rafmacalaba/datause-encoder-data huggingface dataset:
other response patterns overwhelms variance in responses that is driven by the latent traits that are intended to be measured. Clearly a factor analysis that is driven by phrasing rather than actual content is of little interest to the researchers, hence the prior correction for the acquiescence bias is fundamental. Although far from making it perfect, the correction improves the items’ ability to capture a somewhat coherent factor structure.

The findings in this paper hence suggest it is advisable to first correct for acquiescence bias, and systematically analyze the latent factor structure through exploratory factor analysis when using noncognitive skills data. Naïve interpretation of item aggregation following pre-existing constructs without such analysis is likely to lead to erroneous conclusions regarding noncognitive skills.

# **4.7 Explaining the noise in the measure of technical skill**

As the technical skills questions attempt to measure knowledge, one would expect them to be less affected by systematic response biases. They require respondents to choose between a series of answers that do not have a clear ranking, or to give open answers, and while respondents certainly can (and do) guess, systematic bias of all questions in a certain direction is less likely. The results in this paper indicate however that random measurement error is much more important for technical than for cognitive skills. This can be inferred from the low test-retest statistics, low Cronbach’s alpha, and the gains in precision and predictive power when using means of test and retest, or the gains in precision from using factor analysis.

As discussed above, respondents’ difficulties in understanding the technical knowledge questions may partly explain the measurement error. Insights from the qualitative field work provide some additional insights for why the technical measure is noisy. To effectively assess a skill, a question needs to have only one correct answer and have enough variation in the responses to be informative of the respondents’ knowledge. However, after working with agronomists to identify the most fundamental knowledge that can affect the farmer’s productivity and piloting the questions, we found that most of them fell into one of the two following categories. Questions with unambiguous correct answers were answered correctly by the vast majority of farmers.<sup>39</sup> In contrast, questions that had sufficient variance in the responses often were questions for which

> 39 This may be different when farmers have recently been exposed to new information (for instance through an extension intervention) as differences in exposure and internalization of the new messages may create more empirical variation in knowledge of this new information.

30
Policy ReseaRch WoRking PaPeR 4861

# **Abstract**

The current account can be understood as the outcome of investment decisions made by domestic and foreign investors. These decisions can be decomposed into a portfolio rebalancing and a portfolio growth component. This paper provides empirical evidence of the importance of portfolio rebalancing for the dynamics of the current account. The authors evaluate the predictions of a partialequilibrium model of the current account with dynamic portfolio choices, in which portfolio rebalancing is driven by changes in investment opportunities. Using data for the United States and Japan, the authors find evidence supporting innovations in investment opportunities as an important mechanism to explain international capital flows.

This paper—a product of the Office of the Chief Economist, Latin America and the Caribbean Region—is part of a larger effort in the department to understand the driving forces of international capital flows and their implications for macroeconomic policies. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at tdidier@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
48

## TABLE 29

## SAVING ABILITY IN RELATION TO EDUCATIONAL LEVEL, PLACE OF ORIGIN, AND LIVING ARRANGEMENT

# (%)

|**I.Educational**<br>**Level**||||
|---|---|---|---|
|SD|92|58.7|41.3|
|SMPU|83|68.7|31.3|
|SMAU|39|82.1|17.9|
|SMAK|38|81.6|10.4|
|>SMA|2|100.0||
|2. Place of Origin||||
|factory|78|56.4|43.6|
|West Java|33|69.7|30.3|
|Other Java|116|75.0|25.0|
|Other Island|27|81.5|18.5|
|3. Living<br>Arrangement||||
|Dormitory|15|66.7|33.3|
|With parents|47|48.9|51.1|
|<br>Husband|32|62.5|37.5|
|Rent|125|77.6|22.4|
|Relatives|20|80.0|20.0|
|With owners|7|57.1|42.9|

The pattern of migration suggests the mere fulfillment of basic needs is the main purpose for migration: 49 percent of the respondents who are permanent workers say they would prefer to be home since the only reason for moving away was to earn money. Only 30 percent of the respondents plan to stay to where they migrated and 21 percent have not decided if they will stay or return home. Many also still go home for visits with 90 percent saying they have visited their hometown with various frequencies: 79 percent visit at least once a year. A further relationship between workers and their origin is shown by the fact that workers from the same place tend to live in the same area.
8 postneonatal mortality across malaria zones reflects this phenomenon.<sup>6</sup> Table 2 shows neonatal, postneonatal, and infant mortality rates by characteristics, pooling data from 2003 and 2008. Neonatal mortality rates are statistically identical across malaria risk zones (approximately 36 per 1000 live births), while postneonatal mortality is lowest in low malaria risk zones (16 per 1000), slightly higher in intermediate malaria risk zones (20 per 1000), and three times as high in high malaria risk zones (62 per 1000).

ITN ownership rates have increased across Kenya, and the postneonatal survival gains have been chiefly in the high malaria risk areas, particularly rural Nyanza province but also urban Nyanza province as well as both urban and rural Western province. Figure 2 shows a scatter plot by province and urban/rural classification of postneonatal mortality rates vs. ITN ownership using the 2003 and 2008 DHS data. Figure 3 shows a similar plot for neonatal mortality. The geography of shifts over time that have taken place in neonatal mortality show no obvious correlation with malaria patterns.

It is important to recognize that the ITN measure used throughout this paper is _household ownership_ of at least one ITN at the time of the DHS survey. This differs from actual _usage_ of ITNs by individual children. Because information on ITN usage by deceased children is not available, it is not possible to use DHS data to examine the association between ITN usage and mortality. We thus take ITN ownership by the household is used as a proxy for ITN usage. ITN usage by children is highly correlated with the household‘s ownership of at least on net. In the sample, 82 percent of living children in households with at least one ITN slept under an ITN the night before the interview.

Table 2 summarizes comparisons of neonatal, postneonatal, and infant mortality rates by basic characteristics. As noted, postneonatal mortality is greater in high malaria risk zone and is significantly lower in households that own at least one ITN. This difference is significant both overall and within high malaria risk zones. Postneonatal mortality is also lower for infants from

> 6 We categorize survey clusters from the DHS into ―low malaria risk zones,‖ ―intermediate malaria risk zones,‖ and ―high malaria risk zones‖ using data assembled by the Malaria Atlas Project.  The Annex provides a description of this classification. The risk zones are defined in terms of prevalance of Plasmodium falciparum, which is the most deadly strain of malaria and is estimates to be responsible for 98 percent of malaria cases in Sub-Saharan Africa (World Health Organization 2008).
where _T_ = {1,0} is the treatment variable and _X_ is the vector of pre-treatment observable characteristics. Rosenbaum and Rubin (1983) show that if treatment is random within cells defined by _X_ , it is also random within cells defined by the values of the propensity score _p_<sup>(</sup> _X_ ) . So, if the propensity score _p_ ( _X i_ ) is known, the impact of the treatment (which they call Average effect of Treatment on the Treated or ATT) can be estimated as follows: where the outer expectation is over the distribution of ( _p_ ( _X i_ |) _Ti_ = 1 ) and _Y_ 1 _i_ and _Y_ 0 _i_ are the outcomes in the two counterfactual situations of (respectively) treatment and no treatment. More on propensity PSM technique can be found in Becker and Ichino (2002).

The advantage of PSM is that it does not assume a predefined functional relationship between the outcome and control variables, a limitation of any regression model. However the limitation of PSM is that it cannot screen the sample for unobservable characteristics. Another shortcoming of PSM is, if a good portion of the original sample is discarded due to matching, the remaining sample may become unrepresentative of the population.

In this paper, we used PSM to identify a matched sample of panel data, not to estimate treatment impacts. This is done using the baseline data (that is, treated and untreated households are matched based on baseline characteristics) and then same households included from the follow-up 20
Table 7: Change in the share of high-skill occupations and tech adoption

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|ABF Int|-0.056*|-0.061|||||
||(0.032)|(0.038)|||||
|GBF Int|||-0.059**|-0.069**|||
||||(0.027)|(0.029)|||
|SBF Int|||||-0.041*|-0.043*|
||||||(0.022)|(0.022)|
|Ln (Employment 2016)|0.014|0.016|0.037|0.040|0.012|0.015|
||(0.011)|(0.011)|(0.024)|(0.028)|(0.010)|(0.010)|
|Constant|0.064*|0.104*|0.008|0.040|0.049*|0.089*|
||(0.039)|(0.058)|(0.044)|(0.056)|(0.029)|(0.049)|
|Observations|1,038|967|1,711|1,617|1,038|967|
|R-squared|0.004|0.014|0.010|0.020|0.004|0.014|
|Firm characteristic|NO|YES|NO|YES|NO|YES|
|Sector FE|NO|YES|NO|YES|NO|YES|
|Region FE|NO|YES|NO|YES|NO|YES|

_Note:_ Robust standard errors in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1

# **8 Concluding Remarks**

This paper has provided a granular view of technology use and adoption in Senegal that can be summarized in three stylized facts. First, the adoption of general-purpose ICT technologies, such as computer, Internet, and cloud computing for business purposes is low, but very heterogeneous and positively associated with size and formal status. Second, most firms still rely on pre-digital technologies to perform general business functions, such as business administration, production planning, supply chain management, marketing, sales, and payment. Third, most firms, including large and formal, still rely on manual methods, or manually operated machines to perform critical production tasks that are sector specific, such as harvesting in agriculture, or packaging in food processing.

The analysis also suggests that promoting technological upgrade of existing firms (formal and informal with 5+ employees) should be a key priority for Senegal. Senegalese firms of all sizes are far from the technological frontier, across all business functions. Upgrading technologies is critical to generate better firms, which are needed for better jobs. The Recensement G ́en ́eral des Entreprises (RGE), which was the last establishment census led by the Agence Nationale de Statistique et de la D ́emographie suggests that formal and informal firms with 5 or more employees cover almost half of the workers and about 80% of the total sales measured by the RGE data, despite the fact that they represent only 6% of the establishments. These firms are very important for the economy of Senegal, but still, the large majority, including those that are large, formal, and located in Dakar, present low 39
39 while shwe earned by capital was declining. The reason for these wage inreases has been the presence of inflexible labor contcts negotiated during tight labor market conditions around 1983. The wage level of new enarats did not increase On the contrazy, one observes a fill in marginal wage for new entrats from 19S4-M85. There is little evidence to link wage rises to the increase in energy prices. Table 2.4 gives selected macroecononmc indicators for ihe Malaysian economy during 1982-87.

_Table 2 4: Malaysia: Selected Macroeconomic Indicators, 1982-87 (M million)_

||1982|1983|1984|1985|1986|1987|
|---|---|---|---|---|---|---|
|GDP|50430|53582|57741|57150|57859|_60595_|
|(Percent growth)|_5.9_|6.3|7.8|-1.0|1.2|4.7|
|Total revenues|16690|18603|20805|21114|19518|18143|
|(Percent growth)|-|11.5|11.8|1.5|-8%|-7%|
|_Change inPriceIndex(.p.a.)_|||||||
|General price level|3.3|4.9|5.2|-1.5|-8.4|5.0|
|CPI|_5.8_|3.7|3.9|0.32|0.7|_1.5_|
|Food prices|8.26|0.91|3.7|-2.46|0.24|0.49|
|Gross rent, fuel & power|6.28|6.53|7.5|4.2|1.58|<br>0.57|
|Transport<br>& comnication|3.34|2.42|4.99|2.17|0.41|3.09|

Using the estimate of price elasticity of demand for diesel equal to -0.9 and -0.81 for kerosene, we can assess the revenue effect of ihe price increases. Had the energy price chanes not bee implemened, the estimaed shorffa1l in revenues in 1985 due to ihe 1984-85 price increas would have beem equivalent to SM million 398 or 2 percent of lhe government revenues in the same year. Moreover, actual data for t period indicates a substnta reduction in deficits from 1983 onward and th by 198S the fiscal deficit was reduced to SM million 5617 or 7.2 percent of G(P. Although tldis was a peiod of adjus nt and change i govarmment reveue resulting from a number of fiscal poicies, an incras domestic petroleun prices most lky woiud have been one source of reduction in Oh public sector ' See Wol Bank (1988), p _31-35._ 2 Sm **ox** 1.7 for detaiat to cdaculai of **Revem** effect. **Se abso** dicsion of i **iqpd** in **CbRL** L
a rather heterogeneous population group which cannot be easily discerned to have been “left behind” in income terms during the 1957/8-1983/4 survey period.

To what extent are the findings pointed to in Palanpur part of a broader pattern of social exclusion reflected, for example, also in education levels, or landholdings? If households in Palanpur were classified into groups based on education levels of the household head, would we uncover similar evidence of a partitioned income distribution as was found, for example, between Jatabs and the rest of the population? Investigations along such lines are common in the literature on inequality decomposition, and can help point to underlying causes of group differences. If, say, all Jatabs in Palanpur were illiterate in 1983/4 while the rest of the village enjoyed at least some education, then a breakdown of the village population into “illiterates” versus “the rest” would yield a similar ELMO statistic as was found for a caste-wise breakdown between Jatabs versus the rest. This might then point to education disparities as a possible explanation for the differences observed between these two caste-groupings. Land holdings, could provide an alternative basis for defining groups – capturing a key wealth dimension.

In the event, in Palanpur in 1983/4, a variety of different ways of defining groups in terms of levels of education of household head, and of household landholdings, fail to reveal a similar degree of partitioning as was observed for Jatabs versus the rest of the population (or Jatabs and Muslims together, versus the rest). In terms of education, the greatest degree of partitioning is observed when the population is broken up between those households with household head with 12 or more years of education versus everyone else. But the ELMO statistic here takes a value of 10.3% only – far lower than the nearly 50% observed for a breakdown between Jatabs and the rest.<sup>xviii</sup> For groups defined in terms of landholding, the highest value of the ELMO statistic occurs when the population is broken down into the four 21
the distance to the mean in absolute terms, whereas spi-like anomalies directly relate the intensity of a hazard to its probability of occurrence.

Since there is little consensus around an optimal underlying variable for describing the RSQ, we test a range of usual inputs including precipitations, the Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI) and Dry Matter Productivity (DMP). We acknowledge that other variables typically used for monitoring droughts and agricultural production could be employed and leave it to future work to test other indices such as the Standardized Precipitation Evapotranspiration Index (SPEI), the Palmer Index, other vegetation indices, temperature-based indices or more elaborate direct measures of agricultural production and availability of pasture. That said, our results suggest that RSQ measurement choices matter less than the choice of heterogeneity structure (i.e. vulnerability factors) in the optimization of the model explaining power of the performance.

One final consideration for the calculation of RSQ indices is on the temporal dimension with the choice of time window ∆. First of all, in any given year _t_ , we account for differences in the onset of the rainy season that may exist across administrative units. For any geographic unit _a_ , we calculate monthly historical averages for our underlying variable _x_ and considering any time window width of _m_ months, ∆is defined such that it covers the _m_ consecutive months where _x_ is historically highest. Second of all, shorter time scales imply that conditions at the peak of the rainy season are the prime determinant of future food consumption whereas larger time scales consider that aggregate measures over the entire rainy season are more relevant to appraise agricultural campaigns. We do not favour one assumption over the other and simply test different time scales ranging from one to months.<sup>15</sup> Gridded precipitations are retrieved from the CHIRPS-2.0 product of the Climate Hazard Group. Monthly precipitations at a resolution of 0.05<sup>_◦_</sup> are available for the period 1981 to present. We produce monthly grids of NDVI and EVI at a 500m resolution from NASA’s MODIS Terra satellite products that cover the period 2000 to present. Similarly, we create monthly 1km grids of DMP using data from the Global Land Service of Copernicus (PROBA-V satellite mission) for the period 1999 to 2019. We aggregate monthly gridded – – values at administrative levels from regions (level 1) to communes (level 3) and we calculate RSQ indices at various temporal scales.

We provide a correlogram of department-level RSQ indices in Figure 4 to appreciate differences between index types<sup>16</sup> . While we observe high pairwise correlations between

> 15The maximum time scale of five months corresponds to the typical duration of the rainy season in Mauritania.

> 16We also provide a similar correlogram in Figure B.1 considering a shorter time scale for our four indices (1 month) in appendix B and make comparable observations.

15
Nine million children are currently benefiting from the program, 1.9 million of which are in high school.

Currently, the targeting for eligibility for the benefits of the 4Ps program is aimed primarily towards alleviating chronic poverty among eligible poor households and not towards identifying those that are vulnerable to poverty. Beneficiaries are objectively selected through the National Household Targeting System, also known as _Listathanan,_ which is based from a survey of the physical structure of their houses, the number of rooms and occupants, their access to running water, and other factors affecting their living conditions (Velarde, 2018). The program has one of the most comprehensive poverty targeting databases in the world today, covering 75% of the country’s population. It has been used extensively to identify poor and near-poor beneficiaries for national and local government programs.

The rationale of scalable social protection programs is to expand coverage in the event of a disaster to include not only the poor but also some of those believed to be vulnerable to falling into poverty when exposed to a shock. The objective is to mitigate the direct impact on household asset and welfare losses in the direct aftermath of a disaster, but also to prevent some of the intermediate term impacts – including forced displacement, and negative coping mechanisms used by poor households to handle the shock – such as selling off productive assets.

This paper builds on the existing literature assessing _ex-post_ the quantitative effects of natural disasters on different dimensions of household welfare (e.g. Anttila-Hughes and Hsiang, 2013; Strobl, 2019; Ishizawa and Miranda, 2016) to make progress towards the _ex-ante_ identification of households vulnerable to poverty due to natural disasters. A wind model for the Philippines is employed to estimate local wind speeds at any particular locality where a tropical typhoon directly passes over or nearby. The estimated wind speeds are merged to the household FIES survey at the barangay level and consumption expenditures are regressed against windspeed (or a related damage index) and socioeconomic household characteristics. The paper shows that different specifications of typhoon effects have negative impacts on consumption levels, especially food. The estimated coefficients from the regression model are then used to estimate _ex-ante_ household vulnerability to poverty in the event of future natural disasters of different intensity.

The proposed approach applied to the case of typhoons is fully complementary to the proxymeans targeting (PMT) method used by _Listahanan_ and the 4Ps, in the sense that in addition to the chronic poor currently identified by the PMT method of the 4Ps program, it allows identification (or targeting) of the households that are likely to fall below the poverty line in the event of a natural disaster shock. The _ex-ante_ identification of those who are vulnerable to poverty combined with timely support for these households makes it possible to provide targeted support in a timely and cost-effective manner.

3