rafmacalaba/datause-encoder-data huggingface dataset:
survey, we are left with a sample size of 1,493. Data were collected from September 2007 to August 2008. All households in a village completed their diaries over the same 14 days. Survey work in each district was completed in less than two months.<sup>6</sup> Each study household maintained a transaction diary for 14 days. The diary took the form of a paper log book, with a separate row for each transaction. On each day during the observation window, diary keepers noted the quantity, unit, value, and description of every item that entered or exited the household. If multiple items were purchased during a single shopping trip, each item received its own entry (row) in the diary. Purchases, gifts, own production, and stock adjustments were recorded separately. Because this paper deals with purchasing behaviors, we use only those rows of the transaction diaries that indicate purchases, unless otherwise specified.

After collecting the diaries from respondents, project staff used the item descriptions to assign each purchased item to one of 73 categories, covering 58 food items and 15 non-food items. These categories are similar to those that would be available in a typical consumption survey. However, because we have access to the raw diary data, we use the detailed descriptions of each item to further narrow the definitions of the study items. We do this by dropping entries for which the description does not match that of the modal entry within the item category. For example, we drop “unrefined sugar” from “Sugar,” retain only “dried beans” (excluding soy beans) from the original category of “Peas, beans, lentils and other pulses,” keep only “immature coconuts” in the “Coconut” category, restrict the “Dried fish” group to only “dried sardines,” excluding larger fish, and so on. This removes much of the bias from grouping goods of different variety under a single heading. The end result is a data set in which items are far more uniform than those in a typical consumption survey.

We also drop items with too few observations, and drop perishable items that cannot be stored for two weeks by most households (such as beef, milk, and fresh fish). We do not drop or retain items based on whether the price schedule exhibits bulk discounts. After selecting the items, we remove outliers by dropping any observations for which the quantity or unit price is more than 4 standard deviations from the item-level mean, and dropping

> 6More details are available at the project page, accessible here: http://edi-global.com/publications/. Data are available by contacting the lead SHWALITA researchers listed on the project page.

11
That is, the difference between the two growth rates of group average incomes is also a weighted average of the difference between the anonymous and non-anonymous growth incidence curve. As before, the difference between the anonymous and non-anonymous growth incidence curves depends on �� − �������� ≥0 . Whether the anonymous growth rate exceeds or is smaller than the non-anonymous rate � now depends on the sign of the integral �Φ<sup>��</sup> (�)����(�)�� . While a closed-form solution for this � integral exists and is given in the proof of Proposition 4 in Appendix A, this term cannot be signed in � general. However, for the particular case where 0 ≤�< �≤0.5 , it is clear that �Φ<sup>��</sup> (�)����(�)�� ≤ � 0 since Φ<sup>��</sup> (�) ≤0 for 0 ≤�≤0.5 . This means that for any subgroup in the bottom half of the income distribution, the anonymous group average growth rate is lower than the corresponding non-anonymous growth rate. Naturally, this argument is symmetric: when 0.5 ≤�< �≤1 , the anonymous growth rate of group average incomes for any subgroup in the top half of the income distribution exceeds the corresponding non-anonymous growth rate.

We can quantify the difference between anonymous and non-anonymous growth rates using our estimates of the mobility parameter ���� based on aggregate data from the WID and PovcalNet databases. We begin by calculating the average annual anonymous growth rate of the bottom 40 and top 10 percent over the most-recent 10-year period available for each country. These anonymous growth rates would be the ones typically reported when true panel data are not available. We then compute the lower and upper bounds on the corresponding annual average non-anonymous growth rates, using the bounds on ���� reported in Tables 2 and 3. The results of this calculation are summarized in Figure 6. The top and bottom panels report results from the WID and PovcalNet samples, respectively. In each panel, we plot the anonymous growth rate on the horizontal axis, the non-anonymous growth rate on the vertical axis, and the diagonal line corresponds to the 45-degree line. The points above the 45-degree line correspond to growth rates of the bottom 40 percent, reflecting our result that non-anonymous growth rates exceed their anonymous counterparts for group averages within the bottom half of the income distribution. Conversely, the points below the 45-degree line correspond to growth rates of the top 10 percent, for which the non-anonymous growth rate is smaller than the anonymous growth rate. The triangles (for the bottom 40 percent) and squares (for the top 10 percent) indicate the midpoint of our range for the non-anonymous growth rate, and the vertical lines display the range between the lowerand upper-bound estimates of the non-anonymous growth rates. The anonymous growth rates and the bounds on the anonymous growth rates are reported by country in Tables 4 and 5.

23
42 Table 4 Coverage of population and active labor<sup>force in Latin America, 1980</sup>

|Country|Population covered<br>in sickness and<br>maternity program<br>(percent)|Active labor<br>force covered in<br>pension program<br>(percent)|
|---|---|---|
|Low income|||
|Haiti|1|2|
|Lower middle-income|||
|Bolivia|25|18<br>|
|Honduras|7<br>|13<br>|
|Nicaragua|9<br>|19<br>|
|Dominican Republic|8<br>|14<br>|
|El Salvador|6<br>|12<br>|
|Paraguay|18<br>|14<br>|
|Peru|17<br>|37<br>|
|Ecuador<br>|8<br>|23<br>|
|Cuatemala|14|33<br>|
|Costa Rica|78<br>|68<br>|
|Colombia|12<br>|22<br>|
|Chile|67|62|
|Upper middle-income|||
|Brazil|96|96<br>|
|Uruguay|69|81<br>|
|Mexico|53|42<br>|
|Panama<br>|50<br>|46<br>|
|Argentina|79|69|
|Venezuela|45|50|

Source: Carmelo Mesa-Lago (1986), "Comparative<sup>Study of the Development of</sup> Social Security in Latin America", International Social Security Review 39, 2: 127-152, Tables 1 and 3.
today’s EPAs on national exports.

The objectives of EPAs are to help exporters understand and find markets for their products. The services offered by EPAs can be divided into four broad categories: 1) country image building (advertising, promotional events, but also advocacy); 2) export support services (exporter training, technical assistance, capacity building, including regulatory compliance, information on trade finance, logistics, customs, packaging, pricing); 3) marketing (trade fairs, exporter and importer missions, follow-up services offered by representatives abroad); and 4) market research and publications (general, sector, and firm level information, such as market surveys, on-line information on export markets, publications encouraging firms to export, importer and exporter contact databases).

The economic justification for government involvement in export promotion is based on the theory of asymmetric information and other market failures. There are important externalities associated with the gathering of foreign market information related to consumer preferences, business opportunities, quality and technical requirements, etc. Private firms alone will not provide foreign market information, as companies hesitate to incur research and marketing costs that can also benefit competitors. The same applies to pioneer exporters, who make a considerable investment in attempts to open foreign markets, cultivating contacts, establish distribution chains and other costly activities that can be used by their rivals (Hausmann and Rodrik, 2003). The uncertainty associated with trading across markets with different regulations has also been put forward as a justification for export insurance schemes supported by the public sector.<sup>4</sup> The argument for public funding of EPAs would ideally be based on an assessment of the social costs and benefits associated with the activities of the EPA. Social benefits are likely to be larger than the social costs if there are large positive externalities associated with higher current exports across firms, sectors or time and within the exporting country.<sup>5</sup>

> 4See Greenaway and Kneller (2005) for a recent survey of the literature on trade and externalities. For a more skeptic view, see Panagariya (2000).

> 5Note that some of these externalities may travel across borders. It is clear that some of the benefits from export promotion activities can be captured by consumers in the importing country for whom search costs are reduced. This undermines the case for national government funding of export promotion programs and calls

2
networks appear to influence the decision to send girls to work, but not the school attendance of either girls or boys. The consistent absence of social network effects in the school attendance of indigenous teenage boys and girls in rural areas suggests that educational interventions in the rural areas to encourage and support selected teenage boys and girls to attend school are unlikely to have any multiplier effects through “peer effects” or role model for other children. The next topic of investigation is the role of social network effects among adult males and females between 20 and 65 years of age (see table 2).<sup>7</sup> Social networks are believed to play a critical role in the decision to migrate since the network of acquaintances one has lowers not only the costs of migration but also the likelihood of getting employment in the destination area. In the case of Mexico, there is plenty of empirical evidence that social network effects play a significant role in migration to the US (see for instance Massey and Espinosa 1997, Orrenius 1999, Munshi 2003). However, little evidence exists on the role of social networks in internal migration. Our indicator of whether an individual is a migrant or not is essentially a measure of internal migration since it is based on a comparison of the current state of residence of the individual with the state that he/she was born.<sup>8</sup> Our empirical estimates in table 2 confirm that the effect of social networks on internal migration is significant in the rural areas, as well as for adult males in urban areas. Furthermore, the effects are significant for both for males and

> 7 The estimates in table 2 are obtained by constructing the variables CA and SN in equation (3) using the full sample of all adults (irrespective of gender).

> 8  The census allows identification of two additional variables related to migration: an individual specific variable on recent migration by comparing based on a comparison of the current federal state of residence and the state of residence five years ago, and a household level variable identifying the total number of individuals in the household who have migrated overseas.

11
However, the adequacy and appropriateness of reserve levels cannot be ascertained without direct knowledge of the business of each insurer. A low level of reserves, especially for outstanding claims, may reflect short delays in settling claims or it may be caused by inadequate reserving in the face of protracted disputes. Similarly, a high level of reserves may reflect conservative reserving in the face of such delays or it may be caused by excessive reserving in order to understate profits and lower tax liability.

Table 10: Reserve Levels of General Insurance, 997-2001

|(percent)|1997|1998|1999|2000|2001*|
|---|---|---|---|---|---|
|UnearnedPremiumReserve/NEP<br>|53.0<br>|49.5<br>|49.1<br>|51.4<br>|47.8<br>|
|OutstandingClaimsRes/NIC<br>Tl|109.6<br>|98.7<br>|94.2<br>|100.7<br>|99.0<br>|
|otaReserves/NEP<br>NEP:NetEarnedPremi<br>*excl<br>Source:Estimatedontheb|117.8<br>ums;NIC:<br>udingSecu<br>asisofdat|117.8<br>NetIncu<br>ra<br>acollecte|118.8<br>rredClai<br>dbythe|125.2<br>ms<br> FSC.|116.0|

Reserve levels in Mauritius vary considerably across companies and across classes of business as well as over time, even within the same companies. In the absence of either a rigorous inspection program or a requirement on companies to employ experienced and qualified auditors and actuaries for ascertaining reserve levels, it is not possible to judge their adequacy and appropriateness.

> As in long-term insurance, there is considerable variation in the performance of

> general insurance companies by size (Table 11). Small companies are preponderantly involved in motor insurance where they generate 76 percent of their premium income against 34 percent for large companies.

Table 11: Performance and Company Size of Gemeral Isurance, 2001

|(percent)|4Large<br>Companies|7Medium<br>Companies|6Small<br>Companies*|All<br>Companies|
|---|---|---|---|---|
|TotalPremiums<br>|56.1<br>|35.1<br>|6.9<br>|100.0<br>|
|TotalMotor Premiums<br>|43.0<br>|41.4<br>|11.9<br>|100.0<br>|
|MotorPrem/GrossPremiums<br>|34.2<br>|52.6<br>|76.0<br>|44.5<br>|
|Reinsurance/GrossPremuums<br>|53.9<br>|38.2<br>|36.8<br>|46.8<br>|
|GrossClaims/GrossPremiums|49.9|53.1|66.5|53.1|
|NIC/NEP<br>|67.9<br>|71.1<br>|64.2<br>|68.9<br>|
|NetComm/NEP<br>|-9.6<br>|-1.1<br>|-1.3<br>|-5.4<br>|
|Admin Costs/NEP<br>|28.7<br>|41.5<br>|40.5<br>|35.1<br>|
|NetCosts/NEP<br>|19.1<br>|40.4<br>|39.2<br>|29.7<br>|
|Combined Ratio/NEP<br>|87.0<br>|111.5<br>|103.4<br>|98.6<br>|
|UnderwritingResult/NEP<br>|13.0<br>|_-11.5_<br>|-3.4<br>|1.4<br>|
|Invest. IncomefNEP<br>|20.7<br>|29.3<br>|37.7<br>|25.8<br>|
|Profit/NEP|33.7|17.8|34.4|27.2|

NEP: Net Earned Premiums; NIC: Net Incurred Claims

> * excluding Secura

> Source: Estimated on the basis of data collected by the FSC.

12
||(0.925)||(0.399)||
|---|---|---|---|---|
|Gross secondary<br>school enrollment|0.991<br>(0.607)||0.983<br>(0.239)||
|Natural Resource<br>E GDP|1.034||0.931||
|xports (% )|(0.377)||(0.211)||
|PR||0.893||1.403|
|||(0.775)||(0.409)|
|Political System||1.372||1.159|
|||(0.230)||(0.479)|
|Mean District<br>Magnitude||0.994<br>(0.484)||1.005<br>(0.454)|
|Democracy (ACLP)||1.090||0.741|
|||(0.868)||(0.473)|
|Baseline controls|Yes|Yes|Yes|Yes|
|Region Dummies|Yes|Yes|Yes|Yes|
|Observations<br>|183|248|186|253|
|Pseudo_R_<sup>2</sup>|0.229|0.169|0.123|0.092|

Logistics regressions; odds ratios reported; p-values in parentheses + p < 0.10, * p < 0.05, ** p < .01, *** p < .001 Programmatic parties and ICRG bureaucratic quality are measured at the project start year; all other variables are averaged over the project period.

The base regressions pool all countries, more and less democratic, since our hypotheses apply equally to both. Table 4 shows that the results are robust, however, to restricting the sample to countries that have exhibited competitive elections and have had at least one democratic change of government; the definition follows, and the data are taken from, ACLP (Alvarez et. al. 1996, Cheibub et. al. 2009). Within democracies and controlling for IEG quality at entry rating and initial bureaucratic quality, a unit increase in the proportion of programmatic parties is associated with a nine-fold increase (8.68 ) in the odds of a successful institutional development impact rating relative to the odds of an unsuccessful rating, and a similar nine-fold increase (9.37) in the odds of a successful overall outcome rating relative to an unsuccessful rating.

34
# **Technical Note 2: Creating a poverty map using the Asset index method**

## **Data and Methods**

In order to create poverty map in this section we apply the asset index methods using data from the 1999 Census of Azerbaijan collected from the State Statistical Committee of Azerbaijan. With this technique the socio economic status of households is defined in terms of assets or wealth, rather than in terms of income or consumption. The 1999 Census included several questions regarding the ownership of consumer durables and the materials used in the construction of the household, along with basic demographic information concerning household size and composition. Table 1 presents some basic descriptive statistics of the potential components of the asset index.

**Table 1: Ownership of assets and household characteristics, Azerbaijan 1999**

||_Mean_|_Standard_<br>_Deviation_|_Min_|_Max_|
|---|---|---|---|---|
|1. Gas Line|0.584|0.492|0|1|
|2. Gas Cylinder|0.209|0.407|0|1|
|3. Electric Oven|0.245|0.430|0|1|
|4. Heating system (private, public)|0.09|0.286|0|1|
|5. Stove|0.391|0.487|0|1|
|6. Water|0.588|0.492|0|1|
|7. Hot water|0.04|0.196|0|1|
|8. Sewage System|0.412|0.492|0|1|
|9. Bathroom|0.385|0.486|0|1|
|10. Telephone|0.269|0.443|0|1|
|11. Total living area (square meters)|65.24|47.929|0|2000|
|12. Number of rooms|2.371|1.498|0|96|
|13. Dwelling Structure(separate house, no)|0.637|0.480|0|1|
|14. Ownership of dwelling|0.739|0.438|0|1|
|15. Rural household|0.430|0.495|0|1|

Each household asset for which the information was collected though the 1999 Azerbaijan Census was assigned a weight or factor score generated through principal components analysis. The principal components analysis (PCA) is a dimension reduction technique (Chatfield and Collins 1980). This multivariate statistical technique is used to examine the relationships between a set of correlated variables.

57
# **5. Conclusion**

We present two main findings. The first is that in four of the five sectors for which we have data, donor agencies’ project outcome ratings provide no information as to the long-run impact of their projects. It follows that they are a poor measure of aid effectiveness, though they may still be useful for monitoring other aspects of project performance. Our second finding is that the single most important correlate of impact is what appears to be a proxy for the degree of contextualization of project design to country circumstances, far ahead of country institutional quality, project size or other commonly identified factors. Our methods enable us to explain around 70% of the residual variance in development outcomes, a much higher proportion than previous analyses of the determinants of project outcome ratings.

These findings have significant implications for how we think about project preparation. Further work is needed to establish what the embeddings mean. But our results at least suggest that greater attention be paid to country contextualization. This does not appear to correlate with the standard determinants of project quality identified in the literature, such as project manager’s age, education or prior experience. Nor does it correlate with whether the staff is based in headquarters or in the field - consistent with IEG’s own assessment that corporate field staffing targets have failed to ensure that decentralization is tailored to country and program needs or applied to areas where it can bring the most benefits (Independent Evaluation Group 2016). While we find some evidence that the length of project preparation and existence of prior analytical work are positively associated with impact, we are unable to say to what extent they matter.

Our analysis also has implications for how we think about project evaluation. On the one hand, it is encouraging that ICRs provide sufficient information to accurately predict the likely contribution of projects to long-run outcomes. On the other hand, the incorporation of this information into summary ratings appears to have resulted in them becoming disassociated from development impact. This may warrant a more careful reading of the qualitative evidence in ICRs, as well as more attention to how project outcome targets are calibrated, perhaps by requiring teams to specify an ex ante counterfactual absent the World Bank’s involvement.

17
# **6. Concluding Comments**

The floods that occurred during the 2014/15 growing season in Malawi had significant and large impacts on maize yields and value of crop production per capita. Households located in the most highly affected areas faced average yield losses of 35 percent for local maize and over 50 percent for hybrid maize. Impacts on food expenditures per capita were more muted, and even more so for calories consumed per capita. Looking at the direct impact of the value of crop production on food expenditures, we find that the elasticity is 6.7 percent, meaning that a 50 percent drop in value of crop production would translate into just a 3.35 percent drop in food expenditures per capita. One explanation lies in the fact that food prices were lower in 2015 versus 2013, and food expenditures are higher when maize flour prices are higher. Looking at the spatial price variation in 2015, we note a negative correlation between maize flour prices and food aid delivery data at the district level, but the data proved too coarse to test this hypothesis. However, the flooding had a particularly pronounced impact on food consumption scores, leading to 10 percent lower scores for those in medium-affected areas, and 12 percent lower scores in high-affected areas. Overall, then, the primary impact was to reduce the quality of the food consumption basket rather than quantity per se.

Further, we found that the risk coping variables were of limited use in helping households buffer the effects of floods. A number of social safety net programs did help households maintain food quantity and quality, particularly for those located in medium-affected areas. In high-affected areas, only access to food aid was effective, but access to MASAF and school feeding programs were not. Thus, the evidence suggests that ensuring access to these two programs in areas highly affected by extreme weather events like flooding can help ensure households maintain both food quantity and quality. While careful planning ahead of a disaster is needed to make sure such programs are accessible during and immediately after a severe weather event, our data were collected within 9 to 11 months after the floods, and all communities were accessible by that time.

On the other hand, as the descriptive statistics show, the proportion of households with access to direct food aid was actually lower in 2015 versus 2013, though the difference is not statistically different. As we noted earlier, we did not have households located in the most severely-affected areas, where presumably much of the food aid was delivered. Nonetheless, this is a surprising finding, and highlights the need for expanded access to food aid to communities in areas that suffered significant crop damage, even if not located in the most severely-affected areas. Additionally, access to MASAF work was lower in 2015 versus 2013, even while such access has positive impacts on food expenditures and calories per capita. On the whole, the evidence suggests a great deal of scope for aligning different social safety net programs with disaster risk management and emergency food aid programs.

18