rafmacalaba/datause-encoder-data huggingface dataset:
the endogenous social effect parameter, we need data on an exogenous shock which affect schooling of some but not all school peers. To this end, we use data on “fraction of peers exposed to arsenic contaminated water-wells at home” as an instrument for Eb[Sbi] and follow an the instrumental variable approach to estimate equation (1) to resolve the identification problem.

# **3. Data and survey description**

The data used in this paper has been collected in 2005 as part of a larger study to assess school quality in rural Bangladesh. The primary sampling unit of the survey was chosen to be unions<sup>7</sup> . To account for regional variation in school participation rate and so on, 60 unions were selected with proportional allocation from 6 divisions in the country. In the second step, all secondary (recognized secular and religious) schools in each of the sample unions were selected for data collection. In total, 321 schools could be identified in the 60 unions (or school catchment areas). A mathematics test was administered to all students in enrolled in grade 8 in our sample schools, who were present on the day of the survey<sup>8</sup> . Each student taking the test was asked to answer a number of questions relating to their family background and pre-secondary school history. To identify arsenic-exposure, we asked sample pupils whether there was a tube-well at home and if so, whether they were arsenic contaminated<sup>9</sup> . 13% of sample kids belong to households with arsenic contaminated tube wells (see Asadullah and Chaudhury (2007a) for a descriptive discussion of the sample by arsenic exposure).

# **4. Results**

Table 1 reports the regression estimates of the endogenous social effect. Certain parts of rural Bangladesh have remained less unexposed to ground water poisoning of arsenic leading to spatial variation in arsenicosis. Therefore, the regressions always condition on a large number of region dummies. We start with a parsimonious model, regressing individual test score on mean test score of peers (model 1). Correcting for endogeneity of peer variable returns a slightly smaller albeit significant estimate of social effect (model 2). Next, we

> 7 Union is an administrative unit bigger than a village but smaller than thana/upazila. 8 The test instrument was constructed by adopting 20 items previously used in the Trends in International Mathematics and Science Study (TIMSS), 1999; see Asadullah et al. (2007b) for details.

> 9 By the year 2004, the Government of Bangladesh had completed a nationwide screening of tube wells in rural areas. Following this exercise, wells identified as contaminated were painted _red_ whilst those ascertained as being safe were painted _green_ (BAMWSP, 2005). Therefore, contamination status was ascertained by asking kids whether tube well at home was painted _red_ or _green_ .

4
- _-_ 6

# II. URBAN PROPERTY TAX ADMINISTRATION

2.01 Although the municipalities enjoy considerable autonomy in their administration of the property tax, certain characteristics are commo ily observed throughout the country. The legal base of the property tax is defined by federal law, and consists of all real property--land and buildings--within the urban zones of a municipalities. The specific boundaries of an urban zone are formally defined by municipal ordinance. To be included in an urban zone, a property must either be currently provided with two of five urban services (sidewalks, water, street lighting, or a public school or health post within three kilometers) or be defined as "urbanizable" on the basis of approved subdivision plans. Certain exemptions (for property owned by government, religious and charitable institutions) are mandated by federal law.

# A. Identification of Tax Object

2.02 Most municipalities have, at some time, assembled a parcellary map of the urban areas within their jurisdiction and a set of property records, listing owners of record and property characteristics. The tax maps serve as the basic tool for identifying properties for tax purposes, in the absence of reliable systems of street addresses.

# B. Valuation

2.03 Value, for tax purposes, is legally defined as market, or capital, value. Vacant land and common types of residential and commercial structures are valued using mass appraisal techniques.

2.04 Mass appraisal uses a small number of physical characteristics to derive an estimate of a property's value. In Brazil, the mass appraisal process begins with the calculation of standard "unit costs" for land and structures. The cost of land in different neighborhoods is calculated and expressed in terms of a cost per square meter of plot size. The cost of buildings of various types is calculated and expressed in terms of a cost per square meter of floor area. Individual properties are valued by applying these standard unit cost to the respective plot size and floor area of specific properties. Adjustment factors are applied to these valuations to reflect other characteristics--slope or susceptibility to flooding in the case of land, age and condition in the case of structures--which would affect their value.

2.05 The validity of such valuations depends on the accuracy of the unit costs. This is a persistent source of difficulty in Brazil. In industrial countries, unit costs for structures are typically based upon the cost of replacing the structure, less an allowance for depreciation. Replacement costs are based upon current construction prices, derived from industry sources.a' _2/ In the U.S., for example, several prlvate firms publish standard construction cost schedules for varlous classes of buildlngs._
# **Table 1 – Evolution of firm level productivity volatility**

|||Firms s|urvived fo|r at least f|ive conse|cutiveyea|rs[t-2,t+2]<br>|
|---|---|---|---|---|---|---|---|
||Firms<br>survived<br>the entire<br>10 year<br>period|2001|2002|2003|2004|2005|Change in<br>volatility<br>between<br>2001 and<br>2005|
|Total|48.2|43.71|42.69|42.41|42.54|42.76|-0.95|
|Firm size||||||||
|Less than 50|56.0|53.53|52.10|51.40|52.32|51.43|-2.11|
|50-100|49.2|46.90|45.06|44.65|45.04|45.01|-1.89|
|100-500|47.8|43.07|42.05|41.74|41.64|42.04|-1.03|
|More than 500|44.3|37.16|37.45|37.25|36.04|36.65|-0.51|
|Firm’s age||||||||
|Less than 5 years|49.3|48.89|47.25|46.58|48.39|48.86|-0.03|
|6-10 years|47.9|43.50|42.27|41.90|42.12|43.13|-0.36|
|11-20 years|47.2|42.63|41.65|41.48|40.63|39.94|-2.69|
|More than 20 years|47.1|41.69|41.44|41.29|39.84|39.36|-2.33|
|Main part of paid-in capital||||||||
|State capital|49.3|44.48|44.65|44.12|43.56|42.12|-2.36|
|Collective capital|50.0|44.10|42.54|43.68|42.84|42.86|-1.25|
|Corporate capital|48.9|44.09|43.52|43.76|44.10|45.10|1.00|
|Personal capital|48.9|43.15|42.42|42.13|42.92|43.07|-0.08|
|Hong Kong SAR, China;||||||||
|Macao SAR, China; and Taiwan,||||||||
|China, capital|48.0|44.93|42.13|40.82|40.36|40.72|-4.21|
|Foreign capital|45.3|40.01|38.72|38.26|38.56|38.87|-1.15|
|Region||||||||
|Inland province|51.3|45.43|44.49|44.59|45.87|46.92|1.49|
|Coastal province|46.6|42.66|41.79|41.45|41.01|40.88|-1.79|
|Number of observations|29483|49375|46045|51929|68965|78330||

Note: The volatility is measured as standard deviation of annual growth output per worker at the firm level. The output value in each year is adjusted to 1998 price using Producer Price Index for Manufactured Goods provided by China Statistical Yearbooks.

Using Kernel density, we examine the distribution of productivity volatility by firm size. Consistent with the literature, small firms are more volatile than large firms. Figure 3 shows that, as firm size increases, both the level and variance of productivity volatility decrease. Similarly, we observe that young firms are more volatile than old firms, and firms in the inland region are more volatile than firms in the coastal region. Firms with foreign capital as the main part of paidin capital are the least volatile. During the period of study, productivity volatility declined in the coastal region, but increased in the inland region.

9
Table 2. Balancing tests before and after matching, Metropolitan Lima and rural areas, by year, 2003–17

||||_Kernel_|||_Metropolita_<br>_Neare_|_n Lima_<br>_st neighbor_||||_Radius_||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||_R2 before_|_R2 after_|_Rubin's B before_|_Rubin's B after_|_R2 before_|_R2 after_|_Rubin's B before_|_Rubin's B after_|_R2 before_|_R2 after_|_Rubin's B before_|_Rubin's B after_|
|2003|0.213|0.008|121.7|21.6|0.213|0.039|121.7|45.9|0.213|0.000|121.7|0.0|
|2004|0.268|0.004|138.8|14.8|0.268|0.046|138.8|49.0|0.268|0.000|138.8|0.0|
|2005|0.183|0.002|111.7|9.4|0.183|0.009|111.7|21.9|0.183|0.000|111.7|1.2|
|2006|0.227|0.008|128.4|20.9|0.227|0.008|128.4|20.9|0.227|0.000|128.4|0.0|
|2007|0.243|0.016|134.3|29.2|0.243|0.021|134.3|33.5|0.243|0.000|134.3|5.2|
|2008|0.272|0.008|142.1|20.6|0.272|0.011|142.1|24.6|0.272|0.000|142.1|2.9|
|2009|0.257|0.008|137.4|21.1|0.257|0.019|137.4|31.8|0.257|0.000|137.4|0.0|
|2010|0.240|0.016|129.9|29.2|0.240|0.039|129.9|46.4|0.240|0.000|129.9|0.0|
|2011|0.215|0.002|122.3|10.8|0.215|0.006|122.3|18.9|0.215|0.001|122.3|7.4|
|2012|0.244|0.001|135.6|7.6|0.244|0.021|135.6|33.4|0.244|0.000|135.6|0.0|
|2013|0.219|0.003|125.5|12.2|0.219|0.005|125.5|17.0|0.219|0.000|125.5|0.0|
|2014|0.195|0.003|117.1|12.3|0.195|0.005|117.1|16.5|0.195|0.000|117.1|1.7|
|2015|0.214|0.004|121.7|15.2|0.214|0.004|121.7|14.6|0.214|0.000|121.7|0.6|
|2016|0.167|0.008|104.4|21.0|0.167|0.006|104.4|17.7|0.167|0.000|104.4|0.0|
|2017|0.206|0.001|117.8|8.0|0.206|0.007|117.8|20.1|0.206|0.000|117.8|0.0|
|||||||_Rural a_|_reas_||||||
||||_Kernel_|||_Neare_|_st neighbor_||||_Radius_||
||_R2 before_|_R2 after_|_Rubin's B before_|_Rubin's B after_|_R2 before_|_R2 after_|_Rubin's B before_|_Rubin's B after_|_R2 before_|_R2 after_|_Rubin's B before_|_Rubin's B after_|
|2003|0.174|0.025|117.2|37.5|0.174|0.055|117.2|55.3|0.174|0.002|117.2|9.1|
|2004|0.211|0.015|127.3|29.4|0.211|0.030|127.3|41.5|0.211|0.000|127.3|2.0|
|2005|0.222|0.011|135.9|24.4|0.222|0.023|135.9|35.6|0.222|0.001|135.9|9.0|
|2006|0.187|0.011|126.8|24.7|0.187|0.017|126.8|30.7|0.187|0.007|126.8|19.5|
|2007|0.293|0.021|160.2|34.4|0.293|0.089|160.2|72.7|0.293|0.002|160.2|11.2|
|2008|0.244|0.012|142.3|25.5|0.244|0.019|142.3|32.9|0.244|0.000|142.3|0.9|
|2009|0.251|0.018|145.2|31.9|0.251|0.036|145.2|45.5|0.251|0.001|145.2|5.3|
|2010|0.258|0.011|148.1|25.1|0.258|0.027|148.1|39.3|0.258|0.000|148.1|3.2|
|2011|0.272|0.007|157.3|19.3|0.272|0.051|157.3|52.2|0.272|0.000|157.3|1.3|
|2012|0.229|0.007|136.9|20.2|0.229|0.009|136.9|22.4|0.229|0.005|136.9|17.1|
|2013|0.216|0.014|136.7|28.3|0.216|0.019|136.7|32.5|0.216|0.001|136.7|5.9|
|2014|0.235|0.014|144.1|28.0|0.235|0.030|144.1|41.0|0.235|0.001|144.1|8.4|
|2015|0.236|0.010|139.7|23.9|0.236|0.015|139.7|28.9|0.236|0.000|139.7|4.0|
|2016|0.187|0.004|123.4|15.7|0.187|0.011|123.4|24.2|0.187|0.000|123.4|4.7|
|2017|0.195|0.006|128.2|18.4|0.195|0.022|128.2|34.9|0.195|0.001|128.2|5.9|

_Source:_ Based on National Household Survey data.

17
|(6)<br>Non-Agri.|-0.179<br>(0.166)<br>7282<br>841.418<br>41.195|4.337<br>(15.669)<br>2109<br>863.127<br>11.702<br>Yes<br>Yes<br>Yes<br>Yes<br>rument for the<br>n parentheses._∗_<br>head identier.<br> of land-owned,<br>quality (Gini),<br> Eastern Terai.|
|---|---|---|
|Male<br>(5)<br>.<br>Agri.|0.205<br>(0.148)<br>7282<br>18469.293<br>41.195|-5.819<br>(4.098)<br>4006<br>662.555<br>35.274<br>Yes<br>Yes<br>Yes<br>Yes<br> used as an inst<br>tandard errors i<br> and household <br>ildren, amount <br>literacy rate, ine<br>stern Terai, and|
|(4)<br>Any Self Emp|0.141<br>(0.120)<br>7282<br>7294.166<br>41.195|-10.252<br>(6.781)<br>5160<br>2193.401<br>42.797<br>Yes<br>Yes<br>Yes<br>Yes<br>in a VDC in 2001 is<br>ered at VDC level. S<br>tion, marital status,<br>f elderly, share of ch<br> rate, poverty rate, il<br>ls, Eastern hills, We|
|(3)<br>Non-Agri.|-0.098<br>(0.087)<br>9597<br>703.258<br>70.938|-24.345<br>(16.457)<br>1557<br>504.186<br>10.609<br>Yes<br>Yes<br>Yes<br>Yes<br>tional migrants <br> errors are clust<br>2, years of educa<br>e adults, share o<br>, unemployment <br>reas, Western hil<br>ular sector.|
|Female<br>(2)<br>Agri.|0.308***<br>(0.113)<br>9597<br>15715.418<br>70.938|-6.460*<br>(3.787)<br>5550<br>434.614<br>58.076<br>Yes<br>Yes<br>Yes<br>Yes<br>hare of interna<br>s and standard <br>ols are age age<br>ale and femal<br>vel controls are<br> other urban a<br>d in that partic|
|(1)<br>Any Self Emp.|Panel A: Labor market participation<br>Household with migrant<br>0.280***<br>(0.089)<br>Observations<br>9597<br>Wald _χ_2<br>8883.913<br>_F_-test 1stage<br>70.938|Panel B: Hours supplied<br>Household with migrant<br>-13.030***<br>(4.559)<br>Observations<br>6358<br>Wald _χ_2<br>525.033<br>_F_-test 1stage<br>61.724<br>Controls<br>Individual characteristics<br>Yes<br>Household characteristics<br>Yes<br>VDC characteristics<br>Yes<br>Region Fixed Eect<br>Yes<br>Note: 2SLS estimates are reported in the table. S<br>estimations. Sample is working age (18 to 60) adult<br>_p <_ 0_._1, _∗∗p <_ 0_._05, _∗∗∗p <_ 0_._01. Individual contr<br>Household controls are female HH head, share of m<br>house ownership and social caste. Similarly VDC le<br>and urban/rural location. Regions are Kathmandu,<br>Sample in Panel B is conditioned on being employe|

30
|Observations|10,667|2,957|5,536|2,145|
|---|---|---|---|---|
|Adjusted R square|0.297|0.437|0.346|0.283|
|Entertainment|0.550***|0.159|0.335*|0.341***|
||(0.145)|(0.146)|(0.179)|(0.107)|
|Observations|10,641|2,952|5,516|2,144|
|Adjusted R square|0.283|0.315|0.303|0.326|
|Automobiles|0.388***|0.229|0.132|0.330**|
||(0.124)|(0.177)|(0.154)|(0.144)|
|Observations|10,534|2,935|5,465|2,106|
|Adjusted R square|0.234|0.333|0.233|0.243|
|Other vehicles|0.154|-0.048|0.025|0.022|
||(0.141)|(0.295)|(0.196)|(0.119)|
|Observations|10,626|2,947|5,507|2,144|
|Adjusted R square|0.396|0.412|0.403|0.396|
|Durable goods|0.320***|-0.225|0.024|0.062|
||(0.120)|(0.358)|(0.122)|(0.128)|
|Observations|10,841|2,984|5,610|2,218|
|Adjusted R square|0.429|0.488|0.445|0.409|
|Medical|0.170**|0.548**|-0.041|0.192|
||(0.082)|(0.247)|(0.120)|(0.158)|
|Observations|10,733|2,947|5,550|2,207|
|Adjusted R square|0.377|0.379|0.384|0.382|
|Health and fitness|0.273**|0.322*|-0.044|0.057|
||(0.110)|(0.164)|(0.104)|(0.107)|
|Observations|10,844|2,980|5,615|2,220|
|Adjusted R square|0.303|0.434|0.335|0.287|
|Education|-0.143|-0.552**|-0.124|0.040|
||(0.114)|(0.228)|(0.113)|(0.223)|
|Observations|10,775|2,954|5,573|2,219|
|Adjusted R square|0.222|0.230|0.219|0.279|
|Home repair|-0.089|-0.144|-0.112|-0.400***|
||(0.080)|(0.328)|(0.120)|(0.131)|
|Observations|10,666|2,959|5,531|2,147|
|Adjusted R square|0.450|0.435|0.462|0.453|
|Gifts|-0.004|0.437|0.002|-0.310**|
||(0.101)|(0.413)|(0.115)|(0.145)|
|Observations|8,839|2,258|4,701|1,857|
|Adjusted R square|0.111|0.140|0.117|0.160|

Note: Extended model with additional variables of control, including, lagged log value of household expenditure per capita of the consumption category in question, household average years of schooling, household gender ratio, average age/10, age squared/100, dependency ration, and urban-rural dummies. Robust standard errors clustered at the county level are presented in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

·· 34
**Table B6** Treatment-Control Balance at Baseline: Education and Economic Empowerment

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|**Outcome**<br>|**ELA Only**<br>**Control Mean**|**Boys-ELA**|**Supply-ELA**|**No Goal**<br>**Control Mean**|**Goal -**<br>**No Goal**|
|**Panel A. Whole Sample**||||||
|Time spent studying|1.01|-0.140*<br>(0.078)|0.037<br>(0.101)|0.970|0.003<br>(0.059)|
|Education aspiration|0.828|-0.002|-0.022|0.817|0.022|
|||(0.029)|(0.030)||(0.014)|
|Income generating activity|0.161|-0.026|-0.026|0.135|0.009|
|||(0.027)|(0.028)||(0.014)|
|Personal business|0.134|-0.012|-0.018|0.116|0.007|
|||(0.025)|(0.027)||(0.013)|
|Has savings|0.218|-0.060**|-0.027|0.186|-0.006|
|||(0.028)|(0.028)||(0.015)|
|Has loan|0.034|-0.007<br>(0.013)|0.025<br>(0.019)|0.038|-0.007<br>(0.008)|
|Observations|1,074||3,178|2,313|3,178|
|**Panel B. Balanced Sample**||||||
|Time spent studying|1.015|-0.124|0.064|0.991|0.014|
|||(0.081)|(0.108)||(0.065)|
|Education aspiration|0.821|0.001|-0.013|0.815|0.020|
|||(0.033)|(0.033)||(0.015)|
|Income generating activity|0.149|-0.013|-0.017|0.129|0.016|
|||(0.027)|(0.029)||(0.016)|
|Personal business|0.125|-0.004|-0.013|0.111|0.013|
|||(0.026)|(0.029)||(0.015)|
|Has savings|0.226|-0.069**|-0.030|0.189|-0.005|
|||(0.030)|(0.031)||(0.017)|
|Has loan|0.033|-0.010|0.017|0.036|-0.012|
|||(0.013)|(0.018)||(0.008)|
|Observations|871||2,591|1,900|2591|

_Notes._ Column 1 shows ELA only control arm means and column 4 shows control means for females not assigned to the _Goal_ arm. Columns 2–3 and column 5 shows the difference between the control group means and the treatment arm means, controlling for randomization strata. Estimates in columns 2–3 come from a single regression, and estimates in column 5 come from another. Time spent studying is hours spent studying in the past day, Education aspiration is a binary indicator that the female aspires to a post-baccalaureate degree, Income generating activity is a binary indicator of being involved in an income generating activity in the past year, Personal business is a binary indicator that the female owns her own business, Has savings is a binary indicator that the female has savings. Has loan is a binary indicator that the female has taken out a loan. Standard errors, clustered at the club level, are presented in parentheses below the coefficient estimates in columns 2–3 and column 5. .01 ***, .05 **, .10 *. _Source._ Female baseline data.

74
definitions between surveys and the data underlying global trade and production models. The alternative that we use links household models containing detailed information from household surveys with national general equilibrium models. Where our experiments involve indirect impacts—such as changes in wage rates brought about by changes in food prices—we use the national general equilibrium models to estimate the impacts of the relevant price shocks on factor prices in each region. These prices are then passed to the household models used to assess the impacts of the shock on the real income of each household.

In order to ensure consistency between the behavior of the household models and the national CGE models which we use to generate the changes in factor prices, we evaluate z z pp pw �zwp zww<sup>� for each household using the same expenditure and production structure as in the</sup> national model, but taking into account the specific resource endowments of the household and z z pp pw their initial allocation. Decomposing the net output effects �zwp zww<sup>� into the difference</sup> π π e e pp pw pp pw between production and demand effects �πwp πww<sup>�−�</sup> ewp eww<sup>�, we ensure consistency on</sup> both the production and consumption sides.

On the household factor supply side, we assume that the household’s labor supply elasticity is zero, and hence eww ≡0. This is done for consistency with the national models, in which we do not allow for any adjustment in the quantity of labor supplied by households. As a consequence, epw ≡ewp ≡0. This means that the only way the household can change its supply of labor to non-agricultural activities is by reallocating household labor between farm and nonfarm activities. To specify epp **,** we use the semi-flexible Constant Difference of Elasticities (CDE) demand system with the household’s expenditure shares and the estimated substitution parameters used in the GTAP database (Hertel 1997). This provides us with a set of calibrated own- and cross-price elasticities for each household and commodity that are broadly consistent with the substitution possibilities in our economy-wide model.

Following Hanoch (1975 p. 415), we define a matrix of compensated elasticities ε for CDE preferences as: when i≠j, and 9
**Table 5: Characteristics of successful and unsuccessful projects**

||Low revenue|High revenue|
|---|---|---|
|**Community level variables**|||
|Number of people on legal entity|**27.56**|**_9.14_**|
|Revenue per person median|**_-9.0_**|**_10552.1_**|
|Community has taken a loan|9.6%|**_42.9%_**|
|Trust or CPA are main decision-makers|90.0%|66.7%|
|Manager is main decision-maker|**_3.3%_**|**_25.0%_**|
|Individual characteristics|||
|Per capita expenditure mean|**_346.22_**|259.93|
|Per capita expenditure median|228.73|189.90|
|Share of poor beneficiaries|**73.7%**|**_81.4%_**|
|No.of household members|5.45|5.57|
|Head's age|50.21|51.70|
|Head's education|3.15|2.81|
|Female headed|30.1%|34.6%|
|Household accessed housing subsidy|**7.0%**|**_1.6%_**|
|Household owns cattle|25.9%|32.4%|
|Household owns car|16.5%|11.9%|
|Household owns telephone|**_21.5%_**|**_9.1%_**|
|Household has access to electricity|42.3%|41.6%|
|Household has no running water|56.1%|48.9%|
|Roads are bad|72.1%|74.5%|
|Roads are impassable most of the time|45.0%|42.6%|
|Household has pit latrine or no toilet|62.6%|**_85.1%_**|
|Household does not live in a permanent building|53.6%|56.9%|
|Household makes cash contribution to project|**_32.0%_**|**_53.5%_**|

Source: DLA Land Reform Beneficiary Survey Note: Bold coefficients are significantly different at 10% and those in **_bold italics_** at 5% or higher.

26
# **Annex table 1: Impact of a** **<u>change in the price of cereals on consumers- Urban and Rural</u>**

||**-25%**|**-12.5%**|**No change **|**12.5%**|**25%**|**50%**|**100%**|
|---|---|---|---|---|---|---|---|
|||||**Rice**||||
|**Poverty, Urban**||||||||
|Headcount index of poverty|23.81|24.36|**25.51**|26.68|27.37|29.01|32.39|
|Poverty gap|7.36|7.55|**7.76**|7.99|8.26|8.84|10.26|
|Squared poverty gap|3.19|3.26|**3.35**|3.44|3.55|3.79|4.46|
|**Poverty, Rural**||||||||
|Headcount index of poverty|56.42|56.95|**57.63**|58.15|58.93|60.05|62.59|
|Poverty gap|20.41|20.59|**20.78**|20.98|21.20|21.68|22.79|
|Squared poverty gap|9.99|10.08|**10.17**|10.27|10.37|10.61|11.21|
||||**Mille**|**t and sorghu**|**m**|||
|**Poverty, Urban**||||||||
|Headcount index of poverty|24.82|24.99|**25.51**|25.74|25.80|27.10|28.86|
|<br>Poverty gap|7.59|7.67|**7.76**|7.85|7.94|8.15|8.63|
|Squared poverty gap|3.27|3.31|**3.35**|3.39|3.43|3.53|3.75|
|**Poverty, Rural**||||||||
|Headcount index of poverty|56.65|57.08|**57.63**|58.30|58.63|59.69|61.61|
|Poverty gap|20.43|20.60|**20.78**|20.98|21.19|21.64|22.71|
|Squared poverty gap|9.98|10.07|**10.17**|10.27|10.38|10.62|11.22|
|||||**Corn**||||
|**Poverty, Urban**||||||||
|Headcount index of poverty|25.43|25.51|**25.51**|25.64|25.64|25.76|26.51|
|Poverty gap|7.70|7.73|**7.76**|7.78|7.81|7.87|8.01|
|Squared poverty gap|3.31|3.33|**3.35**|3.37|3.39|3.42|3.51|
|**Poverty, Rural**||||||||
|Headcount index of poverty|57.61|57.63|**57.63**|57.65|57.65|57.71|57.81|
|<br>Poverty gap|20.74|20.76|**20.78**|20.80|20.83|20.87|20.96|
|Squared poverty gap|10.15|10.16|**10.17**|10.18|10.19|10.22|10.27|
||||**Wh**|**eat and bread**||||
|**Poverty, Urban**||||||||
|Headcount index of poverty|25.15|25.43|**25.51**|25.55|25.73|25.91|27.04|
|Poverty gap|7.67|7.71|**7.76**|7.80|7.85|7.94|8.15|
|Squared poverty gap|3.31|3.33|**3.35**|3.37|3.39|3.43|3.53|
|**Poverty, Rural**||||||||
|Headcount index of poverty|57.17|57.43|**57.63**|57.72|57.75|57.89|58.21|
|Poverty gap|20.68|20.73|**20.78**|20.83|20.89|20.99|21.21|
|Squared poverty gap|10.11|10.14|**10.17**|10.20|10.23|10.29|10.41|
||||**All id**|**entified cerea**|**ls**|||
|**Poverty, Urban**||||||||
|Headcount index of poverty|22.46|24.13|**25.51**|27.23|28.76|31.34|37.07|
|<br>Poverty gap|7.09|7.40|**7.76**|8.17|8.65|9.75|12.66|
|<br>Squared poverty gap|3.05|3.19|**3.35**|3.53|3.73|4.23|5.75|
|**Poverty, Rural**||||||||
|Headcount index of poverty|55.24|56.22|**57.63**|58.87|59.98|62.74|68.31|
|Poverty gap|19.94|20.35|**20.78**|21.26|21.78|22.94|25.85|
|Squaredpoverty gap|9.74|9.94|**10.17**|10.41|10.68|11.29|12.97|

Source: Authors’ estimation using ELIM 2006.