rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 9973

# **Abstract**

How has globalization affected the relative taxation of labor and capital, and why? To address this question, this paper builds and analyzes a new database of effective macroeconomic tax rates covering 150 countries since 1965, constructed by combining national accounts data with government revenue statistics. Four main findings are obtained. (1) The effective tax rates on labor and capital have converged globally since the 1960s, due to a 10 percentage-point increase in labor taxation and a 5 percentage-point decline in capital taxation. (2) The decline in capital taxation is concentrated in high-income countries. By contrast, capital taxation has increased in developing countries since the 1990s, albeit from a low base. (3) Consistently across a variety of research designs, the findings show that the rise in capital taxation in developing countries can be explained by a tax capacity effect of international trade: trade openness leads to a concentration of economic activity in formal corporate structures, where capital taxes are easier to impose. (4) At the same time, international economic integration reduces statutory tax rates, due to increased tax competition. In high-income countries, this negative tax competition effect of trade has dominated, while in developing countries, the positive tax-capacity effect of international trade appears to have prevailed.

This paper is a product of the Development Research Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at pbachas@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
The qualitative data is based upon semi-structured in-depth interviews with several key players<sup>in</sup> each community: JSIF project coordinators, community leaders (elected officials, community<sup>elders,</sup> pastors, etc.), and members of the JSIF committee in each community who helped organize and manage<sup>the</sup> project. Three extended focus group discussions were also conducted in each community with<sup>groupsof</sup> people who were, as far as possible, selected to represent the entire community. Data from<sup>these interviews</sup> is supplemented with observations from field visits by Ruel Cooke, one of the consultants who supervised the data collection, and Rao who conducted field visits in six of the ten communities in the sample. The report on Amett Gardens is also supplemented by a report by Duncan (2001) commissioned for this<sup>paper,</sup> based upon several additional interviews with "Dons," politicians, and other men and women who live in the community.

We should note that the focus groups, in-depth interviews were not conducted on a random sample. Moreover, despite efforts to avoid the problem, it is possible that members of the community who participated in the focus group discussions were more likely to have participated actively in the social<sup>fund</sup> process. This could, potentially, result in biased findings. Therefore, the qualitative findings should not be examined on their own, but in conjunction with the survey results that are based upon a representative random sample. The qualitative information is useful, however, to sketch a narrative of participation and collective action in the JSIF and non-JSEF communities. We will outline the key players, the manner in which community mobilization occurred, and the perceived impact of the project the in community. The non-JSIF community will also be very briefly sketched to provide a sense of the difference between the matched pairs.

Community Pair A:

# Port Royal and Rennock Lodge

Port Royal is an old and historic town that was the capital of Jamaica when it was under Spanish rule. It subsequently became a haven for pirates and buccaneers. Most of the old Spanish town was submerged after a series of hurricanes and earthquakes in the 1 9gh century. The town also has a large fort dating from the 18h century and has significant but underdeveloped potential as a tourist site. It is also a community that has over the years, through generations of inter-marriage, become extremely tight knit, but

> requires an understanding of their history. For these reasons we have retained their real names. The names of all the individuals quoted have also been changed with the exception of prominent people who would be difficult<sup>todisguise.</sup>

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# _3.4. Secondary markets_

**Secondary markets are generally shallow in MENA-5 as a result of excess liquidity, inappropriate issuance policies, non-diversified investor base and inadequate market institutional organization.** Therefore, reforms in all building blocks mentioned in this report are pre-conditions for secondary market efficiency. The most critical aspect and, at the same time, relatively easy to be tackled by country authorities is the issuance policy as discussed in the previous section. The yield curve should be lengthened gradually through consolidation of benchmark maturities and in volumes large enough to support secondary market trading. In parallel, the institutional organization of secondary markets needs to be addressed.

Secondary markets in MENA-5 may be classified into three different profiles:

**In the first group, Egypt shows the most active T-bill market and a gradually increasing trade volume in the T-Bond market, reflecting an improved issuance policy**<sup>**34**</sup> **.** This is the result of a more systematic benchmark building strategy in the primary market through a predictable and regular calendar of auctions covering all maturities. As a consequence, the maturity profile of the debt is more balanced, and it is easier to price issues by using recent auctions as reference.

**In the second group, comprising Morocco and Tunisia, the combination of longterm average maturity and scarce secondary market liquidity has led to a disproportionate and risky use of repos to manage liquidity that would otherwise be locked in long-term debt portfolios.** In advanced economies, repo markets are in general larger than spot markets. Yet, the latter needs to reach a minimum volume threshold to ensure that the collateral is properly valued and that in the event of default it can be liquidated. In Morocco, the general repo legal framework work is robust, but the spot market to sustain credible valuation of the collateral is missing.   Repos account for 99 percent of all trading activity, and report a 25 times turnover with average maturity of the debt at 5.9 years.  In Tunisia, with similar average maturity of debt at 5.3 years, the legal framework is weaker. Formal repos have not taken off, but a non-regulated substitute called _ventes à rémérés_ is used instead by banks for liquidity management purposes. Trades are not formally reported.

**In the third group, Jordan and Lebanon have almost zero secondary market trading as a result of excess liquidity, a very fragmented debt structure, and poor market infrastructure.**

_Trading venues and reporting obligations_

**Government securities are predominantly traded in over-the-counter (OTC) wholesale markets and marginally on exchanges, and they are subject to trade**

> 34 Reported figures for 2009 show a daily average of US$300 million in T-Bills and US$18-45 million in T- Bonds.

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The non-CF communities had a total of 3835 family members, with an average family size of 5.91 and an average age of 27.94.

Agriculture is main occupation in both CF (34.5%) and non-CF (30.8%) communities followed by foreign employment (7.38% in CF and 6.75% in non-CF) and household work (6.18% in CF and 7.48% in non-CF). Given that the CF and non-CF households and communities are similar allows meaningful comparisons to be made between them in terms of preferences for REDD+ contracts.

# **6. Results and Discussion**

Given that the choice experiment surveys for the two respondent groups (CF and non-CF) have different attributes, we analyze the two groups separately and compare findings. We present four sets of results that correspond to specification (13) - (15). Tables 3 and 4 present results for the main effects specifications analyzed using a conditional logit model (column 1), the main effects specification analyzed using a MMNL model (column 2), the attribute interactions effects specification analyzed using a MMNL (column 3), and the beliefs and attitude interactions effects specification analyzed using a MMNL (column 4) for the non-CF and CF communities respectively. The significance of the standard deviation estimates for random coefficients from the MMNL is indicated with a “SD” next to the standard errors. As can be seen many of the variables exhibit individual heterogeneity and therefore it is necessary to account for this in the analysis by using a MMNL model.

The overall results from the four specifications indicate that the percentage of the payment going to the community, the required firewood reduction, the required grazing reduction (for non-CF households) and the payment amount are all significant variables in 20
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# **Table A1: African Development Bank Country Policy and Institutional Assessments (ADB)**

|**Data Provider**|African Development Bank.|
|---|---|
|**Description**|Multilateral development bank headquartered in Abidjan, Cote D'Ivoire|
|**Website**|www.afdb.org|
|**Data Source**|Country Policy and Institutional Assessments|
|**Type**|Expert Assessment|
|**Respondents**|African Development Bank country economists, subject to centralized review for<br>comparability|
|**Frequency**|Annual since 1998|
|**Country Coverage**|African Development Bank client countries|
|**Public Access**|Since 2005|
|**Description**|Indicators on 16 dimensions of policy and institutional performance. Responses are coded<br>on a 6-point scale. CPIA indicators are used to allocate concessional loans by the African<br>Development Bank. Note that we cannot display 2006 and 2007 data because they were<br>not publicly released by the African Development Bank.|

|**Voice and Accountability**<br>|2008|2007|2006|2005|2004|2003|2002|2000|1998|1996|
|---|---|---|---|---|---|---|---|---|---|---|
|NA|..|..|..|..|..|..|..|..|..|..|
|**Political Stability and Absence of Violence**|||||||||||
|NA|..|..|..|..|..|..|..|..|..|..|
|**Government Effectiveness**|||||||||||
|Policies to improve efficiency of public sector|X|X|X|X|X|X|X|X|X|..|
|Budget Management|X|X|X|X|X|X|X|X|X|..|
|Efficiency of Public Expenditures|X|X|X|X|X|X|X|X|X|..|
|Management of public debt|X|X|X|X|X|X|X|X|X|..|
|Quality of Public Administration|X|X|X|X|X|..|..|..|..|..|
|**Regulatory Quality**|||||||||||
|Trade policy|X|X|X|X|X|X|X|X|X|..|
|Competitive environment|X|X|X|X|X|X|X|X|X|..|
|Labor Market Policies|X|X|X|X|X|X|X|X|X|..|
|**Rule of Law**|||||||||||
|Property rights|X|X|X|X|X|X|X|X|X|..|
|**Control of Corruption**|||||||||||
|Transparency / corruption|X|X|X|X|X|X|X|X|X|..|
|**Country Coverage**|**53**|**52**|**52**|**52**|**52**|**50**|**50**|**51**|**51**|**..**|
|**Year of Publication**|**2008**|**2007**|**2006**|**2005**|**2004**|**2003**|**2002**|**2000**|**1998**|**..**|

40
0,−1 error that results from re-scaling all incomes by the GDP per capita growth rg instead of the 0,−1 income growth ry . This is the equivalent to the _Pass-through Error_ (PTE); this error will get closer to zero whenever we use a pass-through similar to the actual one. Finally, [P(μg0, L−1′) − P(μg0, L−2′)] is the error that arises from using information from the past that does not reflect the present information. This error is equivalent to the _Informational error_ (IE) in the PE method.

# **4 Data and empirical approach for validating poverty nowcasts**

_4.1 Per capita household income from harmonized household surveys in LAC_ In order to measure country-specific and regional poverty in this paper, we need to increase cross country comparability of welfare measures and poverty lines. Since the LAC region is the focus of the paper, we use the $4 per person per day moderate poverty line in 2005 PPP to measure poverty. In addition, we use the SEDLAC micro data as the primary source of the comparable welfare aggregate. The SEDLAC project is a harmonized micro database of LAC’s main households’ surveys produced by the poverty group at the World Bank in partnership with the Center for Distributive, Labor, and Social Studies (CEDLAS, for its acronym in Spanish) at the Universidad Nacional de La Plata in Argentina.<sup>12</sup> The main goal of this project is to increase crosscountry comparability of many socioeconomic indicators, including total household per capita income, from more than 300 household surveys within 18 countries spanning more than 20 years of surveys.<sup>13</sup> The SEDLAC project has been increasingly used by researchers in the analysis of poverty and inequality in LAC. However, unlike many other past studies, this paper uses the micro data instead of country-level indicators produced by SEDLAC. All nowcasting methods are validated for all countries in LAC for which data are frequently collected during the 2003-14 period.<sup>14</sup> Table 1 introduces all surveys (and their coverage) used in this paper.

> 12 See Castaneda et al. (2016), Bourguignon (2015), and Gasparini, Cicowiez, and Escudero (2013) for a more detailed description of the SEDLAC project.

> 13 Since the main goal of the SEDLAC project is to enhance cross-country comparability, poverty measures in this paper are not comparable to those published by NSOs in the region (Castaneda et al. 2016). 14 Guatemala and Nicaragua have fewer than four years of data and therefore they are excluded from the validation (section 3) and sensitivity (section 4) analysis.

15
- [2] Bell, C. (2011b), ‘Estimating the Social Profitability of PMGSY: A Bumpy Ride’, mimeo, University of Heidelberg, September. (To appear in the World Bank’s Working Paper Series.)

- [3] Binswanger, H., Khandker, S., and Rosenzweig, M. (1993), ‘How Infrastructure and Financial Institutions Affect Agricultural Output and Investment in India’, _Journal of Development Economics_ , 41(2): 337-366.

- [4] Deaton, A. (1997), _The Analysis of Household Surveys_ , Baltimore: The Johns Hopkins University Press.

- [5] Fan, S., Hazell, P., and Thorat, S. (2000), ‘Government Spending, Growth and Poverty in Rural India’, _American Journal of Agricultural Economics_ , 82(4): 10381051.

- [6] Jacoby, H.G., and Minten, B. (2009), ‘On Measuring the Benefits of Lower Transport Costs’, _Journal of Development Economics_ , 89(1): 28-38.

- [7] Khandker, S.R., Bakht, Z., and Koolwal, G. B. (2009), ‘The Poverty Impact of Rural Roads: Evidence from Bangladesh’, _Economic Development and Cultural Change_ , 57: 685-722.

- [8] Klemik, H., Leonard, K.L., and Masatu, M.C. (2009), ‘Defining Access to Health Care: Evidence on the Importance of Quality and Distance in Rural Tanzania’, _American Journal of Agricultural Economics_ , 91(2): 347-358.

- [9] Sinha, B. N. (1999), _Geography of Orissa_ , New Delhi: National Book Trust.

- [10] Spriggs, J. (1977), ‘Benefit-Cost Analysis of Surfaced Roads in the Eastern Rice Region of India’, _American Journal of Agricultural Economics_ , 59(2).

- [11] van Dillen, S. (2008), ‘Income and its Variability in a Drought-prone Region: Seasonality, Location and Household Characteristics’, _European Journal of Development Research_ , 20(4): 579-596. DOI: 10.1080/09578810802464912.

- [12] World Bank (2010), ‘Project Appraisal Document’, Report No. 57081-IN, Washington, DC.

- [13] Warr, P. (2010), ‘Roads and Poverty in Rural Laos’, _Pacific Economic Review_ , 15(1): 152-169. DOI: 10.1111/j.1468-0106.2009.00494.x.

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# **Returns to Investment in Education: A Decennial Review of the Global Literature**

George Psacharopoulos* Harry Antony Patrinos** JEL codes: C13, J31 Keywords: Returns to schooling; Investments in education ____________________________

- Georgetown University, <u>gpsach@rcn.com.</u>

** Corresponding author, The World Bank, Washington, DC 20433, USA, hpatrinos@worldbank.org

We are grateful to all those who over the years have shared with us their work on the returns to education and to Sean Kelly for efficient research assistance.
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**Table 3 (Cont’d): Impacts of Increased Biofuel Production on Food Prices**

|**Study**|Coverage & KeyAssumptions|KeyImpacts of Biofuels on Food Prices|
|---|---|---|
|**Rosegrant et al.**<br>**(2008)**|2000‐2007; Scenario with<br>actual increased biofuel<br>demand compared to<br>baseline scenario where<br>biofuel demand grows<br>according to historical rate<br>from 1990‐2000|Increased biofuel demand is found to have<br>accounted for 30 percent of the increase in<br>weighted average grain prices, 39 percent of the<br>increase in real maize prices, 21 percent of the<br>increase in rice prices and 22 percent of the rise<br>in wheat prices.|
|**Fischer et al.**<br>**(2009)**|(1) Scenario based on the<br>IEA’sWEO 2008 projections;<br>(2) variation of WEO 2008<br>scenario with delayed 2<sup>nd</sup>gen<br>biofuel deployment;<br>(3) aggressive biofuel<br>production target scenario;<br>(4) and variation of target<br>scenario with accelerated 2<sup>nd</sup><br>gen deployment|Increase in prices of wheat, rice, coarse grains,<br>protein feed, other food, and non‐food,<br>respectively, compared to reference scenario:<br>(1) +11%, +4%, +11%, ‐19%, +11%, +2%<br>(2) +13%, +5%, +18%, ‐21%, +12%, +2%<br>(3) +33%, +14%, +51%, ‐38%, +32%, +6%<br>(4) +17%, +8%, +18%, ‐29%, +22%, +4%|

Banse et al. (2008) show that if the mandatory 5.75% biofuel blending in EU member states is implemented, it would cause real prices of cereals, oilseeds and sugar in 2010 to be 2.75%, 3.5% and 2.5% higher than that in the reference scenario. In the case of the implementation of mandatory 11.5% biofuel blending, the corresponding price changes would be 7%, 10% and 9.75% (see Table 3). Rosegrant et al. (2008) show that increased biofuel demand accounted for 30 percent of the increase in weighted average grain prices in 2000-2007 compared to the historical baseline. More specifically, increased biofuel demand is estimated to account for 39 percent of the increase in real maize prices, whereas it is estimated to account for 21 percent of the increase in rice prices and 22 percent of the rise in wheat prices.

Some of the rises in food commodity prices are not caused by market forces, such as the price of gasoline, pertaining to biofuels, but rather by policy induced demand growth. McPhail et al. (2008) argue that the elimination of federal tax credits and tariffs, and to a far lesser extent, mandates, in the US would reduce ethanol production by 18.6 percent, resulting in the decline of the price of corn by 14.5 percent. However, if gas prices are high enough, i.e., $3 per gallon or higher, biofuel production may be profitable without support policies; ethanol production can be 21
Secondly, the study translates the absolute emission reduction targets to emission intensity based targets using projected data form the 2007 International Energy Outlook of the U.S. Department of Energy (USDEE) (EIA, 2007b)5. Intensity based targets appear politically palatable because these are interpreted as not reducing economic outputs. In 2002, the U.S. administration announced an intensity based target to reduce GHG intensity by 18 percent over the next 10 years (The White House, 2002). In 2007 April, the Canadian Government announced mandatory cuts in industrial sector GHG intensities by 18 percent from 2006 level by 2010 and 2 percent reduction per year thereafter (Environment Canada, 2007). Argentina was the first non-Annex I country to commit an intensity based emission reduction target (Argentine Republic 1999). Theoretically, both absolute emission based targets and emission intensity based targets result in the same level of mitigation as long as these target are set appropriately (see e.g., Ellerman and Wing, 2003). Our study shows that targets based on emission intensity would be higher than that based on absolute emissions because economic outputs are expected to increase in future.

The paper is organized as follows. Section 2 develops the methodology and describes data sources followed by a brief discussion of historical as well as projected CO2 emissions through 2030 in Section 3. Also discussed in Section 3 is the historical and projected trend of CO2 intensities. Section 4 links the global GHG mitigation implied by the Kyoto Protocol during its first commitment period with the atmospheric stabilization of CO2 concentrations; this section also presents the two scenarios developed for the purpose of this study. This is followed by an analysis of emission

> 5 The reasons behind the use of data from USDOE will be discussed in Section 2.

-5-