rafmacalaba/datause-encoder-data huggingface dataset:
predicted on the basis of only _t_ and a constant. Yet, when we restrict the sample to include only countries with initial enrollment rates of less than 70 percent, R-squared increases to approximately 0.5 (see Appendix).

Yet, the unexplained and possibly random variation may not only be around the time path; parameters may also be different across different groups of countries.<sup>7</sup> And indeed for primary enrollment, _β_ , which determines the shape of the adjustment curve, is heterogeneous across the sample. If we restrict the sample to the subsample just described, _β_ turns out to be twice as high; that is, countries achieve higher levels of enrollment much more quickly. This is illustrated in Figure 2, where the grey line shows the corresponding transition pattern. Increasing enrollment from 20 to 80 percent would then only take around 30 years. For child mortality, the parameter estimates for a reduced sample of countries with initially high underfive mortality rates (higher than 50 per 1000 births) do not differ much from those in the original sample.

This may indicate that for latecomers to universal enrollment there has been a considerable acceleration of progress in recent years. This evidence can be corroborated by taking a closer look at some of the oft-cited “success” stories. These successes include Ethiopia, Malawi, Rwanda, and Uganda, all poor countries whose primary enrollment rates have strongly increased in the past 20 years, potentially enabling some to achieve universal enrollment by 2015. However, these cases may have particular circumstances: a catching-up process after conflicts in Rwanda and Uganda and relatively high rates of economic growth in Ethiopia and Uganda (and thus higher expected returns to education). Rapid progress in enrollment is also likely to be associated with a decrease in the quality of education, for example, in the case of Uganda (Kappel et al., 2006). Still, these cases indicate that progress in enrollment may be more rapid than in the past. Clemens (2004), however—referring to the same cases plus Togo, Botswana, and Indonesia—warns against such a conclusion. He suggests the careful evaluation of these experiences and points to problems of increased repetition rates masked by aggregate enrollment statistics, for example, in Togo and Malawi. Countries may also experience “enrollment bubbles,” that is, temporarily higher enrollment due to public policy, combined with a temporary period of very rapid economic expansion and high expectations. This was the case in Botswana and Indonesia, for example, where enrollment levels stagnated or even fell in the early 2000s.

While the regression results for child mortality seem to show that the scope for very fast improvement is limited, quite a number of examples suggest the opposite. In fact, decreases in

> 7 We do not discuss heterogeneity over time here.

12
**Table 1: Hungary: Capital Income Taxes, 1988 - 1997**

||1988|1989|1990|1991|1992|1993|1994|1995|1996|1997 (planned)|
|---|---|---|---|---|---|---|---|---|---|---|
|Corporate<br>Income Tax|not existing|_50%_<br>(40% on first<br>HUF 3 mio<br>profit)|40%<br>(35% on first<br>HUF 3 mio<br>profit)|40%+4%<br>supplement|40%|36%|36%|Retained<br>Distributed<br>profits: 33.3%|Retained<br>Distributed<br>profits: 33.3%|18%|
|Withholding<br>Tax on<br>Capital|Dividends:<br>20%<br>Interest:<br>20%|Dividends:<br>20%<br>Interest:<br>20%|Dividends:<br>20%<br>Interest:<br>20%|Dividends:<br>20%<br>Interest:<br>20%|Dividends:<br>20%<br>Interest:<br>20%|Dividends:<br>20%<br>Interest:<br>20%|Dividends:<br>20%<br>Interest:<br>10%<br>(as of Nov 1:<br>dividends: 10%,<br>interest: 0%)|Dividends:<br>10%<br>Interest:<br>0%|Dividends:<br>10%<br>Interest:<br>0%|Dividends:<br>20% (27% on<br>dividends)<br>Interest:<br>0%|
|Resulting Tax<br>Rate on<br>Dividends||60%|52%|55.2%|52%|48.8%|48.8%<br>(<br>f N<br>I<br>42.4%)|40%|40%|34.4% (40.14%<br>on ,,excessive"<br>dividends)|

Source: Intemational Bureau of Fiscal Documentation. GET V: Taxation and Investment in Central and East European Countries. Amsterdam (loose leaf). 0:\BOOK\KTAX2.DOC
Table 11: Second generation health impact by mother's district of birth

||(1)<br>Height for<br>age sd|(2)<br>Weight for<br>height sd|(3)<br>Weight for<br>age sd|(4)<br>Body mass<br>index sd|
|---|---|---|---|---|
|Mother's age 22 to 29 _×_|0.016|-0.007|-0.001|-0.011|
|Mother's exposure to conict|(0.013)|(0.008)|(0.009)|(0.008)|
|Mother's age 9 to 15 _×_|-0.006|-0.014**|-0.016**|-0.015**|
|Mother's exposure to conict|(0.007)|(0.006)|(0.006)|(0.006)|
|Mother's age 4 to 8 _×_|-0.003|-0.013**|-0.012**|-0.015**|
|Mother's exposure to conict|(0.006)|(0.006)|(0.006)|(0.006)|
|Mother's age 0 to 3 _×_|-0.003|-0.013**|-0.012**|-0.016***|
|Mother's exposure to conict|(0.006)|(0.006)|(0.005)|(0.005)|
|Observations|2,165|2,163|2,169|2,164|
|Adjusted R-squared|0.181|0.094|0.130|0.095|
|Mother and household controls|Yes|Yes|Yes|Yes|
|Children controls|Yes|Yes|Yes|Yes|
|Mother's district of birth xed eects|Yes|Yes|Yes|Yes|
|Number of clusters|74|74|74|74|
|Control mean (Age 16 to 21)|-1.836|-0.580|-1.472|-0.423|

Note: Conict is dened at district level i.e. mother's district of birth. Sample is children aged 0 to 59 months at the time of the survey. *** (**) (*) indicates signicance at the 1% (5%) (10%) level. Treatment on the treated estimates are reported based on a dierence-in-dierence specication. Standard errors are clustered at village level. Mother's age in the table refers to mother's age at the start of the war. Comparison cohort is children born to mothers whose age was 16 to 21 at the start of the war. Children born to mother's cohort 22 to 29 is a second comparison group that serves as a placebo test. Mother's controls are mother's years of birth xed eects, mother's month of birth xed eects, region specic trends. Household controls are indicator for high caste, female headed households, and whether residing in a rural area. Child controls are indicator if child is a girl, a twin, birth order xed eect, and xed eects for child years of birth, month of birth, and month of anthropometric measurements. Reported outcomes are z-scores based on the WHO anthropometric measurement standards.

49
# **Multidimensional Poverty in Ethiopia: Changes in Overlapping Deprivations**

Alemayehu Ambel<sup>*</sup> , Parendi Mehta, and Biratu Yigezu<sup>†</sup>

**Key words:** Multi-dimensional poverty, Ethiopia **JEL classification:** I31, I32

> * Corresponding author: aambel@worldbank.org.

> † The authors acknowledge financial support from the World Bank. An earlier version of this paper is a background paper for a larger World Bank study of the 2014 Ethiopia Poverty Assessment that was task managed by Ruth Hill. The detail of the larger study is available at <u>http://www.worldbank.org/en/topic/poverty/publication/ethiopia-poverty-assessment. We thank her for her</u> guidance and comments. We would like also to thank Dean Joliffe and Maria Ana Lugo for their helpful comments on an earlier version. Any remaining errors are our own.
# **I. Introduction**

India ranks fourth in the world in energy consumption, placed after United States, China, and Russia. India is not well endowed with abundant energy resources, making questions of energy security and sustainability a matter of national importance. Of course, as the world’s secondlargest country, how India manages its industrialization and urbanization process is a matter of global importance. These processes and India’s energy usage will directly impact worldwide energy prices, pollution levels and climate change, and much beyond.

India is slowly working towards sustainable equilibrium in its energy consumption patterns.<sup>1</sup> For instance, the energy intensity of GDP in India, defined as the energy input associated with a unit of GDP, has declined from 1.09 kilogram unit of oil equivalent (koe) per USD in 1981 to 0.66 koe per USD in 2011 (Planning Commission, 2012; also see Prayas, 2009). Using GDP at purchasing power parity, India’s energy intensity drops to 0.19, which is at par with the world average, but higher than most of the European countries. Specifically, China’s energy intensity is roughly 1.5 times that of India (World Energy Outlook, 2011).<sup>2</sup> These aggregate trends and their future trajectories are comprised of spatial and industrial developments in energy usage within India. This study charts out these detailed and localized patterns of electricity consumption for India’s manufacturing sector. We specifically consider the formal and informal manufacturing sectors in India and a time span stretching from 1989 to 2010. Understanding these patterns from a cross-sectional or static perspective is important so as to identify areas of best potential for improvement (e.g., heavy consumption industries, leading vs. lagging states). While such detail is important, assembly of data surrounding energy usage is difficult for a host of reporting factors depicted below. A very important part of this project is the development of a comprehensive data platform to provide metrics on these levels of activity.

Beyond these static depictions, however, lies our real focus on the dynamics of India’s electricity usage and how they relate to India’s changing economic geography. India’s manufacturing sector

> 1 For an overview of the power sector in India, see World Bank (2013), Abeberese (2013), and Alam (2013). 2 Recent estimates by McKinsey & Company (2014) suggests that India’s overall energy intensity at 0.56 koe/USD, which is high compared to even other developing countries like Brazil at 0.25 koe/USD or Malaysia at 0.40 koe/USD, indicating significant improvement potential.

3
<!-- Start of picture text --> H<br>θ = 0<br>1<br>0 < θ < 1<br>θ = 1<br>Dominated by A<br>Larger EU<br>B than A<br>A<br>H(A)<br>Smaller EU<br>Dominates A<br>than A<br>LE<br>0<br>LE(A)<br><!-- End of picture text -->

**Figure 3:** A and B are robustly ranked even though _H_ ( _A_ ) _< H_ ( _B_ ) and _LE_ ( _A_ ) _< LE_ ( _B_ ).

**Table 2:** An example of robust comparison: Pakistan and Bangladesh in 2019.

||Headcount|Life|Poverty|Poverty Free|
|---|---|---|---|---|
||ratio|Expectancy|Expectancy|Life Expectancy|
||||(_LE ∗H_)|_LE ∗_(1_−H_) =_PALE_1|
|Pakistan|4.3%|64.8|2.8|62.1|
|Bangladesh|6.7%|73.6|4.9|68.8|

differs from the ranking provided by _H_ . Proposition 5 describes the conditions under which _PALEθ_ comparisons are robust.

**Proposition 5.** _(Robust comparisons with PALEθ)_

_(i) For any two societies A and B, PALEθ_ ( _A_ ) _≤ PALEθ_ ( _B_ ) _for all θ ≤_ 1 _if and only if_ _(ii) There exist societies A and B for which PALEθ_ ( _A_ ) _≤ PALEθ_ ( _B_ ) _for all θ ≤_ 1 _even though H_ ( _A_ ) _< H_ ( _B_ ) _. These societies are such that H_ ( _A_ ) _< H_ ( _B_ ) _and LE_ ( _A_ ) _< LE_ ( _B_ ) _._ _Proof._ See Appendix J.

In Appendix K, we study the conditions under which comparisons by _EDθa_ ˆ are robust to the plausible values for its parameters.

# **4 Real world implications**

We now turn to data on poverty and life expectancy spanning the period 1990-2019. The data come respectively from the World Bank’s Poverty and Inequality Platform (World Bank, 2023) and the Global Burden of Disease Project (Global Burden of 15
||||(0.00643)||||(0.00650)||||(0.00656)||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Remittances & Others|||0.00463||||0.00639||||0.00328||
||||(0.01206)||||(0.01252)||||(0.01161)||
|Property income and investments|||-0.01232||||-0.01134||||-0.01301||
||||(0.00935)||||(0.00928)||||(0.00954)||
|Transfers and other benefits|||-0.00699||||-0.00884||||-0.00536||
||||(0.00867)||||(0.00872)||||(0.00879)||
|Own > median size of land (2.5 acres)|||0.00539||||0.00650||||0.00367||
||||(0.00663)||||(0.00674)||||(0.00654)||
|Observations|5,896|5,893|4,128|4,125|5,896|5,893|4,128|4,125|5,896|5,893|4,128|4,125|
|Root MSE|0.420|0.420|0.446|0.434|0.422|0.422|0.448|0.436|0.420|0.420|0.445|0.433|
|**_Included controls_**|||||||||||||
|Exogenous controls (age, agesq, sex<br>“male==1”)|_No_|_Yes_|_Yes_|_Yes_|_No_|_Yes_|_Yes_|_Yes_|_No_|_Yes_|_Yes_|_Yes_|
|Other controls (HH size, marital status,|||||||||||||
|education, occupation, other income sources,<br>land ownership)|_No_|_No_|_Yes_|_Yes_|_No_|_No_|_Yes_|_Yes_|_No_|_No_|_Yes_|_Yes_|
|Year fixed effects|_Yes_|_Yes_|_Yes_|_Yes_|_Yes_|_Yes_|_Yes_|_Yes_|_Yes_|_Yes_|_Yes_|_Yes_|
|Cluster fixed effects|_Yes_|_Yes_|_Yes_|_No_|_Yes_|_Yes_|_Yes_|_No_|_Yes_|_Yes_|_Yes_|_No_|
|Household fixed effects|_No_|_No_|_No_|_Yes_|_No_|_No_|_No_|_Yes_|_No_|_No_|_No_|_Yes_|

24
Policy Research Working Paper 9859

# **Abstract**

While the distributional impacts of the COVID-19 pandemic have been well-documented in high-income countries, studies in low- and middle-income countries have been relatively rare due to data limitations. This paper uses pre-pandemic household welfare data and high-frequency household phone survey data from seven middle-income countries in East Asia and the Pacific, spanning May 2020 to May 2021, to analyze the distributional impacts of the pandemic and their implications for equitable recovery. The results indicate that employment impacts at the extensive margin have been large and widespread across the welfare distribution during times of stringent mobility restrictions (low mobility). When mobility restrictions have been relaxed, however, employment impacts have been larger among poorer workers who have found it more difficult to return to employment. Data on the loss of labor income also suggests that the pandemic has exacerbated existing inequalities. In addition to being more susceptible to employment and income shocks, poorer households in East Asia and the Pacific are at higher risk of experiencing long-term scarring from the pandemic – due to rising food insecurity, increased debt, distress sale of assets, and fewer distance/interactive learning opportunities for their children. Taken together, the findings indicate that inequality has worsened during the pandemic, raising concerns about the prospects for an inclusive recovery in the absence of appropriate policy measures.

This paper is a product of the Poverty and Equity Global Practice and the Office of the Chief Economist, East Asia and the Pacific Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at at ykim14@worldbank.org, mlugo1@worldbank.org, amason@worldbank.org, and iuochi@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
SENEGAL’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE Moreover, studies conducted under the PST2 have identified a backlog in road rehabilitation needs of $400 million.<sup>4</sup> Assuming that the backlog for rehabilitation is financed overnight directly by the government with support from donors and that the network is in good or fair (that is, maintainable) condition, funding routine maintenance will require around $100 million–$10 million per year based on a 40-year life cycle for roads. These calculations lead to an optimal fuel levy of $24.8 cents per liter—10 times higher than the actual fuel levy of $2.6 cents per liter, as of 2009 (figure 7a).

The road fund has been funded primarily by transfers from the budget and by donor contributions to finance road maintenance. The level of the fuel levy has been a critical element of the sector’s policy agenda. In fact, in 2011, the tax on gasoline was essentially doubled (decree 2011-336 of March 16, 2011). This measure is expected to substantially reduce the funding gap that must be covered by the government and donors. Yet results remain to be seen. According to the estimates described above, the fuel levy may still fall short of covering all maintenance needs. In 2009, taking into account internal and external contributions, the total amount spent on maintenance was equivalent to 0.44 percent of GDP (of which budget allocations were 0.33 percent of GDP) about 60 percent short of the needs (figure 7b).

**Figure 7. Senegal’s expenditure is not enough to catch up with its road rehabilitation backlog**

# a. Actual and optimal fuel levy

<!-- Start of picture text --> b. Deviation from the norms<br><!-- End of picture text --> <!-- Start of picture text --> 35 80<br>30 60<br>40<br>25<br>20<br>20<br>0<br>15 -20<br>10 -40<br>5 -60<br>-80<br>0<br>-100<br>Existing Optimal for maintenance maintenance rehabilitation<br>US cents per liter<br>spending as % of requirements<br><!-- End of picture text --> _Source:_ Gwilliam and others 2008.

Other efforts to diversify funding sources for maintenance and rehabilitation are noticeable, particularly those involving the private sector. Yet, there is an overdependence on public funds and an inadequate allocation of resources while financing transport infrastructure. Private sector financing of transport infrastructure is still quite limited, except for the recently approved Dakar-Diamniadio toll highway (World Bank 2010c). At the same time, there is no clear optimization of resource allocation to different projects in this sector. The need for greater selectivity and efficient prioritization of projects for

> 4 Some sector experts suggest that this is a conservative estimate and that rehabilitation needs are on the order of $1.4 billion.

12
# _<u>Rainforest stations</u>_

<!-- Start of picture text --> IQUITOS JUANJUI<br>500 400<br>400<br>300<br>300<br>200<br>200<br>100 100<br>0<br>0<br>-100<br>-200 -100<br>-300<br>-200<br>-400 may-48 oct-53 abr-59 oct-64 mar-70 sep-75 mar-81 ago-86 feb-92 ago-97 feb-03<br>may-48 oct-53 abr-59 oct-64 mar-70 sep-75 mar-81 ago-86 feb-92 ago-97 feb-03<br>PUCALLPA TARAPOTO<br>500 250<br>400 200<br>300 150<br>200 100<br>100 50<br>0 0<br>-100 -50<br>-200 -100<br>-300 -150<br>may-48 oct-53 abr-59 oct-64 mar-70 sep-75 mar-81 ago-86 feb-92 ago-97 feb-03 may-48 oct-53 abr-59 oct-64 mar-70 sep-75 mar-81 ago-86 feb-92 ago-97 feb-03<br>TINGO MARIA YURIMAGUAS<br>600 400<br>500<br>300<br>400<br>300 200<br>200<br>100<br>100<br>0 0<br>-100<br>-100<br>-200<br>-300 -200<br>-400<br>-300<br>-500<br>may-48 oct-53 abr-59 oct-64 mar-70 sep-75 mar-81 ago-86 feb-92 ago-97 feb-03<br>may-48 oct-53 abr-59 oct-64 mar-70 sep-75 mar-81 ago-86 feb-92 ago-97 feb-03<br>average for 1960-1990 (Milimeters)<br>Precipitation Anomalies compared to<br>average for 1960-1990 (Milimeters)<br>Precipitation Anomalies compared to<br>Precipitation Anomalies compared to average for 1960-1990 (Milimeters) Precipitation Anomalies compared to average for 1960-1990 (Milimeters)<br>average for 1960-1990 (Milimeters)<br>average for 1960-1990 (Milimeters) Precipitation Anomalies compared to<br>Precipitation Anomalies compared to<br><!-- End of picture text --> 34
8 Chapter 2 above unity (1.28). The contrast suggests (though our observations are too few to demonstrate) that the long-run elasticity is substantially higher in the group of developing countries than in the high-and-middle-income group. We hazard the proposition that the value of the elasticity in developing countries has, say, 1.25 as its lower limit.

Table 2: Regression Results - Road Tkm

|Am =**KME**<br>GDP = ICP**Intern'l**|**$**(mllion|s)|Dependent<br>ain millions|Vaiiable - <br>)|Road Tbn||Dependent<br>(MOlns)|Var. =**INM**|**(oad Thnjd**||
|---|---|---|---|---|---|---|---|---|---|---|
|Sample|Indep.<br>**Var.()**|* ot<br>Obs.|X<br>Cot.|T<br>Value|Cmlant|Adj.<br>R|X<br>_C ut._|T<br>Value|Conal|AdJ.|
|A: Toal Sabl|An|33|O.OM83|2.969|26,97S3|0.20|0.66965|3.029|_1.6_|0.20|
|A: Ta*l Sampb|GDP|33|0.17721|IS.=10|1,993.8|0.89|126663|12.324|(13.9)|0.83|
|A: Tod Sampl|An&<br>ODP|33|_010065_<br>0.17033|_1336_<br>X<br>14.020|<br>7008|0.89|0.02D71<br>1.2SS29|_0.169_<br>10.329|(13.9)|O2|
|B: Doek,ped|.-<br>An*|-..<br>17|0.03340|-<br>1.441-|-<br>..<br>46,180.1|0.06|.<br>-<br>O.SSB63|-<br>2.639|-<br>3.6|-....:<br>0.27|
|B: Daewloed|GDP|17|0.17169|9.810|6,8f6.0|0.86|1.09033|12.022|(10.3)|0.90|
|B: Dcwlqed|_Am_<br>GDP|17|0.01474<br>0.16552|1.677<br>9.774|2,579.2|0.87|0.12990<br>1.01735|_I 4_<br>10.204|(10.5)|0.91|
|C: Do 'd+DeI'<br>d|<br>A+De|28|0.03466|2.6DI|29,037.9|0.18|0.60|i8s39|I2.|0.21|
|C: DewAId+Duwl'Ih%|GDP|28|0.17827|14.418|1,163.7|0.8|13048I|12.535|(14.7)|0ES|
|lC:Dvwd+Dcwl'ig|Anm<br>GDP|28|_OS_<br>017144|1.273'<br>12.45|_(428.7)_|0.89|0.07<br>1.28131|038'<br>10.499|(14.8)|0.5|
|D: Dewrd+Socallets|Ama|22|003803|1.809|38,312.1|0.11|0.5721i|3.188|3.4|030|
|D: D[wld+Socialiats|GDP|22|0.17219|11.917|6,484.8|0.87|1.03421|220|(9.|0.8|
|D- Dvel'd+Soalla|Ai|22|0.01465|1.957P|3,064.5|0.89|0.13458|1.821'|(.4)|0.89|
||GDP||0.16489|11.755|||Q95792|20363|||
|AlU vaodabka hi asbual lo|S.|Noltalga|filmia**d5%**<br>w|IL|||||||

# Rail Freight

28. Demand for rail ton-kms (Table 3) is governed by essentially similar determinants in our samples of developed and developing countries. Of the two independent variables, it is clearly country area that provides the dominant explanation of inter-country variation. The explanatory power of GDP when taken on its own is very low and the estimate of the simple coefficient on GDP is normally not significantly different from zero. Adding GDP to Area in the regressions does practically nothing for the model's power of explanation (the R<sup>2</sup> s).

29. By the test of R<sup>2</sup> and t-ratio, our model seems no less adequate as an explanation of intercountry variations of rail freight than of road freight. But the almost total failure of GDP to enter into the explanation so far is nevertheless surprising and somewhat disappointing: there appears to be no growth effect in the use of railways. One naturally thinks of other factors that