rafmacalaba/data-use-mentions-extended huggingface dataset:
4 U.N. 1984 assessment. Other sources of mortality data include Eurostat (1987), the U.S. Bureau of the Census (1985), Celade (1987), and various surveys. These sources often report mortality levels for periods other than 1985-90, or give indices other than life expectancies, or both. Some estimation and projection is then necessary to obtain the right index. In some cases, a mortality estimate is made first for an earlier period, and an estimate for 1985-90 obtained by applying the projection methodology being described here. Some Bank sources are also cited in the Appendix: Bank sector reports; Bank assessments, involving unpublished analysis of census or survey data; and Bank estimates, or informed judgments for which no further source can be cited (see Appendix).

The future trend in mortality in all countries was represented by steady increments to life expectancy, with smaller increases at higher life expectancy levels, and with different schedules of increments where few or where many females are in school.

The schedules of increments for female life expectancy were developed in earlier analysis involving regressions of change in female life expectancy between 1965-69 and 1975-79 on initial life expectancies in 196569, separately for countries with different levels of female enrolment. Life expectancies were obtained from Bank files and female primary school enrolment in 1965-69 from Unesco (1977). Female enrolment was used as a control because preliminary analysis confirmed a strong relationship (R<sup>2</sup> _ 0.50 over 30 developing countries) of female enrolment with current life - expectancy and a slightly stronger relationship (R<sup>2</sup> 0.53) with life expectancy lagged ten years. Countries were divided into two female enrolment groups: those with female primary school enrolment of 70 percent or more and those with female primary school enrolment of less than 70 percent.

Figures 1 and 2 plot changes in female life expectancy against initial levels for the two groups of countries. Also plotted are lines estimated through quadratic
|(0.020)|(0.030)|(0.018)|
||r0.818s|r0.108s|r0.059s|r0.010s|
|Observations|4175|4164|4175|4175|
|R-squared|0.432|0.237|0.148|0.209|

*** ** * _Notes_ . Standard errors in parentheses; P-value in brackets. p<0.01, p<0.05, p<0.1. Payment outcomes are derived from the billing data and compound outcomes come from the property owner listing exercise in August/September 2018. Settlement fixed effects included.

32
reports of area and productivity reductions combined with yet relatively normal realized production leads the loss-to-production ratio to be considerably larger in ESS15 with a few unrealistically large outliers in loss estimates (cases where the loss per hectare exceeds the highest realized yield on any plot in the sample for the respective year).

> 31 Higher losses for sorghum are not in line with the literature on crop drought resilience though and should thus be a reflection of sorghum being planted more often where drought (and thus losses) is/are more severe. This is confirmed by comparing GIS rainfall data to 14-year average rainfall - for maize and sorghum plots, respectively.

17
. A recent survey of electricity consumers in twelve African countries found that in all but Rwanda more than half of respondents reported at least one outage per day; in Ghana, that figure is 68% of electricity consumers ( **?** ). Despite both public and private efforts to develop new capacity, major power crises plagued Ghana in 1997-98, 2006-07, and 2012-15. Historically dominated by hydropower, from 2006 to 2016 generation capacity doubled, with thermal now accounting for nearly 60% of installed capacity. Still, the hydro/thermal power generation mix leaves Ghana susceptible to capacity reduction from drought and natural gas supply challenges, and the system suffers from massive distribution losses due to obsolete equipment, a tariff structure that is not cost-reflective, and pervasive non-payment by electricity consumers (including government ministries) ( **?** ).

Dumsor, or Dum So, derives from the words for off and on in the Asante Twi, Akuapim Twi, and

> workers are working on a production process in conjunction with worker labor inputs. **?** , **?** , and **?** present evidence on the labor supply of vehicle and bicycle taxi drivers; **?** consider the labor supply of pear packers; **?** examine labor supply in fisheries; and **?** studies the labor supply of stadium vendors.

4
self-employment, informal employment, and employment outside the formal sector. All informal employment measures are expressed as a share of total employment.

17. **For output informality estimates in Indonesia, the paper prefers the MIMIC-based estimates.** In the case of output informality in Indonesia, the model-based MIMIC estimates are used mainly in cross-country comparisons, while the SAKERNAS estimates are used in the withincountry comparison (see below for details). The MIMIC model, detailed in Schneider et al. (2010), is estimated in the paper (see Annex 4). According to Feige (2016), one of the limitations of Schneider et al. (2010) is that the base year value used for benchmarking is taken from an unspecified currency demand model. To overcome this limitation, the paper uses SAKERNAS estimates on output informality in 2000 to re-benchmark MIMIC estimates on output informality in Indonesia. It is also worth noting that MIMIC estimates are preferred here as they seem to capture the variation of informality better than DGE estimates in the case of Indonesia, while they are highly correlated with the DGE estimates in cross-country comparisons.

18. **SAKERNAS surveys are used to construct output and employment informality in Indonesia for national, regional, and sectoral level analyses.** These estimates are mainly used for within-country analyses. In addition to national-level estimates, output informality and employment informality are estimated for 34 provinces, 3 major sectors, and 9 subsectors over the period 2000-2022. The estimations use data on informal employment status and monthly income from Sakernas. The following equations are used to compute the share of output informality at the national (excl. i), regional (region =i), or sectoral (sector=i) level in year t:

1. Informal output (% total GDP, simple)it =

2. Informal output (% total GDP, weighted)it = j=nit ∑j=0 informal employment status dummyjit∗monthly incomejit∗weightjit j=nit . ∑j=0 employment status
# **3.2 Results**

There is considerable spatial variation in poverty and environmental risks across Vietnam. As is indicated by the dark red districts in Figure 1, the incidence of poverty is highest in the Northern Mountains and in the Central provinces. The dark blue districts in Figure 1 suggest that air pollution and temperature variability are highest in the Northern districts. The Northwest region and Central Highlands face high tree cover loss and steep slopes, while the Central Coastal zones, the Red River and Mekong River Deltas face high rainfall variability and flood hazards. Areas with high land degradation and flood hazards appear across the country ‐ mostly in the Northwest and Southeast region and the Mekong River Delta. These findings suggest that some areas face various risks so that their populations are exposed to multiple hazards.

Across Vietnam there are some large hotspots of high environmental risks and poverty. As indicated by the dark districts in the maps (Figure 1), Northern districts face a combination of high poverty and high air pollution, tree cover loss, steep slopes, temperature variability and drought hazards. In the Central Highlands, tree cover loss, steep slopes and rainfall variability pose high risks on poorer districts. And in the Mekong River Delta there are a few poorer districts that face high land degradation, flood and drought hazards. When looking at absolute poverty numbers, a few shifts in these patterns emerge from sparsely populated districts, as for example, in the Central Highlands, to districts with larger concentrations of poor people (Figure A.1).

Generally, in high risk districts the poverty rate is higher than in low risk districts. The difference is most pronounced for tree cover loss and slope, but also significant for all the other environmental risks (Table 1). Only for flood hazards, poverty is significantly higher in low risk districts than in high risk districts. This observation can be explained with the higher incidence of flood hazards in prosperous coastal regions and river deltas (Figure 1, also see Bangalore et al.
|.|0.2|0.5|
|Total|328.7|19.5|9.2|100.0|100.0|

Source: GMD, UNDESA, WDI, PovcalNet

# **4. Sensitivity to alternative equivalence scales**

The estimates of poverty by age group reported are based on per capita consumption, and therefore rest on the fundamental assumption that per capita consumption reflects individual economic welfare both within and across households. This assumption, while standard, has been widely questioned on three main grounds. First, it ignores potential disparities in resource allocation within the household. Since we do not have access to data from a large number of countries on consumption associated to specific household members, it is difficult to address this issue. However, failing to account for intra-household inequalities likely understates child poverty; Dunbar et al (2011) estimate that child poverty rates in Malawi would increase from 91.3% to 97.4% when unequal allocations within the household are considered. Similarly, Bargain et al (2013) find that per capita measures of consumption underestimate child poverty estimates for Côte d’Ivoire, especially for larger households, once both intra-household allocation and economies of scale are considered. Second, the use of per capita consumption ignores the relative difference in the costs of satisfying children’s and adults’ needs; specifically, food needs of children, especially younger children, are less than those of adults, although other needs of children, such as for schooling and healthcare may be higher. Finally, households with more members are able to achieve greater economies of scale on household goods such as rent, utilities, some durable goods, and other shared aspects of household activity and expenditure. Together, the latter two assumptions -- that adults and children have equal-cost relative needs and that there are no economies of scale -- have been relaxed by employing ‘equivalence scales’ that adjust household size, typically according to their member’s age and the number
**Table A5** Impact of Treatments on Males: IPV Attitudes and Perpetration <u>(Restricted</u> to Ever had Sex at Baseline, Male Data)

||(1)|(4)|(3)|(4)|
|---|---|---|---|---|
||**Boys**<br>**Treatment**|**Goal**<br>**Treatment**|**Endline**<br>**Control Mean**|**Observations**|
|Violence attitudes index|||||
|treatment x post|-0.413***|-0.100|-0.000|1,100|
||(0.138)|(0.143)|||
|treatment x post x goal|0.205||||
||(0.206)||||
|Tolerate violence from|||||
|husband/partner|||||
|treatment x post|-0.204***|-0.057|0.107|1,100|
||(0.062)|(0.061)|||
|treatment x post x goal|0.069<br>(0.086)||||
|Men can beat women under<br>certain circumstances|||||
|treatment x post|-0.069|-0.008|0.214|1,100|
||(0.088)|(0.079)|||
|treatment x post x goal|0.072<br>(0.115)||||
|IPV often index|||||
|treatment x post|0.015|-0.060|
Research that studies the climate change externality challenge posed by income growth posits that no single durables owner or buyer has an incentive to internalize the social costs caused by fossil fuel energy consumption. As documented by Davis and Gertler (2015) and Gertler et al. (2018), there is a rising demand for durable goods across the developing world. For the case of air conditioning, Davis and Gertler (2015) estimate ownership increases by 2.7 percentage points per $1,000 of annual household income. They use their estimates combined with income growth projections, data on utilization, and an estimate of the grid’s future carbon intensity (i.e., tons of GHG emissions per MWh of power generation) to predict the impact of the world’s growing middle class on future GHG emissions. Biardeau et al. (2020) document how cooling degree days in developing nations with extremely hot summers translates into increased electricity consumption as air conditioning ownership rates increase. Despite dramatic improvements in energy efficiency over time, economic growth in LDCs is not less energyintensive than past growth in developed countries (van Benthem 2015).

We build on this past research by presenting new Engel curves estimates and we focus on what these Engel curves mean in terms of stakeholders in the status quo carbon intensive economy. We augment these data with national data on the emissions intensity of the grid to discuss how the social costs of operating these various durables differs across nations.

In developed nations, we present new evidence on the political economy challenge posed by durable internal combustion engine vehicles and by high-carbon cities within nations. Using data on California vehicle registrations, we document the slow progress in increasing the share of electric vehicle ownership. For each European nation, we document the variation in the crosscity carbon emissions per-person. The results from these data sets document that even in rich nations there are many people whose carbon footprint is large and thus they bear more of the economic incidence of carbon pricing (Glaeser and Kahn 2010).

The growth
If the average height-for-age of children who died was twice as far below the age and gender reference mean as that of children who survived (–3.83 rather than –1.91), the height-for-age z-score for the total sample would rise from –1.91( _Zs_ ) to –2.17( _Zall_ ), or by 13.5 percent. If a height-for-age z-score of –5.0 (the lower bound recommended as the cut-off for outliers; WHO 1995) is imputed to a sample of children who died, the overall z-score would rise from –1.91 to –2.32, or by 21.8 percent. Similar tendencies are observed for the 1998 and 2005 samples.

The impact of the imputations for height-for-age on the total sample is proportional to the mortality rates. For example, the imputation of a height-for-age z- score twice as low as the average to the sample of girls who died before age 36 months results in a 13.0 percent change in the overall mean z-score in 1992 but only a 8.4 percent change in 1998 and a 6.6 percent change in 2005, reflecting the decline in girls‘ mortality rates from 0.077 in 1992 to 0.056 in 2005.

But not all deaths before age 36 months were caused by malnutrition. The next simulation is based on the results from the literature that estimates the contribution of malnutrition to child mortality. Puffer and Serrano (1973) found that malnutrition was an underlying cause in 54 percent of deaths for children ages 2–4 years. Pelletier (1994) explored 28 prospective datasets and found that the population-attributable risk of mortality associated with anthropometric deficits varied from 17 percent to 74 percent in eight studies for Asia and Africa. Pelletier and others (1994) applied to prospective surveys in Ethiopia, Guatemala, India, and Malawi a new methodology for determining the association of malnutrition and mortality by the severity of malnutrition. They demonstrated that 42–57 percent of deaths of children ages 6
# **Table A4 Continued: Ghana 2013 Management Survey**

|**Management Practice**|**Topic**|**Ghana 2013 questions**|**Score 1 (2013)**|**Score 3 (2013)**|**Score 5 (2013)**|
|---|---|---|---|---|---|
|**CS-Incentives/Monitoring**|**Performance**<br>**Incentives**|Examines whether the<br>organization is able to deal<br>with underperformers|Poor performance is not<br>addressed or is inconsistently<br>addressed. Poor performers<br>rarely suffer consequences or<br>are removed from their<br>positions.|Poor performance is<br>addressed, but on an ad hoc<br>basis. Use of intermediate<br>interventions, such as training<br>is inconsistent. Poor<br>performers are sometimes<br>removed from their positions<br>under conditions of repeated<br>poor performance.|,<br>Repeated poor performance is<br>systematically addressed,<br>beginning with targeted<br>intermediate interventions.<br>Persistently poor performers<br>are moved to less critical roles<br>or out of the organization.|
|||Examines how well the<br>organization follows up on and<br>enacts plans to fix identified<br>problems (department/<br>process-level)|<br>Failure to achieve agreed<br>objectives does not carry any<br>consequences. Responsibility<br>for following up is poorly<br>defined.|Responsibility for follow-up<br>may be allocated, but there<br>would be no other specific<br>actions beyond this. Failure to<br
types of infrastructure present greater and different challenges than others. Moreover, infrastructure as a constraint must be understood in the context of Togo’s growth strategy as a regional trade hub in addition to its goal of broad‐based growth and the need to connect firms outside Lomé. Taken together, Togo must ensure that the increased focus on infrastructure remains as donors re‐engage and support public investments.

_Access to credit_ for the private sector is yet another potential constraint that gives varied signals. Credit emerges as a severe and high‐ranked constraint to business according perception surveys at an enterprise level; however, this is the case in most countries surveyed. To this end, it is unclear if the low amount of credit is a result of lack of demand (i.e. given the level of economic opportunities today, few firms are prepared to take a loan and make risky investments), or a lack of supply mainly in terms of financial intermediation now that international finance has returned to Togo. In this sense, the possibility of financial intermediation as a potential binding constraint is – in a similar fashion to infrastructure – already being addressed through several measures. At this point, it is crucial to, at the very least, implement the necessary banking sector reforms and continue to pay close attention to access to credit as a constraint. Improvements will have to be continuously realized as economic opportunities and demand for credit arises.

A prominent area in terms of constraint to growth in Togo is _governance issues_ , in particular political instability, corruption, and governance efficiency. In nearly every measure of governance, Togo’s performance ranks last when compared to neighboring countries with very little documented progress to date. Political stability has improved since the latter part of the 1990s and more prudent macroeconomic reforms and policies have been implemented. However, if and how these improvements translate into increased trust among investors is debatable according to perception data from domestic investors and the limited FDIs recorded. Not least, the low governance efficiency in Togo has been
5 6<br>Years around default<br>Start of holding period: 2 years before default<br>Cumulative total return index<br><!-- End of picture text --> _Note:_ This figure shows total cumulative returns on sovereign external bonds around default events, using real, monthly return data and taking into account losses due to missed coupons and haircuts. The total return series are normalized to one in year two (24 months) prior to the default. The bold black line shows the average across all defaulted bonds, while the dotted grey lines show upper and lower quartiles. We include all 92 default episodes for which we have sufficient pricing data before and after the default.

Figure 11 shows a time series of total returns around all the sovereign bond default events in our sample for which we have sufficient bond price data, in the run-up and the aftermath of default. These are 92 cases of a total of 161 sovereign bond defaults in the sample. (This exercise does not include the many defaults on bank loans for which no prices exist; see Figure 1.) The total cumulative return series is 35
data on both the consumer price index (CPI) and key macroeconomic variables in order to investigate the direction of causality and the information content of the key economic variables. Finally, the third approach applies co-integration and error-correction techniques to the inflation process -- a process which is outlined by a simple theoretical model. The paper concludes by highlighting the main policy implications of our empirical findings.

# **I. COMPOSITION AND THE STRUCTURE OF THE CPI**

Albania's consumer price index covers 221 products, and the weight of each product is derived from the 1993 expenditure survey involving 3,000 families in the Tirana area. Table 1 shows the components and weights of the CPI. As seen in Table 1, foods, beverages and tobacco represent 72 percent of the consumption basket, with bread and cereals alone accounting for over 15 percent of the total. Three main factors affecting food prices dictate the behavior of the CPI, and these include: the exchange rate (owing to the high import content of foodstuffs), domestic inputs, and weather conditions.

_5_
OLS estimate of the Zipf’s coefficient in linear and quadratic formulation in much greater detail, we simply use the Hill estimator to check for the robustness of our results.

# **_Measuring participation and the knowledge content of GVCs_**

Besides domestic content, the value added in gross exports of a country can be decomposed into the following three categories (Koopman et al., 2010):

- (i) Value added exported in intermediates directly to the importers

- (ii) Value added exported in intermediates re-exported to third countries, and,

- (iii) Valued added exported in intermediates that are re-imported home.

Each of these categories represents a measure for capturing a country’s participation in GVCs. The OECD TiVA database offers several indices for measuring participation in GVCs. Some examples include the following measures (as a percent of gross exports): (i) DVA in exports of intermediate products, (ii) DVA embodied in foreign exports, and (iii) re-imported DVA. These indices are defined in Annex table A.1.

Participation in GVCs is, however, not an indicator of the knowledge content of participation. We rely on sectoral measures to measure knowledge content. Within manufacturing, we follow Banga (2013) in defining the high-tech manufacturing to include the following sectors: Electrical and optical equipment; Machinery and equipment, nec; Transport equipment; and Chemicals and non-metallic mineral products. Low-tech manufacturing includes the following sectors: Textiles, textile products, leather and footwear; Food products, beverages and tobacco; Agriculture, hunting, forestry and fishing; Mining and quarrying; and Manufacturing nec; recycling. For services, we use the extent of forward participation in GVCs in the services sector, in general, and in highly knowledge intensive R&D and business services, in particular, to draw comparison within and across the sectors.

# **_Endogeneity of GVCs with city size distribution_**

We are interested in capturing the _causal_ effect of GVCs on city size distribution -- however,
to education. As the economy opened to international market, demand for skilled workers increased which provided incentives for getting education. As a result, a virtuous circle was created where education and growth reinforced each other and both contributed to welfare.

**Table 2. GINI Index of Education**

|_Countiy_|_1970_|_1975_|_1980_|_1985_|_1990_|_1995*_|
|---|---|---|---|---|---|---|
|Brazil|0.413|0.367|0.373|0.369|0.314|0.260|
|Chile|0.279|0.275|0.267|0.263|0.266|0.270|
|China|0.450|0.452|0.447|0.442|0.411|0.379|
|Colombia|0.415|0.386|0.397|0.399|0.409|0.420|
|India|0.370|0.394|0.363|0.393|0.422|0.452|
|Korea|0.439|0.382|0.351|0.296|0.242|0.189|
|Mexico|0.420|0.415|0.428|0.410|0.365|0.320|
|Malaysia|0.445|0.439|0.426|0.413|0.398|0.383|
|Peru|0.428|0.424|0.392|0.391|0.379|0.367|
|Philippines|0.368|0.320|0.314|0.313|0.309|0.305|
|Thailand|0.378|0.369|0