rafmacalaba/datause-encoder-data huggingface dataset:
Table A5. Sensitivity analysis: Undercoverage and leakage by household size and age of head, for target population below bottom 25% and cutoff score at bottom 30%

||Samurdhi<br>beneficiaries|Under<br>coverag<br>e|Model 1<br>Leakag<br>e|PMT<br>eligible<br>share|Under<br>coverag<br>e|Model 2<br>Leak<br>age|PMT<br>eligible<br>share|Under<br>covera<br>ge|Model 3<br>Leakag<br>e|<br>PMT<br>eligible<br>share|
|---|---|---|---|---|---|---|---|---|---|---|
|Household|||||||||||
|size|||||||||||
|1|21.5|‐|‐|‐|‐|‐|‐|‐|‐|‐|
|2‐3|17.3|72.3|39.6|6.4|76.6|40.9|5.5|83.1|37.6|3.8|
|4‐5|18.2|40.7|4‐|23.2|40.4|39.4|23.2|40.8|40.5|23.4|
|6+|21.7|24.0|30.8|46.4|24.8|31.4|46.3|24.7|31.8|46.6|
|Age<br>of|||||||||||
|household|||||||||||
|head|||||||||||
|< 15|0.0|‐|‐|‐|‐|‐|‐|‐|‐|‐|
|15‐29|11.0|42.5|26.5|22.4|33.0|32.0|28.2|34.3|31.7|27.5|
|30‐45|16.1|37.4|36.2|24.4|32.3|37.6|27.0|32.5|38.2|27.2|
|45‐60|21.3|40.8|36.9|22.4|42.3|37.1|21.9|43.8|37.3|21.4|
|60+|19.1|38.9|35.7|24.9|44.9|33.4|21.6|46.3|34.3|21.4|

Table A6: Estimated PMT Models by sector

||Urb<br>|an|Rur<br>|al|Esta<br>|te|
|---|---|---|---|---|---|---|
|Variables|Coeff.|S.E.|Coeff.|S.E.|Coeff.|S.E.|
|_HH head characteristics_|||||||
|Female Head|0.02|(0.02)|0.01|(0.01)|‐0.07*|(0.04)|
|Married|‐0.10|(0.07)|0.05|(0.03)|0.02|(0.14)|
|Widowed|‐0.09|(0.07)|0.04|(0.03)|0.08|(0.14)|
|Divorced|‐0.14|(0.12)|0.03|(0.06)|0.22|(0.35)|
|Separated|‐0.06|(0.08)|‐0.02|(0.04)|0.04|(0.16)|
|Age|‐0.00**|(0.00)|‐0.00***|(0.00)|‐0.00**|(0.00)|
|Employment:|||||||
|Govt/semi‐govt employee|0.07***|(0.03)|0.11***|(0.01)|0.04|(0.05)|
|Private employee|0.06***|(0.02)|‐0.01|(0.01)|‐0.01|(0.03)|
|Employer|0.21***|(0.04)|0.20***|(0.02)|0.00|(.)|
|_HH demographics_|||||||
|Household size (relative to 6 or more): 1 member|0.75***|(0.08)|0.82***|(0.04)|0.83***|(0.12)|
|2 members|0.53***|(0.04)|0.59***|(0.02)|0.53***|(0.06)|
|3 members|0.39***|(0.03)|0.41***|(0.01)|0.43***|(0.05)|
|4 members|0.24***|(0.02)|0.28***|(0.01)|0.30***|(0.04)|
|5 members|0.15***|(0.02)|0.16***|(0.01)|0.18***|(0.03)|
|Highest education level of members not currently e|nrolled:||||||
|Grade 10|‐0.03|(0.06)|0.11***|(0.02)|0.07*|(0.04)|
|O/L|0.06|(0.06)|0.15***|(0.02)|0.13**|(0.05)|

29
governments. There is less progress in creating regional approaches to education and in systematically addressing regional health issues.

**_Making agriculture more productive_** . Sustained growth that reduces rural poverty will require that more countries achieve 5 percent annual growth in agricultural value added. Although growth in agricultural value added has been strong since 2000, averaging 4.6 percent in 2004, too little of it has come from higher productivity or yields.<sup>37</sup> Although land productivity is increasing in 38 of 46 countries, only 6 have a rate of increase of 5 percent or more.<sup>38</sup> Labor productivity is increasing in 29 countries, with 10 achieving increases of 3 percent a year or higher.<sup>39</sup>

Productivity growth will require an expansion of irrigated areas, as well as better performance of rain-fed agriculture. But less than 4 percent of cultivated land is irrigated. Because of the long lead time before investments are completed and operational, this proportion changed little in recent years. Improvements in management of soil fertility have been slow, as has been the adoption of better seeds. Spending for agricultural research and technology remains low, although it is starting to increase along with overall spending on agricultural programs in the region (Africa Partnership Forum 2006a). On a positive note, there has been an increase in the use of water management techniques (water harvesting, reduced tillage).

**_Using natural resource rents well_** Resource-based rents are widespread and growing because of new discoveries and favorable prices. During the 1990s, 65 percent of all FDI was concentrated in oil, gas, and mining. Between 2000 and 2010, $200 billion in oil revenue will accrue to African governments. The 2004 oil windfall alone on average resulted in a 26 percent increase in government revenue in oil-exporting countries.

However, in Africa, mineral-dependent countries tend to do worse in social indicators than non-mineral countries at the same income level, including having higher poverty rates, greater income inequality, less spending on health care, higher prevalence of child malnutrition, and lower literacy and school enrollments. But mineral-exporting economies can achieve shared growth. Indonesia, and Malaysia have all used natural resource wealth to provide a basis for a more diversified economy in which the poor have been able to participate and contribute to the process of growth (figure 27).

> 37 Growth in Angola, Burkina Faso, Cape Verde, the Republic of Congo, Eritrea, Ethiopia, Ghana, Mauritius, Mozambique, Nigeria, and Tanzania has been through an expansion of cropped area. 38 This rate is the five-year moving average based on 2001–05.

> 39 This rate is the five-year moving average based on 2000–04.

45
inward FDI”. Henisz (2000) demonstrates that political and contractual hazards may go hand in hand since transactions that originally should have been effectively organized through contractual agreements are more likely to appeal to arbitration or courts. Equally detrimental to inward FDI growth are the financial instability, corruption and social disorder of the host country. Corruption is believed to induce uncertainty in the business environment and discourages FDI inflows. Lack of legality indicates weak protection in case of need and therefore further aggravates the investment environment. We adopt the International Country Risk Guide (ICRG) group index of political stability risk (PSR) as a proxy for the institutional and political quality of the host country. The index incorporates 12 risk categories (bureaucratic quality, corruption, democratic accountability, ethnic tensions, external conflict, governmental stability, internal conflict, investment profile, law and order, military in politics, religious tension, and socioeconomic conditions) and is calculated as a weighted sum out of 100 (a higher score corresponds to less risk and stronger stability).

Tax burden is another factor that concerns potential foreign investors when choosing a location for FDI. As one would expect, a high income tax rate for business could imply a narrower profit margin, which may deter FDI. It is supported in Wei (2000) that a significant negative relationship exists between the corporate income tax rate and FDI attraction. Also argued in the literature is the host country’s infrastructure quality.

According to the survey from World Investment Directory (2003), large FDI projects are partially driven by the infrastructural quality of the host economy. World Economic Forum’s Chief Economist, Augusto Lopez-Claros (2005), has stated “an inadequate supply of infrastructure is rated by business as the biggest obstacle to operation in foreign 16
about 1-1.5 hours (per person) to administer. The household questionnaire included modules on (i) basic demographic characteristics, (ii) economic participation, (iii) housing conditions, (iv) asset ownership, and (v) participation in the 4P program. The individual questionnaire includes modules on (i) decision making, (ii) time use, (iii) self-efficacy, (iv) well-being, (v) social capital, (vi) attitudes towards gender norms, violence against women and violence against children, (vii) pregnancy history, (viii) exposure to genderbased violence, and (ix) exposure to transactional sex. The last three modules are only administered to the women in the sample. The sub-sample of female respondents was randomly split in two groups for modules (viii) and (ix): half of the sample was interviewed using Face-to-Face Direct Questioning (FTFDQ) technique, another half responded to the same question via Audio Computer Assisted Self-Interview mode (ACASI). This was done to account for any social-desirability biases that may creep in while answering questions on socially sensitive topics like GBV and transactional sex in-person or face-to-face. Alternatively, the ACASI method, where respondents provide answers through a phone/tablet and not to the enumerator directly, helps in minimizing human interaction; thereby, increasing the respondents’ perception of privacy protection and inducing self-disclosure. However, we do not find differences in the prevalence rate of violence as reported via FTFDQ versus ACASI in our sample; therefore, we pool the responses in the current analysis, but control for the type of the interview conducted.<sup>10</sup> While collecting GBV data, we closely followed data collection protocols prescribed by the World Health Organization (WHO 2007), such as working with same-sex enumerators, providing specialized training to enumerators, developing protocol for potential interruptions, and providing information on support/referral services for GBV survivors. We received research ethics review approval from Health Media Labs IRB.

Further, we complemented the in-person data collection with a phone survey in April-May 2020 to collect appropriate data on receipt of the 4P benefits: in our initial round of data collection the question about receipt of 4P benefits was addressed to the respondent of the household questionnaire (i.e., the most knowledgeable person in the household), which resulted in high number of missing values and inconsistencies. To bridge this data gap and resolve data irregularities, the phone survey, which included a limited set of questions, was administered to the _target_ respondents.

4.1 Primary Outcome of Interest Violence against women is the primary outcome of interest. We record data on 12 types of violence, following 2 types of classification. The first classification categorizes violence by form of violence, namely

> 10 Results available upon request.

9
dropping by about 73, 37 and 23 percent respectively.<sup>45</sup> We find that Indian merchandise export growth has decelerated both in value and volume terms.<sup>46</sup> While the deceleration in Indian export values is significantly sharper than the volume of exports, volume growth turned negative in 2009, and again in 2016. The decline in India’s trade volume is larger in comparison to the global decline in trade volume, resulting in India’s reduced share in global exports. This is indicative of the role of India-specific factors in determining the export slowdown, or deteriorating external conditions specifically for India’s export basket.

**Figure 25: Export Growth: India and Global, in Value and Volumes**

25 A: Value v/s Volume for World - Growth 25 B: India’s Share in World Exports <!-- Start of picture text --> 2<br>Value Volume<br>30 1.8<br>Value<br>1.6<br>20<br>1.4 Volume<br>10 1.2<br>1<br>0<br>0.8<br>-10 0.6<br>0.4<br>-20<br>0.2<br>0<br>-30<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016<br>2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016<br><!-- End of picture text --> Note: Data is from WDI indicators on export values and from UNCTAD on export volumes. Years are calendar years. The global volume index is calculated as the weighted average of countries’ volume indices with weights equivalent to countries’ (value-based) share in global merchandise exports.

- India’s export basket is broad based. The share of services exports in 2016 was approximately 36 percent of total exports, core merchandise exports (i.e. non-oil non-gold exports) accounted for about half of all exports, oil exports accounted for 10 percent, and gold exports accounted for 4 percent. Exports slowdown is pervasive across merchandise and services exports.

# **Figure 26: Exports of Goods and Services**

> 45 The figures present the decline from peak prices in June, March and July 2014 for oil, agriculture and metals, respectively, to the trough in January 2016, and are drawn from the World Bank’s Global Economic Monitor database.

> 46 We use trade data measured in current USD and focus on merchandise trade for data availability reasons. For a globally heterogeneous export basket, using national deflators (either from the US or India) is unlikely to yield credible estimates of constant export values. Hence, we rely on volume indices to decompose trade into volumes and prices.

43
55

- Obermayer, Judith H. "Examining the Role of Government R & D Contract Funding in the Early History of High Technology Companies." In Babson College Entrepreneurship Research Conference Proceedings. Edited by the Center for Entrepreneurial Studies of Babson College. Wellesley, 1981.

- OECD. Venture Capital: Context, Development and Policies., OECD, 1986.

- Perez, Robert C. Inside Venture Capital: Past, Present, and Future. New York: Praeger Publichers, 1986.

- Poindexter, E.A. "The Efficiency of Financial Markets: The Venture Capital Case." Unpublished doctoral dissertation, New York University. Naw York, 1976.

- Poterba, James M. "Venture Capital and Capital Gains Taxation." Massachusetts Institute of Tech:noloav Department of Economics Working Paper No. 508. November 1988.

- Poterba, James M. "Capital Gains Tax Policy Toward Entrepreneurship." National Tax Journal, Volume XLII, No. 3, September 1989.

- Pratt, Stanley. E., ed. Guide to Venture Capital Sources. Wellesley: Capital Publishing Corporation, 1981 and 1986.

- Premus, Robert. Venture Capital and Innovation. Study prepared for the Joint Committee of the U.S. Congress. Washington: U.S. Government Printing Office. 1984.

- Sandberg, William R. and Hofer, Charles W. "A Strategic Management Perspective on the Determinants of New Venture Success." In Babson Collece Entrepreneurship Research Conference Proceedings. Edited by the Center for Entrepreneurial Studies of Babson College. Wellesley, 1982.

- Saunders, A.; and Lim, J. "The Underpricing of Initial Public Offerings (IPOs) in Singapore: Public Policy Issues and Possible Solutions." ASEAN Economic Research Unit. Research Notes and Discussions Paper No. 66, 1989.

- Silver, A. David. Venture Ca2ital: The Complete Guide for Investors New York: Wiley & Sons, Inc, 1985.

- Sotelo Navalpotro, Justo. "El Capital Riesgo (Venture capital) como Instrumento de Financiacion en Espana." Informacion Economica Espanola. Revista de Economia. Ministerio de Economia y Hacienda. Num 659, Julio 1988.

- Stiglitz, Joseph E. and Weiss, Andrew. "Credit Rationing in Markets with Imperfect Information." American Economic Review. 71, 1981.

- Testa, Richard J. "The Legal Process of Venture Capital Investment." In Stanley E. Pratt (Ed.) Guide to Venture Capital Sources. Wellesley: Capital Publishing Corporation, 1986.
Policy Research Working Paper 9852

# **Abstract**

Whereas the tax compliance literature emphasizes verifying tax liability amounts, this paper highlights two other tax capacity dimensions of particular relevance in lower-income countries: identifying taxable entities and enforcing collection of known liabilities. Leveraging a newly-digitized property database, a randomized experiment in Liberia finds that including identifying information (owners’ names and property photographs) in tax notices more than quadruples the payment rate, from 2 percent to almost 10 percent, when the notice also details noncompliance penalties. A second experiment finds that signaling greater collection probability to delinquent taxpayers _further_ increases compliance. These results highlight the importance of targeted investments in tax capacity.

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 author may be contacted at ookunogbe@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
optimal allocation model, with a brief review of the methodology developed in Buys, et al. (2003). Assuming continuity with the past scale and thematic composition of lending, Section 8 uses the model to estimate lending and AAA opportunities by country and environmental theme for the period 2004-2009.<sup>2</sup> Section 9 interprets our findings using two country cases, and Section 10 provides a summary and conclusions.

# **2.  Environmental Indicators**

Building on prior work by Buys, et al. (2003), we construct country indicators for six environmental problems: greenhouse gas emissions; health damage from air and water pollution; the threat of natural resource degradation on fragile lands; threats to biodiversity; problems related to water resources; and problems with environmental policies and institutions. All of our indices reflect recent research on the cross-country incidence of environmental problems.

For global greenhouse gas emissions, our indicator is total metric tons of carbonequivalent in 2000 from fuel combustion (CO2), land-use change (CO2) and other sources (methane (CH4), nitrous oxide (N20), hydrofluorocarbons (HFC’s), perfluorocarbons (PFCs), and sulfur hexafluoride (SF6)). We draw our emissions estimates from the World Resources Institute’s Climate Analysis and Indicators database.<sup>3</sup> Our estimate of pollution damage is total DALY (disability-adjusted life year) losses from air and water pollution. We draw our DALY estimates from recent research

> 2  The supporting database and an accompanying atlas can be downloaded from the Environment Department (lnweb18.worldbank.org/ESSD/envext.nsf/41ByDocName/Environment), or from the Development Research Group (www.worldbank.org/nipr). 3  The World Resources Institute’s Climate Analysis and Indicators database is available online at http://cait.wri.org.

4
14 21. Infrastructure proceeds grew over time as more countries started to tackle these sectors in the late 1990s. They are overwhelmingly concentrated in telecommunications and power, accounting for 50 and 36 percent respectively of total infrastructure proceeds between 1990 and 2003. Nearly 65 developing countries (or 42 percent of developing countries in the PPI database) had generated telecoms proceeds through private participation in one form or another, concentrated largely in Latin America, Europe/Central Asia, East Asia, and more recently the Middle East and North Africa. In electricity and natural gas, 72 countries (or 46 percent of all developing countries) had some form of private participation in existing utilities, with activity concentrated largely in Latin America, Europe/Central Asia, and East Asia. The share of telecom proceeds remained more or less steady over time, while electricity grew slightly mainly due to recent sales or concessions in Europe/Central Asia. Proceeds in the transport sector declined from 14 percent in the 1990s (mainly Latin America) to 8 percent between 2000 and 2003 (in China and Malaysia). And while activity in the water sector grew from 1 to 3 percent, it still continues to account for a very small share of total infrastructure proceeds, again mostly concentrated in Latin America (management contracts, which are excluded from this paper, have been a frequent method of bringing in private sector participation in the water sector).

22. On the whole, privatization of existing infrastructure assets raised $204 billion since 1990, compared to new green field investments which were significantly higher at $350 billion (Figure 9).

**Figure 9:  Total PPI and infrastructure privatization, 1990-2003**

<!-- Start of picture text --> Total PPI and Infrastructure Privatization, 1990-2003<br>Billions of USD<br>120<br>100<br>80<br>60<br>40<br>20<br>0<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003<br>Total PPI Infrastructure Privatization Difference<br><!-- End of picture text --> Source: PPI and World Bank databases

# _Financial sector_

23. The share of financial sector proceeds has almost doubled since the 1990s, increasing from 12 percent to 19 percent of total proceeds in recent years. Banking and other financial service firms represented 88 percent of financial sector revenues, with insurance firms accounting for 10 percent and real estate the rest. The increase in financial sector revenues came mainly from banking transactions in Eastern and Central Europe, driven by EU accession
|P006059|1997|2003|6/9/2003|13 Closed|Argentina|
|---|---|---|---|---|---|
|P006059|1997|2004|6/30/2004|16 Closed|Argentina|
|P006059|1997|2005|4/26/2005|18 Closed|Argentina|
|P006059|1997|2006|4/26/2005|18 Closed|Argentina|
|P006059|1997|2020|3/13/2020|19 Closed|Argentina|
|P006060|1995|1995||3 Closed|Argentina|
|P006060|1995|1996||6 Closed|Argentina|
|P006060|1995|1997||9 Closed|Argentina|
|P006060|1995|1998||10 Closed|Argentina|
|P006060|1995|1999||12 Closed|Argentina|
|P006060|1995|2000|5/18/2000|14 Closed|Argentina|
|P006060|1995|2001|6/27/2001|17 Closed|Argentina|
|P006060|1995|2002|5/23/2002|19 Closed|Argentina|
|P006060|1995|2003|5/28/2003|21 Closed|Argentina|
|P006060|1995|2004|6/2/2004|23 Closed|Argentina|
|P006060|1995|2005|5/9/2005|25 Closed|Argentina|
|P006060|1995|2006|12/6/2005|26 Closed|Argentina|
|P006062|1994|1994||1 Closed|Argentina|
|P006062|1994|1995||2 Closed|Argentina|
|P006062|1994|1996||4 Closed|Argentina|
|P006062|1994|1997||8 Closed|Argentina|
|P006062|1994|1998||10 Closed|Argentina|
|P006062|1994|1999||12 Closed|Argentina|
|P006062|1994|2000|6/29/1999|12 Closed|Argentina|
|P006062|1994|2019|5/7/2019|13 Closed|Argentina|
|P006065|1981|1985||0 Closed|Bahamas,The|
|P006065|1981|1986||0 Closed|Bahamas,The|
|P006065|1981|1987||0 Closed|Bahamas,The|
|P006065|1981|1988||0 Closed|Bahamas,The|
|P006066|1982|1985||0 Closed|Bahamas,The|
|P006066|1982|1986||0 Closed|Bahamas,The|
|P006066|1982|1987||0 Closed|Bahamas,The|
|P006066|1982|1988||0 Closed|Bahamas,The|
|P006067|1987|1987||0 Closed|Bahamas,The|
|P006067|1987|1988||0 Closed|Bahamas,The|
|P006067|1987|1989||5 Closed|Bahamas,The|
|P006067|1987|1990||6 Closed|Bahamas,The|
|P006067|1987|1991||8 Closed|Bahamas,The|
|P006067|1987|1992||10 Closed|Bahamas,The|
|P006067|1987|1993||12 Closed|Bahamas,The|
|P006067|1987|1994||13 Closed|Bahamas,The|
|P006067|1987|1995||14 Closed|Bahamas,The|
|P006070|1989|1989||3 Closed|Bahamas,The|
|P006070|1989|1990||6 Closed|Bahamas,The|
|P006070|1989|1991||8 Closed|Bahamas,The|
|P006070|1989|1992||10 Closed|Bahamas,The|
|P006070|1989|1993||11 Closed|Bahamas,The|
**Table A.7: Evolution of Mobility by Birth Cohorts**

||**CHI**|**NA **|**IND **|**IA**|**INDO**|**NESIA**|
|---|---|---|---|---|---|---|
|Mobilit CEF|||||||
|y<br>Specification|Linear|Quadratic|Linear|Quadratic|Linear|Quadratic|
|OutcomeVar|Res. Sq.|Res. Sq.|Res. Sq.|Res. Sq.|Res. Sq.|Res. Sq.|
|1980-1989|-0.766***<br>(0.101)|-0.772***<br>(0.106)|-0.644***<br>(0.034)|-0.644***<br>(0.033)|-0.478***<br>(0.050)|-0.478***<br>(0.050)|
|1970-1979|-0.445***<br>(0.055)|-0.452***<br>(0.056)|-0.294***<br>(0.041)|-0.291***<br>(0.041)|-0.299***<br>(0.065)|-0.297***<br>(0.065)|
|1960-1969|-0.435***|-0.441***|0.053|0.061|-0.328**|-0.324**|
||(0.046)|(0.047)|(0.056)|(0.054)|(0.139)|(0.139)|
|1950-1959|-0.275***|-0.281***|0.028|0.022|0.462**|0.470**|
||(0.070)|(0.068)|(0.077)|(0.073)|(0.228)|(0.228)|

_Notes_ : (1) The data are from children who were born between 1950 and 1989 using the CFPS 2010 round for China, the IHDS 2012 round for India, and the IFLS 2014 round for Indonesia.

43