rafmacalaba/datause-encoder-data huggingface dataset:
# **Figure 3. Estimated commodity consumption growth**

## **A. Income elasticities at 2017 income levels**

<!-- Start of picture text --> Elasticity<br>Lower Median Upper<br>1.2<br>0.8<br>0.4<br>0.0<br>-0.4<br>-0.8<br>C. EM7 consumption growth, 2010-16<br>Percent<br>14<br>12 Estimated Actual<br>10<br>8<br>6<br>4<br>2<br>0<br>Aluminum Copper Zinc Oil Gas Coal Rice Wheat Maize Soybeans<br>Aluminum Copper Zinc Oil Gas Coal Rice Wheat Maize Soybean<br><!-- End of picture text -->

## **E: Sectoral use of energy and metals inputs**

**B. Income elasticities in EM7 and G7 countries, 2010-16**

<!-- Start of picture text --> Elasticity<br>1.5 EM7 G7<br>1.0<br>0.5<br>0.0<br>-0.5<br>-1.0<br>D. Commodity consumption growth, by<br>country and group, 2010-16<br>Percent<br>12 Estimated Actual<br>10<br>8<br>6<br>4<br>2<br>0<br>EM7 China Other EM7<br>Metals Energy Agriculture Metals Energy Agriculture Metals Energy Agriculture<br>Aluminum Copper Zinc Oil Gas Coal Rice Wheat Maize Soybeans<br><!-- End of picture text -->

## **F: Intensity of metals and energy consumption**

<!-- Start of picture text --> Ratio Manufacturing<br>Services<br>20<br>Consumption<br>Investment<br>15<br>Exports<br>10<br>5<br>0<br>Energy Metals<br><!-- End of picture text --> <!-- Start of picture text --> Toe per $1,000 Kg per $1,000<br>0.8 6<br>Energy<br>0.6 Metals (RHS)<br>4<br>0.4<br>2<br>0.2<br>0.0 0<br>China Russia Other G7<br>EM7<br>1996 2010 2016 1996 2010 2016 1996 2010 2016 1996 2010 2016<br><!-- End of picture text --> Sources: Aguiar et al. (2016), BP Statistical Review, U.S. Department of Agriculture, World Bank, World Bureau of Metals Statistics. A.B. Income elasticity is defined as percent change in commodity consumption for each 1 percent increase in commodity prices. B. Elasticities at median incomes over 2010-16, Vertical bars are 95 percent confidence intervals.

C.D. Estimated in-sample fitted values based on regression coefficients in Table 4.

E. Use of energy and metals inputs by different sectors of the economy. Calculations show the gross value added of an input (e.g., energy) used by a sector (e.g., manufacturing) as a share of total gross value added of that sector. Values capture both direct and indirect use. The inclusion of sector 32, petroleum and coke, in manufacturing significantly increases its energy use; excluding this sector would reduce the energy use of manufacturing from 16 to 8.7.

F. Toe stands for tons of oil equivalent. Intensity of consumption calculated as consumption of energy or metals (in volumes) relative to output in constant 2010 U.S. dollars. Other EM7 includes Brazil, India, Indonesia, Mexico, Russia, and Turkey.

10
12 sustained, net foreign liabilities cannot grow systematically faster than GDP. 1 Given net foreign liabilities (debt and equity) in a range equivalent to 25%50% of GDP, it follows that foreign liabilities (i.e., foreign saving) are growing at an annual rate in excess of 10% (5 divided by 50), which is about three times the rate of output growth in 1990-92 or more. Around three-quarters of the increase in foreign saving, i.e., the decline in the current account balance, and public saving between 198B-89 and 1992 was accounted for by declining private swring; only about one-quarter was accounted for by higher investment (Table 2). 3 Even if domestic saving was at a high enough level to support sustainable economic growth, a continuation of the recent downward trend in domestic and, in particular, private saving (Table 2) could turn growth unsustainable.Note that without the sharp improvement in net public saving (public saving minus public investment) the decline in domestic saving and, hence, the decline in the current account balance, would have been substantially larger.

# V. Will saving imnrove? If so. can a recession be avoided?

Private saving should improve in the future (Subsection V.1). However, the contractionary effects of the slowdown in consumption are unlikely to be offset in the short term by stronger net exports (Subsection V.2). Over the medium term, structural factors are likely to induce an investment-driven and, eventally, export-driven recovery (Subsection V.3).

21 This coandition stems from the differentiating the foltowing steady state condition: NFL/Y c (=) c (NFL is net foreign liabiLities, Y **is GDP and** c is a constant).

> **12** The estimate of private saving was based **on** current price data; using **constant** price national accounts, the share of investment to **GDP** increases 2.9 percentage points (instead of 2.2 p.p.) **and,** ceteris paribus, would **impty** that investment **is** accounting for a reLativeLy larger share of the deterioration in the current account deficit. Current price national accounts data for 1993 was not avaitabte and so, TabLe 2 only shows the decLine in domestic saving to 1992. Measured in constant prices, the private consuaption-GDP and investment-GDP ratios declined sLightLy during 1993.
_10_ <!-- Start of picture text --> Age Profile of Workers by Type of Employers<br>(% of group total)<br>40%<br>-0- Labor<br>Force<br>30%/ - :,-e --<br>20%<br>4-Public<br>Enterprises<br>-- Private<br>O w . -: W W " M *Companies<br>0/o<br>a> st 0 U 0 m a t<br>cmN N ct cn LO e Ue D<br><!-- End of picture text --> Source: Demographic Institute, Faculty of Economics, University of Indonesia Life expectancy at birth, for the general population, is currently 61 years for males and 64 for females. These figures are expected to increase to 69 years (males) and 72 years by 2025. For members of pension plans, post retirement life tends to be even longer, with expected duration of life after retirement (at age 55) now at 15 years and rising to 20 years by 2025. The old age dependency ratio, which gives the size of those in retirement relative to those at the working age, for the general population is given in the table below. At the current retirement age of 55, the age old dependency ratio will nearly double in the next 30 years, from 17 percent in 1995 to 30 percent in 2025. If, however, the retirement age were to be raised to 60 in the near future, then the dependency ratio in 2025 would be kept at about 20 percent, not much higher than it is today.

||**O**|**ld Age Dependency**<br>(percentage)|**Ratio**||
|---|---|---|---|---|
||1995|2005|2015|2025|
|Population<br>Age 55 +|17.3|20.0|23.3|30.4|
|Pop. Age 15_- 55_|||||
|Population<br>Age 60 +|11.0|13.6|14.7|20.0|
|Pop. Age 15 - 60|||||
# **Links between Growth, Inequality, and Poverty: A Survey**<sup>*****</sup>

Valerie Cerra Ruy Lama International Monetary Fund International Monetary Fund <u>vcerra@imf.org rlama@imf.org</u> <mark>Norman V. Loayza</mark> World Bank <u>nloayza@worldbank.org</u> JEL Classification Numbers: D31, D63, I32, 047, 015.

Keywords: Growth; Inequality; Poverty; Income Distribution; Economic Development; Inclusive Growth

* We thank Izzati Ab Razak and Jaime Sarmiento for their superb research assistance. We also thank Barry Eichengreen, Andrew Berg, Piergiorgio Carapella, Reda Cherif, Fuad Hasanov, Maksym Ivanyna, Aart Kraay, Futoshi Narita, Marco Pani, Martin Schindler, Nikola Spatafora, Xin Tan, Junjie Wei, Younes Zouhar, and participants in the Inclusive Growth book seminar series organized by the IMF Institute for Capacity Development for their comments. This is a draft of a chapter that has been accepted for publication by Oxford University Press in the forthcoming book titled, _How to Achieve Inclusive Growth,_ edited by V. Cerra, B. Eichengreen, A. El-Ganainy, and M. Schindler, due for publication in 2021.
PROVISION OF WATER TO THE POOR IN AFRICA

**Figure 1.1   Typology according to source and technology employed**

||Relation<br>Dependent<br>(source supplied by utility)|ship to source<br>Independent<br>(develop own source)|
|---|---|---|
|<br>Small piped network (SPN)|Community SPN<br>Private SPN|Community SPN<br>Private SPN|
|y employed<br>Point source|Public standpipe<br>Private standpipe (kiosk)<br>Household reseller (informal standpipe)|Private standpipe (kiosk)<br>Community standpipe|
|Technolog<br>Mobile distributor|Water tankers<br>Carters:<br>Animal traction carters<br>Hand carters<br>Water bearers|Water tankers<br>Carters:|

Source: Kariuki and Schwartz, 2005 Mobile distributors, often referred to as vendors, supply a large share of the urban unconnected population in only a few countries. Private standpipes dependent on network water are even more rare. Non-network sources include small-scale independent providers (SSIPs)—businesses or people that sell water from sources they have developed or found such as boreholes, wells, or even rivers. In the past decade, these various sources, which together comprise the “ informal”water market, have gained more attention from the development community.

This paper summarizes the available knowledge on standpipes and the informal water sector, focusing primarily on coverage, price transmission mechanisms, regulation, and standpipe management in urban Africa. It identifies gaps in the research and outlines priorities in terms of future field work to fill those gaps. In preparing the paper, we performed an extensive review of all of the available literature on the topic and analyzed data from a subset of African countries (see below). Another objective of the study is to design indicators that show cross-country comparisons of the structure of the informal water market and its relative importance.

The paper draws upon two data sets, the AICD DHS/MICS database 2007 and the AICD WSS database (2007). The first database relies on 63 national Demographic and Health Surveys (DHS) covering households in 32 countries. This DHS database provides information on water services at the urban level based on household surveys. The second database, AICD WSS, is based on surveys of government officials and utility staff in the largest city of each of 24 countries in Africa<sup>1</sup> . Existing independent case studies based on sector-specific field research were used to fill specific data gaps and to test the validity of some utility-reported data appearing in the AICD WSS database (2007).

A study on the unconnected market is complicated by data constraints. The AICD DHS/MICS database (2007) on water coverage is very comprehensive, but the breakdown of water by source does not include household water resellers of water as a separate category; it therefore misses the coverage

> 1 With the exception of Kaduna, Nigeria, the AICD 2007 Database is based on information collected during January – July 2007 in 24 countries in Africa, and included a module on SSIPs for the largest city in each country.

3
In Panel B we explore gender differences and once again find that effects are concentrated among male CO members. Men offered business training are 4.4 percentage points more likely to borrow, representing an increase of 15 percent in the probability of borrowing, relative to those not offered business training. The model predicts a higher likelihood of borrowing if training makes technology _S_ (which does not require capital) less attractive to participants, shifting them towards technologies that require some capital and hence a loan.<sup>42</sup> Since only lottery winners have an incentive to borrow when they already have an existing loan, the model predicts that the probability of borrowing should also be higher among lottery winners. Indeed, Table 8 column 5 indicates that lottery winners do increase loan uptake by 5.8 percentage points.<sup>43</sup> Male lottery winners also take our larger loans, but the differences remain small (Rs 1,698 or USD 29). Female CO members assigned to be lottery winners (or offered business training) borrow significantly less than their male counterparts. As for repayment, while there is lower default among women offered business training and assigned to be lottery losers, women assigned to be lottery winners have significantly higher amounts due by the 20<sup>th</sup> of the month, but not at maturity.<sup>44</sup> In Table 7 we also explore whether business training led to more informed financial decisions and thus less scope for mistakes (column 5). Again, consistent with the model, male CO members with a lower ex-ante probability of default are more likely to borrow during the lottery if they were offered business training. Taken together, the results of Table 7 are remarkable because they suggest that training leads to increased knowledge about one’s entrepreneurial ability thus contributing to better labor and financial decisions. The fact that effects are concentrated among male CO members suggests again that females are restricted in their labor supply and may not be able to decide whether and how much to borrow on their own as they need the approval of their husbands.

> 42 More formally, the probability of borrowing among individuals offered business training will be higher if . ( )     ( ) (   )  ( )

> 43 According to the model the increase in loan uptake is given by . (   ) (   ( ))

> 44 This is consistent with evidence that women borrow but provide the proceeds to their husbands or another male household member. Administrative data from the lender for this time period show that about 65 percent of loans taken out by female borrowers are not used by them but by their husbands or sons.

27
# **Long-run Returns to Private Equity in Emerging Markets**<sup>1</sup>

Shawn Cole<sup>a</sup> Martin Melecky<sup>b</sup> Florian Mölders<sup>c</sup> Tristan Reed<sup>d</sup> Originally published in the Policy Research Working Paper Series on _August 2020._ This version is updated on _October 2024._ To obtain the originally published version, please email prwp@worldbank.org.

<u>JEL Classifiication:</u> E13, F36, G15, O16, O19 <u>Keywords:</u> private equity, venture capital, international capital market integration, impact investing

> 1We thank Mohan Manem especially for providing an understanding of the data. Seminar participants in the Harvard Business School Finance Unit, International Finance Corporation, World Bank Development Research Group, and IMF African Department provided helpful comments and questions, along with Paddy Carter, Adam Fegan, Penny Goldberg, Neil Gregory, Oghale Ighoavodha, Victoria Ivashina, Jessica Jeffers, Kostas Kollias, Josh Lerner, Ita Mary Mannathoko, Fanele Mashwama, Paolo Mauro, Camilo MondragónVélez, Jacob L. Otto, Tommaso Porzio, Raghuram Rajan, Thomas Rehermann, Roberto Rigobon, Antoinette Schoar, and Jesse Schreger. Anshul Maudar provided excellent research assistance. Shawn Cole gratefully acknowledges support from the Division of Research and Faculty Development at Harvard Business School. The views expressed in this paper are those of the authors and do not necessarily represent those of the World Bank Group. All results have been reviewed to ensure that no confidential information is disclosed.

a: Harvard Business School and NBER, scole@hbs.edu; b: World Bank, mmelecky@worldbank.org, c: International Finance Corporation, fmoelders@ifc.org; d: Corresponding author, Phone: +1 (202) 505-0925, 1818 H St NW, Washington, DC, 20422, treed@worldbank.org.
We use the regression model listed below to explain the regional poverty rates: AROP� � β� �β�X� � β2country� � β3�X� �country��� β�population density� � β�GDP per capita� � ε� X represents a series of variables including different categories of the CAP funds and normalized agricultural indicators such as agricultural share of area. Population density is a proxy for the urban rural typology.

For each variable used to explain the regional poverty, margins of responses for each country are calculated to show the country‐specific strength of associations between poverty and the specific variable. Tables 2, 3, 8, and 9 show the signs of the results, whereas the zeros indicate insignificant marginal effects at 10% significance level. In Figure 8, the results were standardized by the standard errors to become better comparable between variables.

# **Appendix 3:** Analysis methods comparisons for CAP

||Book Chapter 4|Our replication|Our analysis 1|Our analysis 2|
|---|---|---|---|---|
|Data|||||
|Outcome|NUTS1/2 level<br>apportioned to<br>NUTS3 level:<br>GDP per head,<br>Unemployment rate,<br>Population change|NUTS3 level:<br>GDP per head,<br>Population change,<br>Poverty rate, Share of<br>the country poor<br>NUTS2 level:<br>Unemployment rate|NUTS3 level:<br>Poverty rate,<br>Share of the<br>country poor|NUTS1 and<br>NUTS2 level:<br>Poverty rate|
|CAP|NUTS1/2 level<br>apportioned to<br>NUTS3 level:<br>CAP support per<br>hectare of utilizable<br>agricultural area, CAP<br>support per<br>agricultural work unit|NUTS3 level:<br>CAP support per<br>hectare of utilizable<br>agricultural area, CAP<br>support per<br>agricultural work unit|NUTS3 level:<br>Total CAP<br>support|NUTS1 and<br>NUTS2 level:<br>CAP support per<br>capita|
|CAP data source|FADN database;<br>appointed RD budgets|Clearance Audit<br>Trail System (CATS)<br>database|Clearance Audit<br>Trail System<br>(CATS)database|Clearance Audit<br>Trail System<br>(CATS)database|
|CAP categories|Pillar1: Market price<br>support and direct<br>incomepayments|Pillar1: Market price<br>support and direct<br>incomepayments|Pillar1: coupled<br>and decoupled<br>payments|Pillar1: coupled<br>and decoupled<br>payments|
|Method|Pearson correlation<br>coefficients|Pearson correlation<br>coefficients|OLS regressions|Fixed effects<br>regressions|
|Period|1999|2011|2011|2005-2013|
|Country coverage|EU15|EU28 except<br>Lithuania and Czech<br>Republic|EU28 except<br>Lithuania and<br>Czech Republic|EU28 except<br>Germany|

**The table above shows comparisons of the different methods we adopted in order to study the correlation between poverty and the CAP support.** The Book here refers to “The

23
7 household), in Panama to 57 percent (from 20 percent), in Uruguay to 66 percent (from 34 percent), in Venezuela to 65 percent (from 42 percent) and in Guatemala to 30 percent (from 20 percent).

The effect of children is mixed depending on their age. As a general rule, results from countries ihich could not take into account the age of childien suggest that the probability of participation drops by about 3 to 5 percentage points for each child. When the age of children could be taren into account, the results for young children (aged less than 6 years) suggest that the effect is even stronger. However, the presence of older children increases the probability of female participation in some cour'_-es. This can be explained by the fact that older children may be substitutes for women's services at home, as older children can both supervise their younger siblings and also contribute toward other areas of family production.<sup>16</sup> I must also -e stressed, that fertility and labor force participation could be a joint decision, raising important endogeneity/estimation issues.

Individual country studies also report effects of variables whl:h are country specific. For example, in Bolivia the probability of a Spanish speaking woman to be in the labor market is 42 percent while the corresponding probability for an indigenous woman is only 22 percent.<sup>17</sup> Also, variables relating to ie physical health of a woman have the predicted effect of women, that is, ill-health affects adversely the probability of participation. In addition, the presence of adult non-workers il the household decreases the probability of female participation. This may suggest that there is higher demand for domestic services when household size increases and this translates into an increase in women's shadow wage at home.'<sup>8</sup> Finally geographical location is another significant factor for women's decision to participate. As: general rule women in urban areas have a much higher probability of participation than their counterparts in rural areas.

Another consistent result in the participation functions is that, after controlling for other factors, women's propensity to work for pay is high even during the childbearing age (the coefficient on age increases up about the age of 40 to 45 years in all country studies, and it is both sizeable and statistically significant). In this respect, women's behavior appears to be ex ante similar to that of men. However, the actual age profile of female participation dips during Boserup (1970) and Stand j (1981).

7 For example, indigenous women have a lower probability of being in the labor force compared with women of other origin. Also in Brazil there are substantial differences in the participation probabilities of women belonging to different racial groups (white, black, Asian and Mulatto).

'8 These estimates may understate the effect of the presence of non-working adults in the household upon the probability of female participation as such aduits may also assist in some household tasks performed by women.
**4**

Land demarcation is a key stage in the formalization of land rights. The demarcation process consists of the documentation of land claims through the consensual delimitation and mapping of all agricultural lands within a rural village, and the surveying and physical demarcation of corresponding parcels. During the land demarcation activities, land conflicts are discussed and settled in the presence of stakeholders (including neighbors), primary land right holders are identified, and landholdings are demarcated through the implantation of cornerstones. The cornerstones serve as immediate, long-lasting benchmarks to detect and resolve future land encroachment disputes. Moreover, they represent a more standardized substitute to traditional methods, such as tree-planting, used by landholders to mark the frontier of their parcels.<sup>4</sup> In that sense, demarcation in the context of the PFR corresponds to a first key step in securing land rights, akin to that used by Hornbeck (2010) to examine the case of barbed wire fencing in the Great Plains of North America. Demarcation _per se_ does not necessarily amount to enforceable land rights, but the process of laying cornerstones clarifies frontiers and may protect farmers from encroachment. The process that surrounds demarcation allows each community to unify competing and overlapping conceptions of land rights. This is particularly relevant to contexts where several individuals claim rights over the same parcel (Lavigne-Delville, 2014). Yet the resolution of overlapping land claims could be to the detriment of the least powerful (Goldstein and Udry, 2008). Women in particular often only obtain secondary land use rights through a male spouse or relative (Lastarria-Cornhiel, 1997).<sup>5</sup> Since land demarcation is the first step of the land certification program, landholders with demarcated parcels expect to receive land certificates at some unspecified point. As such, we document the impact of land demarcation pending certification.

Documenting the effect of a critical first step in formalizing land rights, in the form of communitybased land demarcation, is of particular policy relevance. The sum of bureaucratic processes required for a government to issue proof of property are typically prohibitive. The World Bank’s 2014 _Doing Business_ report indicates that registering property in Sub-Saharan Africa involves on average six different procedures, taking 59 days each to complete, and generates costs of about 9 percent of the property value.<sup>6</sup> At this rate, it is unsurprising that the final stage of

> 4Indeed, tree-planting, which is a customary practice to demarcate land borders, is seen in some places as a tantamount to land ownership. Landholders with secondary land rights are usually discouraged from planting trees on their landholdings.

> 5At inception of the PFR, secondary rights were meant to be recorded during the process. In practice, the main focus was on recording ownership claims.

> 6The figures reported in the case of Benin are four procedures of 120 days each, for a cost amounting to 11.7 percent of the total property value.