rafmacalaba/datause-encoder-data huggingface dataset:
conduct just such a comparison, using high quality data from Vietnam. Specifically, we use the 2010 and 2012 Vietnam Household Living Standards Surveys (VHLSS) and market prices from spatial cost of living surveys fielded in conjunction with the VHLSS. In each year, prices of up to one hundred goods and services were surveyed in sixteen hundred different markets, with surveyors given detailed pictures of the desired specifications to ensure consistency over time and space.

The spatial deflators and spatially disaggregated estimates of temporal inflation derived from the food Engel curve are a poor proxy for the deflators obtained from the multilateral price indexes. The Engel curve deflators suggest costs of living in some rural regions exceed those of the capital city. The derived changes in the cost of living from 2010 to 2012 vary widely over space, with the Engel curve suggesting deflation in some regions while the multilateral indexes give regional price changes of between 14–26% (the CPI rose 26% between the 2010 and 2012 surveys). These differences matter to conclusions about the location, level, and trend in poverty and inequality. For example, if the Engel curve deflator is used, the national Gini index rises to 0.47 from the 0.43 calculated in nominal terms; in contrast, using the multilateral price indexes leads to a lower Gini of 0.40 in real terms. The Engel deflator also causes the headcount poverty rate to be ten percentage points higher and skews the poverty profile to finding more rural poverty, especially in some regions that are already poorest.

Our results cast doubt on the Engel curve method, but its proponents may claim that our multilateral price indexes do not get the right cost-of-living and are a poor benchmark. The Engel method relies on food shares falling as income rises, so preferences cannot be homothetic. Thus any benchmark price index consistent with homothetic preferences may be considered an unfair test. But a “fair” test of the Engel curve method is hard to design. Beatty and Crossley (2012) show 4
**Table 15 A: Annual value added per worker in presumed comparative advantage industries (in million LCU) at 2000 prices**

|**Two Digit Industry (ISIC Rev. 3.1)**|**UBI**<br>**Sample**<br>**total**||**Estimated**<br>**total**||**Sample**|**Estimated**|
|---|---|---|---|---|---|---|
||**2002**|**2009**|**2002**|**2009**|**Annual**<br>**growth**|**Annual**<br>**growth**|
||||||**rate(% pa)**|**rate(% pa)**|
|**Primary sector:**|||||||
|Crop and animal production|2.0|9.2|2.0|0.6|51.8|‐10.1|
|Fishing and aquaculture|2.7|4.1|2.1|0.9|7.2|‐8.3|
|Extraction of crude petroleum||41.1|||||
|Mining of metal ores|0.2|60.3||14.2|||
|**_Primary sector total_**|2.0|10.6|1.9|0.6|60.8|‐9.8|
|**Manufacturing: **|||||||
|Food and beverages|11.0|18.2|3.5|8.2|9.4|19.3|
|Tobacco products|36.4|2.9|29.1|3.2|‐13.2|‐12.7|
|Textiles|6.0|20.7|2.9|5.6|34.9|12.8|
|Garments|1.1|13.7|0.3|3.7|163.1|150.1|
|Leather and leather products|13.9|43.2|1.3|9.0|30.0|83.3|
|Coke, refined petroleum products|2.1|148.7|0.7|60.6||1207.6|
|Basic metals|20.0|4.5|14.3|2.2|‐11.1|‐12.1|
|**_Manufacturing total_**|11.2|18.8|3.3|6.7|9.6|14.9|
|**Other industries**|||||||
|Electricity, gas,steam and water|62.1|814.5|11.3|1097.2|173.0|1373.5|
|**Services:**|||||||
|Land transport; transport via pipelines|11.0|12.8|8.2|3.1|2.4|‐9.0|
|Water transport|2.4|6.3|2.5|4.9|23.4|13.9|
|**_Total services_**|10.8|12.6|8.2|3.2|2.3|‐8.7|
|**Grand total**|9.9|19.9|3.9|4.8|14.2|3.3|

50
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levels of insufficient or basic both in sixth grade and in twelfth grade. There is however a fair amount of movement over time between categories including both improvements and falling to lower categories over time.

Table 5a: Mathematics ENLACE Scores, 6th Grade versus 12th Grade, Schools in Areas of Medium to Very High Margination 12th grade score, 2013-2014

|||Insufficient|Basic|Good|Excellent|Total|
|---|---|---|---|---|---|---|
||Insufficient|2,287|1,256|400|233|4,176|
|||54.77|30.08|9.58|5.58|100|
||Basic|4,123|5,231|2,585|1,546|13,485|
|6th Grade||30.57|38.79|19.17|11.46|100|
|2008|||||||
||Good|563|1,287|1,381|1,653|4,884|
|||11.53|26.35|28.28|33.85|100|
||Excellent|87|104|180|656|1,027|
|||8.47|10.13|17.53|63.88|100|
||Total|7,060|7,878|4,546|4,088|23,572|
|||29.95|33.42|19.29|17.34|100|

Source: Author’s calculations, ENLACE standardized achievement tests.

Table 5b: Mathematics ENLACE Scores, 6th Grade versus 12th Grade Indigenous Students 12th Grade Score, 2013-2014

|||Insufficient|Basic|Good|Excellent|Total|
|---|---|---|---|---|---|---|
||Insufficient|533|246|88|54|921|
|||57.87|26.71|9.55|5.86|100|
||Basic|674|671|311|205|1,861|
|6th Grade||36.22|36.06|16.71|11.02|100|
|2008|||||||
||Good|56|108|94|136|394|
|||14.21|27.41|23.86|34.52|100|
||Excellent|4|9|15|33|61|
|||6.56|14.75|24.59|54.1|100|
||Total|1,267|1,034|508|428|3,237|
|||39.14|31.94|15.69|13.22|100|

Source: Author’s calculations, ENLACE standardized achievement tests.

# _C. Impact results_

We now turn to the principal impact estimation results. We begin with the estimations for all sixth graders in primary schools located in areas with middle to very high levels of margination, 18
Project (SLMP), an intervention very similar to the one discussed here, found that a farmer’s participation in the project was not associated with significantly higher production (Schmidt and Tadesse 2017).

One reason for finding limited or insignificant impacts from SWC interventions may be that such structures mature slowly; and even if they increase profitability in the long term (Kassie et al. 2008), sustaining them until benefits are realized requires short-term sacrifices that may be unaffordable for small farmers faced with subsistence constraints and limited credit access.<sup>3</sup> Adoption of SLM practices can be affected by many factors, among them tenure security (Melesse and Bulte 2015), the time profile of benefits (Adgo, Teshome and Mati 2013), credit market functioning (Tesfaye et al _._ 2016), partial appropriability of benefits (Shiferaw and Holden 1999), limited risk-bearing capacity or social capital (Wossen, Berger, and Di Falco et al _._ 2015), and coordination failures deeply rooted in local history and socioeconomic structure (Smit et al. 2017).

Ethiopian farmers are well-aware of soil fertility problems (Tefera and Sterk 2010) and accept contracts to control soil erosion if the benefits exceed the cost (Tesfaye and Brouwer 2012). This has led to a consensus that intervention packages that are adapted to local conditions and pair SWC activities with infrastructure investments and support for the necessary inputs or compensation for labor are most conducive to longterm success (Schmidt et al. 2017). While GIS-based terrain modeling, combined with field observations, can be used to identify hotspots for interventions (Tamene et al _._ 2017) and SWC measures are likely to have clear impact (Schmidt and Zemadim 2015), also needed are economic analysis and a link to the biophysical relationships of individual farms (Ekbom 2008) or larger agro-ecological units (Kassie et al _._ 2008). In principle, real-time feedback based on remotely sensed imagery could help make it possible not only to assess the extent to which desired impacts are realized, but also to adjust interventions in an iterative evidence-driven process. Yet although there are numerous studies of deforestation as an outcome variable, there has been little use of such data at finer spatial and temporal resolution.

# **_2.3 Remotely Sensed Imagery and SLM Impacts_**

Plants absorb light in the 0.6-0.7μm wavelengths and reflect strongly in near infrared. Sensors for land surface analysis collect data in bands approximating these wavelengths to generate indicators of vegetation density, plant phenology, and photosynthetic activity or soil water content. Indices for these parameters can be obtained by differencing or computing ratios of red and near infrared (NIR) bands, possibly drawing on additional bands to eliminate effects of atmospheric aerosols or soil background (blue reflectance) and short-wave infrared (SWIR) to estimate leaf water content (Reed, Schwartz, and Xiao 2009).

> 3 Alternative explanations might be benefit spillovers to non-project areas or measurement errors.

6
and _DLC_ ) minus the firm s current assets (Compustat item _ACT_ ) which includes cash, inventory, and marketable securities. After dropping top and bottom 1% outliers in _Q_<sup>_TOT_</sup> , we define _Q star_ as a dummy variable that takes the value 1 if the firm’s _Q_<sup>_TOT_</sup> is above the 90<sup>_th_</sup> percentile of _Q_<sup>_TOT_</sup> across all firms in the US economy in a particular year and 0 otherwise.

While _Q_<sup>_TOT_</sup> has the advantage of using a market valuation of the firm’s prospects, the measure is prospective in that it captures the value of the firm’s investment opportunities given the market’s view of its investment plans (e.g. Novy-Marx [2007]). Thus, it may not measure current product market performance, and we do not use it as the primary metric of star status.

In Table 7, we use the alternate definition of star status based on Tobin’s Q defined in equation (14). The estimates in Table 7 confirm our previous results on the role of industry skill as a determinant of star status. In high skilled industries (high complex problem solving skill, high nonroutine cognitive skills, and low routine manual skills), large firms and those with high markups are more likely to be top performers in terms of Tobin’s Q.

# **4.2 Measurement of Excess Cash**

There is a great deal of controversy in how to treat a firm’s cash holdings in the computation of a firm’s invested capital. It is standard financial reporting practice to include a firm’s cash holdings in the definition of its invested capital. However, financial analysts routinely subtract a large fraction of cash holdings, say any cash in excess of 2% of annual revenues, from the firm’s calculated investment capital (e.g. Koller et al. [2017]). The rationale for that is that the excess cash is unnecessary to support operations and confounds valuations of product market opportunities. This view is also supported by a large body of academic work (e.g. Jensen [1986]; Harford et al. [2008]; Dittmar and Mahrt-Smith [2007]) which argues that large cash holdings are a reflection of agency conflicts between managers and firms shareholders, and are not relevant to the valuation of a firm’s operations.

A second reason to subtract excess cash from invested capital is to circumvent the policy of many large U.S. multinationals to stockpile cash in low-tax jurisdictions in order to manage their tax liabilities (e.g. Faulkender and Petersen [2012]; Faulkender et al. [2017]. Against that, there are 27
Policy ReseaRch WoRking PaPeR 4878

# **Abstract**

With the exception of South Africa, local financial markets in sub-Saharan Africa remain underdeveloped and small, with a particular dearth of financing with maturity terms commensurate with the medium- to longterm horizons of infrastructure projects. But as financial market reforms gather momentum, there is growing awareness of the need to tap local and regional sources. Drawing on a comprehensive new database constructed for the purpose of this research, the paper assesses the actual and potential role of local financial systems for 24 African countries in financing infrastructure. The paper concludes that further development and more appropriate regulation of local institutional investors would help them realize their potential as financing sources, for which they are better suited than local banks because their liabilities would better match the longer terms of infrastructure projects. There are clear signs of positive change: private pension providers are emerging in Africa, there is a shift from defined benefit toward defined contribution plans, and African institutional investors have begun taking a more diversified portfolio approach in asset allocation. Although capital markets remain underdeveloped, new issuers in infrastructure sectors—particularly of corporate bonds—are coming to market in several countries, in some cases constituting the debut issue. More than half of the corporate bonds listed at end-2006 on these countries’ markets were by companies in infrastructure sectors. More cross-border listings and investment within the region—in both corporate bonds and equity issues—including by local institutional investors, could help overcome local capital markets’ impediments and may hold significant promise for financing cross-country infrastructure projects.

This paper—a product of the African Sustainable Development Front Office, Africa Region—is part of a larger effort in the region to gauge the status of public expenditure, investment needs, financing sources, and sector performance in the main infrastructure sectors for 24 African focus countries, including energy, information and communication technologies, irrigation, transport, and water and sanitation. Policy Research Working Papers are also posted on the Web at http://econ. worldbank.org. The author may be contacted at jirving@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
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Policy ReseaRch WoRking PaPeR 4714

# **Abstract**

In recent years donor countries have committed to dramatic increases in the supply of foreign aid to developing countries. Meeting and sustaining such commitments will require sufficient support among donor country voters and taxpayers. The determinants of public opinion in donor countries on foreign aid have received little attention. This paper examines attitudes to foreign aid with a large, multi-level, cross-national study. It outlines a theoretical rationale for support for foreign aid, discussing the importance of both individual factors and economic and social structures. The theory is tested with multi-level models, including both individuallevel and country-level variables to predict positive attitudes. Two datasets are used to measure attitudes in donor countries: (1) the 1995 _World Values Survey_ has information from approximately 6,000 individuals in nine countries and asks a rich battery of questions at the individual-level, and (2) the 2002 Gallup “Voice of the People” survey asks fewer questions of individuals but includes 17 donor countries. Using both surveys combines their distinct strengths and allows tests of individual and national-level theories across disparate samples. The results generally support the predictions that attitudes toward aid are influenced by religiosity, beliefs about the causes of poverty, awareness of international affairs, and trust in people and institutions.

This paper—a product of the Human Development and Public Services Team, Development Research Group—is part of a larger effort in the department to make aid more effective in promoting economic development. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at sknack@ 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
**_<u>________________________________________________________</u>_**

# **2.2.1 Standards and Technical Regulations**

# _<u>World Bank Technical Barriers to Trade (TBT) Database</u>_

The Bank conducted a survey of 690 manufacturing firms in 17 developing countries for the period 2001-02 to obtain information on relevant standards, government regulations, and technical barriers to trade (TBT) confronting exporters wanting to expand output, exports, or enter developed country markets. The country and sectoral coverage is relevant to allow comparisons. The information will include data on:

- Production costs, including fixed costs;

- Output measures, including domestic sales, exports;

- Measures for assessing market structure;

- Major obstacle facing firms;

- Assessment by firm managers of the cost compliance with TBTs.

Firm managers carried out assessments of the costs of compliance with TBTs in 4 activity types (1) investing in equipment and plant, (2) investing in product re-design, (3) hiring labor for production, (4) hiring labor for product testing. The release date is June 7, 2004.

# _<mark>World Bank: Global Economic Prospects –</mark> Wilson, Mann, Otsuki 2004_

This database by Wilson, Mann, Otsuki, includes four variables, and is constructed for 75 global countries from survey data across countries published in year 2000. Each measure is defined as an arithmetic average of multiple inputs from several data sources that are normalized to be a mean of one. A raw series (which is an observation representing a country) of survey inputs is indexed to the maximum of all the countries’ value for the raw series (e.g. global best practice). The maximum is used as a benchmark since this easily indicates how far a country’s performance is from the best practice country whose indexed value is 1.0. The data set builds on Kaufmann, Kraay and Zoido-Lobaton (2002) (KKZ) and World Economic Forum Global Competitiveness Report (GCR), and World Competitiveness Yearbook 2000 (WCY).

An average of the two indexed inputs is used to form each of four trade facilitation measures. Those survey inputs are:

- Port efficiency: designed to measure the quality of infrastructure of maritime and air ports.

- Port facilities and inland waterways (GCR);

- Air transport  (GCR);

- Customs environment: designed to measure direct customs costs as well as administrative transparency of customs and border crossings. `o` Hidden import barriers  (GCR);

- Irregular extra payments and bribes (GCR);

12 __________________________________________________________________________________________________________
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85 Gundlach, Erich (1995). “The Role of Human Capital in Economic Growth: New results and Alternative Interpretations.” _Weltwirtschaftliches Archiv_ . Vol. 131, No. 2, pp. 383-402.

Hall, Bronwyn H., Adam B. Jaffe, and Manuel Trajtenberg (2002). “The NBER Patent-Citations Data File: Lessons, Insights, and Methodological Tools.” In _Patents, Citations and Innovation: A Window on the Knowledge Economy._ Jaffe, Adam B. and Manuel Trajtenberg (eds). The MIT Press, Cambridge Massachusetts, USA.

Hall, B. H., A. B. Jaffe, and M. Tratjenberg (2001). "The NBER Patent Citation Data File: Lessons, Insights and Methodological Tools." NBER Working Paper 8498.

Hall, Robert E. and Charles I. Jones (1999). “Why Do Some Countries Produce So Much More Output per Worker than Others ?” _Quarterly Journal of Economics_ . Vol. 114, February, pp. 83-116.

Hanushek, Eric A. and Dennis D. Kimko (2000). “Schooling, Labor-Force Quality, and the Growth of Nations.” _American Economic Review_ . Vol. 90, No. 5 (December), pp. 1184-1208.

Irwin, Douglas A. and Marko Tervio (2002). “Does Trade Raise Income? Evidence from the Twentieth Century.” _Journal of International Economics_ . Vol. 58, pp. 1-18.

Islam, Nazrul (1995). “Growth Empirics: A Panel Data Approach.” _Quarterly Journal of Economics_ . Vol. 110, Vol. 4, pp. 1127-1170.

Jaffe, Adam B. and Manuel Trajtenberg (2002). _Patents, Citations and Innovation: A Window on the Knowledge Economy._ The MIT Press, Cambridge Massachusetts, USA.

Jong-Wha Lee (1995). “Capital Goods Imports and Long-Run Growth.” _Journal of Development Economics_ . Vol. 48, pp. 91-110.

Jorgenson, Dale W. and Kevin Stiroh (2000). “Raising the Speed Limit: U.S. Economic Growth in the Information Age.” _Brookings Papers on Economic Activity_ . Vol. 1, pp. 125-211.

Joy, Mazumdar (2001). “Imported Machinery and Growth in LDCs.” _Journal of Development Economics_ . Vol. 65, pp. 209-224.

Kaufmann, Daniel, Aart Kraay and Pablo Zoido-Lobaton (2002). “Governance Matters II: Updated Indicators for 2000/01.” _Policy Research Working Paper Series_ . No. 2772. The World Bank. January.