rafmacalaba/datause-encoder-data huggingface dataset:
# **Economic and Social Development along the Urban-Rural Continuum: New Opportunities to Inform Policy**

Andrea Cattaneo<sup>1</sup> | Anjali Adukia<sup>2</sup> | David L. Brown<sup>3</sup> | Luc Christiaensen<sup>4</sup> | David K. Evans<sup>5</sup> | Annie Haakenstad<sup>6</sup> | Theresa McMenomy<sup>1</sup> | Mark Partridge<sup>7</sup> | Sara Vaz<sup>1</sup> | Daniel J. Weiss<sup>8,9</sup>

- 1 Agri-Food Economics Division, Food and Agriculture Organization (FAO) of the United Nations, Rome, Italy

- 2 Harris School of Public Policy, University of Chicago and NBER

- 3 College of Agriculture and Life Sciences – Global Development, Cornell University

- 4 Jobs Group, World Bank

- 5 Center for Global Development

- 6 Institute for Health Metrics and Evaluation (IHME), University of Washington

- 7 Department of Agricultural, Environmental, and Development Economics, The Ohio State University

- 8 Curtin University, Bentley WA 6102, Australia

- 9 Telethon Kids Institute, Nedlands WA 6009, Australia

**JEL:** O18, O20, R11, R58

**Keywords:** rural–urban linkages, city–region systems, territorial development, poverty analysis, access to health, access to education.
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37
consumption basket of farmers, who spend a larger portion of their income on food than the richer urban consumers. As a result, the extreme poverty headcount in agriculture remains virtually unchanged between the biofuels scenario and baseline, while the headcount for households with a primary income source from non-agriculture activities rises by 1.3 percentage points. Therefore, nearly all of the poverty increase at the global level is accounted for by the rise in urban poverty, although this statement does not hold at the regional level (Figure 9).

**Table 7 Biofuels impact on poverty**

||**Pov**<br>|**erty headco**<br>|**unt**<br>||**Number of poor**<br>||
|---|---|---|---|---|---|---|
||Circa<br>2005|Baseline,<br>2010|Biofuels,<br>2010|Circa 2005|Baseline,<br>2010|Biofuels,<br>2010|
|_US$1.25 (PPP) per day poverty line_|||||||
|East Asia and Pacific|16.96|7.42|7.42|307,152,633|137,376,331|137,441,961|
|Eastern Europe and Central Asia|5.38|3.07|3.04|20,747,445|11,748,843|11,656,271|
|Latin America and Caribbean|8.13|5.93|5.48|39,872,727|30,501,838|28,203,873|
|Middle East and North Africa|2.88|1.14|1.13|5,889,996|2,522,362|2,483,783|
|South Asia|39.32|26.51|28.57|566,604,647|400,893,876|433,458,721|
|Sub-Saharan Africa|49.70|37.30|37.52|268,110,910|215,159,468|216,962,042|
|Developing countries|24.80|15.78|16.38|1,208,378,358|798,202,718|830,206,651|
|_US$2.50 (PPP) per day poverty line_|||||||
|East Asia and Pacific|51.72|36.15|37.71|936,465,080|669,278,004|698,355,547|
|Eastern Europe and Central Asia|24.23|16.03|16.13|93,394,142|61,314,686|61,852,500|
|Latin America and Caribbean|21.45|16.84|15.78|105,239,042|86,580,829|81,254,933|
|Middle East and North Africa|29.72|18.54|18.77|60,874,303|40,913,398|41,440,242|
|South Asia|85.81|80.04|81.06|1,236,590,090|1,210,566,763|1,229,975,339|
|Sub-Saharan Africa|80.46|71.90|72.27|434,028,868|414,785,230|417,889,848|
|Developingcountries|58.84|49.10|49.95|2,866,591,525|2,483,438,909|2,530,768,410|

_Source_ : Authors' simulations with the GIDD and ENVISAGE models

**Figure 9 Decomposition of poverty impact of biofuels**

<!-- Start of picture text --> 2.5<br>Households with primary income source in non-agriculture<br>2.0<br>1.5<br>1.0 Households with primary income source in agriculture<br>0.5<br>0.0<br>-0.5<br>-1.0<br>East Asia Eastern Latin Middle East South Asia Sub- Developing<br>and Pacific Europe and America and North Saharan countries<br>Central Asia and Africa Africa<br>Caribbean<br>Source : Simulations w ith World Bank’s GIDD model.<br>Contribution to total change in the extreme poverty headcount ratio<br><!-- End of picture text --> 22
can be taken to be Pareto efficient. This is both a rationality postulate, replacing that of simple utility maximization in the one-person household model, and a solution procedure. Once the household allocation is assumed to be Pareto efficient, all the results of general equilibrium theory apply in a straightforward way, as pointed out in Apps and Rees (1988). In particular, the idea of an intra-family sharing rule is simply an application of the Second Theorem of Welfare Economics. The household can be modeled as choosing its time and consumption allocations directly, or equivalently, on the standard assumptions, as decentralizing this procedure by first sharing its full income amongst its members and then letting them choose their optimal allocations subject to the appropriate equilibrium prices.<sup>25</sup> The focus of the Chiappori (1988) analysis is on the sharing rule. He shows that, in his model, the partial derivatives of the rule can be derived from empirical estimates of the market labor supply functions. This result however, is crucially dependent on omitting household production. As Apps and Rees (1997) demonstrate analytically, and Chiappori (1997) concedes, when his model is extended to include household production, the result on the partial retrievability of the sharing rule no longer holds in general, but requires specific restrictions on preferences and the household technology, which may or may not be considered reasonable.

An example is provided by the model presented in the first part of Chiappori (1997). This model essentially reproduces the results in SSS, in that it defines household production to be essentially the same as on-farm production. Given the resulting separation, Chiappori is able to show that his original results continue to hold. However, as emphasized in Section 2, the essence of household production is that while market goods may be close substitutes for some household goods, they are not perfect substitutes. Thus endogeneity and household specificity of implicit prices of household goods are a necessary feature of the equilibrium. Furthermore, with empirical work limited to the estimation of models on household survey data with missing information

> 25 Note that, in the initial formulation of the model, in which the utility of one household member is maximized for a given utility level of the other, individual incomes are pooled to obtain the household budget constraint.

19
It has long been known that birth rates tend to be higher for the poor in the developing world, which is the main reason why household size tends to be negatively correlated with household consumption or income per person.<sup>14</sup> The same analyses of the DHS data summarized above provide estimates of the differentials in fertility rates.<sup>15</sup> Averaged over 45 developing economies, the total fertility rate is estimated to be 6.2 for the poorest quintile as compared to 3.3 for the richest quintile, with a population average of 4.7. However, to estimate _BRD_ it is simpler to use instead the overall age-specific fertility rate calculated as the number of births per 1,000 women aged 15-49.<sup>16</sup> The overall birth rate per capita of women 15-49 is 154.0 for the poorest quintile versus 113.4 for the population as a whole. The implied _BRD_ is 1.4, under the assumption that the population share of women 15-49 is the same for the poor as the population as a whole. (That assumption may not hold in reality, but it is probably not far wrong.) There are regional differences, with implied _BRD_ values of 1.8 for East Asia, 1.3 for EECA, 1.9 for Latin America, 1.3 for the Middle East and North Africa, and 1.2 for both South Asia and Sub-Saharan Africa.

Returning to the Bidani-Ravallion method, analogously to Figure 2, Figure 3 plots the crude birth rate (CBR) against the poverty rate. (Notice that the correlation with poverty is stronger for the birth rate; the simple correlation coefficient is 0.77, versus 0.51 for the death rates in Figure 2.) For the sample as a whole, the implied birth rate for the poor is 61.3 (standard

> 14 Economies of size in household consumption will attenuate, though probably not eliminate, these differences; for further discussion see Lanjouw and Ravallion (1995).  The present analysis is confined solely to the (standard) method in which the ranking variable is per capita. 15 See http://www1.worldbank.org/prem/poverty/health/data/index.htm .

> 16 To derive the _BRD_ from the differential in the total fertility rate (TFR) between the poor and population as a whole one would need to assume that the age distribution of poor women is the same as that for the population as a whole, which seems unlikely to hold. If instead one bases the calculations of _BRD_ on the general fertility rate then one can get a way with a weaker assumption that the overall population share of women 15-49 is the same for the poor versus non-poor. (Note that the TFR is obtained by aggregating the age specific fertility rates by year for women 15-49 years.)

14
# **Dynamics of Income Inequality and Welfare in Latvia in the Late 1990s**<sup>**1**</sup>

By

## Hippolyte Fofack* and Celestin Monga**

**Abstract** : This paper analyzes the dynamics of poverty and income inequality during the recovery phase of the transition that characterized the Republic of Latvia in the late 1990s. Despite a continued rise in income inequality, empirical evidence suggests an improvement in living standards, owing largely to a significant surge in per capita income growth, particularly in urban areas. In a context of rising income inequality and widening urban-rural income and poverty gaps, the benefits of growth were not equally distributed, and poverty persisted in a number of regions (particularly the regions of Latgale and Vitzeme) and among some socioeconomic groups (particularly households deriving their main income from social benefits). In addition to income inequality and asset endowments, poverty appears to be highly correlated with a number of labor market-related variables, particularly unemployment, suggesting that the labor market could be an important transmission channel from growth to poverty. However, though positive, the association between poverty and unemployment is non linear, especially in urban areas, where the labor market and demand are the most important channels of transmission through which growth and macroeconomic development affect household income and living standards.

### _JEL Classifications_ : C25; D31; E24; I32

**Keywords** : income inequality-driven poverty reduction deficit, poverty incidence curve, urbanrural employment gaps, urban-rural poverty gaps, urban-rural predicted probability gaps.

World Bank Policy Research Working Paper 3336, June 2004 _The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

> 1 *World Bank Institute and **World Bank ECA Region. The authors gratefully acknowledge the support and collaboration of the Latvian Central Statistics Bureau, and particularly the support provided by Mr. Edmunds Vaskis, the Director of the Social Statistics Department. We would like to thank Andrejs Jakobsons, Xiao Ye, Raj R. Nallari, Branko Milanovic, Uwe Deichmann, Nanak Kakwani and Olivier-Jean Blanchard and Latvian authorities for their useful assistance and comments, and Pierre-Richard Agénor and Bernard Funck for guidance and support on various aspects of this study.
Table 2: Early Life Exposure to Coffee Mills and Test Scores

|||All Subjects|||STEM|||Non STEM||
|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|||||A. Stand|ardized Gra|de Points||||
|CatchmentArea X Post|0.0905***|0.1015***|0.0882***|0.0699***|0.0717***|0.0595***|0.0976***|0.1143***|0.1009***|
||(0.0269)|(0.0224)|(0.0214)|(0.0242)|(0.0211)|(0.0206)|(0.0278)|(0.0230)|(0.0219)|
||[ 0.002 ]|[ 0.001 ]|[ 0.001 ]|[ 0.006 ]|[ 0.002 ]|[ 0.002 ]|[ 0.001 ]|[ 0.001 ]|[ 0.001 ]|
|R-squared|0.3866|0.4260|0.4261|0.3796|0.4152|0.4153|0.3610|0.4030|0.4031|
|||||B. Grade P|oints (unsta|ndardized)||||
|CatchmentArea X Post|0.7162***|0.7879***|0.6791***|0.2449***|0.2219***|0.1824***|0.4713***|0.5660***|0.4967***|
||(0.2058)|(0.1702)|(0.1633)|(0.0728)|(0.0623)|(0.0614)|(0.1373)|(0.1134)|(0.1080)|
||[ 0.002 ]|[ 0.001 ]|[ 0.001 ]|[ 0.002 ]|[ 0.001 ]|[ 0.002 ]|[ 0.002 ]|[ 0.001 ]|[ 0.001 ]|
|Mean dep var|15.4019|15.4015|15.4015|4.8608|4.8607|4.8607|10.5410|10.5408|10.5408|
|R-squared|0.4085|0.4478|0.4478|0.4092|0.4451|0.4452|0.3768|0.4189|0.4190|
|Controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|School FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Exam Year FE|Yes|No|No|Yes|No|No|Yes|No|No|
|YOB FE|Yes|No|No|Yes|No|No|Yes|No|No|
|Municipality X Exam Year FE|No|Yes|Yes|No|Yes|Yes|No|Yes|Yes|
|<br>Municipality X YOB FE|No|Yes|Yes|No|Yes|Yes|No|Yes|Yes|
|CatchmentArea X Exam Year FE|No|No|Yes|No|No|Yes|No|No|Yes|
|R-squared|0.3866|0.4260|0.4260|0.3796|0.4152|0.4152|0.3609|0.4030|0.4030|
|Observations|1487355|1487036|1487036|1487358|1487039|1487039|1487354|1487035|1487035|

Notes: In panel A, the dependent variable is the standardized aggregate grade points in all subjects in the Rwanda Primary School National exam. The dependent variable in panel B is the (unstandardized) aggregate grade points in all subjects. STEM subjects include Math and Science while Non STEM courses include English, Social Studies, and Kinyarwanda. CatchmentArea X Post is an indicator variable equal to 1 if a student was born within the vicinity ( _≤_ 2.5km) of an operational coffee mill and 0 if otherwise. Controls include gender of the student, average annual temperature and precipitation in the exam year, as well as baseline controls such as coffee suitability and road density interacted with time trend. Robust standard errors clustered at school level in parenthesis. False discovery rate (FDR) adjusted p-values for CatchmentArea X Post are reported in square brackets (Anderson 2008). The outcome variables accounted for in the p-values adjustments include the standardized test scores of: All subjects (aggregate), STEM, and Non-STEM subjects, as well as the subject-specific test scores i.e., Maths, Science, Social, English, and Kinyarwanda. _∗_ Significant at 10 percent level _∗∗_ Significant at 5 percent level _∗∗∗_ Significant at 1 percent level 32
most cases, the calculated Ginis lie fairly close to the IPF. In terms of absolute distance, the countries falling farthest below the IPF curve are the most “modern” pre-industrial economies: England and Wales in 1688 and 1801-3, and Holland in 1732. The maximum possible Ginis in these cases range from 72 to 80 while the estimated Ginis are between 45 and 63.<sup>13</sup>

# [Table 2 and Figure 2 about here]

If we used Maddison’s subsistence level of $400, then four estimated Ginis would be significantly greater than the maximum Gini (at their level of income) implied by the IPF: three of these are based on data from India, and the fourth is from Nueva España.<sup>14</sup> Recalling our definition of the IPF, these four cases can only be explained by one or more of these five possibilities: (i) a portion of the population cannot even afford the subsistence minimum, (ii) the actual ε is much smaller than the assumed ε=0.001, (iii) inequality within the rich classes is very large, (iv) our estimate of inequality is too high, and/or (v) the subsistence minimum is overestimated. We have already analyzed and dismissed the first three possibilities. The fourth possibility is unlikely: since our estimates of inequality are based only on a few classes, they are likely to be biased downwards, not upwards. The last possibility offers the more likely explanation. It could well be that the subsistence minimum was less than $PPP 400 for some societies.<sup>15</sup> In particular, this is likely to be the case for subtropical or tropical regions where calorie,

> by Becker, Philipson and Soares (2005), who, in calculation of multidimensional inequality (income times life expectancy), use a calibration to transform these two variables into one.

> 13 Naples, with very low inequality, also lies deeply inside the Inequality possibility frontier.

> 14 The Old Castille is also slightly above the IPF.

> 15 Another possibility is that our Maddison-based estimates of mean incomes for these four cases are too low. If that was true, all four points should be moved horizontally to the right, thus falling inside the IPF.

15
This allows us to verify the robustness of our findings to the variations in the sample. Namely, we can limit the sample to miscarriages late in the pregnancy, when the option of abortion is likely to have already been exercised by those who intended to do so.

# **3.2. How do women underreport abortions?**

As mentioned earlier, one of the major threats to the identification strategy is the possibility of abortions being reported as miscarriages. The present section looks at this issue in more depth. Substantial underreporting of abortions _is_ present in ENADID data. Juarez et al. (2008) construct an estimate of the number of induced abortions in Mexico in 2006, based on data on hospitalization for complications due to induced abortion, and a survey of medical personnel. The results show a low and a high estimate of the abortion rate: 28 and 39 abortions per 1000 women aged 15 to 44 (Table 2).

According to ENADID data, there were 8,438 induced abortions in Mexico in 2005<sup>9</sup> – slightly more than 1 percent of the low estimate of the number of induced abortions estimated in Juarez et al., 2008 (Table 2). Nevertheless, underreporting of abortions will impede identification only if women report abortions as miscarriages (as opposed to just not reporting pregnancies that terminated in abortion). Using epidemiological data on the likelihood of a miscarriage, we demonstrate that this scenario is highly unlikely.

Epidemiological studies show that the probability of miscarriage varies depending on the time elapsed since conception. The probability of very early miscarriage (within six weeks after the last menstrual period [LMP]) is quite high – 25 percent (Wilcox et al., 1999; Wang et al., 2003). This statistic however, is based on studies that use very sensitive pregnancy tests. Within normal settings, women are likely to mistake such early miscarriages for delayed menstrual periods, without realizing they were pregnant. Six weeks after the LMP, the probability of miscarriage decreases dramatically – clinical miscarriages are estimated to occur in 7.9 percent of pregnancies (Wang et al., 2003). At this

> 9 We use miscarriages reported to occur in 2005 for two reasons: (i) the survey was administered in JanuaryMay of 2006, so we would not be able to capture only abortions which occurred in the first 5 months of 2006; (ii) the respondents are only asked whether their last abortion was spontaneous or induced. Of 266 abortions reported in 2005, 260 (or 98 percent) were the last abortions. In previous years this proportion is lower.

8