rafmacalaba/datause-encoder-data huggingface dataset:
lower than that of the rural-dwellers that are more spread-out (60 percent are poor). At this point it may be worth remembering that Pará is a rich state, with no semi-arid areas as in the Northeast region of Brazil, hence rural-dwellers have easier access to food items that possibility add calories to their daily diet, than urban-dwellers have. Therefore, the monetary poverty picture may not reflect adequately the poverty differences in rural and urban living. Unfortunately, no “environmental income” data are available in Brazil.

In the last decade Pará has made creditable headway in reducing urban poverty **.** Analyzes of poverty trends in urban Pará based on PNADs 1981-2001 reveal that during 1993-2001, the state’s extreme urban poverty, measured by P0, fell 9 percentage points in urban areas and reached 33 percent in 2001 (Figure 4.2, Table 4.1, and Appendix B show urban poverty measured by P0, P1, and P2 for Brazil, and the state of Pará for 1981-2001). There is little room for complacency, however, because extreme poverty is still very high at 33 percent in urban areas. This translates to over 1.4 million people of the urban population (4.1 million) who still live in extreme poverty, which means that they do not have sufficient income to buy a minimum basket of food. This is a poverty rate more than 50 percent higher than that of urban areas in Brazil (22 percent) and 10 percent higher than that of urban areas in the North as a whole (30 percent), see Table 4.1.

**Figure 4.2:     Indigent Poverty Rate (P0) in Urban Areas, Brazil, the North, and Pará Selected Years during 1981-2001** **<u>(Percent)</u>**

<!-- Start of picture text --> 50.0<br>Brazil North<br>45.0<br>Pará<br>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>1981 1985 1990 1992 1993 1995 1997 1998 1999 2001<br><!-- End of picture text --> _Source:_ Authors calculations based on selected years of PNADs in 1981-2001.

Urban poverty did not fall monotonically over the last two decades **.** The P0 increased from 29 per cent in the beginning of the 1980s, increased before the federal government’s implementation of the inflation-beating Cruzado Plan of 1986, increased when the plan collapsed, and increased even more during the crisis of 1990 where it hit an all-time high of 45 percent urban poverty rate (Figure 4.2). The P0 embarked on a downward path only after the most recent inflation-beating Real Plan of 1994 was implemented and the minimum wage increased. There was a sharp decline in poverty 17
**Table 5a. Concentration indexes of selected child health status indicators in Ethiopia (20002014)**

|Year||CI<br>S|E|norm CI|SE|abs CI|SE|
|---|---|---|---|---|---|---|---|
|_Stunting_||||||||
||2000|-0.033|0.007|-0.076|0.016|-0.019|0.004|
||2005|-0.042|0.011|-0.083|0.022|-0.021|0.005|
||2011|-0.064|0.009|-0.115|0.016|-0.028|0.004|
||2014|-0.085|0.015|-0.144|0.025|-0.035|0.006|
|_Wasting_||||||||
||2000|-0.034|0.020|-0.039|0.022|-0.004|0.002|
||2005|-0.117|0.028|-0.133|0.032|-0.015|0.004|
||2011|-0.159|0.022|-0.176|0.025|-0.016|0.002|
||2014|-0.099|0.037|-0.109|0.041|-0.009|0.003|
|_Underwei_|_ght_|||||||
||2000|-0.054|0.009|-0.093|0.016|-0.023|0.004|
||2005|-0.075|0.015|-0.113|0.022|-0.025|0.005|
||2011|-0.129|0.012|-0.182|0.017|-0.038|0.003|
||2014|-0.118|0.019|-0.161|0.027|-0.032|0.005|
|NMR||||||||
||2000|0.050||0.052||0.002||
||2005|0.036||0.037||0.001||
||2011|-0.085||-0.088||-0.003||
||2014|0.080||0.083||0.003||
|IMR||||||||
||2000|0.038||0.042||0.004||
||2005|-0.016||-0.018||-0.001||
||2011|-0.083||-0.088||-0.005||
||2014|0.013||0.013||0.001||
|U5MR||||||||
||2000|0.015||0.018||0.002||
||2005|-0.049||-0.056||-0.006||
||2011|-0.105||-0.114||-0.009||
||2014|-0.021||-0.023||-0.002||

Source: Authors’ compilation from the DHS (2000, 2005, 2011 & 2014) data.

Note: CI is concentration index; norm CI is normalized concentration index; and abs CI is absolute concentration index.

25
<!-- Start of picture text --> Percentage<br>change in prices<br>ME <.45<br>Ml >-4.5--3<br>[9 >-3--1.5<br>{-] >-1.5-0<br>Ml >0-0.5<br>MB >os ams<br>vod - Lm os<br>(AN<br>0 250. S00 Km N<br>0 155-310 Miles v<br><!-- End of picture text -->
# **Couple with two young children and a pensioner in the house**

We now assume that the family described above now lives with a pensioner who receives the average pension of SK4,550. The MSL then becomes SK10,670, while child allowance payments remain the same.

A. Unemployed spouse

|**IndividualandHHincome**|**UI**<br>**First3 months**<br>(RR**50%)**|**UI**<br>**Afterfirst3 months**<br>(RR45%)|**SA(Objective)**|
|---|---|---|---|
|**Individualincome(AW)**|5,400<br>(0.49)|4,927<br>(0.45)|3,230<br>(0.29)|
|HU**income(AW)**|12,350<br>(0.72)|12,350<br>(0.72)|12,350<br>(0.72)|
|**Individualincome (MW)**|3,230<br>(0.90)|3,230<br>(0.90)|3,230<br>(0.90)|
|**HHincome(MW)**|12,350<br>(1.00)|12,350<br>(1.00)|12,350<br>(1.00)|

# B. Spouse employed at average wage

|**IndividualandHHincome**|**UI**<br>**First3 months**<br>**(RR50%)**|**UI**<br>**Afterfirst3 months**<br>**(RR45%)**|**SA(Objective)**<br>**(noUl)**|
|---|---|---|---|
|**Individualincome(AW)**|5,400<br>(0.49)|4,927<br>(0.45)|0|
|**HHincome(AW)**|22,000<br>(0.83)|21,527<br>(0.81)|16,600<br>(0.63)|
|**Individualincome(MW)**|1,800<br>(0.50)|1,620<br>(0.45)|0|
|**HHincome (MW)**|18,400<br>(0.96)|18,220<br>(0.96)|16,600<br>(0.87)|

# **C.** Spouse employed at minimum wage

|**IndividualandHHincome**|**UI**<br>**First3 months**<br>**(RR 50%)**|**Ul**<br>**Afterfirst3 months**<br>**(RR45%)**|**SA(Objective)**<br>**(noUI)**|
|---|---|---|---|
|**Individualincome(AW)**|5,400<br>(0.49)|4,927<br>(0.45)|**0**|
|**1Hincome(AW)**|15,230<br>(0.80)|14,757<br>(0.77)|12,350<br>(0.65)|
|**Individualincome(MW)**|2,520<br>(0.63)|2,520<br>(0.63)|2,520<br>(0.63)|
|**HHincome (MW)**|12,350<br>(1.16)|12,350<br>(1.16)|12,350<br>(1.16)|

43
**Figure 4: Cost-effectiveness of improving learning - percentage of times each program is as cost-effective as Girls’ merit scholarships, across 100,000 draws**

<!-- Start of picture text --> Percentage of draws as cost-<br>effective as Girls' merit<br>scholarships<br>Minimum conditional cash transfers, Malawi<br>Read-a-Thon, Philippines<br>Girls’ merit scholarships, Kenya Girls' merit scholarships, Kenya<br>Individually-paced computer assisted learning, India<br>Extra contract teacher + streaming, Kenya<br>Village-based schools, Afghanistan<br>Camera monitoring, India<br>Remedial education, India<br>Textbooks for top quintile, Kenya<br>Teacher incentives (year 2), Kenya<br>Electing school committee and linking to local government, Indonesia<br>Linking school committee to local government, Indonesia<br>Streaming by achievement, Kenya<br>Providing earnings information, Madagascar<br>0% 20% 40% 60% 80% 100%<br><!-- End of picture text --> Source: Data adapted from J-PAL (2014a). Calculations by authors.

27
- Ordonana, Juan, Francisca Gonzalez-Javier, Laura Espin-Lopez, and Jesus Gomez-Amor. 2009. “Self-Report and Psychophysiological Responses to Fear Appeals.” _Human Communication Research_ 35 (2): 195–220.

- Orimadegun, Adebola, Akinlolu Adepoju, and Olusegun Akinyinka. 2014. “Adolescent Girls Understanding of Tetanus Infection and Prevention: Implications for the Disease Control in Western Nigeria.” _Frontiers in Public Health_ 2:24.

- Oruamabo, RS. 2007. “Neonatal tetanus in Nigeria: does it still pose a major threat to neonatal survival?” _Archives of Disease in Childhood_ 92 (1): 9–10.

- Pebley, Anne, Noreen Goldman, and German Rodriguez. 1996. “Prenatal and Delivery Care and Childhood Immunization in Guatemala: Do Family and Community Matter?” _Demography_ 33 (2): 231–247.

- Petrosino, Anthony, Carolyn Turpin-Petrosino, Meghan Hollis-Peel, Julia Lavenberg, and Alexia Stern. 2014. “Scared Straight and Other Juvenile Awareness Programs for Preventing Juvenile Delinquency.” _Crime Prevention Research Review_ 9 (5): 1–31.

- Rainey, Jeanetter, Margaret Watkins, Tove Ryman, Paramjit Sandhu, Anne Bo, and Kaushik Banerjee. 2011. “Reasons related to non-vaccination and under-vaccination of children in low and middle income countries: findings from a systematic review of the published literature, 1999-2009.” _Vaccine_ 29 (46): 8215–8221.

- Robertson, Laura, Phyllis Mushati, Jeffrey Eaton, Lovemore Dumba, Gideon Mavise, Jeremiah Makoni, Christina Schumache, Tom Crea, Roeland Monasch, Lorraine Sherr, Geoffrey Garnett, Constance Nyamukapa, and Simon Gregson. 2013. “Effects of unconditional and conditional cash transfers on child health and development in Zimbabwe: a cluster-randomised trial.” _Lancet_ 381 (9874): 1283–1292.

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30
4

# I} Existini empirical evidence

There are tbree strands of empirical evidence which have failed to support the common sense view of the negative economic consequences of population growth.

First, there has been at best a weak correlation between the rate of growth of population and GDP per capita growth in the historical experience of the now developed countres. Kuznets (1967) surveyed the very long growth experience of a number of (now developed) countries and found no correlation between population growth and economic performance. This held true even excluding the areas of recent setaement to reduce the impact of endogenous changes in population through migraton responding to economic opportwity3.

Second, economic growth and population growth have clearly acceleratedjointly over the course of human history<sup>4</sup> . Demogtaphers have pointed out that world population growth has acceleated rapidly in recent centuries, rsing from near zero anciently to rates above 2 percent (before faling recently). Economists have poited out that growth rates of per capita income have also accelerated rapidly in the last centuries-especially recently. As Kuznets pointed out, sice current income levels are the result of cumulative economic growth over past centaies, the fact that many countries began only recentiy growing rapidly from a very low base suggests 3 lu the last two centuies the "areas of recent settlement' have had very rapid grovwth of both population and GDP per capitaL All counies were able to sustain very rapid rates of GDP growth to allow growth per capita to be at least as rapid in the countries with very rapid popuation growth as opposed to the demogaphically more stable counuer-parn. It is difficult to believe that GDP per capita in the U.S. (or Argendna) in 1930 would have been higher had immigration been curtailed in 1880, or 1820.

-4 There is something missing in the argument that since population is going to double in the _next_ forty years this necessarly _iples_ some negative consequences, because populaion gwth doubled in the List forty years-and the last forty years are arguably the best forty years for progress in the history of mankind.
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have a non-linear relationship with the size of the migrant labor force and we expect growth in the migrant network to decline after initially increasing with distribution of IDs. In Figure 2, we show a lowess plot of the relationship between years since IDs were distributed and the number of migrants from the village from year t � 1 to t. Note the sharp increase in migrants from the time that IDs are distributed and then a slowing of the increase over time (which would imply an even slower growth rate). This pattern suggests non-linearity in the relationship between ID distribution and new participants in the village migrant labor force. We thus specify our instrument as a dummy variable indicating that IDs had been issued interacted with the years since they had been issued, and then experimented with quadratic, cubic and quartic functions of years-since-IDs-issued. We settle on the quartic function for our instruments because, as we show below, it ...ts the pattern of expanding migrant networks better than the quadratic or the cubic functions.

Since ID distribution was the responsibility of county level o¢ ces of the Ministry of Civil A¤airs, which are distinctly separate from agencies involved in setting policies a¤ecting land, credit, taxation and poverty alleviation (the Ministry of Agriculture and Ministry of Finance handle most decisions that a¤ect these policies at the local level), it is plausible that ID distribution is not be systematically related to unobservable policy decisions with more direct relationship to household consumption. Ideally, a policy would exist that was randomly implemented, a¤ecting the ability to migrate from some counties but not others. As the di¤erential timing of the distribution of ID cards was not random, we must be concerned that counties with speci...c characteristics or that followed speci...c policies were singled out to receive ID cards earlier than other counties, or that features of counties receiving IDs earlier are systematically correlated with other policies a¤ecting consumption growth. These counties, one might argue, were “allowed”to build up migrant networks faster than others.

In two earlier papers, de Brauw and Giles (2008a and 2008b) address several possible concerns with use of the years-since-IDs quartic as instruments for the size of the village migrant labor force. They ...rst show that timing of ID distribution appears to be related to remoteness of the village, but not systematically related to village policies that may a¤ect consumption growth, with village administrative capacity, or with the demand for IDs within the village. They thus argue in favor of including a village ...xed e¤ect to control for features 22
_budget required to increase household coverage by Y percent? iii) What is the coverage or poverty reduction we can obtain with a given Z budget? iv) Can the program’s efficiency be improved by shifting the poverty line?_ The answers to these questions can help donors make funding decisions and humanitarian organizations make targeting choices. We show that this approach can be applied to existing programs such as food voucher programs administered by the World Food Program (WFP) relying on available micro data collected by the United Nations High Commissioner for Refugees (UNHCR). Our main contribution is to show how to operationalize such approach in the context of welfare improving humanitarian operations using relatively simple visual devices suitable for policy makers. In this respect, the paper is a complement to the broader and relatively new literature on targeting food and cash programs in humanitarian contexts (Mollis et al., 1999, Maxwell et al., 2011, Coll-Black et al., 2012).

The paper is organized as follows. The next section describes the food voucher program that we use to illustrate the methodology proposed. Section 3 describes the data, Section 4 outlines the models employed to caliber the methodology, section 5 outlines results and section 6 concludes.

# 2. The 2014 food voucher program for refugees in Jordan

As a case study, the paper uses the 2014 food voucher program administered by WFP to Syrian refugees in Jordan in 2014. Similarly to the populations mentioned in the introduction, this is a population that experienced multiple shocks that eventually led to poverty and refugee status in neighboring countries (Verme et al, 2016). There are also some important differences. Jordan is a middle-income country and has a very different capacity to provide for displaced populations as compared to countries such as South Sudan or Somalia. The quality of data available for refugees in Jordan was also superior to the quality of data that is currently available in Sub Saharan Africa. However, we will show that the approach proposed can be implemented using micro data that the WFP and UNHCR routinely collect and that this is a method not beyond the existing capacity of these organizations.

The material assistance provided to refugees in Jordan in 2014 was essentially structured in two programs: i) a cash program administered by the UNHCR and ii) a food voucher program administered by the WFP. In 2014, the UNHCR cash program provided 50 Jordanian Dinars (JD)<sup>7</sup> per month to cases<sup>8</sup> including one or two members, 100 JD to cases with 3 to 5 members and 120 JD to cases with more than five members.

> 7 A Jordanian Dinar in 2014 was roughly equivalent to one Euro. 8 In the UNHCR jargon, a “case” is the household or family unit used to register refugees.

4
Figure 1: Distribution of Household Per Capita Expenditures with and without Household Size Weights, 1985 and 1986 <!-- Start of picture text --> C6te d'Ivoire 1985<br><!-- End of picture text --> <!-- Start of picture text --> 100<br>80<br>.2<br>70-<br>a 60<br>0<br>- ~~~50<br>v ~~40-<br>30<br>0~~~<br>1.1 0 1 10 l 1826 I 1 4.2 1 42.5 5 S2.53 65.5 3 90.8 1200.4<br>6 14 22 30 38.2 46.9 57.7 74.9 1ZI1.<br>Per capita expenditure (10.000 CFA)<br>with H-S weights<br>without H-S weights<br>Cote d'lvoire 1986<br>100<br>80<br>c<br>0~~~7<br>0.~~~7<br>a ~~60<br>50<br>0~ ~ 3<br>0~~~2<br>.2~~~~~~~~~2<br>20<br>6 14 822 30 38.2 5 46.9 57.%7 .74.9 MIA1O*<br>Pe aiaexpenditure (10.000 CFA)<br>with H-S weights<br>-without H-S weights<br><!-- End of picture text -->
share of the working-age population has been accompanied by higher capital accumulation and faster productivity growth (Bloom and Canning 2004; World Bank 2018).

The recent demographic shifts towards older population structures affect potential output in several ways. Population aging may reduce the working-age population, which directly reduces potential labor supply. Aging populations have also been associated with slower labor productivity growth for various industries and occupations (Maestas, Mullen and Powell 2016). Russian demographic trends are worse than those found in other EMDEs, as the country’s low total fertility rate in the early 1990s accelerated population aging. Russia’s total fertility rate remained low until the mid-2000s (Figure 2B). The decline in the total fertility rate began to take a toll on the working-age population after 15 years, with potential labor force growth peaking in 2007 at 0.7 percent before declining to -0.7 percent in 2017.

Over the past five decades, global growth was supported by rapidly growing working-age populations—until the mid-1980s in advanced economies and around 2010 in EMDEs. With the retirement of the baby boom generation and lower fertility rates, demographic trends have turned less favorable to growth and will continue to do so over the next decade. In advanced economies, the working-age share of the population is set to decline from 65.4 percent in 2015 to 62.3 percent by 2025. In EMDEs, the working-age share of the population peaked at 65.8 percent in 2015 and is expected to stabilize around this level for the next 10 years.

Another important driver of increased labor supply is labor force participation among less represented groups, including women, young, and old workers. Rising female labor force participation rates have been attributed to better educational attainment (opening access to higher-earning jobs), lower fertility rates, a technology-driven shift towards non-manual skills, and cheaper home production (lowering the opportunity cost of working). While the Russian labor market participation rate is on an upward trend, it is not enough to offset the rapid decline in the population.

# **IV. Russia’s potential growth: Outlook**

Factors weighing on potential growth for Russia are likely to persist over the next decade. Demographic trends are expected to become less favorable. This will weigh on potential growth even if trend improvements in human capital and labor force participation continue. Short of unexpected surges in productivity growth, these trends imply an outlook of mediocre potential growth.

9
The assessment of **operational efficiency** depends on data availability. Some information on transmission and distribution losses for the system as a whole is available from the beginning of the period studied, and separate data is available for KE (privatized in 2005) and PEPCO (the DISCOs that had been unbundled in 1997). Figure 8 compares the evolution of T&D losses of these two groups and reveals several important features. The overarching finding is that during the whole period T&D losses were generally large and even at their lowest point (2016) did not drop below 20 percent for either the public sector or the private sector.

The privatized utility KE has seen a steady improvement, particularly from about 2009 onwards, with losses falling from an extremely high 35 percent to about 20 percent. The change in ownership, following a management takeover in 2009 by Abraaj Capital, appears to have improved this aspect of efficiency, albeit from an exceptionally high level of losses. There has been only modest improvement in the performance of the state-owned DISCOs in the 16 years shown. This suggests that there has not been an effective incentive to these utilities to improve performance.

**Figure 8: T&D losses for public sector DISCOs and for Karachi Electric (percent)**

<!-- Start of picture text --> 40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br>PEPCO KE<br><!-- End of picture text --> Source: State of Industry Reports, various years.

One of the policy tools that was intended to improve this aspect of performance was the way in which the revenue-requirement tariff calculated by NEPRA set a target rate for T&D losses for each DISCO. If the actual loss turned out to be larger than the target, then the DISCO would not be recompensed for this in the TDS. It was hoped that by incremental tightening of the target rates over time the actual rate would improve as the DISCOs tried to avoid making deficits.

35