findings on the significance of jati in the participation of two very different poverty alleviation programs may have important policy implications for the design of policies in contemporary India. The recently released Economic Survey of India for example, highlights the existence of 950 poverty alleviation programs at the national level, and hundreds more at the statelevel. The report acknowledges targeting inefficiencies in the form “exclusion error” and argues that “leakages” are largely due to the presence of loopholes, the lack of transparency and failures of accountability (Government of India 2017: 177). Our results suggest that the program benefits may be affected by social identity (Kumar and Somanathan 2017). Our findings also corroborate the findings of past studies that highlight the difficulty of targeting more generally (Galasso and Ravallion 2005).

# **5.  Conclusion**

This paper has examined the relationship between caste and inequality in Bihar. We use data from a large sample survey collected in rural and relatively poor districts of the state in 2011 to compare patterns of inequality that emerge from two definitions of caste. The first is the conventional broad categorization that is used by the government (“Scheduled Caste”, “Scheduled Tribe”, etc.); and the second is the narrower categorization of jati identity through which caste is practiced in everyday life. Keeping in mind that the data only tell us about the poorest rural areas of the state, we find some caste-based differences in average monthly per capita expenditures. SC and ST populations are on average poorer than households from higher ranked castes, but there is considerable variation by jati.

We find that jati has a great influcence on inequality. When we use broad caste groups, the contribution of between-caste inequality to total inequality is relatively low, just 0.8 percent. Consistent with the previous studies, we find most inequality occurs within rather than between castes. When we use jati-level groupings however, this number goes up to 3.2 percent. Even within some broad-caste categories, we find that a great deal of inequality is driven by jati-level variation. Of Forward Caste inequality for example, 6.5 percent is explained by between-jati inequality. Some jatis, such as the Musahars, appear to be “truly disadvantaged” within the disadvantaged groups.

All this has implications for targeting anti-poverty programs. We examine jati-level variation in participation in the MGNREGS and the State Livelihoods Programs, two of rural India’s most important efforts to alleviate poverty. We find that after controlling for a variety of socio-economic variables, there remains a lot of variation at the jati level. Doms and Adivasis are less likely to participate in the livelihoods program, while Dobhas and Sardars are less likely to have an MGNREGS card.

9