rafmacalaba/datause-encoder-data huggingface dataset:
# TABLE **OF** CONTENTS

||PageNo|
|---|---|
|INTR|ODUCTION..........................<br>,0-0<br>.. 1|
|I.|ECONOMICS OF FUELWOOD FEE SYSTEMS .........<br>3<br>Economic Justificationfor<br>StumpageFees.<br>4|
||Problems of Open Access<br>........<br>...<br>Market Failure.................<br>***.....5**<br>Conclusions from Market Failure.......<br>........<br>8<br>Benefits of Charging for Fuelwood Resources ...........11<br>Costs of Charging for Fuelwood Resources ...o.....12|
||Costs to Consumers<br>......<br>.........<br>.<br>12|
||Costs of Fee Collection<br>........<br>3|
||Types of Stumpage Fees<br>.14<br>OptimalFuelwood Stumpage Fee.<br>............<br>15|
||Other Forest Revenue Systems<br>.17|
||Charges on Forest Products<br>.......so......o<br>.......... 17|
||Charge on Productive Factors<br>.....<br>.<br>**..**<br>**18**|
||Chargesfor Services<br>.........<br>.......|
|II.|METHODS FOR ESTIMATING STUMPAGE VALUES ................<br>20<br>MarketApproaches<br>.............<br>Residual Stumpage.<br>..........<br>.<br>.....<br>.......<br>20<br>Auction Sale/Tender<br>...<br>23<br>Surrogate Market Approaches<br>.........-............<br>24<br>Opportunity Cost of Wood Gatherer's Time. .........24<br>Differential Land Values<br>.<br>.......<br>........<br>Cost-Based Approaches<br>........<br>......<br>Alternative<br>Fuel<br>Substitution.<br>...............<br>28<br>Wood Replacement Cost<br>...............|
|III.|FUELWOOD STUMPAGE FEE SYSTEMS IN PRACTICE ..............<br>35|
|IV.|CONCLUSIONS...<br>.<br>.<br>.......<br>...<br>43|
|**TABL**|**ES**|
|1.1|<br>Charcoal Producers' Margin: Jamaica<br>..<br>10|
|2.1|<br>1986 Cost Components of Retail Charcoal Prices<br>in Khartoum, Sudan, for Charcoal Produced in|
||the Blue Nile Province<br>..<br>21|
|2.2|<br>Results of a Village Fuelwood Collection Survey<br>........ 25|
|2.3|<br>Stumpage Value Based on Opportunity Cost of Wood .......26<br>Gatherer's Time|
|2.4|1988Kerosene Substitute Price for Charcoal:|
||Nairobi, Kenya .30|
|2.5|<br>Malawi Second Wood Energy Project: Stumpage|
||Calculation for Government Plantation|
||(3,000<br>ha at Lilongwe)...<br>.........................<br>32|
**<u>Table 3: No. of registered transactions leading to transfer of ownership in 2014/15 by region</u>**

|**Total**|**Kigali city**||**Provin**|**ce**||
|---|---|---|---|---|---|
|||**Southern**|**Western**<br>|**Northern**|**Eastern**|
|**Pane**<br>**Totals**|**l A: Transact**|**ions involving**|**residential land**|||
|All<br>31,209|16,710|4,234|2,805|2,141|5,319|
|Sales<br>21,367|15,145|1,504|1,240|827|2,651|
|Sale value ($/m<sup>2</sup>)<sup>a</sup><br>30.07|39.38|9.92|14.41|12.41|4.75|
|Others<br>10,038|1,665|2,762|1,606|1,319|2,686|
|**Mean no of transactions per ce**|**ll and year**|||||
|All<br>7.26|51.89|3.98|2.61|2.59|5.29|
|Sales<br>4.97|47.03|1.41|1.15|1.00|2.64|
|Others<br>2.34|5.17|2.60|1.49|1.59|2.67|
|**Share of registered parcels tra**|**nsferred per y**|**ear**||||
|All<br>1.16|6.23|0.93|0.33|0.72|0.64|
|Sales<br>0.80|5.64|0.33|0.15|0.28|0.32|
|Others<br>0.37|0.62|0.61|0.19|0.45|0.33|
|**Panel**<br>**Total number of transactions**|**B: Transacti**|**ons involving a**|**gricultural land**|||
|All<br>22,850|6,635|3,996|2,390|3,994|5,835|
|Sales<br>14,497|6,087|2,178|1,234|1,971|3,027|
|Others<br>8,510|609|1,855|1,180|2,033|2,833|
|Sale value ($/m<sup>2</sup>)<sup>a</sup><br>4.45|6.65|3.70|3.57|3.91|1.47|
|<br>**Mean no. parcels per annum**||||||
|All<br>5.32|20.61|3.76|2.22|4.82|5.80|
|Sales<br>3.37|18.90|2.05|1.15|2.38|3.01|
|Others<br>1.98|1.89|1.74|1.10|2.46|2.82|
|**Percent of registered parcels p**<br> <br>|**er annum**<br>|||||
|All<br>0.16|1.67|0.10|0.06|0.10|0.23|
|Sales<br>0.10|1.54|0.05|0.03|0.05|0.12|
|Others<br>0.06|0.15|0.05|0.03|0.05|0.11|
|**Panel**<br>**New registrations by gender**|**C: Gender of**|**newly register**|**ed land’s owners**|||
|Female only<br>0.15|0.16|0.16|0.14|0.12|0.15|
|<br>Male only<br>0.23|0.22|0.25|0.22|0.19|0.25|
|Joint<br>0.62|0.62|0.58|0.64|0.68|0.60|
|No. of cells<br>2148|161|532|538|414|503|

_Source:_ Rwanda Natural Resource Authority (RNRA), Land Administration Information System (LAIS). _Notes:_<sup>a</sup> Two residential and two agricultural parcels from Kigali with values above 5,000 $/m<sup>2</sup> have been dropped.

18
services can be produced with more or less labor and labor of different kinds as dictated by economic conditions and the relative prices of labor and capital. Manpower requirement ratios do change in response to economic circumstances. The mechanical use of these requirement ratios in manpower lorecasting ignores the potential and incentives for the substitution of expensive labor or capital and can lead to erroneous results in the estimation of labor requirements.

The conversion of labor requirements by occupation nto educational requirements is also inconsistent with experience. This approach is based on a misunderstanding of the labor supply function for many occupations. Reverse tracer studies for occupations frequently show the diverse paths by which people arrive in occupations. The number of occupations in which formal education serves as the principal entry point is quite small. People perform the same jobs with various ! Vels and types of education and training. Thus, predicted skill shortages are often met through many alternative ways other than the expansion of formal education programs. Except in occupations with well-defined education requirements, the manpower requirements model can lead to overinvestments in formal education in response to pro,ected skill shortages.

The weaknesses of the model for forecasting skill needs are most evident when it is used for long-term forecasting. This is because given adequate time emplovers will find ways to adjust to skill shortages and rising wage costs. With time, they are able to develop and employ capital to do the work of skilled workers who are in short supply. Employers are able to adopt new production technologies that require different skill mixes. They are often able to respond to skill shortages by finding more costeffective means to increase the supply of skills. In many cases, this leads to a bypassing of lengthy formal education reducing the need for large public investments in schools and training centers. It is not surprising to find that manpower requirements forecasting, which discounts the likelihood of these adjustments, often produces large forecasting errors.

5
collected data across 9 topic areas: anthropometry, clinical and health, dietary, socioeconomic, food security, government nutrition and health program participation, maternal health and nutrition, infant and young child feeding and MDG health related outcomes. For the analyses in this report, 10 NNS modules made available from DOST-FNRI were used (Table 1), representing 42,310 households and 202,570 household members. Data on household income and consumption from the 2015 Family Income & Expenditure Survey (FIES) are also used, given that households in the 2015 FIES can be matched to the 2015 NNS, since both samples use the same sampling frame.

**Table 1**

|**Survey program**|**Module**|**Population**|**Observations**|
|---|---|---|---|
|2015 Family Income &<br>Expenditure Survey (FIES)<br>(1st semester)|--|Households|43,882|
|||Household members (all ages)|202,570|
||Form 1.1|Household members (fathers)|28,140|
|||Household members (mothers)|31,654|
||Form 1.2|Households|42,310|
||Form 1.3|Households|42,310|
||Form 1.5|Households|42,310|
|2015 National Nutrition|Form 1.6|Households|42,310|
|Survey||Anthropometry (children < 61 months)|18,629|
||Form 2.1|Anthropometry (fathers)|28,140|
|||Anthropometry (mothers)|31,654|
||Form 3.1|Mothers & pregnant women with children|8,568|
||Form 4.1|Children (0-71 months)|20,303|
||Form 4.2|Children (0-71 months)|22,332|
||Form 4.3|Children (0-23 months)|6,653|

The NNS anthropometry module contains data on 18,629 children spanning the ages of 0 and 1,856 completed days of age (60.9 months) which can be matched to the WHO’s international growth standards reference table. However, for 8.7 percent of these cases, height measurements are missing, leaving 17,014 children for whom height-for-age z-scores (HAZ) can be computed.<sup>9</sup> Of these children, 141 had biologically implausible z-scores (> 6 sds or < -6 sds), leaving 16,873 children under the age of 61 months with valid HAZ measurements (Table 2).

3
# **1. Introduction**

The paper provides a review of the empirical literature that has attempted to test the relative income and relative deprivation hypotheses in economics. The relative income and relative deprivation hypotheses are treated as one and the same hypothesis. As discussed in more detail in the next section, in economics the relative income hypothesis as put forward by Duesemberry (1949) in the context of savings and the relative deprivation hypothesis formalized by Runciman (1966) in the context of social status converged empirically into testing the same hypothesis simply stated as follows: _The evaluation of individual utility depends - among other factors - on relative income, one’s own income relative to the income of others._ The empirical literature on the relative income and relative deprivation hypotheses is vast and cuts across the social sciences. To set the boundaries of the review, the paper covers only published papers in economics journals as found in JSTOR and Econpapers by using the search terms “relative income” and “relative deprivation” in titles and keywords. This initial search provided over 200 hits. From this initial set of papers, we kept only papers that used relative income as one of the explanatory factors in the specification of the empirical models, therefore removing titles in specific areas of economics that tested for non-income dimensions of deprivation. A second screening was undertaken using the references in these papers. This left a final core of approximately 50 papers.

The relative income hypothesis as stated by Duesemberry (1949) emerged as one of the core issues in economics for a short period of time after its publication before being replaced by the life-cycle hypothesis put forward by Modigliani and others. The relative deprivation hypothesis stated by Runciman (1969) has been very popular across the social sciences since its publication and started to become popular in economics from the 1980s following an article by Yitzhaki (1979) that attempted to operationalize the relative deprivation hypothesis into a measure of relative deprivation. This literature focused on relative income as a measure of relative deprivation and mainly concerned itself with the measurement of relative deprivation rather than with the test of the relative income hypothesis. It has been only during the past decade and thanks to the popularity of the literature on happiness that the relative deprivation/relative income hypothesis has become mainstream in economics. As a result, tests of the relative income hypothesis have started to appear in high-ranking journals including the _Journal of Economic Literature_ , the _Economic Journal_ and the _Journal of Public Economics_ , bringing to new life the original relative income hypothesis proposed by Duesemberry (1949).

2
to have remained constant throughout the period and estimates are based on data available only for 1991 in Davis (1994). As noted in the table, book reserves are not included in the asset totals. The U.S. figures were adapted from Brankato (1994).

A recent study by Helmut Reisen of the OECD Developinent Centre provides another set of data on private pension fund assets in the OECD for 1992 which is reported in Annex Table 7 below. Portfolio allocation by different investment type are presented in Figures 5.1 and Table A.12 and are taken from Davis (1993).

For the Chilean AFP system, total asset data were taken from Acufla and Iglesias (1992) for years before 1991. For example, Figure 6.1 uses this source in the denominator of the ratio of administrative costs to total assets through 1990. Assets for 1991 are taken from Valdes-Prieto (1993) and from Abuhadba (1994) for 1992. Assets of Chilean AFPs by size are used in the denominator of Issue Brief figure 5.2 and are adapted from Abuhadba (1994). Further explanations of these figures are given below in Section III.C.

**Annex Table 7 Private Pension Fund Assets Selected Countries, 1992**

|Country|Total<br>(US$ billions)|Share<br>of GDP|
|---|---|---|
|United States|2265|38.5|
|Japan|362|9.8|
|United Kingdom|544|52.2|
|Netherlands|147|45.9|
|Canada|108|19.2|
|Switzerland|125|51.9|
|Germany|85|4.8|
|Sweden|-||
|Australia|24|11.8|
|Denmark|21|14.7|
|Ireland|-|32.8*|
|Italy|-|0.9*|
|Norway|4|_3.5_|
|Spain|-|0.9*|
|Belgium|-|0.2*|
|Portugal|-|2.40|

Source: Reisen 1994

* Includes some publicly-managed funds

46
- Bjorner, T. and Jensen, H.: 2002, Interfuel Substitution within Industrial Companies: An Analysis Based on Panel Data at Company Level, _The Energy Journal_ **23** (2), 27–50.

- Blundell, R. and Bond, S.: 1998, Initial Conditions and Moment Restrictions in Dynamic Panel Data Models, _Journal of Econometrics_ **87** (1), 115–143.

- Borenstein, S., Cameron, A. and Gilbert, R.: 1997, Do Gasoline Prices Respond Asymmetrically to Crude Oil Price Changes?, _Quarterly Journal of Economics_ **112** (1), 305–339.

- Boucekkine, R., Germain, M. and Licandro, O.: 1997, Replacement Echoes in the Vintage Capital Growth Model, _Journal of Economic Theory_ **74** (2), 333– 348.

- Brack, D., Grubb, M. and Windram, C.: 2000, _International Trade and Climate Change Policies_ , Earthscan.

- Burnside, C.: 1996, Production Function Regressions, Returns to Scale, and Externalities, _Journal of Monetary Economics_ **37** (2), 177–201.

- Chari, V. V. and Hopenhayn, H.: 1991, Vintage Human Capital, Growth, and the Diffusion of New Technology, _Journal of Political Economy_ **99** (6), 1142– 1165.

- Cho, W., Nam, K. and Pag ́an, J.: 2004, Economic Growth and Interfactor/Interfuel Substitution in Korea, _Energy Economics_ **26** (1), 31–50.

- Diewert, W. E.: 1980, Aggregation Problems in the Measurement of Capital, _in_ D. Usher (ed.), _The Measurement of Capital_ , University of Chicago Press, pp. 433–538.

- Diewert, W. and Fox, K.: 2008, On the Estimation of Returns to Scale, Technical Progress and Monopolistic Markups, _Journal of Econometrics_ **145** (1-2), 174– 193.

- Drud, A.: 1996, _CONOPT: A System for Large Scale Nonlinear Optimization_ , ARKI Consulting and Development, Bagsvaerd, Denmark.

- Enevoldsen, M. K., Ryelund, A. V. and Andersen, M. S.: 2007, Decoupling of Industrial Energy Consumption and CO¡ sub¿ 2¡/sub¿-Emissions in EnergyIntensive Industries in Scandinavia, _Energy Economics_ **29** (4), 665–692.

32
penultimate section develops a cost-benefit analysis of the Village Fund, and the concluding remarks address the issues of why the Village Fund has not grown (or substantially shrunk) over time, whether the benefits exceed the cost, and whether it is a true financial intermediary, or an instrument for channeling public funds to selected individuals.

# **2. The Village Fund in Comparative Perspective**

In this section we ask a very basic question: How large, and how important to households, is the Village Fund?

A good starting point is to compare the size of the Thailand Village Fund with microfinance institutions in other countries. The relevant data are shown in Table 1, which is based on information reported to the Microfinance Information Exchange (www.themix.org ). The VF did not exist in 2001, and in that year the largest micro lender was Bank Rakyat Indonesia, with 2.8 million borrowers and gross loans of $939 million. Three years later, when the VF was fully operational, it was lending to 7.5 million borrowers – almost twice as many as any other institution – and had a loan portfolio of $3 billion. Five years on, in 2009, the VF had

|**Table 1. Thailand Village**|**Fund in Inter**|**national Co**|**mparative Per**<br>Average loan|**spective**<br>Average loan|||
|---|---|---|---|---|---|---|
|Name|Country|Gross loan<br>portfolio<br>US$m|balance per<br>borrower,<br>US$|balance per<br>borrower / GNI<br>per capita|Number<br>of active<br>borrowers|Total<br>women<br>borrowers|
|**2001**|||||||
|Bank Rakyat Indonesia|Indonesia|939|337|46%|2,790,192|195,313|
|ASA|Bangladesh|91|64|18%|1,414,931||
|**2004**|||||||
|**Village Fund**|**Thailand**|**3,003**|**402**|**16%**|**7,463,000**|**3601000**|
|Bank Rakyat Indonesia|Indonesia|2,045|637|57%|3,210,678|1,605,339|
|Vietnam Bank for Social Policy|Vietnam|855|229|42%|3,740,179||
|Grameen Bank|Bangladesh|338|91|22%|3,700,000|3,540,000|
|BRAC|Bangladesh|243|61|15%|3,993,525|3,941,276|
|**2009**|||||||
|At least $1 billion in assets|||||||
|Vietnam Bank for Social Policy|Vietnam|3,929|521|50%|7,536,960||
|**Village Fund**|**Thailand**|**2,917**|**461**|**12%**|**6,334,516**|**3,050,965**|
|SKS|India|961|166|16%|5,795,028|5,795,028|
|Grameen Bank|Bangladesh|817|127|23%|6,430,000|6,223,597|
|Spandana|India|787|215|21%|3,662,846|3,368,115|
|BRAC|Bangladesh|636|102|18%|6,241,328|5,921,753|
|ASA|Bangladesh|456|114|20%|4,000,401|3,535,145|
|**Summary statistics**|||||||
|**Total/Average for 2009**||**64,770**|**1,405**|**64%**|**82,501**|**63,767**|
|Number reporting||1,110|1,103|1,099|1,109|982|

_Source:_ Microfinance Information Exchange. http://www.mixmarket.org/mfi/indicators?page=1 [Accessed February 20, 2011.] For Thailand, 2004 data from Village Fund central office; 2009 data estimated based on Thailand Socio-Economic Survey. The comparator microfinance institutions shown here include the largest by gross lending, and by number of active borrowers, in each year, as well as a selection of other institutions of general interest.

3
# Contents

|**1 Introduction**|**1**|
|---|---|
|Data|2|
|**2 Comparing cities**|**4**|
|The development diamond: a multidimensional view of poverty in the slums|4|
|Assessing quality: the living conditions diamond|7|
|The infrastructure polygon|10|
|**3 Inequality within slums: comparing poor and nonpoor households**|**15**|
|The development diamond|15|
|The living conditions diamond: dwelling quality and tenure|16|
|The infrastructure polygon: The poor are worse off|17|
|**4 Spatial heterogeneity in access to services**|**20**|
|Electricity connections: Poverty, tenancy, and location matter|21|
|Piped water: tenancy and location matter, but poverty does not|22|
|**5 Conclusions**|**23**|
|**References**|**24**|
|**Tablular appendix**|**25**|
|**Appendix A Spatial heterogeneity in service access in Johannesburg**|**48**|
|Electricity connections|48|
|Piped water connections|50|
|**Appendix B Supply-side vs. demand-side determinants of infrastructure access**|**52**|
|The heuristic computational approach|52|
|Application of the approach to our data for the three cities|53|

iii
_Table 2: Occupational clusters used in the experiment_

|**Occupational cluster**|**Share of cluster in**<br>**total number of vacancy**<br>**ads**|**Included occupations (according to online**<br>**portal’s classification)**|
|---|---|---|
|Accounting|9%|Accounting, Audit, Finance|
|Marketing|11%|Marketing, Business Development|
|Sales|19%|Sales|
|IT|20%|IT, Engineering, R&D|

Note: Average shares of cluster in total number of vacancy ads are calculated using the ad counts in the online job portal between January and February 2015.

# 2.2.  Treatments and resume construction

# _Socioemotional skill signals_

A particular challenge associated with this study is the difficulty in defining and measuring socioemotional skills, which also includes personality traits like the Big Five (Heckman and Kautz, 2012). For our purposes, however the Big Five are too general to signal in a CV, and it is not clear whether employers may understand or value a statement that signals, for example, “high conscientiousness." Depending on data availability, some studies use more specific measures, such as misbehavior in childhood (Segal, 2013), leadership positions or behavioral reports in high school (Kuhn and Weinberger, 2005; Protsch and Solga, 2015), and skills such as locus of control, aggression, and withdrawal (Groves, 2005), but most of these studies use information on skills and labor market outcomes for real individuals, which presents a challenge for causal inference.<sup>11</sup> One problem is the potential association between different aspects of an individual’s skill set: for example, there is evidence of a significant correlation between an individual’s IQ (i.e. cognitive skills) and openness to experience (i.e. socioemotional skills).<sup>12</sup> The other challenge arises because job experience is not orthogonal to socioemotional skills once the individual has started their career: A person with high teamwork skills might be likely to get a job that requires teamwork, but working in a team would improve teamwork skills as well, making it difficult to disentangle the effect of socioemotional skills from that of job experience. Thus, the ideal but unattainable case study would be random assignment of socioemotional skills to two identical individuals and subsequent observation of their performance on the labor market. A more feasible second-best is the audit study methodology, which creates CVs for fictitious similar candidates that significantly differ only on the variables of interest (here, gender and socioemotional skills) and tracks real employers’ recruitment decisions for these fictitious candidates.

For this study, the selection process for one of our main variables of interest - occupation-specific socioemotional skills - involved two steps. In the first step, we carefully reviewed the organizational psychology literature and the O*NET occupation descriptors to identify the most relevant socioemotional

> 11 Apart from Protsch and Solga (2015), which uses an experimental methodology with fictitious CVs.

> 12 E.g. McRae (1987), Ackerman and Heggestad (1997).

7
# **Dreams and Barriers**

# Aspirations, Expectations, and Schooling Outcomes of Indonesian Students

Hillary C. Johnson `r` Noël Muller<sup>1</sup> World Bank

**Keywords** : Aspirations, expectations, education, gender, socioemotional skills.

**JEL** : D84, D91, I24, J16, J24.

> 1 We thank Diego Catalán Molina, Samantha De Martino, Elizaveta Perova, Daniel Pinzón Hernandez, Kali Trzesniewski, and Renos Vakis for continuous conversations and feedback while developing this paper and related joint work. We also thank Achim Schmillen and participants from the 2020 conferences of the Missouri Valley Economic Association and the Society for Research in Child Development for their comments. Our names are listed in random order.

Emails: Hillary C. Johnson, hjohnson1@worldbank.org; Noël Muller, noel.muller@hotmail.fr.
# TABLE OF CONTENTS

||**PAGE**|
|---|---|
|**INTRODUCTION ...................................................**|**1**|
|**I.BACKGROUND ...................................................**|**1**|
|A. THE NEED FOR PENSION REFORM ...........................................................|2|
|Insufficient Contributions||
|Generous Benefits||
|Variable Coverage||
|B.ESSENTiALDATA FORASSESSING THE HEALTH OF PENSION SYSTEMS ...........|...............<br>3|
|Demographic and Economic Data||
|Indicators of Financial Health of Pension Systems||
|Pension Fund Cash Balance||
|Contribution Rate Needed for Positive Cash Balance||
|Income Replacement Rate||
|Retirement Age||
|Net Pension Liabilities||
|Rate of Return on Pension Funds||
|C. ECONOMIC CONSIDERATIONS<br>IN PENSION REFoRM ..........................................|............<br>5|
|Paying for Benefits for Present Retirees||
|Macroeconomic Effects of Reform||
|**II.**NEW DIRECTIONS FOR REFORM**...............................**|**5**|
|A. CHILE ...............................|6|
|B. PERU .....................................|8|
|C. ARGENTINA .....................................|9|
|D. MEXIco ....................................|11|
|E. BRAziL ..................................|15|
|F. COMPARING PENSION REFORMS IN LAC ....................................|15|