rafmacalaba/datause-encoder-data huggingface dataset:
externality inherent to green innovation, it may be that demand-side policies are also needed to create sufficient incentives to make the leap to greener products.

**Figure 7: Export of Green + Close-to-Green Goods and Services** (as % of all exports)

<!-- Start of picture text --> 20%<br>16%<br>12%<br>8%<br>4% 2000<br>0%<br>2005<br>2010<br><!-- End of picture text --> Source: COMTRADE and Proximity Matrix based on 6-digit COMTRADE

# **2.5 Little BoP green innovation to-date**

BoP (Base-of-Pyramid) innovations are defined as innovations to meet the needs of poor consumers. They include formal innovations for the poor, namely innovations by global and local private companies and public institutions, whether fully privately provided, supported by public subsidies, or produced through public-private partnerships (such as medicines for neglected diseases and seeds for ̳neglected‘ soil types and climates). They also include informal innovations by local grassroots inventors largely through improvisation and experimentation.<sup>14</sup> Often facilitated by co-creation with poor consumers themselves, the innovations typically seek to better meet the needs of poor households at dramatically lower costs per unit, aided by significant scale-up in volumes – hence they seek ―to create more (products) with less (resources) for more (people)‖.<sup>15</sup> An exhaustive survey of green BoP innovations indicates that very few BoP (and related low-tech) green innovations have been sufficiently scaled-up to-date. Whether there may be a need for more focused policy efforts in this area requires a better understanding of the constraints, both on the supply and demand side, impeding scaled-up commercialization, and the benefit-cost of appropriate policies and their implementation to improve market outcomes. Box 1 highlights a few representative green BoP innovations that have been documented.

## **<u>Box 1. Illustrative BoP innovations</u>**

**_Indoor non-electric cooking stoves:_** Nearly 3 billion people who don‘t have easy and/or affordable access to some form of clean, modern energy use indoor cooking stoves burning biomass crop waste, wood, coal or dung. Smoke from the stoves produces black carbon, an important GHG, in addition to indoor air pollution. Patent counts based on inventor country highlight the importance of developing countries as developers of such technologies, with China

> 14 See Utz and Dahlman (2007) for examples of BoP innovations across technologies in India.

> 15 See, among others, Prahalad and Mashelkar (2010).

10
# Table A4 - Summary of Rates of Return to Education

|||ROR<br>|ROR<br>|
|---|---|---|---|
|Education Level|PrecedingLevel|1998|2006|
|Primary||2.72%|1.83%|
|Preparatory|Primary|2.19%|3.21%|
|General secondary|Preparatory|19.40%|4.50%|
|3-year vocational edu(agriculture)|Preparatory|4.64%|5.37%|
|3-year vocational edu(industry)|Preparatory|9.49%|4.22%|
|3-year vocational edu(commerce & others)|Preparatory|7.32%|5.58%|
|5-year vocational edu|Preparatory|6.08%|8.16%|
||General secondary|0.57%|6.08%|
|Post secondar|3-year vocational edu(agriculture)|14.76%|5.20%|
|y|3-year vocational edu(industry)|9.67%|6.36%|
||3-year vocational edu(commerce & others)|11.89%|4.99%|
|4-year universityedu|General secondary|2.99%|8.36%|
|5-year universityedu|General secondary|5.40%|11.03%|
|Postgraduate|4-year universityedu|17.27%|13.43%|

# **_Different Paths of Education by Gender_**

In 2006, for males who were to enter the labor market after secondary education, 5-year vocational education was the most rewarding. Among males who decided to join the labor market after postsecondary education, those who graduated from 3-year vocational education specializing in commerce earned the highest returns. However, for general secondary graduates, 5-year university education was the most rewarding (see Figure A1).

For females, joining the labor market after attaining a post-secondary certificate after general secondary education was the most rewarding educational path in 2006. However, surprisingly, primary level female graduates earned returns that were the second highest, even higher than those with postgraduate studies. Among females who joined the labor market after secondary education, graduates of 3-year vocational education specializing in commerce earned the highest returns, followed by those with 5-year vocational education (see FigureA2).

33
- Semba, R. D., S. de Pee, et al. (2008). "Effect of parental formal education on risk of child stunting in Indonesia and Bangladesh: a cross-sectional study." The Lancet **371** (9609): 322-328.

- Sharp, P. T. and P. Harvey (1980). "Malaria and growth stunting in young children of the highlands of Papua New Guinea." Papua and New Guinea medical journal **23** (3): 132140.

Stuart Gillespie and Lawrence Haddad (2001). Attacking the double burden of malnutrition in

- <u>Asia and the Pacific., Asian Development Bank. Manila, Philippines and the International</u> Food Policy Research Institute, Washington DC, United States.

- Subramanyam, M., I. Kawachi, et al. (2011). "Is Economic Growth Associated with Reduction in Child Undernutrition in India?" PLos Med **8** (3): :e1000424.

- Temple, V., P. Mapira, et al. (2004). "Iodine deficiency in Papua New Guinea (sub-clinical iodine deficiency and salt iodization in the highlands of Papua New Guinea. ." J Public <u>Health</u> **27** (1): 45-48.

- The World Bank Group (2012). Ethiopia Community-Based Nutrition Program Helps Reduce Child Malnutrition.

- UNICEF (1990). "The conceptual framework of malnutrition. ."

- Van de Poel, E., A. R. Hosseinpoor, et al. (2007). "Malnutrition and the disproportional burden on the poor: the case of Ghana." International Journal for Equity in Health **6** (1): 21.

- Van de Poel, E., A. R. Hosseinpoor, et al. (2008). "Socioeconomic inequality in malnutrition in developing countries." Bulletin of the World Health Organization **84** (4): 282-291.

- Vollmer, S., K. Harttgen, et al. (2014). "Association between economic growth and early childhood undernutrition: evidence from 121 Demographic and Health Surveys from 36 low-income and middle-income countries." Lancet Glob Health **2** (4 ): e225-234.

- Wand, H., N. Lote, et al. (2012). "Investigating the spatial variations of high prevalences of severe malnutrition among children in Papua New Guinea: results from geoadditive models." BMC Research Notes **<u>5</u>** (288).

- Waterlow, J. (1972). "Classifi cation and defi nition of protein-calorie malnutrition." BMJ **3** : 566–569.

- WHO Multicentre Growth Reference Study Group (2006). WHO Child Growth Standards: Length/height-for-age, weight-for-age, weight-for-length, weight-for-height and body mass index-for-age: Methods and development, Geneva: World Health Organization.

15
the null correlation (Figure 17 in the Appendix).<sup>14</sup> In contrast, we find that these deviations are positively correlated with union density rate, which is obtained from the Database on Institutional Characteristics of Trade Unions, Wage Setting, State Intervention and Social Pacts and is available for 51 countries (see Figure 9).

_Figure 8: Actual minus predicted (general government employment as share of labor force), average over the available years by country_ <!-- Start of picture text --> a) East Asia Pacific b) Latin America and the Caribbean<br><!-- End of picture text --> <!-- Start of picture text --> THA VEN<br>URY<br>SGP TTO<br>SUR<br>PHL SLV<br>PRY<br>NZL PER<br>PAN<br>MYS NIC<br>MEX<br>MNG HND<br>GTM<br>ECU<br>KOR DOM<br>DMA<br>JPN CRI<br>COL<br>IDN CHL<br>BRB<br>HKG BRA<br>BOL<br>FJI BLZ<br>BHS<br>AUS ARG<br>-15 -10 -5 0 5 -10 0 10 20 30 40<br>c) Middle East and North Africa d) North America<br>OMN USA<br>MLT<br>ISR<br>IRN<br>EGY<br>BHR<br>ARE CAN<br>-5 0 5 10 15 0 2 4 6 8<br>e) South Asia f) Sub-Saharan Africa<br><!-- End of picture text -->

> 14 Using the political ideology variable in the model would imply losing a significant part of the sample due to data availability. Hence, we do the correlation analysis between the deviations estimated with the entire sample and the available data on political ideology.

20
57

# **OTHER BIBLIOGtAPHY:**

- Arakawa, Hideo. "The Promotion of Venture Businesses and Venture Capital Industry by Venture Enterprise Center in Japan." Paper presented at the Policy Roundtable on Innovations in Foreign Finance at the Economic Development Institute of the World Bank, Washington, D.C., 1989.

- Araki, A.K. "Legal Requirements and Taxation of Venture Capital in Japan." Tax Planning International Review 12, November 1985.

- Ashton, Raymond. "Cost of Capital & Raising Venture Capital in a Tax Efficient Manner." British Tax Review, 1986.

- The Asian Venture Cacital Journal (Hong Kong), November 1989.

- Attardi, Gabriella and Paterno, Maurizio. "Il 'Venture Capital' in Italia." Rivista di Politica Economica (Italy) 74, February 1984.

- Barry, Christopher B.; Muscarella, C.J.; Peavy, J. W. III; and Vetsuypens, M. R. "Venture Capital and Initial Public Offerings." Texas Christian University, 1988.

- Bhattacharyya, Sudip. "Venture Capital Financing." Economic and Political Weekly. November 25, 1989.

- Bossuyt, Reinhild M. "European Continent and Venture Capitalism: Requirements and Government Incentives." Revue de la Banaue 50 (Belgium).

- Bruno, Albert V., and Tyebjee, Tyzoon T. "The One that Got Away: A Study of Ventures Rejected by Venture Capitalists." In Babson Colleoe Entrepreneurship Research Conference Proceedings. Edited by the Center for Entrepreneurial Studies of Babson College. Wellesley, 1982.

- Bruno, Albert V., and Tyebjee, Tyzoon T. "The Entrepreneur's Search for Capital." In Babson College Entrepreneurshi, Research Conference Proceedings. Edited by the Center for Entrepreneurial Studies of Babson College. Wellesley, 1984.

- Bruno, Albert V., and Tyebjee, Tyzoon T. "The Destinies of Rejected Venture Capital Deals." Sloan Manaaement Review. Winter 1986.

- Bullock, Matthew. Academic Enterprise. Industrial Inovation and the Development of High Technologv Financing in the United States. London: Brand Bros., 1983.

- Byers, Brook H. "Relationship between Venture Capitalist and Entrepreneur." In Stanley E. Pratt (Ed.) Guide to Venture Capital Sources. Wellesley: Capital Publishing Corporation, 1986.

- Bygrave, W.D. and Timmons, J.A. "Networking Among Venture Capital Firms." Unpublished paper, Babson College, Wellesley, 1986.
the degree of aid-dependence of the economy, in ways that are consistent with the implications of some basic theory.

# **7.  Conclusions**

In this paper, I have used a novel dataset of loan-level transactions covering lending by official creditors to developing country governments to estimate government spending multipliers. My identification strategy exploits the substantial lags that occur between loan commitment and the eventual full disbursement of the loan, a process which typically takes several years. The key identifying assumption is that loan approvals, and the decision to embark on the associated spending plans, do not anticipate future shocks to growth. Given this assumption, fluctuations in disbursements that are attributable to fluctuations in past loan approval decisions are plausibly exogenous to contemporaneous shocks, and can be used as an instrument for fluctuations in government spending. Deploying this strategy in a large sample of developing countries, I find reasonably precise estimates of the government spending multiplier that are in the vicinity of 0.4. These results survive a range of robustness checks designed to address concerns about data quality and potential violations of the exclusion restriction. I find some evidence of heterogeneity in estimated multipliers that is consistent with the implications of basic theories. However, these differences typically are not statistically significant.

To put these findings in context, it is useful to compare them with estimates of the government spending multiplier in the empirical literature, which has overwhelmingly been based on evidence from developed countries, most notably the United States. For the United States, Hall (2009) suggests that estimates of the federal government spending multiplier are between 0.5 and 1, while Ramey (2011b) suggests a somewhat higher range from 0.8 to 1.5. Moreover, many of the studies of subnational spending multipliers cited in the introduction are even higher, ranging from 1 to 2. Policy discussions in many countries are also often premised on the assumption that the spending multiplier is substantially above one. The smaller, but nevertheless reasonably-precisely estimated, multipliers estimated in this paper for developing countries stand in sharp contrast to this evidence, and if anything, are more consistent with the modest short-run effects of government spending in emerging markets uncovered by Ilzetzki, Mendoza and Vegh (2010) using VAR-based techniques.

The small multipliers estimated in this paper suggest a rather limited output effect of countercyclical responses of government spending in response to economic downturns in developing 28
<!-- Start of picture text --> 280<br>260<br>240<br>220<br>200<br>180<br>160<br>140<br>120 I The impact of the crisi<br>100<br>SSSSSSasaraggagssgasseeggesaa<br>—After crisis <—Before crisis (2020 strategy)<br><!-- End of picture text --> <!-- Start of picture text --> 18.0<br>sia<br>140<br>10<br>10.0<br>Ei)<br>60<br>”<br>20<br>oo<br>2008-2009 20002011. 2012«-NOLD. «2014 «205 «2016 «OL? «2018 «2019-2020<br>—Belorecrisis baseing = ==After crisis baseline (passive scenaeio)<br><!-- End of picture text --> <!-- Start of picture text --> 18.0<br>16.0<br>14.0<br>12.0<br>10.0<br>a0<br>6.0<br>“0 SEE —————<br>2.0<br>0.0<br>2008 «2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 #2019 2020<br>Alter crisis baseline (witha temporary fiscal adjustment 2010-2012)<br>Alterms aiden crisis baselinenecicuannda(witha permanent fiscal adjustment 2010-2015)<br><!-- End of picture text -->
Policy Research Working Paper 8220

# **Abstract**

This paper proposes a non-parametric adaptation of a recently developed parametric technique to produce point estimates of intra-generational economic mobility in the absence of panel data sets that follow individuals over time. The method predicts past individual income or consumption using time-invariant observable characteristics, which allows the estimation of mobility into and out of poverty, as well as household-level income or consumption growth, from cross-sectional data. The paper validates this method by sampling repeated cross-sections out of actual panel data sets from three countries in the Latin America region and comparing the technique with mobility from panels. Overall, the method performs well in the three settings; with few exceptions, all estimates fall within the 95 percent confidence intervals of the panel mobility. The quality of the estimates does not depend in general on the sophistication level of the underlying welfare model’s specifications. The results are encouraging even for those specifications that include few time-invariant variables as regressors.

This paper is a product of the Poverty and Equity Global Practice Group. 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://econ.worldbank.org. The author may be contacted at llucchetti@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 difference between the two blocks becomes even clearer in figure 1.6, where the cross-plots between GDP per capita relative to the United States and firms’ perceptions are presented. A preliminary analysis of the cross-plots suggests two points: first, that there is an intuitive and negative relation between income level and infrastructure constraints; and, second, that the diversion of the two blocks of countries remains intact, showing now the largest dispersion in the constraint perceptions of figure 1.6 for the lowest per capita income group.

**Figure 1.6 A simple illustration (cross-plots) of the relation between per capita GDP and infrastructure perceptions of severe or very severe obstacles to growth in Africa**

GDP per capita relative to United States <!-- Start of picture text --> A. Telecommunications vs. GDP per capita B. Electricity vs. GDP per capita<br>100 100<br>90 90<br>80 80<br>70 70<br>60 60<br>50 50<br>40 40<br>30 30<br>20 20<br>10 10<br>0 0<br>0 5 10 15 20 25 30 35 40 45 50 0 5 10 15 20 25 30 35 40 45 50<br>GDP per capita (% of US) GDP per capita (% of US)<br>B. Customs clearance vs. GDP per capita D. Transport vs. GDP per capita<br>100 100<br>90 90<br>80 80<br>70 70<br>60 60<br>50 50<br>40 40<br>30 30<br>20 20<br>10 10<br>0 0<br>0 5 10 15 20 25 30 35 40 45 50 0 5 10 15 20 25 30 35 40 45 50<br>GDP per capita (% of US) GDP per capita (% of US)<br>obstacle obstacle<br>% of firms considering telecoms as a severe % of firms considering electricity as a severe<br>obstacle obstacle<br>% of firms considering customs as a severe % of firms considering transport as a severe<br><!-- End of picture text --> _Source_ : Authors’ calculations based on IC data. _Note_ : No data are available on perceptions of transport in Algeria .

The objective of this paper is to assess the impact of the quality of existing infrastructure on the TFP of African firms. This measure is understood to include quality in the provision of the following services: customs clearance, energy, water, sanitation, transportation, telecommunications, and 10
# FIGURE 20: SHARE OF WHOLESALE AND RETAIL TRADE IN EMPLOYMENT: SOVIET REPUBLICS AND OECD

<!-- Start of picture text --> 30%01<br>20%-<br>10%<br>o%L I I I ~~ I ~~I I I I I I<br>7.5 8 8.5 9 9.6 10<br>LOG OF PER CAPITA INCOME<br><!-- End of picture text --> <!-- Start of picture text --> Un<br><!-- End of picture text -->
# Table 69 _(Cont.)_

||Based<br>in D|on Value-A<br>omestic Price|dded<br>s|Based o<br>in|n Value-Add<br>World Prices|ed|
|---|---|---|---|---|---|---|
||1|2|3|4|5|6|
|In (Middle Management)||(0.24B.3)|||(.15E-3<br>0.34E23<br>(027B-3)||
|In (Upper Management)||0.428-3<br>(0288-3)|||_0.451-3_<br>(0.328-3)||
|in (Wage Bill of<br>'remporaiy Workers)<br>InK|0.45E4<br>(0.198-3)<br>0.361*<br>(0.119)|-.9984<br>(0.178-3)<br>0.4S7'<br>(0.096)|-. 41B-4<br>(0.15S-3)<br>0.424'<br>(0.084)|028S-4<br>(0.20E-3)<br>0.491'<br>(0.131)|.023E-3<br>(0.19B-3)<br>0A89<br>(0107)|4.148-3<br> <br>(0.178-3)<br>0A496<br>(O.9S)|
|Time|-0.024<br>(0.043)|-0.091<br>(0.041)|40.063'<br>(0.034)|0.029<br>(0.047)|-0.106!<br>(0.046)|**_-.0A6_**<br>(0.038)|
|R2|0.926|0.910|0.914|0.939|0.924|0.926|
|N|S3|53|53|S3|S3|53|

* - Signifcant at the 5 % atitical test leveL - + Sint at the 10 % statistical tet keveL Our sample in this indust conssts of 13 firms
Table 1. Basic indicators for Sub-Sahara African countries, latest available year

|**Country**|**Population**<br>**1998**<br>**(millions)**|**GDP 1997**<br>**(millions of**<br>**current PPP**<br>**dollars)**|**GDP growth**<br>**1997**<br>**(percent)**|**GDP per capita**<br>**1997**<br>**(current PPP**<br>**dollars)**|
|---|---|---|---|---|
|**_West and CentralAfrica_**|||||
|Benin|6.0|7,377.1|5.6|1,270|
|Burkina Faso|11.3|10,543.1|6.6|1,010|
|Cameroon|14.5|26,406.4|5.1|1,890|
|Central African Republic|3.5|4,546.1|5.1|1,330|
|Chad|6.9|6,918.1|6.5|970|
|Congo, Rep. of|2.9|<br>4,396.7|-1.9|1,620|
|Cote d'Ivoire|15.1|26,133.9|6.0|1,840|
|Equatorial Guinea|0.4|..|76.1||
|<br>Gabon|1.2|8,703.8|4.1|7,550|
|Guinea-Bissau|1.1|..|5.0||
|Mali|10.6|7,637.2|6.7|740|
|Niger|10.0|8,291.6|3.5|850|
|Senegal|9.0|15,229.8|5.2|1,730|
|Togo|4.5|6,463.1|4.7|1,490|
|Gambia, The|1.2|1,730.9|5.4|1,470|
|Ghana|18.5|29,493.9|4.2|1,640|
|Guinea|7.1|<br>13,015.6|4.8|1,880|
|Liberia|3.0||||
|Mauritania|2.5|4,265.2|5.1|1,730|
|Nigeria|121.3|107,959.2|3.9|920|
|Sierra Leone|4.9|1,954.5|..|410|
|**_East and Southern Africa_**|||||
|<br>Angola|11.8|16,704.3|7.6|1,430|
|Botswana|1.5|11,796.2|6.9|7,690|
|Burundi|6.8|4,040.3|0.4|630|
|Congo, Dem. Rep.|48.1|40,881.8|-5.7|880|
|Eritrea|4.0|3,097.0|7.9|820|
|Ethiopia<br>|62.1<br>|30,193.8<br>|5.6<br>|510<br>|
|Kenya|28.7|33,917.5|2.1|1,190|
|<br>Lesotho|2.1|3,751.1|8.0|1,860|
|Madagascar|15.6|13,109.3|3.6|930|
|Malawi|10.5|7,277.7|5.1|710|
|Mauritius|1.2|10,688.9|5.0|9,310|
|Mozambique|190|123241|124|740|
|<br>Namibia|.<br>1.7|,.<br>8,136.9|.<br>1.8|5,010|
|Rwanda|8.1|5,171.8|10.9|660|
|Somalia|10.5||||
|South Africa|39.0|299,577.2|1.7|7,380|
|Sdan|285|433888|46|1560|
|u<br>Tanzania|.<br>32.2|,.<br>18,091.0|.<br>4.1|,<br>580|
|Uganda|20.9|23,622.2|5.4|1,160|
|<br>Zambia|9.7|9,086.6|_3.5_|960|
|Zimbabwe|11.7|26,930.6|3.7|2,350|
|_CFA_**_franc_**_zone_|**96.9**|**132,646.8**|||
|**_West and Central Africa_**|**255.2**|**291,066.0**<br>|||
|**_East and Southern Africa_**|**372.5**|**621,787.2**|||
|<br>**_Sub-Saharan Africa_**|**627.6**|**912,853.3**|||

_Note:_ Countries with fewer than I million inhabitants are not included (except for Equatorial Guinea, a member of the CFA franc zone).

_Source: World Bank 1999b,c._ 4