rafmacalaba/data-use-mentions-extended huggingface dataset:
household diary than in the frequently supervised one. In one example, the frequently supervised diary yielded a consumption value for Nuts and Seeds 17 percent less than the benchmark, while the corresponding yield of the infrequently supervised diary was only 7 percent less. Because the infrequently supervised diary can be transformed into a recall survey over relatively brief periods between supervisory visits if the household does not fully adhere to the diary format, the larger consumption values for the infrequently supervised diary may point to a similar positive reporting error phenomenon as seen in the 7- day recall survey. This error, in turn, partly compensates for the leave out error, thus bringing the total consumption value from the infrequent diaries closer to the benchmark.

Because household diaries are substantially less resource intensive than the benchmark personal diary, they will remain a commonly used tool. As suggested by these results, steps can be taken to improve accuracy. Thus, even if one household member records all consumption, prompting explicit consultation with all adult household members around the consumption of items commonly consumed alone or outside the home may serve to reduce personal leave out errors. The fieldwork of diary supervisors may also be structured so that more frequent supervision is possible (by optimizing work routes exhibiting a constraint on repeat visits). Where this is not possible, bounding notices such as stickers showing the dates of the past and future visits can call attention to the relevant time periods for the consumption records.

Our analysis focuses on the influence of errors generated by forgetting, telescoping, the use of rules of thumb, and personal leave out errors and proposes changes in survey design that may reduce this influence. We conclude with an additional brief discussion of small design changes unlikely to impact survey costs that may reduce the influence of other types of response error not investigated here. Two examples are given below. We refer interested readers to a more comprehensive treatment of this issue in Smith, Dupriez, and Troubat (2014).

First, intentional error could also stem from interviewers subtly guiding respondents to
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accurate welfare metric for poverty estimation in Syria, particularly for the purpose of assessing the evolution of poverty over time using pre-conflict estimates from the HIES as a baseline.

In fact, the consumption module in each of the three rounds of HIES conducted prior to the conflict in 2003, 2007 and 2009 was based on an extensive diary account of household expenditures covering more than 500 items (goods and services). According to Beegle and others (2012), use of diary versus recall, and differences in the number of consumption items collected in surveys drastically affect poverty and inequality measures.

- 3.2 Monetary poverty estimates based on the HNAP 2022 Demographic and WASH survey

While differences in survey instruments between the HNAP 2022 and the HIES constrain the possibility of obtaining accurate pre-post conflict poverty comparisons, it is important to assess whether poverty estimates from the HNAP 2022 can be used to assess the profile of monetary poverty in Syria in 2022.

The welfare metric used for poverty analysis is constructed aggregating all available information on household expenditure, with the sole exclusions of expenditures recorded under the “Other” categories for both monthly and annual consumption items (see Table 4). Compared to a “standard” consumption aggregate for welfare measurement, the consumption aggregate based on information collected in the HNAP 2022 has three major drawbacks:

> 19 Engel’s law – the fact that the expenditure share dedicated to food consumption decreases as income rises – has been proved to be extraordinarily consistent across time and space. As such, the predictive character of this relationship has been widely used in poverty measurement, from the estimation of poverty lines to PPP assessment.
Another strand of the literature examines the idea that disparities are due to social norms and institutions. Although developed countries do not have social customs that limit freedom or inferior property rights such as those described in figures 3 and 4, norms regarding the division of labor in the household, especially those regarding child rearing, may be a limiting factor. In her analysis of the World Value Surveys, Fortin (2005) finds a strong correlation between “traditional family norms” and both female labor-force participation and a wage gap. Ganguli et al. (2010) find that marriage and motherhood are important factors in explaining the correlation between reduced education inequality and female labor-force participation. Using historical microlevel census data, they show that the reduction in education inequality is not matched by a reduction in marriage and the motherhood gap (that is, the difference in labor force participation between married and single women and between women with and without children). Evidence on historical patterns of female labor force participation in the United States is consistent with the idea that gender differences in the production function for children play an important role in explaining gender disparities in economic outcomes. For instance, Albanesi and Olivetti (2009) show that the introduction of formula milk, which reduces gender differences in child-feeding skills, has played a crucial role in explaining the increase in labor-force participation by married women in the United States over the last century.

There is no doubt that technological progress (e.g., formula milk, the contraceptive pill) has reduced women’s comparative advantage in child rearing, but important differences remain in the division of housework and child care. The recent evidence presented in the 2012 World Development Report indicates that, compared with men, women devote much more time to housework and child care at the expense of “market time” in both developed and developing countries.

Parental-leave policies are likely to be a factor in these stark differences in time use. For instance, parental-leave policies in all OECD countries clearly place the mother in charge of caring for
an education system that promotes higher levels of student achievement. However, the fact that the majority of variance in achievement (70–75 percent) is attributable to student-level factors means that policy makers will also have to look at the home environment and broader society, as well as the school environment, in order to raise levels of achievement and close the (currently very wide) gaps in achievement between boys and girls in Saudi Arabia. This student-level variance has been partly explained by variables examined here (e.g., students’ early literacy and numeracy skills, attitudes toward mathematics and science, experiences of bullying, and level of access to home resources for learning), but significant variance remains unexplained.

# 5.3 Limitations

The conclusions that can be drawn from these analyses are limited to being correlational in nature, as TIMSS and NALO are both cross-sectional studies. This means that it would be incorrect to claim on the basis of these results alone that changes in any of the included variables will lead to corresponding changes in student achievement. In some cases, the findings presented here are clearly consistent with theoretical expectations and evidence from other settings—for example, that promoting more regular attendance at a school with a learning-supportive climate may reasonably be expected to have positive implications for student learning. Nonetheless, readers should be aware that the model results need to be interpreted cautiously and with due regard to the wider theoretical and empirical literature. Informed decisions should be based on a broad reading of the literature and the evidence base, including the new results presented in this paper, rather than on any single study.

The results of the models hint at the importance of teachers and teaching quality as contributing factors to student outcomes. However, the strength of any conclusions drawn in this paper related to teaching are constrained by limitations in the available data. For example, the TIMSS variable describing teachers’ qualifications at grade 4 was omitted from analysis due to an error identified in the Saudi Arabia data set for TIMSS 2019. TIMSS collects
Rogoff (2004) and updated by Ilzetzky, Reinhart and Rogoff (2009). This index goes from 0 to 14 and higher values signal a more flexible exchange rate arrangement. Healthier external and fiscal positions are approximated by the current account balance and the central government budget surplus. Both variables are expressed as percentage of GDP and the data are obtained from WDI and IFS. Measures of the health of the financial sector are also included as determinants of sudden stops. Among them we have bank credit, bank deposits, bank assets and the credit to deposit ratio. All these variables are expressed as percentage of GDP except for the credit to deposit ratio which is taken from Beck, Demirguc-Kunt and Levine (2000) and Beck and Demirguc-Kunt (2009).<sup>10</sup> A country’s vulnerability to sudden stops is heightened by its exposure to international good and asset markets. In this respect we include indicators of trade and financial openness. Our measure of trade openness includes the ratio of real exports plus imports to GDP and natural resource abundance. The latter is measured by the net exports per capita of agricultural raw materials, food, fuel, metals and mineral ores. The data is compiled from WDI. Openness to the world capital markets is measured from Kose, Prasad and Terrones (2006), as the ratio of net financial flows to GDP ( _i.e._ net capital flows). To test the consistency of the financial openness indicators we break down this measure into net equity vis-à-vis net debt flows, and its decomposition into FDI, portfolio investment and other investment. Then we distinguish between local and foreign residents’ decisions by including gross inflows and outflows instead of net inflows. The data on net and gross flows is obtained from the IMF’s Balance of Payments Statistics. Finally, external shocks are added to the regression as controls. We proxy terms of trade shocks as the annual percentage change in the terms of trade index, and the data is collected from WDI.
line-up method, which gives elasticities that are significantly higher in magnitude than previously observed. The model estimates plausible poverty rates at higher poverty lines, and predicts greater poverty reduction in faster growing states. The validation exercises show that in 2011-12, the imputation model fared slightly worse than the typical method of applying HFCE growth to older survey data. When predicting back to 2004-05, however, the imputation model performed dramatically better. This evidence, though limited, suggests that a survey to survey approach generates more reliable estimates than the current line-up method.

The 2015 poverty rates reported for urban and rural India were calculated by applying a fractional “pass-through rate” to growth in Household Final Consumption Expenditure, with pass-through rates selected separately for urban and rural areas to match the model’s estimated $1.90 poverty rates. Therefore, the 2015 poverty rate was calculated by scaling up the 2011-12 survey data in a distributionally neutral way, rather than treating the predicted welfare distribution as if it were a new survey. Three considerations influenced this decision. First, using a pass-through rate to line up the estimates more clearly signals that the 2014-15 estimates are model-based predictions rather than actual survey data on welfare. The alternative of lining up the predicted 2014-15 welfare distribution to 2015 could create confusion by blurring the line between predicted and actual data. Second, applying a partial pass-through builds on and improves the existing practice of assuming a full pass-through to line up the estimates. Finally, no study to our knowledge has convincingly shown that survey to survey imputation methods can accurately predict welfare at the very top of the welfare distribution. As a result, it is not clear that this methodology can be used to accurately predict mean welfare and inequality, which unlike poverty are sensitive to the accuracy of the predictions for the richest households. Maintaining a distributionally neutral adjustment provides a way to use a survey to survey imputation for poverty without entering the uncharted territory of using imputed values to estimate mean welfare and inequality. Better understanding whether and how imputation methods can be used
||accelerated depletion of livelihood assets that will lead to food consumption gaps.|
|---|---|
|Emergency|Even with any humanitarian assistance at least one in five HHs in the area have the<br>following or worse: Large food consumption gaps resulting in very high acute<br>malnutrition and excess mortality, OR Extreme loss of livelihood assets that will lead to<br>food consumptiongaps in the short term.|
|Famine|Even with any humanitarian assistance at least one in five HHs in the area have an<br>extreme lack of food and other basic needs where starvation, death, and destitution<br>are evident. (Evidence for all three criteria of food consumption, wasting, and CDR is<br>required to classifyFamine.)|

In Ethiopia, the IPC 2.0 scale is used by the Famine Early Warning Systems Network (FEWS NET) in the development of food security maps. FEWS NET food security maps additionally provide information on the location of the provision of humanitarian assistance. Within Ethiopia’s broader early warning framework, the IPC system could be used to substantiate the results obtained through the existing early warning tools. Specifically, the IPC could be used to act as an overlay that could structure the decision making process to respond to droughts, particularly due to the system’s design that enables the simplification of complex information into actionable knowledge and response objectives.

# IV.7 Potential for use of early warning tools to inform early action

As outlined above, Ethiopia has a set of instruments to predict droughts, thereby enabling early action before the food security impacts of the drought become critical, that is, following the lean season (or dry season in pastoral areas) after the harvest. Based on the available early warning systems, it is possible to define triggering events that would set off a drought response. Potential triggers that could be considered include, for instance, LEAP’s intermediary outputs (WRSI or crop yield
14 Hence, the effective storage is the gross storage minus the dead storage. It can be calculated from the following formula:

# Effective Storage (MCM) = MAX{Gross Storage (MCM) – Dead Storage (MCM),0}

To construct the index, the effective storage from the relevant major tanks is recorded on the 1<sup>st</sup> and 15<sup>th</sup> of every month from November to March to cover the Maha season. The normal monthly effective storage levels are specified for each tank. (The storage level data is also available on a daily basis for some tanks.) For example, for Pavatkulam tank in Vavuniya, the normal monthly effective storage levels, based on seven years of recorded data from 2011 to 2017, are as follows:

**Table 1. Normal Monthly Effective Storage Levels – Pavatkulam Tank**

|**Month**|**Nov**|**Dec**|**Jan**|**Feb**|**Mar**|
|---|---|---|---|---|---|
|**Effective storage**<br>**(MCM)**|9|16|19|17|14|

Source: Author constructed, based on MCM data from Irrigation Department of Sri Lanka.

The effective storage level of major tanks can be used as a parameter for more accurately identifying yield stress for farmers with access to irrigation from them. The indexed pay-outs would then only take place in a situation where adverse weather conditions have occurred while tank storage levels were also low in the same season. For example, if the actual average monthly effective storage level is less than a threshold level (e.g. less than 90 percent of the normal monthly effective storage level) that month can be considered a ‘low tank level’ month. Depending on
and 2013 from the national I-O tables, we applied a nonsurvey-based method (Miller and Blair 2009; Flegg and Tohmo 2011; Kowalewski 2013). Appendix C discusses how we constructed the regional I-O tables.

# **Sectoral Domar Weights and Value-Added Shares**

Appendix figure A3 (panel a) shows changes in Domar weights, which is the ratio of sectoral output to GDP, between 2004 and 2013. In the south, production activities primarily shifted from agriculture and “other services” to mining and “other industry.” The Domar weight increased the most for mining (17 percentage points), followed by “other industry” (6 percentage points). In the north, the Domar weight for agriculture decreased by 36 percentage points. “Other industry” experienced the largest increase in the Domar weight (by 16 percentage points), followed by mining (10 percentage points) and WRT services (10 percentage points).

Appendix figure A3 (panel b) shows the change in value-added shares for each sector between 2004 and 2013. The regional pattern of the change in sectoral value-added shares is comparable to the regional pattern of the change in Domar weights. In the north, the gain in the value-added share in “other industry” (23 percentage points) is larger than the change in the Domar weight in “other industry” (16 percentage points). At the same time, the gain in the value-added share in mining in the south (13 percentage points) is lower than the Domar weight in mining (17 percentage points). The implications of changes in the Domar weight and the sectoral value-added share for intersectoral linkages are twofold. First, “other industry” continues to be the most important sector in the production network of the north. Second, mining plays a dominant role in the production network, especially in the south.

# **Intermediate Input Use and Input Downstreamness Index**

We compute the intermediate use share as the percentage of sectoral output used as intermediate inputs for other
results, large parts of physical infrastructure were destroyed or rendered inoperable, and GDP in 1999 declined by about 40 percent (World Bank (2002a)). Soon after the violence ceased, Timor-Leste began rebuilding itself with the establishment of a United Nations transitional administration and support from the international donor community and NGOs. During 2000 and 2001, Timor-Leste made enormous progress in rehabilitating its economy, with GDP growing at an annual rate of 15 to 20 percent, reconstructing infrastructure, reintegrating refugees and building the key elements of a sustainable political process in an environment of internal peace. This transition has transformed people’s lives fundamentally and in ways that go far beyond purely material endowments. Although the fundamental achievement of gaining independence is overwhelmingly positive, the vast structural changes were accompanied by conflict, destruction, and displacement. This paper is an empirical study of changes in economic well-being (‘welfare’) and empowerment (‘power’) in Timor-Leste from the pre- to post-conflict period.* It draws on the only nationally representative household survey and community census of Timor-Leste to investigate the impact of the systemic changes between the violence of 1999 to end-2001, six months before independence. It demonstrates how subjective and objective measures, based on current and recall information, can be used to develop a differentiated profile of changes in well-being from the pre- to post-conflict period. ° However, in interpreting the findings, it is important to bear in mind one qualification. Recall data are not representative of the historic population in case of large population changes. This is likely to be the case in Timor-Leste, where parts of the population left the country for good - in particular Indonesians and supporters of the old regime. The results in this paper therefore reflect changes in well-being as perceived by the 2001 population rather than the population.

The rest of the paper is organized as follows. The next section describes the data. Section 3 takes a first look at the pattern of changes in self-rated welfare and power between 1999 and 2001. Section 4 investigates
language skills, poverty, etc.) are themselves likely to have been affected by the migration decision. As such, anyone designing migration surveys that are intended to estimate causal impacts using these methods should endeavor to ask retrospective questions about household characteristics at some point in time prior to the migration events.

# **4.2 Panel Data, Fixed Effects, and Difference-in-Differences**

## **4.2.1 Fixed**

Panel data provides multiple observations over time of the unit of observation (e.g., individual, household, or locality).<sup>6</sup> This allows controlling for not just the observable characteristics of these units, but also non-observable time-invariant characteristics that might be correlated with both migration and the outcome of interest. For example, consider estimating the impact of migration of a household member on child schooling in that household. The concern with using regression or matching is that migrant households may differ in unobservable ways from migrant households due to factors that also influence schooling. For example, they may live in communities with worse schools (and migrate to escape poor infrastructure), they may care a lot more about schooling (and migrate to give their children schooling opportunities) or they might speak a different language from non-migrants (that is rewarded more at destination). With panel data, equation 1 is supplemented by adding the individual fixed effect term _αi_ , and time effect _λt_ to give: This individual fixed effect _αi_ will capture the influence of any time-invariant characteristic like how much the household cares about education, the starting infrastructure in their neighborhood, and the language they taught their children, even when we cannot directly observe these characteristics. Estimation of the impact of migration, _β_ , then comes from units that _change_ migration status; households that happen to have a migrant member in all time periods in the data, or no migrant members in any time period, do not contribute directly to the estimates.<sup>7</sup> The fact