rafmacalaba/datause-encoder-data huggingface dataset:
_Figure 6 Growth in household wealth over the life cycle, by type of household_ <!-- Start of picture text --> Residential buildings Financial wealth (excl. loans) Loans<br>60 4 2<br>50 3 1<br>40 2<br>0<br>30 1 2529333741454953576165<br>-1<br>20 0<br>2529333741454953576165 -2<br>10 -1<br>0 -2 -3<br>25 30 35 40 45 50 55 60 65<br>-10 -3 -4<br>Age of the household head Age of the household head Age of the household head<br>Q1 Q2 Q3 Q1 Q2 Q3 Q1 Q2 Q3<br>Q4 Q5 Q4 Q5 Q4 Q5<br>Residential buildings Financial wealth (excl. loans) Loans<br>50 3.5 0.5<br>3<br>0<br>40<br>2.5 25 30 35 40 45 50 55 60 65<br>-0.5<br>2<br>30<br>-1<br>1.5<br>20 1 -1.5<br>0.5<br>-2<br>10<br>0<br>-2.5<br>-0.5 25 30 35 40 45 50 55 60 65<br>0<br>-3<br>25 30 35 40 45 50 55 60 65 -1<br>-10 -1.5 -3.5<br>Age of the household head Age of the household head Age of the household head<br>urban rural urban rural urban rural<br>Months of household cons. Months of household cons. Months of household cons.<br>Months of household cons. Months of household cons. Months of household cons.<br><!-- End of picture text -->

# **7. Conclusion**

The combined forces of population aging, weakening family and village risk-sharing networks, and low formal pension coverage will make financing elderly consumption a major challenge for the future. Life expectancy (and with it, the number of unproductive years to be covered) has increased significantly since the early 1990s. In Pakistan, it went from 60.1 years in 1990 to 67.11 in 2018 (World Bank, World Development Indicators, 2018). While still relatively high compared to peer countries, the fertility rate in Pakistan has almost halved over the last thirty years, from 6.2 births per woman in 1990 to 3.5 in 2018, increasing the dependency ratio and reducing the availability of family or network members for support. Formal pension coverage is likely to remain low as a fraction of the labor force, as large cohorts of young workers enter the labor market, exceeding the availability 18
beyond which the marginal economic return declines with additional rainfall. The impact of temperature is in line with previous findings at a small spatial scale (e.g., BHM 2015). We then demonstrate that as the data are aggregated to larger spatial scales, the impact of precipitation becomes smaller until it ultimately disappears when the spatial unit is the country level.

Our results further indicate that the relationship between rainfall and GDP growth is sharper in low-income countries. However, in contrast, economic activity remains affected by temperatures in the developed economies. As a likely mechanism, we show that the relationship between precipitation and GDP growth is stronger in cells with large areas of croplands.

# **2- Methods**

## **2.A Data**

We use weather data taken from the _Terrestrial Air Temperature and Precipitation Version 4.01_ compiled by the University of Delaware (Willmott and Matsuura 2001). This data set provides monthly mean precipitation and temperature at a 0.5-degree spatial resolution. Data are available for each month between 1901 and 2014. These data are widely use in the economics literature, including in DJO and BHM. Annual precipitation at the cell level between 1990 and 2014 ranged between 3mL in Sudan to 10,187mL in India (659mL average, Figure 1-A). Annual temperature at the cell level between 1990 and 2014 ranged between -32.4oC in Greenland and 37.2 oC in Peru (14.8 oC average, Figure 1-B).

Annual grid-level GDP data between 1990 and 2014 at a 0.5-degree resolution come from Kummu, Taka and Guillaume (2018a). The data are primarily based on sub-national GDP per capita data constructed by Gennaioli, _et al._ (2013) and cover 82 countries, representing 85% of the global population and 92% of global total GDP (PPP) in 2015.

In figure 1, we plot the average cell-level precipitation between 1990 and 2014 against the level of GDP per capita observed in 2014 (y-axis), and cell-level temperature against GDP per capita in 2014. A local weighted scatterplot smoothing (LOWESS) represents the non-parametric correlation between weather variables and GDP across cells. Precipitation and GDP per capita follows an inverted U-shape. Up to a level of 500mL to 700mL of precipitation, an additional drop of rainfall is correlated with a higher level of GDP per capita. The relationship then turns negative (Fig 1-A). Above -20oC, temperature and GDP are negatively correlated (Fig 1- B).

In Figure 1 C-D, we collapse weather data to the country level. Following DJO and BHM, we weight each cell by its total population. Observe that the inverted U-shape correlation between population-weighted precipitation and GDP per capita at the cell level is lost at the country level in Figure 1-C. After an initial decrease in GDP 4
2003 budget envisaged an even higher deficit (3.2 percent of GDP) notwithstanding the high growth rates of recent years. Fortunately, Latvia has been able to finance its fiscal deficit over the past decade mostly with privatization receipts (1997-98), through the banking system (1999-2000), and with some foreign financing (1999 and 2001), without either putting pressure on government debt or crowding out the private sector.

**Figure 2: Financing of the Fiscal Balance (in millions of Lats)**

<!-- Start of picture text --> 150<br>100<br>50<br>0<br>1994 1995 1996 1997 1998 1999 2000 2001 2002<br>-50<br>-100<br>-150<br>-200<br>Privatization Banking system Other domestic Foreign financing Fiscal balance<br><!-- End of picture text --> Following several years of small external current account deficits that were entirely financed by foreign direct investment, Latvia’s current account deficit (including official transfers) rose from 6.1 percent of GDP in 1997 to 9.8 percent in 1999, with only half financed by FDI. This deterioration was largely initiated by the Russian crisis. There was some improvement in 2000 when the current account deficit was brought to just under 7 percent of GDP despite appreciation of the lat against the euro and the rise in oil prices. However, it widened again in 2001, reaching 9.6 percent of GDP. **Figure 3: Balance of Payments, 1998-2002 (percent of GDP)** While the external position Balance of Payments, 1998-2002 improved slightly in 2002 with the 16 (Percent of GDP) deficit falling to 7.8 percent of GDP, 12 this reflected the impact of improved 84 statistical coverage that helped raise -400 private transfers. The fragile -8 external position remains the main -12-16 risk to the Latvian economy. -20 <!-- Start of picture text --> Balance of Payments, 1998-2002<br>(Percent of GDP)<br>16<br>12<br>484<br>-400<br>-8<br>-16-12-16<br>-20<br>-24<br>-28<br>Mar Jun Sep Dec Mar Jun Sep Dec Mar Jun Sep Dec Mar Jun Sep Dec Mar Jun Sep Dec<br>1998 1999 2000 2001 2002 Est.<br>Service Account Trade deficit<br>Income and transfers Current Account<br>Source: Bank of Latvia.<br><!-- End of picture text --> Like many transition economies, Latvia has recorded relatively modest saving rates (19.5 percent of GDP on average over the past five years) even as investment needs during transition have been high (gross domestic investment averaged 28 percent of GDP in 1998-2003). Such conditions suggest that running a sustainable current account deficit, both to smooth consumption over time and to finance much-needed capital projects to catch up with EU countries, may be necessary from a welfare viewpoint. However, sizable investments will be needed to sustain economic growth in the medium term, as well as a potential catch-up in private sector consumption. These factors may compromise improvements in the external current account deficit, as imports will continue to grow 8
- <mark>Statistics Canada. 2022. “Distributions of household economic accounts, income, consumption and saving, by characteristic, annual.” Available at: https://www150.statcan.gc.ca/t1/tbl1/en/tv.action?pid=3610058701&pickMembers%5B0%5D =2.4&pickMembers%5B1%5D=3.2&cubeTimeFrame.startYear=2019&cubeTimeFrame.endYe ar=2020&referencePeriods=20190101%2C20200101</mark>

- <mark>Statistics Korea. 2021. “Household Income and Expenditure Trends in the Fourth Quarter of 2020.” = =</mark>

- <u><mark>http://kostat.go.kr/portal/eng/pressReleases/6/1/index.board?bmode read&aSeq 388364&pa geNo=&rowNum=10&amSeq=&sTarget=&sTxt=</mark></u>

<mark>Statistics Japan. 2022. “Japan Statistical Yearbook 2022.”</mark> <u><mark>https://www.stat.go.jp/english/data/nenkan/71nenkan/index.html</mark></u>

- <mark>Sumner, Andy, Chris Hoy, Eduardo Ortiz-Juarez. 2020. “Estimates of the Impact of COVID-19 on Global Poverty.” UNU-WIDER working paper 2020/43.</mark>

- <mark>United Nations and World Bank. 2020. “Monitoring the State of Statistical Operations under the COVID-19 Pandemic.” Washington, D.C. : World Bank Group.</mark>

- <mark>Valensisi, Giovanni. 2020. "COVID-19 and global poverty: Are LDCs being left behind?" The</mark> _<mark>European Journal of Development Research 32</mark>_ <mark>(5): 1535-1557.</mark>

- <mark>Wieser Christina, Shinya Takamatsu, Nobuo Yoshida, Kexin Zhang, and Danielle Aron. 2022. “Poverty projections and profiling based on Ethiopia’s High Frequency Phone Surveys of households using a SWIFT-COVID-19 package.” Mimeo.</mark>

- <mark>World Bank. 2016. Poverty and Shared Prosperity: Taking on Inequality.” Washington, DC: World Bank. https://openknowledge.worldbank.org/handle/10986/25078 License: CC BY 3.0 IGO.</mark>

- <mark>World Bank. 2022. Poverty and Inequality Platform (version 20220602_2017_01_02_INT). World Bank Group. https://pip.worldbank.org/</mark>

35
# **Annex 1: Ranking of Housing Elasticities**

Ranking of countries based on decadal average of Elasticity of housing supply to urban <u>growth</u>

|||1970-1980||1980-90||1990-00||2001=201|1|
|---|---|---|---|---|---|---|---|---|---|
|||Elasticity<br>of<br>housing<br>supply to<br>urban<br>||Elasticity<br>of<br>housing<br>supply to<br>urban<br>||Elasticity<br>of<br>housing<br>supply to<br>urban<br>||Elasticity<br>of<br>housing<br>supply to<br>urban<br>||
|Countryname|Region|growth|Ranking|growth|Ranking|growth|Ranking|growth|Ranking|
|Mauritius|SSA|8.6|2|-3.3|49.0|5.3|21|22.0|1|
|Iran, Islamic Rep.|MNA|5.0|7|0.6|35|5.9|19|17.7|2|
|Sweden|OECD|4.0|12|2.5|12|17.4|3|16.2|3|
|Tajikistan|ECA|||||||15.9|4|
|Guyana|LAC|-3.7|32|-14.4|53|-22.7|77|14.5|5|
|Jamaica|LAC|||||14.4|5|12.7|6|
|Mozambique|SSA|||||-0.5|60|11.8|7|
|<br>Montenegro|ECA|||||||11.1|8|
|<br>Morocco|MNA|2.2|23|0.7|32|2.1|34|10.6|9|
|Madagascar|SSA|||0.9|27|1.3|40|9.5|10|
|United Kingdom|OECD|||-11.3|52|-1.7|68|9.2|11|
|Mongolia|EAP|||||-9.2|76|8.7|12|
|Kiribati|EAP|||||3.8|25|8.4|13|
|Tuvalu|EAP|||||||8.1|14|
|China|EAP|0.7|26|1.8|16|3.1|28|8.1|15|
|Eritrea|SSA|||||-0.8|63|8.0|16|
|Venezuela, RB|LAC|||-2.8|48|6.2|16|7.5|17|
|Senegal|SSA|||0.9|28|-0.2|56|6.9|18|
|<br>Peru|LAC|||||0.3|51|6.9|19|
|Denmark|OECD|||1.8|17|10.3|9|6.6|20|
|Palau|EAP|||||0.2|53|6.4|21|
|Belize|LAC|4.0|13|3.5|9|1.8|37|6.4|22|
|Brazil|LAC|||||6.0|18|6.3|23|
|Egypt, Arab Rep.|MNA|2.4|22|0.8|30|3.6|26|6.2|24|
|Germany|OECD|||4.5|6|18.1|2|6.0|25|
|Canada|OECD|3.5|17|3.9|8|-0.3|57|5.9|26|
|Tonga|EAP|||||-4.5|73|5.9|27|
|<br>Turkey|ECA|||||8.6|10|5.6|28|
|Finland|OECD|0.6|28|3.0|11|5.9|20|5.6|29|
|Chile|LAC|4.6|9|-3.3|50|8.4|12|5.6|30|
|Indonesia|EAP|3.8|14|1.1|23|0.8|49|5.4|31|
|South Africa|SSA|2.8|19|-1.3|44|-0.6|61|5.4|32|
|Belium|OECD|||||-07|62|53|33|
|g<br>Switzerland|<br>OECD|||||.<br>-6.0|<br>74|.<br>5.3|<br>34|
|Norway|OECD|4.4|11|0.9|24|3.9|24|5.0|35|
|Antigua and Barbuda|LAC|-14.2|33|-5.0|51|10.6|8|4.8|36|
|Philippines|EAP|1.1|25|0.1|37|4.5|22|4.8|37|
|<br>Yemen, Rep.|MNA|||||0.9|48|4.6|38|
|Ethiopia<br>|SSA|||0.6|34|-1.1|66|4.5|39|
|Cambodia|EAP|||||-0.3|58|4.3|40|
|Luxembourg|OECD|||||0.2|52|4.1|41|
|<br>Korea, Rep.|OECD|3.0|18|4.5|7|2.2|33|4.1|42|
|Cape Verde|SSA|||0.8|31|0.0|55|4.1|43|
|<br>Vanuatu|EAP|||0.9|26|1.0|45|3.8|44|
|St Kitts and Nevis|LAC|215|1|-23|47|150|4|32|45|
|.<br>Hungary|<br>OECD|.||.||.<br>-6.4|<br>75|.<br>2.9|<br>46|
|<br>Italy|OECD|2.5|21|||-1.0|65|2.8|47|
|<br>Argentina|LAC|||||1.2|41|2.6|48|
|Australia|OECD|5.3|6|1.4|20|3.2|27|2.6|49|
|Dominican Republic<br>|LAC|||-1.2|43|6.6|15|2.5|50|
|St. Vincent and the<br>Grenadines|LAC|3.7|15|0.8|29|||2.5|51|

33
19 sources, on expenditures on health and nutrition by UN agencies in 1987, and on allocations for health and nutrition under child survival by USAID in 1988. Figures are generally available only for expenditures by international agencies and national or local governments (Mateus 1989 is an exception). Figures for private aAid NGO funding of nutrition interventions (which may be an important component for some countries) are not readily available. To supplement these data, country studies were undertaken for Chile, Malawi and the Philippines (Appendices A, B and C respectively).

The available data are reproduced and summarized here in table 11 (on expenditures by country) and table 12 (expenditures by UN agencies and USAID). Appendix tables 1 and 3 also contain additional information on the percentage of nutrition expenditures financed externally. One finding from tables 11 and 12 is that nutrition interventions account for only about 10% of expenditures on health (9% for Malawi, 10% fo- the Philippines, 11% for Brazil, 13% for UN agencies, and 15% for USAID child survival). Chile however is unusual in that its nutrition programs account for 35% of health expenditure. Nutrition expenditures as a share of GDP vary widely, from a low of 0.06 in the Philippines, to a high of 0.41 in Chile, with the other three countries in the table clustered at 0.16-0.18.

Mateus' data allow comparison of expenditures on feeding programs in 16 Latin American countries (table 11). Whilst these programs do not .ccount for all expenditures on nutrition interventions, they certainly are an impoartant and expensive component. The countries with large total (i.e. domestic plus foreign) expenditures on feeding programs are not necessarily the poorest. Although expenditures on feeding programs are large in Bolivia and Haiti, they are also large in Brazil, Chile, Jamaica, Mexico and Costa Rica which are better off, and small in (relatively poor) Peru. Poorer countries do tend to attract a higher proportion of external financing (Appendix tables 1 and 3). As regards domestic resources applied to feeding programs, the best performers are Chile,
'WI _w ' A

## OLICY RESEARCH WORKING PAPER 1395

# Summary findings

Aron and Elbadawi compare and contrast the design and outcomes of differenr foreign exchange auctions in four countrics in Sub-Saharan Africa and present a typology of such auctions.

They identify two distinct sets of countries in terms of the auctions' fcaturcs, policy interventions, and outcomes.

In Ghana and Uganda, the exchange rate auctions are judged to have been largely on target in exchange rate unification, exchange rate stabilization, and efficient allocation of foreign exchange.

_Tle_ auctions in Nigeria and Zambia, on the other hand, were subject to frequent policy interventions, rcsulting in unsustainable auctions, inefficient allocation of foreign exchange (through ad hoc disqualificadons), limited unification, and a rather volatile exchange rate. The conclusions reached by Aron and Elbadawi are broadly corroberated by a statistical analysis of weekly micro-auction data for all four countries.

This paper -a product of die Macroeconomics and Growth Division, Policy Researh Dcpartment - is part of the departmental project "Foreign Exchange Auction Markets and Exchange Rate Unification in Sub-Saharan Africa" Copies of this paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Raquel Luz, room N11-053, extension 39059 (33 pages). December 1994.

_The PolkyR c Worng Paper Scre_ by _thePocy fserch Re_ D _pras in to encong ter cwhn ;e of abo psaperscarytb-ae xnsof ibLacwsnd shodd bcusa ox.decru~. ieabcgf, interprctatiozs,andw chLond arnedct azudxrs' own and shouldnot c&bei tod iWeortd B-,itsE%c tBoardof Dvrs, orany of its mcenbfrcoYtris_ Producd by the Policy Resach Dissemnation Center
frequently been little links between reform and performance. The relative ownership of countries during the reform process has been a subject of growing debate. Also, the precise filtering of macro policies to the household level has been unclear. Reform programs have frequently been hindered by a difficult political economy of vested interests where reforms are perceived as weakening the status quo. In a ten country study of reform in Africa, it was found that many countries that have faced severe crises in Africa have either collapsed in civil war or had difficulty developing coherent reform programs that can overcome vested interests that benefit from the perpetuation of poor policies (Devarajan, Dollar, and Holmgren, 2001). In a variety of countries, reforms were never sustained because of the absence of a workable coalition that benefited significantly from the reforms to champion them further.

The lesson from China is simple: a visionary leadership, together with a vibrant private sector that responds favorably to a realignment of incentives, can articulate a set of changes in the economy that will benefit all parties. The absence of a zero sum mentality among Chinese policymakers in relationship to reforms can definitely be transplanted to the African context, where the political economies have frequently been inimical to reforms. Thus, the perpetuation of internal inefficiencies in SSA show that the reforms did not make as much impact as envisaged. While there is no cookie-cutter approach to development, there are lessons regarding the structuring of incentives for economic agents that are of cross-country relevance.

# **II. Diasporas and Development**

Second, one of the understated reasons for Chinese economic success has been the success in utilizing the financial and technical capital of the Chinese diaspora, especially those Chinese minorities in neighboring Southeast Asian countries. The Chinese diaspora is large and is located all over the globe. Ethnic networks such as the Chinese play an important role in the international economy and sometimes bypass central governments, while making important 16
## **Edson Queiroz**

Edson Queiroz is located in the western part of Fortaleza. It is the largest of the three surveyed neighborhoods in geographical terms. In 2000, Edson Queiroz had a population of 20,291 people. This neighborhood has benefited greatly from the urban growth in Fortaleza. It hosts the largest private university in Fortaleza (UNIFOR), a large shopping and commercial area, and some of the state government buildings. This has brought improvements to the area, for example, in terms of child care and health care centers. These improvements, in return, may have attracted people with more skills while given new opportunities to old dwellers and, as a result, contributed to the improved HDI. The HDI in Edson Queiroz (0.500) is the highest of the three surveyed neighborhoods (see Table 3).

In terms of infrastructure, Edson Queiroz is one of the better-equipped neighborhoods in Fortaleza (see Table 4). In recent years, the neighborhood has benefited from investments from both the government and private sector.

# **3. Simple Tabulations of Findings**

This section presents several striking findings based on simple tabulations of data collected in the above-mentioned poor urban neighborhoods in Fortaleza. We cannot use these simple findings to establish correlations or causality. It requires further analyses of data to shed light on determinants of risks and other statistical correlations. The findings are organized in accordance with the five thematic areas of the questionnaire: (1) socio-economic background; (2) education; (3) health and sexuality; (4) violence and social capital; and (5) and employment and economic activity. All the numbers in the below sections refer to the survey data collected in Fortaleza.

# **3.1 Socio-economic background**

12
<!-- Start of picture text --> Constitutional approach to legal pluralism<br>Ww<br>N<br>n<br>5%<br>=<br>a]<br>fo}<br>ow<br>co. _ an an ore<br>a 67 MN \--<br>sh a<br>1960 1970 1980 1990 2000 2010<br>year<br>—— Recognize and limit customary law<br>—---—- Recognize without limit customary law<br>— —-—-- Recognize and limit religious law<br>vovenees Recognize without limit religious law<br><!-- End of picture text -->
# **5. Results**

The first columns in Table 5 present the results for weight-for-age using the original randomization classification in 2004, irrespective of whether the villages actually received the program. The approach uses both the baseline and subsequent data and, thus, combines difference in difference with random assignment. The coefficient for the variable _planned intervention_ indicates the difference of the mean value for the villages that were initially assigned treatment status from those expected to be in the control group. We include age dummies of the children in six months age groups, with the children between 30 months and 3 years of age representing the omitted group. The original treatment status assigned to the village irrespective of actual receipt of the program is an imperfect indicator of services actually delivered. As such, using the planned assignment status avoids a correlation with unobserved factors at the possible expense of increasing errors in variable from mismeasurement from which an attenuation bias is expected.

Based on the two assumptions that in the absence of treatment, the villages in both groups would experience a similar trend in malnutrition rates and also that selection was not based on the time varying factors such as the level of the outcome at time the baseline was implemented a difference in difference framework using the actual receipt of treatment would give an accurate assessment of the impact of the services. This approach, however, could introduce a bias and, unlike the bias due to attenuation with the planned treatment, the direction of such a possible bias is unknown. We report the outcome based on the assumption of a time-invariant selection bias in column 3. In all specifications, we allow for the clustering of standard errors at the village level.

15
from 1.3 years in 2000 to 4 years in 2009. Asia gained almost 3 years from 6.7 to 9.4 while Europe gained 8 months from 2.4 to 3.1 years.

**Chart 16**

<!-- Start of picture text --> Average Life<br>10<br>Emerging Markets Asia Emerging Europe Latin America<br>9<br>8<br>7<br>6<br>5<br>4<br>3<br>2<br>1<br>0<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009<br>Source: JP Morgan EM Debt and Fiscal Indicators (2009) and BIS Financial Stability and<br>local currency bond markets (2008)<br>Years<br><!-- End of picture text -->

**Another trend in the structure of the domestic debt over the last decade is the increasing importance of inflation-linked debt.** Even though these instruments are not used, or, have low weights in most EM portfolios, they have been introduced by a number of countries as an alternative to nominal fixed-rate instruments to extend maturities and reduce currency<sup>16</sup> and rollover risk. Some countries also issue linkers to reach an optimal debt portfolio, combining these instruments with fixed-rate securities. South Africa for instance who did not use inflation linked securities in 2000 had 16% of the portfolio in these instruments by 2009; Brazil managed to move the share of linkers from 6% to 22%; and Turkey that started using these instruments in 2006, had reached 10% in 2009. Inflation linkers have found strong demand from pension funds and non residents. This is good news as the literature on government debt provides support for some use of inflation-indexed debt, as much of the government’s revenues (that service the debt) are real in nature<sup>17</sup> .

> 16 For instance, Uruguay has indicated that to shift from FX to LX debt they have relied on inflation linked instruments.

> 17  See Barro, R. J. (1997). Optimal management of indexed and nominal debt. Cambridge, MA: National Bureau of Economic Research. NBER Working Paper 6197; Campbell, J.Y. and Shiller R. J. (1996). A scorecard for indexed government debt. Chicago, IL: The University of Chicago Press.

13