rafmacalaba/datause-encoder-data huggingface dataset:
Deaton report as follows: “We find no objective evidence that the Bank’s research ‘tail’ is longer than that of other leading institutions” (Bourguignon, 2006, p.3).

What do the data suggest? In Table 3 SSCI-indexed journals have been weighted by their impact factor (their h-index, computed by the SCImago Research Group based on the SCOPUS database).<sup>10</sup> The median quality of the journals in which Bank authors publish is 29 (the h-index of the _World Bank Economic Review_ )—lower than the average of the universities, but higher than the average of the multilateral institutions. However, these are clearly not large differences. If we focus on the 25<sup>th</sup> percentile—as an indication of the “lower tail”—the quality of the journals that Bank authors publish in is equal to or better than nine of the 14 universities. This does not support the Deaton report’s claims about the Bank’s “long tail of undistinguished work,” at least if this is interpreted as a tendency for the Bank’s authors to publish in low-quality journals. It is actually at the other tail where the differences are more pronounced; the 75<sup>th</sup> percentile of the distribution of the h-indices of the journals in which Bank authors publish in is lower than all but two universities. Bank staff hit top journals less frequently than university faculty, but Bank authors appear in bottom-tier journals less frequently.

Table 4 reports publication counts for selected themes on which the Bank has published widely, including poverty, inequality, growth, trade, education and health.<sup>11</sup> The Bank is ahead of most of the top universities and all the agencies in the volume of its papers on these topics, except in the fields of education and health if the publications are not restricted to those in economics (hardly surprising given the Bank does not have a medical school!).

# **How much are the Bank’s publications cited by others?**

Clearly, one of the objectives of the Bank in publishing is to influence development thinking. The classic indicator of influence for academic research is the citation count. It is established practice that research and (serious) policy papers cite other papers or books on which

> 10 An h-index for a journal of 82 (the score for the AER) means that the journal has published 82 articles each of which has been cited at least 82 times (in SCOPUS-indexed journals). The QJE’s h-index is 76, the JPE’s 62, the Journal of Development Economics 36, the WBER 29, the WBRO 25, EDCC 22, and the Journal of African Economies 13. See <u>http://www.scimagojr.com</u> for details. The h-index data for this table are not the latest data which were released only recently. The broad conclusions are unlikely to be much affected.

> 11   Recall that ‘poverty” did not appear in word cloud in Figure 3, which was based on economics, finance and econometrics articles in the SCOPUS database, while the counts in Table 4 for poverty relate to all fields (and are based on SSCI data).

11
**_Energy demand models for policy_** **_<u>formulation</u>_**

As the demand pattern in households vary with income level and geographical location (rural/ urban), better results are obtained by disaggregating the demand by income level and rural/ urban areas. The total demand in that case would be sum of demands by all categories and locations.

Studies using this approach include: a) McAleer (1982) for a study of Northern Ireland to perform forecasting for 30 years and analyse what-if question, b) Farahbakhsk et al (1998) for the residential sector of Canada using an end-use model, c) Tanatvanit et al (2003) who used LEAP for analyzing demand in Thailand including the residential sector, etc.

d) Liao and Cheng (2002) analyzed the space heating and water heating demands by the aged people in the USA.

A number of studies by Lee Schipper and his associates (e.g. Schipper et al (1985), IEA (1997), Haas and Schipper (1998), etc.) use end-use approach but focus mainly on the indicators and the decomposition approach for analyzing the residential energy demand at the international level. While these are not forecasting studies per se, the later studies focusing on the climate change issues incorporate forecasting elements.

Michalik et al (1997) used a structural model based on the bottom-up approach considering consumer characteristics and appliance- stock and usage information at a detailed level for residential energy demand in Australia.

The disaggregated approach to residential demand analysis allows better representation of the specific features of developing countries. Spatial differences in housing stocks, consumption behavior and technological choices are commonly captured in these studies. They also cover traditional energies as well as differences in demand by income class. Moreover, the end-use approach reflects the transition of energy use in the residential sector due to income- and policy-induced effects. Accordingly, this approach appears to better suit the developing country needs.

74
2 a tax (Aronson, Johnson and Lambert 1994, Van de Ven, Creedy and Lambert 2001).<sup>1</sup> The change in the Gini coefficient is decomposed into three parts: a part capturing vertical redistribution (people at different points in the initial income distribution experiencing different proportionate changes in income), horizontal redistribution (initial equals experiencing inequalities in income after the growth), and a residual term capturing reranking between the two distributions. The decomposition shows that a near-zero change in the Gini index could have two quite different explanations: everyone experiencing similar proportionate changes in their income; and inequality-reducing vertical redistribution being offset by inequality-increasing horizontal redistribution and reranking. Knowing only that the Gini has not changed much without knowing which of these two explanations applies is problematic if policymakers are not indifferent between the two.

The decomposition method is applied in the paper to panel data from China and Vietnam. Both have experienced rapid income growth during a rapid transition from planned to market economy, China’s per capita GDP growing by an annual average 8.6% during the 1990s and Vietnam’s by 5.6%.<sup>2</sup> During these transitions, income inequality has increased in both countries (Glewwe 2003, Ravallion and Chen 2004). However, while these increases in income inequality are rightly a source of concern in both countries, it is noteworthy that income inequality has risen more slowly than per capita income in both countries: the Gini coefficient grew by 1.5% per year in China during the 1990s (Ravallion and Chen 2004) and by 1.4% per year in Vietnam between 1993 and 1998 (Glewwe 2003).<sup>3</sup> Furthermore, these annual increases in the Gini coefficient are small by comparison to those recorded in European transition economies: of the six economies

> 1  Loshkin and Ravallion (2004) use a different decomposition to decompose the changes in inequality associated with trade reform in Morocco.

> 2  Growth calculations here and below assume exponential growth.  Data for per capita GDP are at constant prices, are for period 1991-2000, and are taken from the World Bank’s _World Development Indicators_ database.

> 3  China’s Gini is for household per capita income, adjusted for cost-of-living differences between rural and urban areas. Vietnam’s is for household per capita consumption, adjusted for regional price differences.
- Thomas, D., J. Strauss, and M.H. Henriques (1991). How does mother’s education affect child height? _Journal of Human Resources_ 26(2):183–211.

- Tohidi, Nayereh. 2000. “Gender and National Identity in Post-Soviet Azerbaijan”, pp 249-292 in Feride Acar and Ayse Gunes-Ayata (eds), _Gender, Identity and Construction_ , Leiden:Brill.

- United Nations. 2013. _World Population Prospects: The 2012 Revision_ , New York: United Nations Department of Economic and Social Affairs, Population Division

- United Nations. 2014. _World Urbanization Prospects: The 2014 Revision,_ New York: United Nations Department of Economic and Social Affairs, Population Division

- <mark>World Bank, 1996.</mark> _<mark>Armenia: Confronting Poverty Issues</mark>_ <mark>, Washington DC: The World Bank, Europe and Central Asia Region.</mark>

- World Bank. 1997. _Azerbaijan Poverty Assessment_ , Washington DC: The World Bank, Europe and Central Asia Region. (online summary at <u><mark>http://go.worldbank.org/CJSA075UN0)</mark></u>

- World Bank. 2000. _Making Transition Work for Everyone:_ Poverty and Inequality in Europe and Central Asia, Washington DC: The World Bank

- World Bank. 2003. _Public Expenditure Review of Armenia,_ Washington DC: The World Bank, Europe and Central Asia Region.

- World Bank. 2007. _Migration and Remittances: Eastern Europe and the former Soviet Union,_ Washington DC: The World Bank, Europe and Central Asia Region.

- World Bank. 2010. _Azerbaijan Living Conditions Assessment Report_ Washington DC: The World Bank, Europe and Central Asia Region.

- World Bank 2011. _Georgia Poverty Dynamics, 2003 – 2010_ , Washington DC: The World Bank, Europe and Central Asia Region

- World Bank. 2013a. _The Little Data Book on Gender_ (compiled from the World Development Indicators), Washington DC: The World Bank

- World Bank. 2013b. _Republic of Armenia: Accumulation, Competition, and Connectivity_ , Washington DC: The World Bank Europe and Central Asia Region.

- World Bank. 2014a. Armenia: Country Gender Assessment, Washington DC: The World Bank Europe and Central Asia Region.

- World Bank. 2014b. The Republic of Azerbaijan: Country Gender Assessment, Washington DC: The World Bank Europe and Central Asia Region.

- World Bank. 2014c. Georgia: Country Gender Assessment, Washington DC: The World Bank Europe and Central Asia Region (draft).

- World Bank. 2014d. _Country Partnership Strategy for Georgia FY 2014-2017,_ Washington DC: The World Bank Europe and Central Asia Region.

- Yi, Zeng, Tu Ping, Gu Baochang, Xu Yi, Li Bohua, and Li Yongping. 1993. “Causes and Implications of the Recent Increase in the Reported Sex Ratio at Birth in China.” _Population and Development Review_ 19(2): 283-302.

- Yüksel-Kaptanoğlu, Ilknur, Mehmet Ali Eryurt, Ismet Koç and Alanur Çavlin. 2014. Mechanisms behind the Skewed Sex Ratio at Birth in Azerbaijan: Qualitative and Quantitative Analyses, Report prepared for the UNFPA, Baku

15
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# TABLE 4. Correlates of Dyadic Differences: Human Capital and Labor Management

|||Eth<br>|iopia<br>|||Su<br>|dan<br>||
|---|---|---|---|---|---|---|---|---|
||[1]<br>Ratio of<br>nonproduction<br>workers to total<br>employment<br>|[2]<br>Any in-house<br>training of staff<br>last year?<br>|[3]<br>Staff sent to<br>formal training<br>course last<br>year?<br>|[4]<br>First principal<br>component<br>|[5]<br>Ratio of<br>nonproduction<br>workers to total<br>employment<br>|[6]<br>Any in-house<br>training of staff<br>last year?<br>|[7]<br>Staff sent to<br>formal training<br>course last<br>year?<br>|[8]<br> <br>First principal<br>component<br>|
|||_yi_ −_yj_|||_yi_ −_yj_|||_yi_ −_yj_|||_yi_ −_yj_|||_yi_ −_yj_|||_yi_ −_yj_|||_yi_ −_yj_|||_yi_ −_yj_||
|_i_&_j_trade with each other|−0.0218|0.0807|−0.0451|−0.0514|−0.0859|−0.121|0.368|−0.200|
||(0.0334)|(0.107)|(0.133)|(0.220)|(0.0879)|(0.263)|(0.340)|(0.336)|
|_i_&_j_have common supplier|0.0136|0.0439|0.0113|0.0857|−0.000342|0.0313|−0.0195|0.0743|
||(0.0141)|(0.0473)|(0.0413)|(0.0799)|(0.0442)|(0.0842)|(0.0549)|(0.147)|
|_i_&_j_have common client|0.0161|0.0396|0.127**|0.215*|−0.0403|0.0826**|0.0210|0.174|
||(0.0237)|(0.0742)|(0.0627)|(0.124)|(0.0288)|(0.0375)|(0.0713)|(0.142)|
|log distance btw_i_&_j_|−0.00193|−0.0168***|−0.0153***|−0.0251***|0.00320|−0.00803**|−0.000397|−0.00612|
||(0.00159)|(0.00299)|(0.00291)|(0.00824)|(0.00195)|(0.00315)|(0.00434)|(0.00847)|
|_i_&_j_belong to same sector|−0.00244|−0.0173|−0.0304*|−0.0480*|0.00212|−0.0341*|−0.00869|−0.0644*|
||(0.00491)|(0.0123)|(0.0177)|(0.0262)|(0.00623)|(0.0189)|(0.0159)|(0.0374)|
|Abs diff firm age|−0.000149|−0.000207|0.00180|0.00225|−0.000522*|−0.000423|0.000882|0.000211|
||(0.000336)|(0.000720)|(0.00114)|(0.00210)|(0.000299)|(0.000662)|(0.000992)|(0.00183)|
|Abs diff managers’ education|−0.00392*|0.0112|0.00644|0.0328|0.00381|−0.000336|−0.0145***|−0.0148|
||(0.00237)|(0.0184)|(0.0160)|(0.0351)|(0.00339)|(0.00576)|(0.00474)|(0.0131)|
|Abs diff managers’ experience|0.000213|−0.00167**|−0.000966|−0.000834|0.000259|−0.00155***|0.000637|−0.000751|
||(0.000477)|(0.000803)|(0.000838)|(0.00165)|(0.000598)|(0.000501)|(0.00108)|(0.00185)|
|Owners’ gender differ|0.00767|0.0181|−0.0120|−0.00539|0.0298*|0.0446|0.0847|0.204*|
||(0.0111)|(0.0258)|(0.0177)|(0.0403)|(0.0176)|(0.0375)|(0.0542)|(0.114)|
|Abs diff log employment|0.0110**|0.0876***|0.0987***|0.230***|0.0182***|0.0567***|0.0780***|0.207***|
||(0.00490)|(0.0155)|(0.0143)|(0.0309)|(0.00569)|(0.0170)|(0.0205)|(0.0499)|

_Source:_ Authors’ computations based on data described in the text.

_Note_ : The table shows ordinary least squares results. A constant is included in all specifications. The numbers in parentheses are bootstrapped standard errors that are robust with respect to heteroskedasticity and cross-observation correlation in the error terms involving the same firms. Statistical significance at the 10 percent, 5 percent, and 1 percent levels is indicated by *,**, and ***, respectively.

40
in compliance with the principle of comparative advantage. However, rational managers might have an incentive to push also for ine¢ cient exports that do not match with their country’s factor endowment.

A product manager can surely expect some additional perks if the ...rm sells “his” product also on foreign markets. Similarly, export status of a ...rm would be certainly not harmful for the status and bene...ts enjoyed by the ...rm’s top management. The export-driven perquisites for managers can range from travelling abroad and spending time at luxury hotels to gaining a better access to domestic politicians who are eager to create national export champions. Managers might even retain rewards from exporting activities after a switch to another employer. Mion and Opromolla (2011) ...nd a 15% wage premium for managers who have previously worked for an exporting ...rm. Interestingly, they do not ...nd such a premium for export experience in the case of non-managerial employees.

Export subsidies might further skew the incentives towards ine¢ cient exporting. These subsidies could be (and often are) justi...ed by the adverse e¤ects of ...nancial frictions on potential exporters. In the presence of capital market imperfections, even promising ...rms might fail to secure up-front ...nancing necessary for successful expansion into foreign markets. However, looking at the export promotion through the lenses of agency approach highlights the possible costs of government intervention. Export subsidies represent additional funds at managers’ disposal that can worsen the problem of free cash ‡ow.<sup>4</sup> For example, management can spend the government’s funds for broad export promotion like establishing distribution networks or various marketing and public relations activities. Once the ...rm has set up this general export infrastructure, managers can use it to promote also products that match poorly with the factor endowment of the country.

The example of export subsidies shows how combining the idea of comparative advantage with the insights from agency literature allows a more precise inference regarding allocative e¢ ciency than in the standard ...nance-trade literature. We do not ask whether ...nance promotes exports of all credit-constrained or ...nancially vulnerable ...rms. Our focus is rather on the allocative and selective roles of ex-

> 4Blanchard et al. (1994) already showed that additional cash coming from won or settled lawsuits often leads to ine¢ cient investment in accordance with agency models from ...nance literature.

6
# 4 Estimating Environmental Income of the Rural Poor: A Snapshot

## 4.1 Overall income

Table 1 summarizes for all households surveyed in the PEN study our results regarding total household income, its cash portion, and its sector origin: crops, livestock, forest and non-forest environmental incomes, wages and other incomes. Forest environmental incomes include all non-cultivated sources such as, for instance, timber and firewood from natural forests, wild fruits and vegetables from grasslands, as well as non-farmed fish. Forest incomes include all environmental incomes that originate from the forest;<sup>11</sup> non-forest environmental income denominates the residual from all other non-cultivated sources. Finally, agricultural incomes include incomes from all types of livestock and crop cultivation. The reported incomes represent gross incomes minus variable costs, including purchased labor costs. However, capital depreciation and family labor were not deducted (Angelsen et al. 2014).

**Table 1: Annual household incomes in the PEN sites. PPP converted 2005 US$ adult equivalent units (aeu)**

||**Latin**<br>**America &**<br>**Caribbean**|**South**<br>**Asia**|**East**<br>**Asia &**<br>**Pacific**|**Sub-**<br>**Saharan**<br>**Africa**|**PEN**<br>**total**|
|---|---|---|---|---|---|
|Total income [US$ pc/year]|4549|1449|1663|1019|1692|
|Total cash income [US$ pc/year]|3558|919|1224|615|1181|
|Share cash in total income [%]|78|63|74|60|70|
|Crop income [US$ pc/year]|754|372|415|371|434|
|Crop cash income [US$ pc/year]|498|132|221|180|226|
|Share of crop income in total income [%]|17|26|25|36|26|
|Share of crop cash income in crop income [%]|66|35|53|48|52|
|Livestock income [US$ pc/year]|438|214|281|104|197|
|Livestock cash income [US$ pc/year]|355|112|224|67|141|
|Share livestock income in total income [%]|10|15|17|10|12|
|Share cash in livestock income [%]|81|52|80|65|72|
|Forest income [US$ pc/year]|1217|175|311|158|338|
|Forest cash income [US$ pc/year]|711|36|186|82|183|
|Share forest income in total income [%]|27|12|19|16|20|
|Share cash in forest income [%]|58|21|60|52|54|
|Environmental income (non-forest) [US$|174|40|77|131|116|
|pc/year]||||||
|Environmental cash income [US$ pc/year]|40|5|24|42|34|
|Share non-forest environmental income in total|4|3|5|13|7|
|income [%]||||||
|Share cash in non-forest environmental income|23|13|31|32|29|
|[%]||||||
|Wage income [US$ pc/year]|1183|207|243|71|278|
|Share wage income in total income [%]|26|14|15|7|16|
|Household size|6|5|5|7|6|
|Number of households|1140|1114|1348|4376|7978|

> 11 We have abstracted here from the small share of forest income (global average around 5%) that comes from forest plantations, and thus is not of environmental origin.

15
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(Andrabi et al. 2007). In India, almost 20 percent of rural students are enrolled in private schools, and almost 30 percent are in private schools in urban areas (Kingdon 2007).

<u>Table 1: Private Enrollment Share, Selected Countries (%)</u>

|||Primary<br>||S<br>|econdary<br>||
|---|---|---|---|---|---|---|
||1990|2005|_Δ_|1990|2005|_Δ_|
|Benin|3|12|_300_|8|25|_213_|
|Bolivia|10|9|_-14_|26|16|_-39_|
|Brazil|14|10|_-28_|35|12|_-65_|
|Burkina Faso|9|14|_56_|41|39|_-5_|
|Chile|39|51|_31_|49|52|_6_|
|Colombia|15|19|_27_|39|24|_-38_|
|Eritrea|31|8|_-74_|11|6|_-45_|
|Indonesia|18|17|_-6_|49|44|_-10_|
|Jordan|23|30|_30_|6|16|_167_|
|Netherlands|69|69|_0_|83|83|_0_|
|Peru|13|16|_23_|15|22|_47_|
|Senegal|9|12|_33_|24|23|_-3_|
|South Africa|1|2|_100_|2|3|_50_|
|Thailand|10|16|_60_|16|13|_-19_|
|Togo|25|42|_68_|17|28|_65_|
|Tunisia|1|1|_0_|12|5|_-58_|
|United States|10|10|_0_|10|9|_-10_|
|Venezuela|14|14|_0_|26|25|_-4 _|

Source: UNESCO, OECD, EDSTATS CBA also needs to estimate the “social” benefits and costs of the intervention by accounting for externalities, redistribution and correcting for other market failures.

_Accounting for Externalities._ The benefits of education may extend to others beyond the individual student. So, the true benefit from a year of education cannot be captured by the difference in earnings as a result of that year. According to Lucas (1988), for example, a worker's schooling enhances his or her own productivity as well as those of co-workers, thereby giving 14
Nevertheless, we control for many baseline variables in all regression analysis as detailed in Section 3.4, including business age, which shows a slight imbalance across the three groups.

# **4.2. Training Attendance**

Table 2 presents attendance, feedback, and evaluation statistics from the business training class for all businesses, and also separately for those assigned to finance training and marketing training, respectively.

Overall attendance was fairly high, with 82% of those invited to a training actually attending at least 1 out of 10 class modules. There is a slightly higher attendance rate for the marketing group (86%) compared to the finance group (77%), however these differences are not significant among participants who completed at least 6 modules, which was the minimum attendance required to receive a completion certificate. Overall, most participants received the completion certificate (70% of those invited), with the average participant attending 6.5 modules.

The feedback from both finance and marketing trainings was very positive. On a 1-7 scale, the overall satisfaction among attendees was higher than 6, with participants particularly satisfied with the program’s business relevance and value for time and money. In addition, participants were willing to pay for such trainings in the future. Finance attendees were agreeable to a slightly higher amount, $122 USD, compared to $105 USD average among the marketing group. In terms of policy, this simple willingness to pay exercise suggests that extensions of such business training programs on a larger scale need not be subsidized down to zero.

Finally, Table 2 presents evaluation results based on test scores and shows that the trainings were effective in improving the aspects of financial knowledge they were targeting. Specifically, while the pre-test average scores on finance and marketing are no different between the two training groups, the post-training finance test scores are significantly higher among the finance group and likewise the post-training marketing test scores are significantly higher among the marketing group.

20
**_Table 14: Borrowing Behavior_**

|Taken out a loan in the last 4|The loan was from an|
|---|---|
|Borrowing behavior<br>months|informal source|
|(1)|(2)|
|_Treatments_||
|Movie<br>‐0.06|‐0.02|
|(0.039)|(0.070)|
|MFB<br>‐0.07*|0.07|
|(0.040)|(0.070)|
|Movie + MFB<br>‐0.06|‐0.08|
|(0.040)|(0.069)|
|_Gender disaggregated interaction effects (female inter_<br><br>|_action)_<br>|
|Movie<br>‐0.06|‐0.07|
|(0.045)|(0.081)|
|MFB<br>‐0.06|0.05|
|(0.047)|(0.081)|
|Movie + MFB<br>‐0.05|‐0.14*|
|(0.047)|(0.081)|
|_Gender disaggregated interaction effects (male interac_|_tion)_|
|Male<br>0.01|‐0.11|
|(0.067)|(0.121)|
|Male*Movie<br>0.01|0.19|
|(0.089)|(0.166)|
|Male*MFB<br>‐0.03|0.11|
|(0.091)|(0.161)|
|Male*(Movie + MFB)<br>‐0.01|0.21|
|(0.089)|(0.159)|
|p‐values δ1+ γ1≠0<br>0.5|0.47|
|for F‐tests δ2+ γ2≠0<br>0.25|0.27|
|δ3+γ3≠0<br>0.36|0.61|
|_Observations_<br>_1,261_|_410_|
|_R‐squared_<br>_0.06_|_0.11_|
|_Controls_<br>_YES_|_YES_|
|_Restricted Model_<br>_YES_|_YES_|
|_Control Mean:_<br>_0.508_|_0.470_|

Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 38