rafmacalaba/datause-encoder-data huggingface dataset:
South Africa (SSA) Time Use Survey 2000 (TUS2000). This was conducted as a separate survey. The second is the time use module included in the Nicaraguan 1998 Living Standards Measurement Survey (Encuestra de Hogares sobre Medicion de Nivel del Vida) (EMNV98). These two surveys are compared in terms of the information they provide on the intra-household distribution of resources and on living standards, and for estimating models within the theoretical framework outlined.

Section 5.1 provides a summary of the main features of these surveys. Section 5.2 discusses gender differences in hours of work and leisure based on data means for the two surveys and on the results of various time use studies in the literature. Section 5.3 examines the limitations of SSA TUS2000 for making welfare comparisons and for estimating behavioral models, and goes on to consider the merits of including a time use module in an LSMS, as in the EMNV98, rather than as a stand-alone survey. To highlight the importance of time use data for policy analysis, the section illustrates the deficiencies of consumption based measures of living standards when there is a high degree of heterogeneity in the allocation of time to market and domestic work across households. The analysis is based on the EMNV98 data and the derived measure of consumption per capita included in the file.

# **5.1 Data**

The SSA TUS2000 was the first of its kind at a national level for South Africa. The survey was designed to contain a number of questions found in other SSA survey questionnaires to allow matching. These included questions on household demographics, labor force participation, industry and occupation. The survey was conducted in three tranches to capture seasonal variation.

The planned sample was 10,800 dwelling units, drawn systematically from all provinces. All households in a selected dwelling unit were interviewed. The final sample contained a total of 8,339 household records. Time use data were collected by diary from two randomly selected individuals, aged 10 years or older, from each multi-person household 43
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Amsden, Alice. 1989. _Asia's Next Giant: South Korea and Late Industrializadon._ Oxford University Press.

Arrow, K.J. 1962. "Tbe Economic Implication of Learning by Doing. _Review of Economic Studies,_ 29 (June). pp. 155-173.

Bacha, Edar L. "Growth with Limited Supplies of Foreign Exchange: A Reappraisal of the TwoGap Model" in Moshe Syrquin, Lance Taylor, and Lany Wespal, eds., _Economic Structure and Performance: Essays in Honor of Hollis B. Chenery._ New York: Acadmc Press, 1984.

Balassa, Bela 1978. "Exports and Growth: Further Evdence", _Journal ofDevelopment Economics_ 5, no. 2, 181-189.

Barro, R. 1991. 'Economic Growth in a Cross Secdon of Countries." _Quartery Jouma of Economis._ May.

Benhabib, J. and M. Spiegel. 1994. 'The role of hma capital in econmic development: Evidence from aggregate cross-country data.' _Joura of Monetary Econoics_ 34, pp. 143-173. Bochove, Conelis A. van. 1982, _Imports and Economic Growth._ The Haue: Marims Nijhoff Publishers.

Chenery, Hollis, and Micheal Bnmo. 1962. 'Development Alternative in an Open Economy: tbe Case of Israel,' _Economic Journal._ Vol. 57, March 1962, 79-103. de Melo, Jaime and Sherma Robinson. 1990. "Productivity and Externis: Models of Exrt-led Growth," Working paper no. 387, The World Bank.
**_Figure 2:_** _Share of shopping trips in Bogota and Buenos Aires, by type of travel chain and trip start time (%)_

<!-- Start of picture text --> a. Bogota<br>45%<br>Round trips Chained trips Reduction potential<br>40%<br>35%<br>30%<br>25% 26.1%<br>20%<br>15%<br>6.2% 13.2%<br>10%<br>3.7%<br>5%<br>0%<br>6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23<br>Hour of trip start<br>b. Buenos Aires<br>45%<br>41.3%<br>40%<br>35%<br>30% 27.6%<br>25%<br>18.3% 20.2%<br>20%<br>15%<br>10% 7.4%<br>0.7%<br>5%<br>0%<br>6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23<br>Hour of trip start<br>Source: Team’s estimate based on EDM Bogota 2019 and ENMODO 2018<br>Percentage of shopping trips<br>Percentage of shopping trips<br><!-- End of picture text --> Shopping trips have a higher weight in short trips, reaching 26 percent for trips under 10 minutes in Bogota and 25 percent in Buenos Aires. Only 10 percent of trips that last 30-40 minutes and less than 5 percent among trips lasting over an hour in Bogota are shopping trips. In Buenos Aires, similarly, the weight of shopping trips falls rapidly with trip length, so that the potential for mobility reduction by e-commerce is above the overall average for trips under 20 minutes.

Existing studies have pointed out that, when assessing the potential traffic reduction effects of e-commerce, one has to consider that trips related to shopping are generally short distance and have a higher modal share of non-motorized modes in dense urban environments (Xi et al. 2020). Indeed, in both cities, trips for shopping purposes have a greater weight among non-motorized trips compared to trips made in other modes (Figure 3). In Bogota, shopping trips account for 18 percent of all non-motorized trips, while in Buenos Aires, the shopping trip share in non-motorized trips is between three and four times higher than in trips by other modes. In both cities, the share of linked trips over total trips for shopping purposes is lower among non-motorized trips. Shopping trips are more prevalent in private vehicle trips (8.4 percent in both cities) than in public transport trips. However, while in Buenos Aires only 12 percent of the private vehicle trips are chained, in the case of Bogota the share is over half, which limits the potential for trip reduction. As a result, the potential reduction of private vehicle trips due to e-commerce is about 5.6 percent in Bogota and 7.9 percent in Buenos Aires. Public transport is the mode with the lowest potential for trip reduction due to e-commerce (2.8 percent in Bogota and 5.7 percent in Buenos Aires).

8
BENIN’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Table 14. Benchmarking ICT indicators**

|Indicator|Unit|**Low-**<br>**income**<br>**countries**<br>2008|**B**<br>2000|**enin**<br>2005|**Middle-**<br>**income**<br>**countries**<br>2008|2008|
|---|---|---|---|---|---|---|
|GSM coverage|% population under signal|63|26|43|—|96|
|Mobile phone|subscribers/100 people|24|1|8|47|95|
|International bandwidth|bits/capita|24.8|0.3|6.0|19.0|209.3|
|Internet|users/100 people|3.5|0.2|2.0|6.5|7.0|
|Landline|subscribers/100 people|0.8|0.8|1.0|1.4|9.2|
|Price of mobile basket|US$ per month|11.0|15.5|12.4|11.8|9.2|
|Price of fixed line basket|US$ per month|10.4|11.0|12.0|11.4|11.4|
|Price of fixed broadband|US$ per month|287|—|116|52|57|
|Price of a call to United States|US$ per minute|0.7|1.6|0.4|0.4|0.6|
|Price of an inter-Africa call|US$ per minute|0.9|1.9|0.4|0.4|1.1|

_Source_ : Adapted from AICD, OTH, ART, SOCATEL, Orange and World Bank ICT at-a-Glance. — = data not available The country has benefited from a huge expansion in coverage and access of mobile communications. Mobile penetration rose from 1 subscription per 100 people in 2000 to 47 in 2008, almost twice the average penetration in low-income countries. However, with only 43 percent of the population under GSM coverage, Benin remains behind the levels of comparable Sub-Saharan peers (table 14). Analysis suggests a large efficiency gap that could be reduced with further regulatory reform that would allow 99 percent of the population to be reached by a GSM signal on a commercial basis (figure 18).

**Figure 18. Efficient market gaps for mobile telephone service in West Africa**

<!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>Existing access Efficient market gap Coverage gap<br>Percent of population<br><!-- End of picture text --> _Source_ : Mayer and others (2009) using GSM coverage figures for 2005. _Note_ : Existing access represents the percentage of the population covered by voice infrastructure in the third quarter of 2006. Efficient market gap represents the percentage of the population for whom voice telecommunications services are commercially viable given efficient and competitive markets. Coverage gap represents the coverage gap—the percentage of the population for whom services are not viable without subsidy. SSA = Sub-Saharan Africa.

32
to another. In a first time, people moved to escape the disaster area and, in a second time, they came back home.

Despite its lack of representativeness, the survey does, however, provide very useful information on the allocation of assistance programs after the earthquake. In particular, using asset index as a proxy for household wealth, we can firstly specify the dynamics of poverty after the earthquake. Second, it is possible to assess the targeting performance of assistance programs.

Previous results based on a similar survey in February, a few weeks after the earthquake, highlighted the limited ability of assistance programs to target households according to their preearthquake household wealth or asset losses after the quake. On the other hand, modelling the impact of various covariates on both assistance allocation and food consumption score, we find that the fact of being in a camp had at that time a sizeable effect on assistance, especially for food assistance. Hence, the emergency assistance principle appears to be connected with selfselection strategies since food-insecure people had little choice but to move to the camps in order to have access to food assistance. We also find that both the pre-earthquake asset index and asset losses have a significant impact on food consumption score, with asset losses making up for about half the effect of the pre-earthquake asset index, which demonstrates that households who enforced coping strategies have been able to buffer the impact of the shock.

So, shortly after the earthquake, assistance programs allocation prove not to have been effective in targeting the most vulnerable people in the directly affected area. Five months after the earthquake, it appears that things had not really changed: although food assistance may have contributed to decrease the prevalence of food insecurity over the period, authorities still seemed unable to provide an efficient allocation of assistance programs. The location of households stands out as the main criterion that emerges from the regression analysis, yet this method of targeting is not sufficient: indeed, assistance also appeared to benefit less to families headed by women and less to households with disabled members, which is contradictory with an "optimal" targeting that would make those most vulnerable eligible for assistance in priority. In addition, participation in an association, such as agricultural organizations and school committees, appeared to favor the allocation of emergency assistance to households.

When focusing of cash and food-for-work programs, we find that these programs are not specifically targeted at people who are most in need, be it because of their low level of subsistence or because of earthquake-related losses. Pre-earthquake participation to programs 26
speed data of historic hurricanes and tropical storms in Central America. This novel windstorm hazard model for Central America has been developed by the WBG Latin‐American and the Caribbean Disaster Risk Management (DRM) team as part of the development of Country Disaster Risk Profiles.<sup>6</sup> The windstorm hazard model, described in more detail in Pita et al. (2015), is a wind field model to estimate surface gust wind speeds and a mechanism to generate synthetic events and trajectories. One of the key outputs of the model is the generation of maximum wind speeds along the trajectory of the hurricane. Wind speeds refer to the tangential wind component of the hurricane and are directly related to the intensity of the event. This model has been tested, calibrated, and validated with observed activity in the East Pacific and North Atlantic basins for the Central America region. The model’s forecasts conform acceptably well with the observed wind speeds, which guarantees the accuracy of the data used.

The wind speed model used is grounded in the asymmetric Holland equation, which is characterized by its simplicity (i.e. the number of model parameters) and its accuracy (i.e. the coincidence of model’s predictions with observed measurements).

The model reproduces the geographical distribution of the tangential gradient wind speeds, _Vg,_ or wind gusts at a height of 10 meters over a 1km<sup>2</sup> grid, using the Holland model as represented in Equation 1. The wind speed is measured at this height because it is here that wind speeds do the most damage.

Where: Δ� is the deficit of central and outer peripheral pressure (in Pascals); � is the air density at the gradient height (kg/m3); ��� is the radius of maximum winds (in meters); � is the radius of any location to the center of the storm (in meters); �� is the hurricane translation speed (in meters per second); � is the Coriolis parameter (in 1/seconds); φ is the angle between the North and the storm heading.

Holland’s shape parameter (B) incorporates some characteristics of the terrain. Roughness, for example, may affect angular momentum due to surface friction. These variables have been calibrated for the Central American region using different sources: terrain roughness from the Global Land Cover Dataset 2000, topography data from the Shuttle Radar Topography Mission data base, speed‐up occurring in escarpments and ridges using the methodology proposed by the American Society of Civil Engineers in 1994, and wind gusts factors using Vickery and Skerlj’s (2005) estimation method.

To assess the model’s accuracy and applicability, the estimates were evaluated against the tracks and wind fields of historical events. Predicted results by the model were compared to the values reported by the United States’ National Oceanic and Atmospheric Administration (NOAA) aircraft for Hurricanes Mitch

> 6 This research has been funded by the World Bank through a Global Facility for Disaster Reduction and Recovery (GFDRR) grant (TF014499) from the Government of Australia (AusAid) under the CAPRA Probabilistic Risk Assessment program (P144982).

4
process is progressive. In column (1), we see that vulnerability as defined by TASAF is indeed pro-poor: vulnerable households are, on average, 5.4 percentage points more likely to be categorized as poor using our consumption aggregate and the lower-bound poverty line. Column (2) indicates that the vulnerability criterion identifies a group that is poorly educated rather than one that is poor conditional on education levels.

Columns (3) and (4) compare the actual beneficiaries of TASAF II VG projects to the entire eligible sample, i.e. the population of vulnerable households, and show that those selected to be recipients of TASAF II VG funds are not significantly poorer than the overall eligible sample. Instead, within those eligible to benefit from VG projects, the very same attributes that were shown to be influential at the national-level also determine membership at the local level: those who are more politically active (measured by attendance at village meetings), wellconnected (measured by being a blood relative of village elites) and living closer to the village center are much more likely to be program beneficiaries than others. Finally, in columns (5) and (6), we compare group leaders to the „rank and file‟ membership and find that they are substantially less likely to be poor, and much more likely to have completed primary education and to own a radio or a mobile phone.

These findings confirm the patterns of targeting performance found at the national level and presented above. Earlier, we found that wards with high levels of civic engagement and that party ties to district officials were more likely to receive funds at the national level. Here, we see that households engaged in local politics and have ties to local officials are more likely to be program beneficiaries as well. Hence, while the targeting of TASAF II projects is mildly propoor both at the national and at the local level, these projects (and funds) are also flowing towards households that have high levels of civic engagement and are connected to the local elites. Again, most of the successful targeting towards the poor in this decentralized development program appears to have come about because of the eligibility rules imposed by the center, rather than decisions taken at the local level.

# _6.2. Awareness of TASAF_

The primary empirical contribution of our national-level analysis is the fact that we observe the universe of applications and, hence, can separate the demand-driven components of the program from the approval process. While we do not have an exact analogy for applications at the individual level (the applications are submitted with the beneficiary group already defined 17
14 Union in 1990, this would be the correct procedure. Another view is that the Soviet Union cut deliveries to Hungary of oil in 1990 to force Hungary to incur some of the switch-over costs in 1990. That is, if the Soviet Union had a continued commitment to the CMEA system, it would have made an effort to maintain deliveries to the CMEA partners, rather than sell to the West for convertible currency. t<sup>2</sup> Then one could counterfactually evaluate what the Soviet "subsidy" to Hungary would have been in 1990, if it had maintained deliveries in 1990 at the level of 1988. Using 1988 quantities and the prices of the previous paragraph, results in an additional cost of the switch-over of $950 million (for a total cost of between 1.75 and 2.15 billion dollars).

Since the prices of energy products are subject to considerable volatility, we also estimate the change in the Soviet "subsidy" to Hungary for each dollar change in the price of oil (taking into account the impact of a change in the price of oil on other energy prodcuts). Based on 1990 quantity weights, we estimate that the switch-over costs to Hungary will change by $76 million for each dollar change in the price of a barrel of oil." 3 Using this estimate of the subsidy per dollar per barrel, the reader may easily calculate the change in the "O0f course, part of the reason for the cut in Soviet deliveries to Eastern Europe was domestic production problems. But, deliveries of oil to Eastern Europe fell more than in proportion to the drop in production or in sales to the West.

'<sup>3</sup> The estimate is derived using the following methodology. In 1990 Hungary imported 32.9 million barrels of oil from the Soviet Union. Thus, a one dollar increase in the price of oil will increase the cost of oil from the Soviet Union by $32.9 million. In addition, a one dollar increase in the price of oil is estimated to increase the cost of non-oil energy imports by $43 million. The latter number is derived from the fact that non-oil energy imports represented 67 percent of the total value of Hungarian energy imports from the Soviet Union in 1990, and Hungarian government and industry sources estimate that the proportional increase in non-oil energy costs will be 65 percent of the - proportional increase in the cost of oil, i.e., $43 million $32.9 million*(67/33)*(.65).
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Measuring poverty consists of two tasks. First, a poverty line must be determined. Second, the poverty level of individuals has to be aggregated. This sub section deals with the first issue, sub-section III.3 tackles the second. Data and numerical results are presented in section IV.

Two methods can be employed to set the poverty line. In the first one a 10 single indicator is used (e.g. nutritional intake) . In the second one a list of commodities considered essential is made. Following the previous discussion, I argue that access to adequate sources of nutrition should provide the benchmark for setting the line of extreme-poverty. Hence, I define the extremely-poor as those individuals who are unable to purchase enough nutrients which, given age and sex, allow to maintain health and performance. The required level of nutrient intake is set at 2250 calories per day for an adult.

Three comments are relevant in relation to this definition. First, caloric intake is taken as the reference point for nutritional status. The bulk of the evidence shows that protei.n and caloric intake are highly correlated: people that fulfill their caloric needs will most likely also satisfy their protein requirements1 l. Second, the definition makes no 10The minimum wage ia sometimes used in Mexico to set the poverty line (e.g. World Bank (1989a)), Lustig (1984)): poor households are those earning less thani -he minimum wage. This poverty line is at times also used by the government to discriminate among households for some of its food subsidy programs (see section VII.2, below). This procedure is probably inappropriate. The real wage is subject to transitory deviations arising from macroeconomic shocks, and may also respond to political considerations. For example, if the real wage declines then, ceteris paribus, the number of poor people falls. Conversely, if for political considerations the minimum wage is increased, poverty increases.

> 1 1"Malnutrition is not primarily a problem of an imbalance between calories and proteins. Most surveys have found that if energy intake is adequate, protein needs are also satisfied, and if not, protein is burned up for energy requirements" (Streeten, 1989b, p. &). Also: "There is today relative consensus that the indicator 'intake of calories' is more representative of the whole nutritional oroblem than the quantities of proteins or other nutrients" (Garcia et. al., 1986, p. 33; my own translation, S.L.). This is not to say that there will be no deficiencies of some vitamins and other key nutrients like iron, iodine, and calcium. But these deficiencies may arise from cultural factors that determine the type of diet, and not from lack of resources; people with higher incomes can show these deficiencies too. As mentioned below, a distinction between undernutrition and malnutrition is required.