rafmacalaba/datause-encoder-data huggingface dataset:
38 Caribbean found in Table 4 and Table 5, important in understanding the role of supply shocks in these years:

**Table 4 Natural Disaster Profile for Costa Rica**

|**Date**|**Disaster**|**Population Impact**|
|---|---|---|
|February 1976|Volcano|70,000 affected|
|April 1983|Earthquake|10 killed|
|October 1988|Hurricane Joan|127,500 affected; 28 killed|
|April 1991|Earthquake|51killed, 19700 affected|
|August1991|Flood|185,021affected|
|November 1993|Flood|38,451 killed|
|February1996|Flood|20,000 affected|
|July 1996|Hurricane Cesar|Affected 500,000; 51 killed|
|October 1996|Wind Storm|216,000 affected|
|October 1998|Hurricane Mitch|16,700 affected, 4 killed|

Source: (CRED 2004;ECLAC and IBD 2000); author’s own work

**Table 5 Natural Disaster Profile for Central America and the Caribbean**

|**Date**|**Country**|**Disaster**|
|---|---|---|
|1976|Guatemala|Earthquake|
|1978|Honduras, Belize|Hurricane Greta|
|1979|Dominica|Hurricane David|
|1979|Dominican Republic|Hurricanes David/Frederic|
|1980|Haiti|Hurricane Allen|
|1982|Nicaragua|HurricaneAlleta|
|1986|El Salvador|Earthquake|
|1987|Dominican Republic|Hurricane Emily|
|1988|Jamaica|Hurricane Gilbert|
|1988|Nicaragua|Hurricane Joan|
|1989|Antigua/Guadalupe|Hurricane Hugo|
|1991|CostaRica|Earthquake|
|1992|Nicaragua|Tsunami|
|1993|Nicaragua, Honduras|Tropical Storm Gert|
|1995|Nicaragua|Heavy Rains|
|1996|Costa Rica, Nicaragua|Hurricane Cesar|
|1996|Nicaragua|Eruption Maderas Volcano|
|1998|Dominican Republic, Haiti|Hurricane George|
|1998|Honduras, Nicaragua,<br>CostaRica|Hurricane Mitch|

Source: (Martine 1999) Any of these factors, _and any which might be omitted_ , can be related to variations in the mark-down measure and to the potential exercise of market power. For example, mills/exporters, in non-spike years, may be less tempted to deflate _pR_ * with a deliberate ‘cheating’ factor since Icafé, in theory, investigates any contract price that is suspiciously deviant from the international p*. In interviews there seems to be an * awareness of a lower limit below which suppressing the reported _pR_ is likely to be detected (see also Garro 2000). In times of extreme price movement, however, Icafé * will not have full information on p* and will have greater difficulty in monitoring _p R_ . More generally, upheavals in the world market, including the suspension of coffee
# **Table 4. Results of Three-Stage Least Squares Estimation of Expenditure System Using Lee’s Estimator**

||||Ex|penditure Cate|gory (Equatio|n)|||
|---|---|---|---|---|---|---|---|---|
|Variable|Food|Durables|Super-markets|Health|Education|Housing|Invest-<br>ments|Other|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Log of Expenditure|-0.05973|0.02058|-0.00090|-0.00786|-0.01862|0.03141|0.04800|-0.01289|
||(-5.78)***|(4.21)***|(-0.15)|(-1.60)|(-3.55)***|(5.74)***|(7.88)***|----|
|Household Size|0.00757|0.00379|-0.00098|-0.00243|0.01222|-0.00608|-0.00588|-0.00821|
||(2.20)**|(2.46)**|(-0.53)|(-1.59)|(4.76)***|(-4.02)***|(-3.25)***|----|
|Number of children|<br>-0.00059|<br>0.00742|<br>-0.00224|<br>-0.00150|<br>-0.01439|<br>-0.00277|<br>0.00281|0.01126|
||(-0.09)|(2.44)**|(-0.62)|(-0.47)|(-4.31)***|(-0.99)|(0.78)|----|
|Age of Household head|0.00001|-0.00068|0.00028|0.00075|-0.00027|-0.00042|0.00031|0.00002|
||(0.02)|(-2.53)**|(0.98)|(3.20)***|(-0.97)|(-1.78)*|(1.09)|----|
|Schooling of Household head|-0.00571|0.00205|0.00057|0.00087|-0.00007|-0.00042|0.00052|0.00221|
||(-2.90)***|(2.39)**|(0.55)|(1.02)|(-0.08)|(-0.53)|(0.50)|---|
|Number of household members|||||||||
|with six grades of schooling|0.00451|0.00028|0.00116|0.00081|-0.00680|0.00329|-0.00387|0.00062|
||(0.90)|(0.13)|(0.44)|(0.36)|(-2.54)**|(1.61)|(-1.47)|----|
|Number of household members|||||||||
|with nine grades of schooling|-0.00490|0.00132|0.00405|-0.00377|-0.00183|0.00140|-0.00111|0.00484|
||(-0.81)|(0.50)|(1.27)|(-1.45)|(-0.60)|(0.56)|(-0.35)|----|
|Number of household members<br>with ten or more grades of|||||||||
|<br>schooling|-0.03661|0.00302|0.00256|-0.00250|0.02544|-0.00615|-0.00017|0.01440|
||(-4.71)***|(0.89)|(0.62)|(-0.75)|(6.77)***|(-1.92)*|(-0.04)|----|
|Landholdings|0.00003|0.00012|-0.00014|0.00005|-0.00010|0.00004|0.00006|-0.00006|
||(0.11)|(1.23)|(-1.15)|(0.49)|(-0.90)|(0.41)|(0.52)|----|
|International Migration Probability|||||||||
|(p1)|0.89101|-0.60435|0.09137|0.04377|0.40833|-0.01302|-1.54803|0.73092|
||(2.10)**|(-3.29)***|(0.41)|(0.24)|(2.01)**|(-0.07)|(-6.94)***|----|
|Log of Expenditure * p1|-0.09644|0.06202|-0.00986|-0.00376|-0.03590|-0.00268|0.14520|-0.05857|
||(-2.42)**|(3.60)***|(-0.47)|(-0.22)|(-1.88)*|(-0.16)|(6.96)***|----|
|Internal Migration|||||||||
|<br>Probability (p2)|-0.65430|0.76899|0.15017|-0.15429|-0.10474|0.04421|0.72713|-0.77718|
||(-2.26)**|(6.04)***|(0.98)|(-1.22)|(-0.75)|(0.37)|(4.79)***|----|
|Log of Expenditure * p2|<br>0.06069|<br>-0.08014|<br>-0.01506|<br>0.01457|<br>0.00935|<br>-0.00332|<br>-0.06678|0.08069|
||(2.16)**|(-6.50)***|(-1.02)|(1.19)|(0.70)|(-0.29)|(-4.57)***|----|
|Inverse Mills Ratio|----|0.03858|-0.05663|-0.06668|-0.04910|0.00808|-0.02064|7.11292|
||----|(1.58)|(-12.84)***|(-4.78)***|(-6.27)***|(0.89)|(-1.91)*|----|

t-statistics in parentheses, *** significant at 1%; ** significant at 5%; * significant at 10% 34
# **LIST OF** FI(URES, **CONTINUED**

|6.1:|Number of Houses by Sector . . . . . . . . .<br>. .<br>.<br>. . . .<br>. . .<br>98|
|---|---|
|6.2:<br>|Population, Rooms and Number of Houses . . . . . . . . . . . . . .<br>98<br>|
|6.3:<br>|Change in Components of Landlord's Present Value . . . . . . . . .<br>103<br>|
|6.4:|Summary Present Value, New Unit<br>.... .<br>.<br>...... . . .<br>.<br>.<br>105|
|6.5:|Internal Rate of Return: New Unit . .<br>. . . . . . . . . . . . .<br>105|
|6.6:|Real Interest Rates . . . . . . . . . . . .<br>. . . . . . . I. .<br>106|
|6.7:|Returns to Financial And Other Investments<br>ared<br>. . . . . . .<br>106|
|6.8:|Willingness to Pay: Decontrolled New Unit<br>1) . . . . . . . .<br>107|
|6.9:|Affordabilicy Over Time . . . . . . . . . . . . . . . . . . . . .<br>107|
|6.10:|Internal Rate of Return, Existing Units . . . . . . . . . . . . .<br>108|
|6.11:|Affordability, Existing Units . . . . . . . . . . . . . . . . . .<br>108|
|6.12:|Internal Rate of Return, Existing Unit, Rents Overshoot<br>Equilibrium . . . . . . . . . . . . . . . . . . . . . . . . . .<br>114|
|6.13:|Willingness to Pay Over Time, Existing Unit, Rents Overshoot . . .<br>114|
|6.14:|Internal Rate of Return, Floating Up and Out . . . . . . . . . . .<br>116|
|6.15:|Willingness to Pay, Floating Up and Out<br>. . . . . . .<br>..<br>.<br>.<br>116|

xiv
- 33 -

control for some unobservables at the firm and much broader levels, e.g., countries and sectors.

Table 7. Price elasticity of energy demand by unbalanced panel analysis

||Unbalanc<br>with fixed|ed SUR<br>effects|
|---|---|---|
|Pooled model|||
|All 7 countries|–0.159|(0.044) ***|
|Separate models|||
|Albania|–0.434|(0.090) ***|
|Bosnia & Herzegovina|–0.261|(0.089) ***|
|Bulgaria|–0.236|(0.056) ***|
|Croatia|–0.276|(0.067) ***|
|Macedonia|–0.370|(0.133) ***|
|Romania|–0.209|(0.077) ***|
|Serbia|–0.111|(0.132)|

*, ** and *** represent the 10%, 5% and 1% level significance, respectively.

By industry, construction and manufacturing seem to have relatively low price elasticities of energy demand, though the frontier analysis generates insignificant coefficients (Table 8). With the pooled SUR regression estimated with the 2005 data and evaluated at the sample means of each industry, the elasticities are estimated at less than 0.2 in the construction and manufacturing sectors (Figure 15).<sup>36</sup> In other industries, which mainly belong to the service sector, the energy demand can be considered more elastic. The main results are found unchanged when the cost function is estimated for each industry separately, even though the level of the elasticities is different.<sup>37</sup> An important policy implication from the Ramsey rule is that governments should charge more on the construction and manufacturing industries to maximize economic efficiency, because their demand are less likely to react to high tariffs. However, as already discussed, an application of the Ramsey rule to the real economy may raise the equity concern and the risk of reducing competitiveness of key industries. It may not be justifiable that two companies consuming the same amount of electricity are charged

> 36 One unexpected result may be that the price elasticity of the manufacturing sector is not statistically insignificant in the SFA model. In general, it is expected that manufacturing would be more energy intensive and more flexible in their technology choice.

> 37 In this case, the country-specific fixed-effects are included, rather than the industry-specific ones. For the mining sector, there is no sufficient observation to estimate the assumed cost function (Equation (1)).
||||**Tulungaggung**||**Malang**<br>|**Sum**|**bawa**|
|---|---|---|---|---|---|---|---|
||**Full Sample**|Treatment 1<br>(Letter)|Treatment 2<br>(Pocket book)|Control|All Schools<br>(SMS)|Treatment<br>(meeting)|Control|
|**Knowledge Index (general)**|1.46|1.72|1.58|1.55|2.21|1.59|1.50|
||(0.93)|(0.85)|(0.82)|(0.88)|(0.73)|(0.90)|(0.88)|
|Heard about BOS|0.80|0.88|0.90|0.84|0.98|0.85|0.87|
||(0.40)|(0.32)|(0.30)|(0.37)|(0.13)|(0.36)|(0.34)|
|Knows what BOS stands for|0.53|0.65|0.56|0.60|0.81|0.52|0.46|
||(0.50)|(0.48)|(0.50)|(0.49)|(0.39)|(0.50)|(0.50)|
|Knows BOS amount|0.14|0.19|0.12|0.11|0.42|0.22|0.17|
||(0.34)|(0.39)|(0.33)|(0.32)|(0.50)|(0.41)|(0.38)|
|Seen BOS use in notice board|0.07|0.04|0.06|0.02|0.31|0.09|0.10|
||(0.25)|(0.21)|(0.24)|(0.15)|(0.47)|(0.29)|(0.30)|
|**Participation Index (sum of 2**|0.16|0.07|0.13|0.08|0.37|0.16|0.19|
|**variables below)**|(0.40)|(0.28)|(0.36)|(0.27)|(0.50)|(0.37)|(0.42)|
|Visits at least 3 times per year|0.08|0.02|0.02|0.02|0.24|0.02|0.03|
||(0.27)|(0.15)|(0.15)|(0.13)|(0.43)|(0.14)|(0.17)|
|Provides feedback to principal,|0.09|0.04|0.11|0.06|0.13|0.14|0.16|
|teacher or SC|(0.28)|(0.21)|(0.31)|(0.25)|(0.34)|(0.35)|(0.37)|
|Number of observations|3,326|135|132|125|121|102|100|
|Tests of joint significance in predic|ting treatment|||||||
|Individual variables:||||||||
|F-statistic||0.38|1.33|||0.48||
|P-value||0.89|0.26|||0.82||
|**Indices:**||||||||
|F-statistic||0.55|1.26|||0.22||
|P-value||0.58|0.29|||0.80||

Note: Standard errors reported in parenthesis clustered at the school level. * used for significance levels, but none of the differences between treatment and control groups are statistically significantly different from zero at 10%. The test for joint significance consists of a joint test that all the individual variables, or the two indices, are significantly different from zero in a regression of treatment on these variables.

The additional set of balancing tests using the endline survey (Table 3) largely confirm the results of the first set for the pocket book and the school meeting interventions, where no statistically significant differences are observed in any of the background variables. In Tulungagung, however, the treatment group selected to receive the letter is more likely to live in urban areas than the control group, which could indicate higher socioeconomic status; but they are also less likely to be employed for wages, thus more likely to be self-employed, and less likely to have the parent of the child as the head of the household, variables generally correlated with lower socioeconomic status. There are no statistically significant differences in household per capita expenditure. A joint test for significance of these variables in predicting treatment rejects that they are jointly equal to zero. However, the results do not seem to indicate a systematic pattern; while some differences are statistically significant, they do not clearly signal systematic bias between the two groups. In the case of Malang, the differences are less robust: 15
The demand for protection through the adoption of measures against “unfair trade” increased after trade was liberalized, especially after the tariff phase-out process initiated in February 1991 and concluded ahead of schedule, in July 1993. Table 4 shows that, during the first year of the implementation of the tariff phase-out schedule, 13 investigations were initiated (counted on the basis of product-country pairs): 8 during the second year and 27 in 1993. In 1994, the number of cases dropped to 10, and to 5 in the following year, involving only one product (iron-chromium) and the countries of former Yugoslavia.<sup>20</sup> Table 4 Number of Antidumping, Anti-subsidy and Safeguard Cases Initiated by Brazil: 1987-2003

|Year|Dumping|Dumping-review|Subsidies|Safeguards|
|---|---|---|---|---|
|1987|0|0|0|0|
|1988|2|0|0|0|
|1989|2|0|0|0|
|1990|2|0|0|0|
|1991|13|0|2|0|
|1992|8|0|13|0|
|1993|27|0|1|0|
|1994|10|0|7|0|
|1995|5|0|0|0|
|1996|16|0|0|1|
|1997|15|7|0|0|
|1998|23|2|0|0|
|1999|27|3|0|1|
|2000|10|2|0|0|
|2001|19|1|1|1|
|2002|16|8|0|0|
|2003|20|12|0|0|
|Total|215|35|24|2|

Source: DECOM, SECEX, MDIC Reports, several years. Author's calculations.

Note: The number of antidumping and anti-subsidy cases is based on a product-country pair. The European Union countries were taken individually, therefore results differ from official statistics. Safeguard measures, instead, are counted by product.

20 The reasons for such an abrupt drop in 1995 are not clear. Perhaps the increase of the GDP explains partially as in this year, the second highest growth rate of 4,2% is recorded.

20
<!-- Start of picture text --> Policy Reowch<br>Inlwnadonl ITrade<br><!-- End of picture text --> WPS 974 This paper- a product of the Intemational Trade Division, International Economics Department- is part of a larger effort in the departmen **t** o evaluate the influence of changes in extemal conditions on developing countries' trade and growth prospects. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Jean Jacobson, room S7-035, extension 33710 (September 1992, 31 pages).

One issue dominating recent discussions on free trade areas and other minilateral associations (preferential trade arrangemeats) is whether such arrangements will detract from furter multilateral trade liberalization on a most-favored-nation basis. But for much of this debate empirical information has been lacking on: . The global importance of minilateral arrangements that have been, or are being, concluded.

* The relative size of other major bilateral trade flows _not_ affected by minilateral amngements, and their suitability for such amTngements.

. The global importance of Europe in this process.

* The possibility that other sorts of arrangements - such as "managed" trade initiatives (arrangements specifying quantitative trade - targets) are a more likely threat as far as trade flows not presently covered by free trade area arrangements are concerned.

Braga and Yeats argue that this lack of relevant data has led to several misconceptions about the movement toward minilateralism. In particular, their statistics suggest that fears about how the further spread of free trade areas will affect world trade volumes may be exaggerated - while the dangers of these blocs becoming hostile to each other may have been underestimated.

Using data recently compiled by the United Nations, Braga and Yeats show that the global importance of minilateral arrangements is now far greater than is often recognized. Almost half of world trade is affected by these arangements.

But major trade flows not covered by minilateral arrangements are dominated by important county-specific problems. In particular, problems relating to high-technology trade between Asian newly industrialized countries (NICs), Japan, and the United States, as well as between Asian NICs, Japan, and Westem Europe, are sufficiently importanm to hinder the formation of additional free trade areas. This suggests that fears about the spread of such arrangements may have been exaggerated.

Braga and Yeat's tabulations and analysis of the "discriminatory" trade barriers applied to these flows indicate that "managed" trade is a far more likely outcome.

ThePolicyResarchWorkgssnatefdgsPorodoedsd **ie** Pytemdsofwo **eee** derwayiheBank.Anobjectiveof seies is to get dhese fin&Wg out quilcly, eveni if presetatons are less than fully polished.'The fmdings, inepetations and conclusions in these papers do not neceaiy rpesent official Bank policy.

Produced by the Policy Research D6ssatdon Center
Table 4: Long-term Effect of FPE on Educational Achievement

|VARIABLES|Treatmen<br>First year|t group:<br> students.|Treatm<br>16-18|ent group:<br> year olds|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|_Total ATT of FPE_<br>|||||
|ATT (<sup>ˆ</sup>_δSDID_)|15.7940***|19.3648***|2.9394|2.3894|
||(2.3166)|(5.5198)|(3.0303)|(8.4694)|
||[3.4080]|[6.4826]|[7.8675]|[18.5473]|
|_Heterogenieties of FPE efect_|||||
|female||0.9700||4.8939|
|||(4.9266)||(6.7208)|
|||[5.6126]||[11.5040]|
|Agriculture||-6.3270||-56.5824***|
|||(9.3363)||(17.3100)|
|||[11.4092]||[26.0263]|
|Health Sciences||11.5125||-18.5925|
|||(8.6084)||(13.2479)|
|||[9.2857]||[27.0102]|
|Humanities||6.1071||46.2779|
|||(9.8383)||(11.4773)|
|||[10.6054]||[32.7043]|
|Law||-11.2598||7.0008|
|||(9.9782)||(13.1108)|
|||[11.8654]||[21.3546]|
|Science & Technology||-15.1297**||-10.4212|
|||(7.8899)||(10.4535)|
|||[7.4746]||[19.2751]|
|Social Sciences||-9.5815||-4.8977|
|||(5.8828)||(8.4315)|
|||[7.0641]||[16.1814]|
|Observations|9,274|9,274|6,127|6,127|

_Notes_ : The point estimates of the FPE effect on achievement (OWM) and standard errors in parentheses are produced using `asdid` Stata command by Houngbedji (2016). Bootstrapped standard errors, clustered by high school attended, in brackets with 1000 replications, and significance levels are indicated as follows: *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. Models (1) and (3) report estimates of the average ATT. Models (2) and (4) show how the ATT varies across different groups of students. All models are estimated using a linear polynomial function of degree 4 to approximate the propensity score. The covariates used in the estimation of the propensity score are APS, gender, district, and age dummies to approximate the cohort effects.

23
Table 5 - Imports of prepared poultry meat (million US$)

|Year|Japan|European<br>Union|Canada|USA|Others|Total<br>Trade|Total<br>Trade<br>(million.<br>Tons)|
|---|---|---|---|---|---|---|---|
|1995|233|40|63|3|191|531|318|
|1996|280|87|62|5|231|665|365|
|1997|302|111|78|7|252|748|409|
|1998|326|140|82|10|218|776|439|
|1999|408|166|75|18|191|857|495|
|2000|517|215|92|27|225|1,075|633|
|2001|615|264|103|40|268|1,291|773|
|2002|702|285|101|43|276|1,407|801|
|2003|733|363|110|51|302|1,558|855|
|2004|790|410|116|63|463|1,841|1,128|
|2005|1,101|684|134|67|512|2,499|1,391|
|2006|1,211|785|154|75|580|2,806|1,530|

Source: COMTRADE Database Note: Excludes intra-EU trade.

Table 5 reports the evolution of the import of prepared poultry meat for the four major importers. With more than 1.2 billion US$ worth of imports in 2006, Japan is the largest importer of prepared chicken. The large increase in imports between 2004 and 2005 (300 million US$) is possibly due both to increases in demand and to shortages of suppliers of fresh chicken, for which prepared poultry is a substitute. A substantial part of chicken consumed in Japan is now being prepared abroad and imported already cooked. The European Union’s imports of prepared poultry have also increased substantially, especially in the last few years. Imports have almost doubled from about 400 in 2004 to about 800 million US$ in 2006. Increases in imports, but to a much smaller extent have also been recorded for Canada and USA, and for all other countries in general.

The increase in demand for prepared poultry has been met largely by three major exporters: Thailand, China and Brazil. In particular, countries such as Thailand and China, which were major exporters of unprepared meat before avian influenza outbreaks, 18
facilities compared to those who remained. The out migration in Narayanganj however was relatively high - almost 30 percent of the households surveyed in the first wave were not found at the time of the endline survey. As mentioned earlier, due to this high attrition rate in the urban sample, the impact evaluation exercise is restricted to the rural sample in Jaldhaka only.

Table 4 presents a comparison of the outcome variables between the treatment and control households before the implementation of the _Shombhob_ pilot. Most of the differences between the treatment and control households are in nutrition outcomes – the treatment households appear to be worse off compared to the control households in the case of incidence of stunting, wasting, underweight, knowledge of breastfeeding, and dietary diversity. The same is true in terms of household consumption. School attendance (defined as the number of classes missed in the last two weeks) and enrollment rates however are almost identical.

**Table 4: Baseline outcome indicators by treatment and control households, Jaldhaka**

||**Treat-**<br>**ment**|**Contr**<br>**C1 + C2**|**ol**<br>**C1**|**All**|**Ban**<br>**Rural**|**gladesh**<br>**Rang-**<br>**pur**<sup>**a**</sup>|**Lowest**<br>**wealth**<br>**quintile**|
|---|---|---|---|---|---|---|---|
|_Consumption (BDT per month)_||||||||
|Per capita consumption|1,117|1,358|1,310|2,565|2,177|1,845|-|
|Total household consumption|5,548|5,938|5,780|11,003|9,436|7,703|-|
|Food household consumption|3,168|3,193|3,153|6,031|5,543|4,783|-|
|_Education (children 6-15 years o_|_ld)_|||||||
|School enrollment|88.7|88.2|87.3|84|83.4|84.7||
|School attendance|5.4|5.3|5.8|-|-|-|-|
|_Nutrition (0-3 years old when en_|_rolled)_||_(_|_0-59 mon_|_ths_<sup>_~~b~~_</sup>_)_|||
|Stunting|47.2|44.4|43.3|41.3|42.7|42.9|53.7|
|Underweight|47.1|44.3|42.9|36.4|38.7|34.5|50.3|
|Wasting|27.8|23.9|22.9|15.6|16.0|13.2|17.5|
|Breastfeeding knowledge<sup>c</sup>|83.1|86.3|88.6|N/A|N/A|N/A|N/A|
|Dietary diversity<sup>d</sup>|12.1|13.7|12.5|24.2|21.8|25.3|13.0|

_a: For consumption data, rural Rangpur only is displayed. For all other statistics, aggregated data for Rangpur (rural + urban) is shown._<sup>_b_</sup> _: nutrition data is taken from the Bangladesh Demographic and Health Survey (DHS) 2011._<sup>_c_</sup> _: Knowledge about breastfeeding is asked to any mother who had a child with the past 36 months._<sup>_d_</sup> _: Dietary diversity is measured on 6-60 month-old babies as the proportion of children having at least one of the 4+ food groups._ Table B3 in Annex II provides descriptive statistics of other household level characteristics of both the treatment and control groups. The data suggest the two groups are quite 20