rafmacalaba/datause-encoder-data huggingface dataset:
breastfeeding. While HCES will capture the demographic composition of the household (age and gender of household members), other information is often not collected (whether women in the household are pregnant or breastfeeding, body weight, height and levels of physical activity).

# **4. DATA**

While the potential sources of mismeasurement in HCES are numerous, this study systematically explores the net effect of questionnaire design on arguably the major category of error, reporting error, using a survey experiment conducted in Tanzania. There were a total of eight alternate designs, which differ by method of data capture, level of respondent, length of reference period, number of items in the recall list, and nature of the cognitive task required of the respondent. These alternative designs were randomly assigned to a national sample of over 4,000 households. Modules 1-4 are recall designs and modules 5-7 are diaries (Table 1). An eighth module is excluded from the current analysis as it did not capture food quantities. The eight designs were strategically selected to reflect the most common methods utilized in low-income countries and are informative of the kind of variation one is likely to find in the type of consumption and expenditure surveys used in the countries where concerns about hunger are most pressing.

In the food recall modules, households report the quantity consumed from three sources (purchases, home production, and gifts/payments). Modules 1 and 2 contain a list of 58 food groups; module 3 has a subset list that consists of the 17 most important food groups that constitute, on average, 77 percent of food consumption expenditure in Tanzania based on the Household Budget Survey 2000/01. To make module 3 comparable, we scale up food quantities for that module (by 1/0.77). Among the recall modules, module 4 deviates from a reporting of actual expenditure over a specified time period. Instead it asks for “usual” consumption, following a recommendation in Deaton and Grosh (2000), where households report the number of months in which the food item is typically consumed by the household, the quantity usually consumed in those months, and the average value of what is consumed in those months. These questions aim to measure permanent rather than transitory living standards, without interviewing the same households repeatedly throughout the year. Hence, module 4 introduces two key differences from the other recall modules: a longer time frame and a different (and more complicated) cognitive task required of respondents.

The three diary modules are of the standard “acquisition type.” Specifically, they add everything that came into the household through harvests, purchases, gifts, and stock reductions and subtract 11
- 26 -

Table 5 (Continued)

# Comparative Analysis of Nominal Transport Costs for Major Caribbean Food and Agricultural Raw Material Exports to the United States

||1986 Exp|orts|U.S.|Nom|inal Freig|ht Rate|
|---|---|---|---|---|---|---|
|||% of|Applied|Caribbean|Other||
|Country/Product<br>1/<br>Val|ue(SOOO)|Total_21_|<br> T-riff 3/|Exporter|Exporters|Difference 4/|
|Guyana|||||||
|Fresh or Frozen Shrimp (1144545)|21,620|34.8|0.0|4.1|4.2|-0.1|
|Sugars and Molasses (1552045)|7,793|12.5|0.0|5.6|6.8|-1.2|
|Live Birds over S5 each (1003100)|1,278|2.1|0.4|8.1|18.2|-0.1|
|Sugar and Mixtures (1554000)|753|1.2|0.0|7.4|25.0|-17.6|
|Subtotals|31,444|50.6|--|4.6|5.8|-1.2|
|Haiti|||||||
|Coffee, Crude (1601020)|5,523|1.6|0.0|2.9|3.7|-0.8|
|Fresh Winter Mangoes (1480300)|2,717|0.8|0.6|57.2|54.0|3.2|
|Goat and Kid Leather (1216200)|1,656|0.5|0.0|2.9|6.9|-4.0|
|Sugar and Mixtures (1554000)|1,555|0.4|0.0|28.4|24.7|3.7|
|Rock Lobster Tails (1144525)|1,298|0.4|0.0|5.6|2.8|2.8|
|Fresh Summer Mangoes (1480600)|791|0.2|0.2|49.0|32.6|16.4|
|Bovine Leather, NES :1216112)|703|0.2|0.0|2.3|5.3|-3.0|
|Subtotals|14,243|4.1|--|18.9|17.7|1.2|
|Jamaica|||||||
|Cigars (1707000)|7,004|2.4|0.1|3.2|2.4|0.8|
|<br>Fresh Yams (1378800)|2,764|1.3|0.1|30.2|27.4|2.8|
|Cordials and Llquers (1689800)|2,890|1.0|0.2|1.5|12.4|-10.9|
|Sugars and Molasses (1552045)|2,280|0.8|0.0|2.9|6.8|-3.9|
|Rock Lobster Tails (1144525)|1,827|0.6|0.0|6.4|2.8|3.6|
|Fresh or Frozen Peppers (1371020)|1,537|0.5|0.0|29.6|42.0|-12.4|
|Subtotals|18,302|6.6|0.1|10.7|12.6|-1.9|
|St. Kitts-4evis|||||||
|Sugars and Molasses (1552045)|3,811|17.1|0.0|4.5|6.8|-2.3|
|Sugar and Mixtures (1554000)|414|1.9|0.0|28.1|24.8|3.3|
|Subtotals|4,225|19.0|0.0|5.3|9.1|-1.7|
|St. Lucia|||||||
|Fresh Tuna (1101012)|1,103|9.0|0.0|5.4|9.4|-4.0|
|Cocoa Beans (156100)|72|0.6|0.0|5.1|7.6|-2.5|
|<br>Subtotals|1,175|9.6|0.0|5.3|9.1|-3.8|
|St. Vincent|||||||
|By-Products of Grain Mills (1847070|)<br>80|1.0|0.0|23.3|23.8|-0.5|
|Pumpkins and Breadfruit (1379300)|62|0.8|0.0|39.1|31.6|7.5|
|Subtotals|142|1.8|0.0|28.7|27.2|-1.5|
8 Table 1: Summary of CGE Results on the Economic Implications of NAFTA for the Rest of the World (ROW) (Percentage changes except where noted)

||Brown,<br>and S|Deardorff<br>tern a/|Soba|rzo b/|Co|x and Harris|ci|
|---|---|---|---|---|---|---|---|
||Case I|Case 2|Case I|Case 2|Case I|Case 2|Case 3|
|Exports from the ROW<br>To NAFTA countries<br>To the US<br>To Canada _|.0.06|||-0.<br>0.|3<br>_*0.65_<br>0.08|-0.64<br>0.07|*t0.9<br>-2.4|
|Net Exports from the ROW d/<br>To NAFTA countries<br>To Mexico|0.35_ei_|-10.88 ei|2.1|17.1||||
|Termb of Trade for the ROW|-0.0|0.2||||||
|Welfare f/<br>ROW|.0.0|-0.0||||||
|Canada|0.7|0.7|||0.003|0.936|-0.117|
|Mexico<br>|1.6|5.0|2.3|2.4||||
|US|0.1|0.3<br>1|<br>|1__||||

- a/ The Brown, Deardorff and Stmrn model (29 sectors of which 23 am tradable goods sectors) assumes that technology in most tradeable sectors is characterized by increasing returns to scale and the market structure is monopolistically competitive. Cuse I (BDS' experiment A) assumes complete removal of tariffs among NAFrA members and a 25 percent increase of US import. quotas which restrain Mexican exports of agriculture. food, textiles and clothing. Case 2 (BDS' experiment B) maintains the same assumptions plus the liberalization of Mexico's capital import controls, resulting in a 10 percent increase in Mexico's capital stock.

- b/ The Sobarzo model identifies 27 production sectors and assumes imperfect competition in most of its 21 tradeable sectors. The model only addresses the impact of a complete removal of tariffs among NAFTA countries. In case I (Sobarzo's version 2). a fixed quantity of capital in Mexico is assumed. Case 2 (Sobarzo's version 3) in tumr assumes that capital is mobile between countries.

- cl The Cox and Harris model identifies 19 sectors with ten of them presenting increasing returns to scale. The trade results reported reflect absolute changes in the market sham of the ROW in total imports of the United States and Canada. The base for comparison is provided by the Canada-US free trade area (CUSFTA) situation. Case I compares a hub-and-spoke fornat (with the United States as the hub and Canada and Mexico as the spokes) with CUSFTA. Case 2 compares the NAFrA with CUSFTA. Case 3 compares a NAFrA combined with an increase of 10 percent of member countries' ad _valorem_ tariffs against the ROW with CUSFIA. Preferential trade liberalization in all cases reflects only tariff removal.

- _df_ Net exports am defined as exports minus imports.

- e/ Values in USS billions.

- fI Welfare changes are measured in terms of the so-called equivalent variation -i.c., they reflect the change in income valued at base prices that would lead to the same change in utility level associated with the liberalization.

Sources: Brown, Deardorff and Stem (1992). H. Sobarzo (1992) and Cox and Harris (1992).
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Policy Research Working Paper 8453

# **Abstract**

The paper uses a detailed household survey to document precautionary wealth accumulation in Afghanistan, with wealth being significantly higher for households facing higher income uncertainty. Annual household expenditure on nondurable goods is also lower for these households. There is no significant difference in the wealth response to income uncertainty across high- and low-conflict provinces.

However, the constituents of precautionary wealth vary drastically. While households in the low-conflict regions rely almost exclusively on livestock to iron out uncertainty, households in the high-conflict areas also build up a reserve of gold and silver. This shift in household portfolio suggests a more substantial decline in real returns of livestock relative to jewelry in high-conflict provinces.

This paper is a joint product of the Finance, Competitiveness and Innovation Global Practice and the Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http:// www.worldbank.org/research. The authors may be contacted at arostom@worldbank.org.

_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 views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
contracts<sup>2</sup> , whereas civil works are governed by administrative law contracts. Private law contracts ensure equivalent rights and obligations to contracting parties. For example, a private contract cannot be changed unless both parties agree to do so. Similarly, parties rely on the judiciary to protect their rights in case of a breach of contract. Conversely, administrative contracts are asymmetric in the sense that governments will usually have exorbitant rights. For example, governments will not need a judiciary decision before modifying or canceling a civil work contract or enforcing penalties in case of default.

Besides, infrastructure networks exhibit characteristics of _natural monopoly_ . A highway linking city A to city B may be the only economically available route between these two cities. If conceded to a private entity, the concessionaire will hold a natural monopoly over the roadway between cities A and B even if the provision of transportation services remains competitive. The same applies to the distribution of water and electricity services. When a city decides to grant the distribution of water or electricity services to one concessionaire, there is little room left to develop competition in the market. Granting concessions in infrastructure sectors therefore entails monopoly or exclusive rights. In order to minimize monopoly rents captured by concessionaires, Sir Edwin Chadwick recommended in 1859 to promote wherever possible “competition for the market”. He went on recommending that concessions rights be _auctioned to the bidder offering the most attractive terms_ – for example the lowest price to consumers. In resurrecting Chadwick’s idea, Harold Demsetz (1968) advocated in favor of periodic rebidding of concession contracts in order to extract monopoly rents prevailing after the concession has been awarded. But one has to admit that re-bidding is not always a practical solution specially when assets are very specific to the concession or cannot be easily disbanded in a secondary market. In such a situation, re-bidding is not a practical solution<sup>3</sup> because of critical incentives problems that arise (Laffont and Tirole, 1993;

> 2 For example, the government may have the privilege to terminate the contract at any time without relying on a judiciary decision. This is the case in most civil law countries such as France.

> 3 In France, only 12% of the incumbent water concessionaires lost their renewing bids in 2000 (see ENGREF, 2001) compared to 8% in 1998 and 18% in 1999. Due to the limited terms of delegation contracts (on average 11 years in water concession or lease), ENGREF has found that out of 336 renewing contracts in 2000: (a) independent operators won 8 new contracts, (b) altogether the three main private

4
Figure B.7: Distribution of Placebo RD Estimates and Randomization Inference P-Values <!-- Start of picture text --> A. Linear Fit B. Quadratic Fit<br>I. Log Number of Card Transactions<br>-.6 -.4 -.2 0 .2 .4 -.8 -.6 -.4 -.2 0 .2<br>August 2014 RD Estimate (0.433) vs Mean Placebo RD Estimate (-0.074) August 2014 RD Estimate (0.330) vs Mean Placebo RD Estimate (-0.093)<br>Estimates T-test: 0.507 (0.144) Estimates T-test: 0.423 (0.195)<br>Randomization Inference p-value: (0.000) Randomization Inference p-value: (0.000)<br>II. Log Volume of Card Transactions<br>-.2 0 .2 .4 -.2 0 .2 .4<br>August 2014 RD Estimate (0.231) vs Mean Placebo RD Estimate (0.002) August 2014 RD Estimate (0.288) vs Mean Placebo RD Estimate (-0.023)<br>Estimates T-test: 0.229 (0.135) Estimates T-test: 0.310 (0.144)<br>Randomization Inference p-value: (0.029) Randomization Inference p-value: (0.029)<br>III. Number of POS<br>-.2 -.1 0 .1 .2 -.2 -.1 0 .1 .2<br>August 2014 RD Estimate (0.079) vs Mean Placebo RD Estimate (-0.002) August 2014 RD Estimate (0.075) vs Mean Placebo RD Estimate (0.003)<br>Estimates T-test: 0.080 (0.065) Estimates T-test: 0.071 (0.065)<br>Randomization Inference p-value: (0.057) Randomization Inference p-value: (0.114)<br>IV. Number of Firms with a POS<br>-.05 0 .05 .1 -.05 0 .05 .1<br>August 2014 RD Estimate (0.022) vs Mean Placebo RD Estimate (0.003) August 2014 RD Estimate (0.019) vs Mean Placebo RD Estimate (0.004)<br>Estimates T-test: 0.019 (0.029) Estimates T-test: 0.015 (0.031)<br>Randomization Inference p-value: (0.114) Randomization Inference p-value: (0.143)<br>3 2.5<br>2<br>Density 2 Density 1.51<br>1<br>.5<br>0 0<br>4<br>4<br>3<br>3<br>2<br>2<br>Density Density<br>1 1<br>0 0<br>8<br>8<br>6 6<br>4 4<br>Density Density<br>2 2<br>0 0<br>20 20<br>15 15<br>10 10<br>Density Density<br>5 5<br>0 0<br><!-- End of picture text --> Notes: This figure shows the distribution of estimates from placebo RD estimations, using equation 1 with optimal bandwidths as per Calonico et al. (2014) and pretending the reform happened in a month other than August 2014 (one estimation per month, using all months between January 2013 and December 2015). The vertical red line shows the estimate for August 2014. We report the point estimate and standard error on a t-test comparing the August 2014 estimate to the placebo estimates, and randomization inference p-values. This figure is discussed in section 4.3.

55
|**(11)**|8.205<br>(1.077)<br>*<br>4.702<br>*<br>(3.352)***<br>-0.772<br>(-1.995)**<br>-1.891<br>(-3.548)***<br>*<br>860<br>3.233<br>0.245<br>1.041<br>1.422<br>gnificance<br>on problem.|
|---|---|
|**(9)**<br>**(10)**|-3.136<br>2.287<br>(-0.421)<br>(0.307)<br>2.362<br>2.243<br>(2.337)***<br>(2.236)**<br>4.239<br>3.102<br>(3.56)***<br>(2.582)**<br>-6.508<br>(-6.244)***<br>*<br>-7.434<br>(-7.73)**<br>859<br>859<br>2.781<br>2.795<br>0.250<br>0.221<br>0.838<br>0.994<br>1.397<br>1.406<br>vel, and *** indicates 1% si<br>here is no overidentificati|
|**(8)**|2.97<br>(0.323)<br>2.967<br>(1.922)**<br>5.206<br>(3.865)***<br>-4.558<br>(-3.605)**<br>634<br>3.168<br>0.232<br>0.849<br>1.415<br>gnificance le<br>m where H0: t|
|**(7)**<br>**012)**|4.082<br>(0.506)<br>2.535<br>(2.555)***<br>4.866<br>(3.137)***<br>-0.599<br>(-1.964)**<br>-1.92<br>(-3.603)***<br>*<br>860<br>3.316<br>0.228<br>0.884<br>1.473<br>indicates 5% si<br>fication proble|
|**(6)**<br>**mple 2002-2**|10.676<br>(1.323)<br>2.611<br>(2.011)**<br>5.317<br>(1.872)*<br>-0.555<br>(-1.799)*<br>-1.298<br>(-2.418)**<br>-6.495<br><br>(-6.041)**<br>847<br>3.261<br>0.231<br>0.891<br>1.487<br>ance level, **<br>or overidenti|
|**(5)**<br>**Corruption (Sa**|10.644<br><br>(1.418)<br><br>2.316<br>(1.876)*<br>-0.556<br>(-1.703)*<br>-1.298<br>*<br>(-2.429)**<br>-6.492<br>*<br>(-6.296)***<br>847<br>2.527<br>0.229<br>0.851<br>1.419<br>es 10% signific<br>ients. J-test is f<br>".|
|**(4)**<br>**ants of Tax**|22.675<br>(5.663)***<br>2.451<br>(2.444)***<br>-0.772<br>(-1.881)*<br>-1.543<br>(-2.937)**<br>-6.775<br>(-6.449)**<br>856<br>3.131<br>0.224<br>0.832<br>1.421<br>esis. * indicat<br>f zero coeffic<br>distribution|
|**(3)**<br>**ion: Determin**|-16.988<br>(-2.368)**<br>1.937<br>(1.97)**<br>5.712<br>(4.803)***<br>872<br>3.193<br>0.187<br>0.950<br>1.350<br>en in parenth<br>ll hypothesis o<br>H0 is "normal|
|**(2)**<br>**nel Regress**|-12.125<br>(-1.797)*<br>6.023<br>(5.101)***<br>872<br>2.939<br>0.186<br>0.890<br>1.550<br>tistics are giv<br>ecting the nu<br>rmality test,|
|**_Dependent variable: Tax corruption_**<br>**(1)**<br>**Table 5 - Pa**|**Constant term**<br>12.409<br>(3.698)***<br>**_Tax simplification_**<br>**log(Tax payments)**<br>2.819<br>(2.918)***<br>**log(Tax time)**<br>**_Political and Political Institution Determinants of Corruption_**<br>**Bureaucracy Quality (higher better quality)**<br>**Democratic Accountability (higher better)**<br>**Government Effectiveness (higher better)**<br>**Burden of government regulation, 1-7 (best)**<br>**_Judicial and Bureaucratic Determinants of Corruption_**<br>**Rule of Law (higher better)**<br>No. of observations<br>881<br>J-statistics<br>2.897<br>Arellano-Bond serial correlation test AR(1)<br>0.182<br>Arellano-Bond serial correlation test AR(2)<br>0.871<br>Jarque-Bera normality test<br>1.452<br>Note: The estimation method is a panel - GMM. Annual data are used. t-sta<br>level. These significance levels are equal to one minus the probability of rej<br>For serial correlation z-tests, H0 is "there is no serial correlation"; and for no|

26
for only 17 districts.<sup>11</sup> A second and equally important limitation of district malaria data arises from the fact that we have to use district ...xed e¤ects to control for the relevant time invariant unobserved factors, and thus it is not possible to use district level malaria data for identi...cation. Drawing on the insights from the literature on Malaria in tropical countries, we devise a way to exploit the DSD level variations in historical malaria prevalence. We use interactions of district level malaria data with DSD level exogenous characteristics that are likely to be informative about malaria susceptibility and transmission.

The district level malaria prevalence data are, by de...nition, average of the malaria prevalence across di¤erent DSDs in a district. Our approach to constructing instruments is based on the idea that if we can ...nd indictors of susceptibility to and transmission of malaria in a DSD in a district, the interaction of district level malaria intensity with the DSD level ‘malaria susceptibility indicators’ will provide us with a measure of di¤erences in historical malaria intensity across di¤erent DSDs. The ‘malaria susceptibility indicators’ can be thought of as the DSD speci...c weights one needs to recover the historical DSD level malaria prevalence from the available district level malaria estimates. Since the Anopheles mosquito is the carrier of malaria parasites (especially Plasmodium Vivax and Plasmodium Falciparum), we use geographic features that a¤ect the ease with which mosquitos can survive and multiply in a DSD as indicators of malaria susceptibility of a DSD. The literature on malaria transmission and prevalence identify a number of such geographic features including water body and elevation. Water body can be a fertile breeding ground for Anopheles mosquitos, but mosquitos ...nd it di¢ cult to survive in high altitude. We use the proportion of land above 1000 feet elevation, and proportion of land under water in a DSD as indicators of malaria susceptibility of a DSD. Thus the interaction of district level malaria with ‘inland water’in a DSD is expected to have a positive sign in the ...rst stage regression for LDO incidence, while the sign of the interaction of district malaria with elevation should be negative. We control for water body and elevation directly in the IV regressions to make the exclusion restrictions imposed on the interactions with district malaria prevalence more credible. Since we control for current malaria parasite infection (data for the year 1999) across DSDs, the historical malaria prevalence cannot pick up the e¤ects of recent malaria incidence. Note also that we

> 11 The 17 districts in 2002 correspond to 15 districts in 1937-41.

7
- 30 -

Table 8. Predicted Unit Bid of Individual Item: Electricity <u>(Million US$)</u>

|Generator installation<br>Bid|amount<br>perunit|Std.<br>Err.|
|---|---|---|
|10 MW|7.78|(2.55)|
|100 MW|1.07|(0.35)|
|500 MW|0.38|(0.14)|
|1,000 MW|0.18|(0.06)|
|Transmission line installation|||
|1 km|96.14|(45.38)|
|10 km|8.92|(4.24)|
|25 km|3.11|(1.52)|
|50 km|1.20|(0.65)|
|100 km|0.27|(0.30)|

Source: Author’s calculation.

From the water-sector bid function, similarly, it is found that a water treatment plant with a daily production capacity of 0.5 million m<sup>3</sup> —which may be usable in a medium-sized city with 1–2 million of population—would cost about 58 U.S. dollars per m<sup>3</sup> per day (Table 9). If a water treatment plant produces only 0.1 million m<sup>3</sup> of water per day—as in a smaller town—it will cost more than 200 U.S. dollars per m<sup>3</sup> .<sup>14</sup> The unit cost is 3 times as high as the procurement package of 0.5 million m<sup>3</sup> of treatment capacity.

Table 9. Predicted Unit Bid of Individual Item: Water and Sewage

||Bid amount|Std.|
|---|---|---|
|Water treatment plant|perunit|Err.|
|0.01 million m3 per day|777.85|(323.11)|
|0.05 million m3 per day|329.94|(49.45)|
|0.1 million m3 per day|202.52|(26.14)|
|0.5 million m3 per day|57.94|(7.62)|
|1 million m3 per day|32.73|(4.52)|
|Waste water treatment plant|||
|0.01 million m3 per day|609.72|(309.97)|
|0.05 million m3 per day|296.32|(28.78)|
|0.1 million m3 per day|185.71|(11.87)|
|0.5 million m3 per day|54.58|(4.84)|
|1 million m3 per day|31.04|(3.22)|
|Iron pipe installation|||
|1 km|9.22|(1.27)|
|5 km|2.14|(0.22)|
|10 km|1.13|(0.11)|
|50 km|0.26|(0.02)|
|100 km|0.13|(0.01)|
|500 km|0.03|(0.00)|

Source: Author’s calculation.

14 These estimated costs are broadly consistent with the actual development costs of water treatment plant construction projects, which may range from 60 to 300 U.S. dollars per m<sup>3</sup> per day.
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