rafmacalaba/datause-encoder-data huggingface dataset:
- ILO (2016) Technical paper. A just transition to climate-resilient economies and societies: Issues and perspectives for the world of work. Geneva: ILO.

- ___. (2019) Skills for a greener future: A global view based on 32 country studies. Geneva: ILO.

- ___. (2021) Global framework on core skills for life and work in the 21st century. Switzerland: ILO

Janser, Markus (2018a) The greening of jobs in Germany: First evidence from a text mining based index and employment register data, IAB-Discussion Paper, No. 14/2018, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg

- ___. (2018b) The greening of jobs in Germany: Online Appendix ‘text Mining & Descriptives’ Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg

- Lewis, P., Morris, J., and Gregory, C. (2022) Green Topics: Identifying Linkages to Occupations and Education Programs using a Linguistic Approach. O*NET working paper. June 2022

- OECD, Eurostat (1999). The environmental goods and services industry: manual for data collection and analysis.

- O*NET (2010) O*NET® Green Task Development Project. The National Center for O*NET Development.

- ___. (2013) Greening of the World of Work: O*NET® Project’s Book of References. The National Center for O*NET Development.

- Oyunbileg, D., Stoevska, V. (2017) Employment in the environmental sector and green jobs in Mongolia. Pilot Study.  International Labour Organization (ILO)

- Stoevska, V. Hunter, D. (2012) Proposals for the statistical definition and measurement of green jobs. Discussion paper prepared for informal consultation, November 2012. International Labour Office. Geneva.

- Stoevska, V., Elezi, P., and Muraku, E. (2014) Report on the pilot project towards developing statistical tools for measuring employment in the environmental sector and generating statistics on green jobs. Albania. Intenrational Labour Organization (ILO)

Stoveska (2017). ILO measuring conference - Measuring green jobs current proposals.pdf

- Ruppert Bulmer, Elizabeth, Kevwe Pela, Andreas Eberhard-Ruiz and Jimena Montoya. 2021. “Global Perspective on Coal Jobs and Managing Labor Transition out of Coal.” World Bank, Washington, DC.

- Rutzer, C.; Niggli, M.; and, Weder, R. (2020) Estimating the Green Potential of Occupations: A New Approach Applied to the U.S. Labor Market. WWZ Working Paper 2020/03

- UNEP, ILO, IOE, ITUC (2008). Green jobs: Towards decent work in a sustainable, low-carbon world. United Nations Environment Program (UNEP), ILO, International Organisation of Employers (IOE), International Trade Union Confederation (ITUC).

Vidican Auktor, Georgeta V. (2020). Green Industrial Skills for a Sustainable Future. Vienna: UNIDO 36
-6-

results in a rapid real decline in assessed values: a 15% annual inflation rate reduces the real value of an assessment by half in five years. Failure to revalue during periods of inflation is often self-perpetuating: as valuations fall in real terms, taxpayers become accustomed to low effective levels of taxation; as he gap between tax valuations and market values widens, the political costs of abruptly revaluing to market levels increases, prompting further delays. (In principle, declining real valuations can be offset by annual increases in nominal tax rates, the political courage to do so is rarely observed.) 2.6 Countries familiar with inflation have addressed this problem through indexation. 'While indexing, over time, tends to yield less accurate valuations than field inspection, the amounts involved are small enough that the loss in fairness is well justified by the gain in buoyancy. The degree of sophistication used in indexing valuations varies. In U.S. jurisdictions, for example, an attempt is made to base the indices on the rate of increase in land and construction costs within the taxing jurisdiction. In Colombia, similarly, the index is (in principle) to be based on the results of an annual property survey. Other jurisdictions are less meticulous. In Tabasco, Mexico, the index is derived from a national consumer price index. In Brazil, no attempt is made to link the rate of indexation to property prices, or even to general increases in the cost of living. In effect, the adjustment factor functions as both an inflation index and a nominal tax rate.f

# B. Broadening the Base

2.7 Broadening the definition of what is taxable can also increase the yield of the tax. Exemptions and favorable treatment for particular types of property can remove significant contributors from the property tax base. The ostensible objective of these policies is to promote a variety of apparently distributional or allocative objectives--to shift the tax burden onto higher income groups, or to influence investment decisions. The validity of these **-** objectives is oeten questionable particularly when local government is pursuing them- -and their effectiveness is limited.

# Reduce Tax Rate Progressivity

2.8 Progressive tax rates- -that is, rates that are higher for higher-valued property--are common in developing countries. The tax rate on residential property in Calcutta ranges from 11% of annual rental value (ARV) for properties valued at less than Rs. 600 to 40% of ARV on properties with values exceeding Rs. 18,000. In the Philippines, similarly, centrally mandated assessment ratios introduce a strong element of progressivity into the property tax structure: residential properties with a market value of less than 30,000 pesos are to be taxed at 15% of their value; properties worth over 500,000 pesos are to be taxed at 80% of value.

**_jf_** Mayors may make these adjustments unilaterally, provided the percentage increase does not exceed the official inflation index for treasury bonds. Higher adjustments merely require the approval of the municipal council.
The empirical analysis draws on the data of the _Rural Permanent Sites Survey_ conducted by the Ministry of Agriculture of China in 1993 and 1995–2000 and uses regional and time variations in the installation of landline phones to identify the causal effect of landline phones on outmigration. Out of 61 villages in our sample, 37 had landline phones in 1993 (i.e., our initial year), 21 installed landline phones at a different time during the sample period, and 3 remained without access to landline phones by 2000. Meanwhile, other telecom technologies, such as mobile phones and the Internet, only started to penetrate in the late 1990s and mostly in rich and coastal cities. Hence, our research setting allows us to separate the effect of landline phones from other competing telecom technologies. Furthermore, our identification is aided by directly controlling for the exposure to other information sources, such as newspapers and televisions. An important advantage of relying on landline phones to identify the effects of telecom technology is that we face a less serious challenge of endogeneity. Individuals can purchase mobile phones, and the access to mobile phones is closely related to personal ability, wealth, and demand for modern technology, which may be strongly related to the migration decision. In contrast, the installation of landline phones at the village level, as we document later, was largely related to several easily observable variables, and thus, its endogeneity for migration can be more easily dealt with. Perhaps because of this reason, our estimates of the telecom effects on migration are quite stable.

Based on the difference-in-difference (DID) approach, we find that the installation of landline phones leads to an increase in the ratio of out-province migrant workers in total rural labor force by 2 percentage points, or 51 percent of the sample mean. The results are robust to a battery of validity checks, such as using DID coupled with matching, controlling for time-varying province heterogeneity, controlling for pretreatment effect, using a flexible estimation method to account for differences in the time trend in outmigration of treatment and control groups, and using an alternative estimation method (i.e., a matched control group). Three placebo tests also confirm our identification assumptions. First, if the telecom effect on migration merely reflects changes in village characteristics, telecom access should affect in-migration (i.e., immigration into the village), but we do not find this to be the case. Second, if the 4
- (iii) We had to map the data in the World Bank database to the Australian based list of restrictions. In many cases this is not a one to one mapping and we have had to take the closest proxies available or modify the subcategories of the Australian list of restrictions.

- (iv) The impacts could change over time, but we are relying on cross-country econometric estimates of the impact of a change in our STRIs on the price or cost of the service that were done about 14 years ago.

- (v) Differences from the average that are not explained by the econometric model are ignored.

- (vi) For some sectors, such as road transportation and communications, no list of restrictions or estimated AVEs was developed by the Australian authors. In these cases, we developed lists of restrictions based on the closest categories from comparable sectors of the Australian authors. These estimates in these sectors are subject to an additional level of skepticism, since we must make a further assumption that the regression results apply for comparable sectors, e.g., the results for banking apply to insurance.

# References

- Albon, R., Hardin, A. and Dee, P. 1997, _Telecommunications Economics and Policy Issues_ , Industry Commission Staff Information Paper, AGPS, Canberra.

- Arnold, Jens M., Beata S. Javorcik and Aaditya Matoo (2011), “Does Services Liberalization Benefit Manufacturing Firms: Evidence from the Czech Republic,” _Journal of International Economics,_ Vol. 85 (1), 136-146.

- Arnold, Jens M., Beata S. Javorcik, Mary Lipscomb and Aaditya Matoo (forthcoming), “Services Reform and Manufacturing Performance: Evidence from India,” _Economic Journal_ .

- Balistreri, Edward J., Thomas F. Rutherford and David G. Tarr (2009), “Modeling Services Liberalization: The Case of Kenya,” _Economic Modeling_ , Vol. 26 (3), May, 668-679.

- Borchert, Ingo, Batshur Gootiiz and Aaditya Mattoo (2014), “Policy Barriers to International Trade in Services: Evidence from a New Database,” _World Bank Economic Review_ , 28(1), 162-188.

- Borchert, Ingo, Batshur Gootiiz and Aaditya Mattoo (2012), “Guide to the Services Restrictions Database,” World Bank Policy and Research Working Paper Number 6108, June.

16
# **I. Introduction**

A growing body of research suggests that low levels of cognitive development in early childhood have adverse long-term consequences for adult wellbeing. Research based on the 1958 British cohort study shows that performance on mathematics and reading tests at age 7 predict test scores at later ages, as well as educational attainment and wages as adults (Connolly et al., 1992; Currie and Thomas, 1999; Robertson and Symons, 2003). Evidence from a later-born cohort indicates that tests administered as early as 22 moths of age are also associated with adult education outcomes (Feinstein, 2003). Several of these authors find that the predictive effects of test performance in childhood on adult education and wages are particularly large for children from households with lower socioeconomic status (SES) (Currie and Thomas, 1999; Feinstein, 2003).<sup>1</sup> These results underscore the importance of understanding the determinants of cognitive development, especially among poor children.

Research on both industrialized and developing countries has focused on identifying environmental factors that affect cognitive development. A common finding in the US literature is that higher SES, as measured by income, wealth, or parental education, is associated with better cognitive development of children.<sup>2</sup> The extent to which this association is causal is a topic of live debate. More resources for children, better parenting skills, increased cognitive stimulation of children, and lower incidence of maternal depression and stress have all been proposed as “pathways” from high SES to high levels of cognitive development of children.

In contrast with the US literature, research on developing countries has placed greater emphasis on child health as a determinant of cognitive development and little emphasis on parenting. Numerous studies report an association between cognitive development and malnutrition (Powell et al., 1995; Pollitt et al., 1997), iron deficiency (Lozoff et al., 2000; Grantham-McGregor and Ani, 2001; Stoltzfus et al., 2001) and, more recently, other micronutrients such as zinc, iodine and vitamin B-12 (Black, 2003). Helminth infections have also been implicated as a route through which SES affects cognitive development. Children infected with intestinal helminths are more likely to be malnourished, and to have iron deficient

> 1 In much of this research, and in what follows, the term “SES” is used as shorthand to refer to a set of socioeconomic factors, including wealth, parental education, family structure and maternal age. We do not mean to suggest that the components of SES measure the same things, and the variables included in SES could be related to children’s cognitive ability in quite different ways.

> 2 Key references include Smith et al. 1997; Blau, 1999; Guo and Harris 2000; Waldfogel et al., 2002; Aughinbaugh and Gittleman, 2003; Baum, 2003; Ruhm, 2004; Taylor et al., 2004.

1
# Fiscal Policy, Public Expenditure Composition, and Growth Theory and Empirics<sup>1</sup>

Willi Semmler,<sup>2</sup> Alfred Greiner,<sup>3</sup> Bobo Diallo,<sup>4</sup> Armon Rezai,<sup>5</sup> and Anand Rajaram<sup>6</sup>

> 1 We want to thank Lars Gruene (Univ. of Bielefeld) for extensive help in the numerical part of the paper, David Bevan (Oxford Univ.) and to Shanta Devarajan for valuable guidance, and the audience at the World Bank workshop on "Growth and Fiscal Policy,” June 2006, for useful comments.  Financial support from the Bank-Netherlands Partnership Program is gratefully acknowledged.

> 2 Schwartz Center for Economic Policy Analysis, New School, New York and CEM, Bielefeld

> 3 Dept. of Economics and CEM, Bielefeld University

> 4 Schwartz Center for Economic Policy Analysis, New School, New York

> 5 Schwartz Center for Economic Policy Analysis, New School, New York

> 6 World Bank, Washington, D.C
where the endogenous weight _ω_ ( **y** ) _∈_ [0 _,_ 1] has the following linear expression: for _<u>y</u> >_ _<u>y</u>_<sup>_~~z~~_</sup> , where _y_ ˆ<sup>_R_</sup> is the average income among only-relatively income poor individuals;<sup>14</sup> _ω_ ( **y** ) := 0 for _<u>y</u> ≤_ _<u>y</u>_<sup>_~~z~~_</sup> . The closer the average income among the only-relatively income poor is to _za_ (respectively _z_ <u>(</u> _<u>y</u>_ <u>)),</u> the closer their weight is to one (respectively zero). Contrasting Equations (3) and (5) reveals that poverty as measured by _HHS_ always lies between the absolute headcount ratio and the societal headcount ratio. More precisely, we have Under some assumption on the distribution of preferences, index _HHS_ corresponds to the expected fraction of individuals who are welfare poor when _<u>y</u> >_ _<u>y</u>_<sup>_~~z~~_</sup> . The probability that an individual who is not income poor is welfare poor is zero, because the poverty line corresponds to the smallest income for which no individual can be welfare poor. The probability that an absolutely income poor individual is welfare poor is one because her own income is smaller than _za_ and her relative income is smaller than _za/y_<sup>_~~z~~_</sup> . The assumption under which index _HHS_ corresponds to EH is that the probability that an only-relatively income poor individual is welfare poor increases _linearly_ between zero and one as her income decreases from the poverty line to the subsistence income. Under this assumption, the probability that any individual _i_ is welfare poor corresponds to the poverty score that _HHS_ attributes to _i_ , which is: As an illustration, we show in online Appendix S2 that, for a certain probability distribution on the class of utility functions _u_<sup>_σ_</sup> , index _HHS_ is equal to EH for all distributions **y** such that _<u>y</u> >_ _<u>y</u>_<sup>_~~z~~_</sup> .<sup>15</sup>

> 14 ˆ _R_ <u>1</u> _y_ := _n_ ( **y** ) _HR_ ( **y** ) <u>�</u> _i∈NR_ ( **y** )<sup>_yi_,where</sup><sup>_NR_(</sup><sup>**y**)isthesetofonly-relativelyincomepoor</sup> individuals.

> 15We also discuss in online Appendix S2 the relationship between our theory and the literature on the fuzzy measurement of poverty.

14
Governance Of Public Pension Funds: Lessons From Corporate Govemance And International Evidence

**Table 2. Allocation of Assets for 26 Pension Funds (% of Portfolio)**

|**Investment-Type'**<br>**:** <br>**---**|>-<br>**Avrag-**|**MedIan>**MlnimC-M'K<br>-<br>MSlwiimzT|
|---|---|---|
|GovernmentBonds|35|20<br>0<br>98|
|Bank Deposits|25|23<br>0<br>93|
|<br>Equities|15|7<br>0<br>63|
|<br>Loans|6|2<br>0<br>39|
|CorporateBonds|4|2<br>0<br>22|
|<br>RealEstate|8|2<br>0<br>52|
|Other|4|1<br>0<br>23|

## **III SOLVING AGENCY PROBLEMS**

# _IIIA Separation of Ownership and Control_

A fundamental problem with public pension funds is how to achieve a workable separation of ownership and control. For example, if the plan participants are taken to be the owners of the fund, problems may result where another group (of, for example, government officials) controls the pension fund. This section considers the implications of the separation of ownership and control on pension plan governance. It considers first the situation of private pension plans and then the more complex problem of public pension plans.

# **_lll.B Ownership and Control in Private Pensions_**

Recent work by Besley and Prat (2002) applies agency theory to private pension fund governance. Their goal was to find the optimal governance structure of defined contribution (DC) and defined benefit (DB) pension plans with respect to three potential sources of agency problems: the responsibility for monitoring the asset manager ("vigilance"), asset allocation decisions, and the plan's level of funding. Governance structure matters because, due to the inability to exactly specify the obligations of all parties, the plan's beneficiaries and sponsor do not have complete contracting ability. Thus, the incentives are important that encourage the parties to monitor or make appropriate asset allocation decisions. The optimal governance structure is one in which the risk-bearer is also the decision-maker (that is, there is no separation of ownership from control).

Determination of the optimal governance structure requires that the owner of the plan (which may also be termned the risk-bearer or residual claimant) be identified and if possible granted decision control responsibility. The residual claimant is the group with the greatest incentive to act with vigilance because it is this group that is best positioned to enjoy the benefits of such actions. For DC plans, the residual claimant is the beneficiary, as benefits suffer from poor financial performance but increase with better financial performance. For DB plans, because the benefits such plans do not change with the performance of their assets the residual claimants are the sponsors, as it is the sponsors that bear the risks of poor financial performance. If the beneficiaries have a comparative cost advantage in acting with vigilance, the plan therefore should be structured as a DC plan. If the sponsors have a comparative advantage, the plan should be structured as a DB plan.

- 14 -
After controlling for household characteristics, location of a household in most of the regions is associated with lower consumption relative to Dhaka region (columns 1 and 3, Table A-1). Sylhet and Kushtia are the only exceptions in the rural and urban samples respectively. Ravallion and Wodon (1999) had found broadly similar results using data from earlier rounds of HES. For example, in the rural sample for 1988, location in all but one region (Chittagong) had a negative and significant effect on household consumption relative to location in the Dhaka region.

In the light of these results, an important question is whether the regional fixed effects are capturing variations in community characteristics below the level of regions, such as availability of infrastructure in the community, connectivity/access to urban markets and size of the non-farm sector? To address this question, a few variables of this type – that are available from the community survey of HIES 2005 (for rural communities) and Census (2001) – are included in the poverty determinant regressions.<sup>6</sup> The coefficients of these variables must be interpreted with caution because the bias caused by the omission of potentially other important location attributes for which no data is available. Taken together, these community variables reduce the size of the regional fixed effects (see columns 2 and 4 of Table A-1). For rural areas, adding travel time from the community to _thana_ and district headquarters and Dhaka city (from HIES community survey), indicators of microfinance coverage at the _thana_ level (from PKSF data) and percentage of households in the _thana_ with electricity connection or owning agricultural land (from the Census) reduce the size of the regional fixed effects. Similar results are seen for urban households, with the microfinance coverage and Census

> 6 Note that since the Census was fielded in 2001, these variables can be interpreted as indicators of the initial condition of development in each Thana.

14
and female workers are much closer.

Table 6 presents results on employment in village firms; Panel A shows estimates in logs and Panel B in levels. Because the data source is the Economic Census, these counts include all work in the village, formal and informal, excluding crop production. These results capture economic activity that takes places in the village, in contrast to Table 5, which describes economic activities for village residents even if they take place outside the village. We present estimates for total non-farm village employment (Column 1), as well as employment in the five largest sectors in the sample (livestock, manufacturing, education, retail and forestry), which together account for 79% of non-farm employment. We estimate an insignificant 25 percent increase in employment in non-farm firms. While the two largest village sectors (livestock and manufacturing) show similarly growth to total employment, the only statistically significant estimate we find is for retail, which we estimate grows 34 percent in response to a new road. In levels, we find no significant results overall or in any sector, with estimates ranging from 1.6 jobs lost in livestock to 2.6 jobs gained in manufacturing.

While the log changes in employment are quite large, the level changes are small because the typical 500- or 1,000-person village has few people engaged in economic activities other than crop production. We estimate that a new road on average creates 3.7 new jobs in a village. In contrast, the estimate from Table 5 suggest that 18.5 workers are exiting agriculture in the average village; only 20% of these workers appear to be finding this nonagricultural work in the village. These small impacts on firms imply that roads are facilitating access to external labor markets rather than growth of jobs in village firms. The proportional changes are the largest in the retail sector, suggesting that non-farm employment growth in the village may be more a function of new consumption opportunities (perhaps due to cheaper imports) rather than new productive opportunities. Unfortunately we are aware of no village-level data that would make it possible to directly test for changes in the availability or prices of consumption goods.

19