rafmacalaba/datause-encoder-data huggingface dataset:
As a result, the welfare costs from increased poverty relative to those from increased mortality are much higher for poorer countries and tend to fall markedly with income per capita. There is, of course, non-trivial variation at each level of GDP per capita, reecting dierences in demography, inequality, and policy choices. But the evidence we present makes it dicult to escape the conclusion that the optimal policy response to the pandemic is not one size ts all. It will almost certainly dier across countries, with policy makers in poorer countries being justiably more concerned with the poverty costs faced by their populations than those in richer countries.

We have not sought to identify the optimal position countries should take when facing policy trade-os. We do not even believe that there necessarily is a trade-o between lives and livelihoods for every policy response to Covid-19. Developing a vaccine, for example, would clearly reduce both lost-years and poverty-years in the future. The same is likely true for early contact-tracing and large-scale testing. Imposing social-distancing early likely dominates doing it late (Demirguc-Kunt et al., 2020), and so on.

What we hope to have contributed is a simple and transparent approach to estimating the relative welfare costs of Covid-induced increases in mortality and poverty. The evidence we have assembled suggests that there are very large poverty costs across the world, and particularly in developing countries. A concern with these poverty eects is clearly ethically legitimate and economically important. We hope our approach can contribute to more informed public debates on policy choices aimed to mitigate the welfare costs of the pandemic.

# References

- Abay, K., T. Kibrom, and A. Woldemichael (2020). Winners and Losers from Covid-19: Global Evidence from Google Search. World Bank, mimeo.

- Acemoglu, D., V. Chernozhukov, I. Werning, and M. D. Whinston (2020). A Multi-Risk SIR Model with Optimally Targeted Lockdown. NBER, Working Paper 27102 .

- Alon, T., M. Kim, D. Lagakos, and M. VanVuren (2020). How should policy responses to the Covid-19 pandemic dier in the developing world? CEPR, Covid Economics, Issue 15 .

- Baland, J., G. Cassan, and B. Decerf (2020). too young to die. deprivation measures combining poverty and premature mortality. Discussion paper .

- Banerjee, A., L. Pasea, S. Harris, G.-I. A., T. A., L. Shallcross, and C. Pagel (2020). Estimating excess 1-year mortality associated with the covid-19 pandemic according to underlying conditions and age: a population-based cohort study. The Lancet .

24
# _(v) South Asia region (SAR)_

The percentage increase in storm surge zone among the countries of the South Asia region is less than the other regions. Approximately 23% to 33% of the countries’ coastal zones will be subjected to inundation risk with SLR and intensified storm surges, and Bangladesh will be worst affected (33.4%).

**Figure 6: Percentage increase in storm surge zone, SAR Region**

<!-- Start of picture text --> 40.0<br>35.0<br>30.0<br>25.0<br>20.0<br>15.0<br>10.0<br>5.0<br>0.0<br>Bangladesh Sri Lanka India Pakistan<br>% Increase<br><!-- End of picture text --> As expected, in absolute numbers, exposure estimates of all indicators—coastal population, GDP<sup>12</sup> , agricultural area, urban extent and wetlands—are larger for India and Bangladesh (Table 7). However, the relative impacts: percentage of coastal population and GDP exposed will be most severe in Pakistan (35.72% and 38.57%, respectively), and the percentage of vulnerable coastal croplands and urban extent will be most acute in Sri Lanka (43.03% and 37.42%, respectively) (Table 7, Columns 5, 7, 9 and 11).

> 12 It is estimated that storm surge intensification would cause additional GDP losses (above the current 1- in-100-year reference standard) of $5.2 billion in India and $2.2 billion in Bangladesh.
The industries are aggregated into 4 main sectors: 1. “Manuf”: Manufacturing and Agriculture (tradable sector), 2. “Metal”: Metal Manufacturing (sector subject to policy change), 3. “Service”: Service except Trade (non-tradable sector), and 4. “Trade”: Wholesale and retail trade (another non-tradable sector). The industries are aggregated mainly in two groups, tradable and non-tradable. Since “wholesale and retail trade” is a relatively large industry, we consider it as a separate sector apart from service.

Table 1 summarizes the distribution of workers across sectors, age, sectoral experience and education groups in both NLSY and CPS samples. Note that the sectoral experience variable is not available for the CPS sample and the NLSY sample includes individuals only up to age 40. Manufacturing and agriculture workers (henceforth manuf.) are approximately 27%, metal workers are about 4%, service workers except trade workers (henceforth service) are about 49% and finally wholesale and retail trade workers (henceforth trade) are close to 20% of the total sample (see Panel A). Panel B shows age distribution and Panel C shows sectoral experience distribution in the sample. As illustrated in Panel D, about 40% of workers have college education in the sample.

Table 2 shows example transition probabilities from different age groups, education groups and sectors. Panel A presents probabilities of sector change for workers with no college education while Panel B shows it for those with at least one year of college education. The effect of education on probability of sector change is ambiguous. However, it is clear that the probability of sector change is decreasing with age for both education groups. Panel C shows transition probability from one sector to another. As one would expect, the probability of moving out of a larger sector is lower than the probability of moving out of a smaller sector, and the probability of moving into a larger sector is higher than the probability of moving into a smaller sector.

# 5 Regression Results

We have two data sets, CPS and NLSY, for the estimation. CPS is missing the sectoral experience and work history variables, while NLSY has enough observations for only limited 18
ZIMBABWE‘S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE has been $0.10 per kilowatt-hour (kWh) historically in Zimbabwe, while the average effective tariff stood at only $0.0753 as of 2009. The discrepancy produced an associated financial burden of about 5.0 percent of GDP (figure 22).

In the water sector, as of 2009 the ZINWA‘s average tariffs stand at $0.30 per m<sup>3</sup> , falling substantially short of the estimated cost-recovery tariff of $0.74 per m<sup>3</sup> . The economic burden caused by this discrepancy, 1.1 percent of GDP, is lower than that for power, but nevertheless is huge. In comparison to the average for low-income, fragile countries, Zimbabwe performs considerably worse when it comes to cost recovery of water and power tariffs.

**Figure 22. Underpricing of power and water in Zimbabwe is burdensome**

Financial burden of underpricing as percentage of GDP <!-- Start of picture text --> 6.0<br>5.0<br>4.0<br>3.0<br>2.0<br>1.0<br>0.0<br>Power Water<br>Zimbabwe LIC-fragile<br>Percenatage of GDP<br><!-- End of picture text --> _Source:_ Derived from Briceño-Garmendia, Smits, and Foster (2009). _Note:_ GDP = gross domestic product; LIC = low-income countries.

Moreover, because of inequitable access to power and water services in Zimbabwe, subsidized tariffs are regressive. More than 90 percent of those that have electricity or piped-water connections belong to the top 40 percent of the expenditure distribution; such connections are nonexistent for poorer households (figure 23). Only the richest quintile has access to piped water, while most of those in the poorest quintiles still rely on surface water. This inequitable distribution of connections virtually guarantees that any price subsidy to these services will be extremely regressive.

Both the power utility ZESA and water utility ZINWA are comparatively inefficient in terms of bill collection. While ratios may fluctuate from year to year, the most recent data available suggest that ZESA manages to collect only 62 percent of its power bills, while the ZINWA collects only 45.7 percent. As a result, the power utility undercollects $225 million per year, and the water utility undercollects $18 million per year.

47
only mixed evidence. In the case of China for instance, Knight, Song and Huaibin (1999) find that migrants of rural origin hold jobs that non-migrants shun. On the other hand, Roberts (2001) mentions the issue of local workers in Shanghai being laid-off by state enterprises and competing with migrant laborers in the formal sector.

# **The effect of migration on poverty at the place of destination**

Finally, the effect of internal migration on urban areas is not limited to an impact on wages and employment. In a country such as South Africa, migration from poor rural areas is often viewed as contributing to a strengthening of residential segregation, an increase in the number of poor, and a costly burden on local public services. These issues have seldom been studied empirically, probably due to the lack of adequate micro-data.<sup>33</sup> One exception is Mohtadi (1986) who uses cross sectional data of cities and rural regions from Iranian censuses in order to assess how migration from rural areas affects urban inequality. Not surprisingly, the finding is that inequality in urban areas tends to increase with migration when migrants are from a poorer, landless group, and to decrease when they are from a better off, landed group.<sup>34</sup>

**Policy implications** -- The considerations above suggest that the inflow of migrants to cities can be problematic if not accommodated properly. Accommodationist policies cover programs designed to assist migrants upon their arrival in urban areas in order to reduce the economic and social costs of migration and to maximize its gains. The idea is to provide rural migrants with an environment that ‘allows them to make use of their energy and skills’ (Skeldon, 1997). Measures can be as diverse as the creation of reception centers for migrants, helping them access essential public goods, or helping them acquire affordable housing (such as the programs launched by the South African

> 33 There is a real need to measure the characteristics of migrants with respect to age, gender, education, health, family structure, ethnicity and poverty at a _local_ level.  Generating  and using localized census data and the construction of neighborhood poverty maps at a metropolitan scale could be a prerequisite for useful research in the  Future.

> 34 This study uses cross sectional data and, therefore, only focuses on short term effects.

44
- 12 -

**<u>Table 3. OLS Regression of the RAI</u>**

|Variable|Coef.|Std.Err.||Coef.|Std.Err.||
|---|---|---|---|---|---|---|
|ln_RdDen_|0.324|(0.031)|***|0.272|(0.046)|***|
|ln_Quality_|0.543|(0.035)|***|0.564|(0.040)|***|
|ln_PopDen_|-0.0001|(0.017)||0.001|(0.021)||
|ln_PopConc_|0.746|(0.048)|***|0.759|(0.060)|***|
|ln_Urban_|0.005|(0.003)||0.013|(0.004)|***|
|_v_(_Ethiopia_)||||-0.391|(0.096)|***|
|_v_(_Kenya_)||||0.014|(0.055)||
|_v_(_Mozambique_)||||-0.039|(0.095)||
|_v_(_Nepal_)||||0.001|(0.069)||
|_v_(_Tanzania_)||||0.005|(0.084)||
|_v_(_Uganda_)||||0.142|(0.056)|**|
|_v_(_Zambia_)||||-0.217|(0.083)|***|
|Constant|0.530|(0.139)|***|0.574|(0.163)|***|
|Obs.|691|||691|||
|R squared|0.8763|||0.8923|||
|Fstatistics|985.68|||542.54|||

_Note:_ The dependent variable is the log of _RAI_ . Robust standard errors are shown in parentheses. *, **, and *** indicate statistical significance at 10, 5, and 1 percent, respectively.

One may wonder if there is any systematic difference between Africa and South Asia, because the two regions have several different characteristics. For instance, population density in Africa is generally much lower than in South Asia, and so is road density. The conventional structural model is used to test this hypothesis. The test statistic is estimated at 23.32, well above the critical value, suggesting that there is a systematic difference in the RAI determinants between the two regions.

Several specific differences are found, although they cannot be overgeneralized because our sample of countries is limited (table 4). First, road density and quality are both important to improve the RAI in Africa. However, road density does not matter for South Asia: the coefficient of _RdDen_ is not statistically significant. This may be because Bangladesh and Nepal already have high road densities. In African countries, the current road networks may remain to be developed.

The evidence provides different policy implications for the two regions. For South Asia, a remaining challenge to improve rural access is primarily to improve the quality of the
immunization, with a mortality rate comparable to the two injection group. However, for the period 21-32 months postimmunization, the rate for this group was no longer significantly lower, but in fact substantially higher than fUr the two injection group, leading the authors to conclude that a single dose did not proviue meaningful protection for longer than 20 months. The findings of the Matlab study by Rahman, et al. led them to conclude one injection of tetanus toxoid did not confer a meaningful level of protection. However, as Figure 8 shows, the results of their study indicated that among the small number of births to women who received only one injection of tetanus toxoid in the 1974 vaccine trial (N= 362), 4-14 day mortality rates were substantially lower than for the unimmunized group and only slightly higher than for the two dose group (34, 25, and 20 per 1000). However, 4-14 day mortality rates among the group who received only a single injection while pregnant through the 197879 immunization program were markedly higher than either of the two immunization groups, and twice as high as for the single injection group in the 1974 vaccine trial. This discrepant finding may be explained by the small numbers of births belonging to this group (N=124), as well as the highly selective nature of those who dropped out of the program after receiving only one injection (see subsequent discussion).

The recent study Matlab study by Koenig and colleagues (1991b) provides additional and contrasting evidence on the effect of a single tetanus toxoid dose. As Figure 7 shows, 4-14 day mortality for the one tetanus toxoid injection group closely parallels that for the two injection group not just in the shortrun, but for the entire period of observation. The authors' analysis showed that relative to the group of births to unimmunized mothers, lower 4-14 day mortality among the one injection group was statistically significant through 1978-79; for a period of up to four years. At the same time, while not attaining statistical significance, mortality for the one injection group remained below one-half that for the unimmunized group in almost all subsequent years. For the 1975-89 period as a whole, 4-14 day mortality for the one injection group was 12.5 per 1000 live births, significantly lower than for the unimmunized group (28.4 per 1000) and even slightly below that for the two injection group (13.8 per 1000).

# 3+ Doses

Empirical evidence on the incremental mortality gains from more than two doses of tetanus toxoid is also slender and generally based upon very small study populations. The study in New Guinea found that three injections of tetanus toxoid during pregnancy conferred a higher level of protection against neonatal tetanus compared to two doses, with a reduction in neonlatal tetanus mortality from 34.2 deaths per 1000 to 5.7 per 1000 27
# **_3.2 Measuring mobility_**

We want to measure the ease of going from an origin to a destination in cities. We focus on the speed of road travel using a motorized vehicle.<sup>17</sup> Measuring the speed of travel in a city raises a number of challenges since trips differ considerably in their length, location of origin and destination, time and day of departure, and mode.

The simplest approach is to compute a measure of mean speed for a given city: where _c_ denotes a city and _i_ is a trip instance. Because we sum the length _Di_ of all trip instances in city _c_ and divide by the sum of trip durations _Ti_ , the ratio _Sc_<sup>_m_is a length-weighted</sup> measure of travel speed. It is straightforward to define the corresponding unweighted mean.

Means are attractive because of their simplicity and ease of computation. However, in our case means may not be comparable across cities for two reasons. First, although we sample a large number of trips, we may not observe trips in different cities taking place under exactly the same conditions such as time of departure. Second and most importantly, our trip generation strategy implies that trip length and distance to the center differ systematically across cities. As we show below, these characteristics are important determinants of trip speed. We can condition them out by estimating the following type of regression: where the dependent variable is log trip speed ( _Si_ = _Di_ / _Ti_ ), _Xi_ is a vector of characteristics for trip instance _i_ , _sc_<sup>_f e_</sup> ( _i_ )<sup>is a fixed effect for city</sup><sup>_c_, and</sup><sup>_εi_is an error term.</sup> If trip characteristics are appropriately centered and the errors are normally distributed, _S_ ˆ _c_<sup>_f e_=exp</sup> _s_ ˆ _c_<sup>_f e_+</sup><sup>_φ_ˆ2/2</sup> is a measure of predicted speed for a typical trip in city _c_ where _φ_<sup>ˆ</sup> is � � the estimator of the standard deviation of the error term _ε_ . Note that for we can simplicity ˆ directly use _sc_<sup>_f e_as an index of mobility.</sup> Equation (2) does not specify the exact content of the vector of characteristics _X_ . In addition to the city within which a trip takes place, we expect the main variables that determine the speed of a motorized trip in our data to be its length, time of departure, distance to the center, and perhaps the type of the trip. We also expect trip speed to be affected by weather

> 17Data from the 2011 Indian census suggests that 46% of urban commutes, and 55% of urban commutes longer than 1 kilometer, are by motorized road transport.

8
<!-- Start of picture text --> ~°<br>2.<br>8<br>co<br>g<br>pt<br>ad<br>[)<br>3<br>N.<br>mht ee ee<br>2000 2005 2010 2015 2020<br>Year<br>——— MENA ——— SSA<br>——— LAC ——— SAR<br>—— ECA<br><!-- End of picture text --> <!-- Start of picture text --> ~2 ———<br>3m2<br>&<br>c<br>2<br>&<br>wn<br>a<br>°<br>oa<br>2<br>uw<br>S<” a eee<br>2000 2005 2010 2015 2020<br>Year<br>——— MENA ——— SSA<br>—— LAC ——— SAR<br>——— ECA<br><!-- End of picture text -->
economic standing. This analysis uses the results from Filmer and Pritchett (1998) which argued that an index of housing characteristics and assets owned by the household members, which are collected in the DHS, is a good rneasure of a household's long run wealth in predicting educational outcomes.

The particular goal here is to investigate the association between educational disparities and gender, household wealth, adult education, and "access" to schools. The analysis leads to four main findings. First, the extent of the female disadvantage in education varies enorrnously across countries. At one extreme there are some countries, primarily located in Western and Central Africa, North Africa, and South Asia where the gaps are large in all the measures used. For example, in India there is a 16.6 percentage point difference between the school enrollment of girls and boys aged 6 to 14. In Benin, the enrollment rate of boys aged 6 to 14 is 63 percent higher than the enrollment rate of girls. At the other extreme there are countries, mostly in Latin America, where there is no female disadvantage in and in fact a small female _advantage_ in some of the measures used. In Colombia, the enrollment r ate of boys is 98 percent that of girls.

Second, while gender gaps are large in a subset of countries, wealth gaps are large in almost all the countries studied. For example, in Senegal the enrollment of 6 to 14 year olds from the poorest households is 52 percentage points lower than for those from the richest households. In Zambia, there is a 36 percentage poilnt difference in the enrollment rate of children from the richest and poorest households. Disturbingly, in some countries where there is a high degree of female disadvantage in enrollment, wealth interacts with gender to exacerbate gaps in educational enrollment among the poor (Niger, Egypt, Morocco, India, and Pakistan). The magnitude of this difference can be quite large. For example, in India there is a 2.5 percentage point difference in the enrollment of male and female children from the richest household whereas the difference is 34 percentage points for children from the poorest households.

4
**Table 3: Country-Specific Proportion of Sibling Correlations Explained by Intergenerational Transmission Estimates (Full Sample)**

|**Country**|**Prop.**|**S.E.**|**N**|**Country**|**Prop.**|**S.E.**|**N**|
|---|---|---|---|---|---|---|---|
|East Asia & Pacific||||Sub-Saharan Africa||||
|Cambodia|0.688|0.010|10521|Benin|0.824|0.018|5480|
|Indonesia|0.780|0.006|33209|Burkina Faso|0.720|0.021|3498|
|Philippines|0.812|0.011|15064|Burundi|0.660|0.024|3897|
|Vietnam|0.824|0.018|3692|Cameroon|0.848|0.021|3663|
|||||Chad|0.721|0.018|2710|
|Europe & Central Asia||||Congo, Rep.|0.853|0.039|1781|
|Albania|0.757|0.043|2311|Cote d'Ivoire|0.791|0.033|1565|
|Armenia|0.767|0.030|3215|Ethiopia|0.713|0.011|7047|
|Kyrgyz Republic|0.661|0.066|1455|Gabon|0.771|0.045|1212|
|Tajikistan|0.641|0.037|3238|Ghana|0.786|0.023|3313|
|Turkey|0.774|0.016|8292|Guinea|0.704|0.034|2589|
|||||Kenya|0.768|0.020|7559|
|Latin America & Caribb|ean|||Lesotho|0.717|0.021|4382|
|Bolivia|0.720|0.012|6971|Liberia|0.690|0.033|1771|
|Brazil|0.752|0.018|2967|Madagascar|0.845|0.014|4376|
|Colombia|0.729|0.009|17607|Malawi|0.752|0.016|6164|
|Dominican<br>Republic|0.691|0.015|8190|Mali|0.745|0.021|3910|
|Guatemala|0.740|0.011|6553|Mozambique|0.823|0.032|2910|
|Haiti|0.640|0.014|6022|Namibia|0.749|0.034|2581|
|Peru|0.755|0.006|34974|Niger|0.704|0.027|1505|
|||||Nigeria|0.704|0.009|11380|
|Middle East & North Af|rica|||Rwanda|0.735|0.021|6848|
|Egypt, Arab Rep.|0.740|0.010|27042|Senegal|0.802|0.024|3850|
|Jordan|0.667|0.015|17023|Sierra Leone|0.668|0.030|2209|
|||||South Africa|0.786|0.040|2407|
|South Asia||||Tanzania|0.659|0.021|5619|
|Afghanistan|0.618|0.016|7585|Togo|0.825|0.043|1615|
|Bangladesh|0.820|0.010|12031|Uganda|0.735|0.020|4176|
|India|0.744|0.003|151142|Zambia|0.746|0.016|5595|
|Nepal|0.658|0.015|5574|Zimbabwe|0.707|0.024|3369|
|Pakistan|0.808|0.006|40964|||||

_Notes_ : This table presents the estimated share of the intergenerational component using the Bingley and Cappellari (2019) method for each of the 53 developing countries in the Demographic and Health Surveys (DHS) using the full sample.

37