rafmacalaba/datause-encoder-data huggingface dataset:
Table 3: Estimates of daily Total Energy Expenditure (TEE) and its com- <u>ponents</u> over time

||Po|pulatio|n|Males|15+|Females|15+|Under|age 1|5||
|---|---|---|---|---|---|---|---|---|---|---|---|
|Year|TEE|REE|AL|TEE|AL|TEE|AL|TEE|AL|Population|share|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)||(10)|
|||||||All India|mean|||||
|1983|2136|1179|1.77|2864|1.95|2244|1.93|1499|1.52||0.40|
|1988|2134|1186|1.77|2858|1.94|2234|1.92|1475|1.50||0.39|
|1994|2158|1200|1.77|2855|1.93|2241|1.91|1463|1.49||0.36|
|2000|2155|1210|1.75|2829|1.91|2225|1.89|1477|1.48||0.36|
|2005|2172|1222|1.75|2825|1.90|2215|1.88|1476|1.47||0.34|
|2009|2159|1243|1.72|2753|1.85|2182|1.83|1460|1.44||0.31|
|2012|2153|1250|1.70|2729|1.83|2158|1.81|1469|1.44||0.30|
||||||Ru|ral secto|r mean|||||
|1983|2168|1165|1.81|2959|2.04|2290|1.99|1505|1.53||0.41|
|1994|2190|1183|1.81|2951|2.02|2289|1.97|1458|1.49||0.37|
|2000|2184|1191|1.79|2930|2.00|2275|1.96|1472|1.48||0.37|
|2005|2199|1201|1.79|2930|2.00|2264|1.94|1469|1.47||0.35|
|2012|2179|1230|1.74|2828|1.92|2197|1.86|1455|1.43||0.31|
||||||T|US State|mean|||||
|1983|2152|1183|1.78|2864|1.95|2280|1.95|1490|1.51||0.39|
|1994|2184|1204|1.78|2857|1.94|2281|1.94|1454|1.48||0.35|
|2000|2190|1214|1.77|2847|1.93|2264|1.92|1462|1.47||0.34|
|2005|2208|1224|1.77|2847|1.93|2260|1.91|1468|1.47||0.32|
|2012|2199|1254|1.73|2776|1.87|2196|1.83|1452|1.42||0.28|

_Source_ : Authors’ analysis based on data from 1998-1999 India Time-Use Survey, National Family Health Survey rounds 2, 3 and 4, and National Sample Survey rounds 38, 43, 50, 55, 61, 66, and 68. _Note_ : TEE is Total Energy Expenditure, REE is Resting Energy Expenditure, and AL is activity level. Columns 1-3 report population means (using sampling weights). Columns 4-9 are population means for subsets of the population. Column 10 is the share of the children aged 14 and under in the population. The reported years correspond to the last year covered by National Sample Survey “thick” survey rounds (38, 43, 50, 55, 61, 66, 68).

31
Table S1.5: ULTRA-POOR’S FOOD TRANSFERS WITH THEIR BASELINE NETWORK

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||_Fraction_<br>_sources_|_of baseline food_<br>_from whom tra_|_transfer_<br>_nsfers..._|_Fraction_<br>_recipien_|_of baseline food_<br>_ts to whom tra_|_transfer_<br>_nsfers..._|
||decreased|unchanged|increased|decreased|unchanged|increased|
|Treatment|-0.003|0.034|-0.031**|-0.053|0.065|-0.012|
||(0.036)|(0.035)|(0.014)|(0.050)|(0.050)|(0.008)|
|Mean in control|0.398|0.514|0.088|0.506|0.457|0.038|
|Adjusted R-squared|0.048|0.053|0.053|0.131|0.140|0.085|
|Observations|12180|12180|12180|3077|3077|3077|

**Source:** Author’s analysis based on original survey data. **Notes:** The table reports ITT estimates based on specification (1) estimated with OLS. The sample includes observations from ultra-poor households surveyed at the midline and endline surveys. All specifications control for survey wave and subdistrict (strata) fixed effects. Standard errors are clustered at the BRAC branch office level (unit of randomization). The dependent variable in column 1 (2) is a dummy variable equal to 1 if the amount of food transfers received from the baseline food transfer sources decreased (increased) at midline/endline. The dependent variable in column 3 (4) is a dummy variable equal to 1 if the amount of food transfers given to baseline food transfer recipients decreased (increased) at midline/endline.
those prevailing in the regional or global infrastructure markets. In other words, there is a risk that operators delivering services in the same sector across countries could engage in coordination with outcomes in terms of prices or quantities, equivalent to those prevailing under a cartel.<sup>4</sup> With this background in mind, the main purpose of this paper is twofold. First, we provide quantitative indicators on the residual degree of concentration in each sub sector after a decade of reforms across sectors and across regions. We cover electricity generation and distribution, fixed and mobile telephony, water and sanitation. Second, we discuss the extent to which there may be a case for more coordination between country specific agencies or even a case for a supra-national agency to address any concern for excessive concentration in a sector.

To conduct the assessment, we built the database from scratch from the partial information available on the web, mostly from international agencies specialized in some sectors (i.e. International Energy Agency, Water and Sanitation Program of the World Bank or the International Telecommunication Union) and from specialized consulting and credit rating firms. Because the database does not cover the full supply spectrum, we also had to approximate the concentration indices typically used by competition agencies. The approximation provides a range for the market power rather than the point estimate typically relied on by competition authorities to take decisions. Accordingly, we propose an adjusted associated rule to assess market power. The results seem however to be robust enough to identify the sector and regions in which a case for a supranational competition authority or coordinated regulation could be made.

The rest of the paper is organized as follows. Section 2 presents the methodology used to approximate the degree of concentration in each market and each region of the world. Section 3 presents the results. Section 4 discusses some of the policy implications. Section 5 concludes.

> 4 See for instance Pepall, Richards and Norman (2002) , pp349-405

3
CAMEROON’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

- Eberhard, Anton, Vivien Foster, Cecilia Briceño-Garmendia, Fatimata Ouedraogo, Daniel Camos, and Maria Shkaratan. 2009. ―Underpowered: The State of the Power Sector in Sub-Saharan Africa.‖ AICD Background Paper 6, Africa Region, World Bank, Washington, DC.

- Foster, Vivien, and Jevgenijs Steinbuks. 2009. ―Paying the Price for Unreliable Power Supplies: In-House Generation of Electricity by Firms in Africa.‖ Policy Research Working Paper 4913, World Bank, Washington, DC.

- Helio International. 2009. ―Energy Systems: Vulnerability—Adaptation—Resilience (VAR) 2009: Regional Focus Cameroon.‖ Paris.

- Husband Charles, Gary McMahon, and Peter van der Veen. 2009. ―The Aluminum Industry in West and Central Africa: Lessons Learned and Prospects for the Future.‖ Extractive Industries and Development Series #13, World Bank, Washington, DC.

- Nkama, Arsène Honoré Gidéon. 2007. ―Analyzing the Impact of ICT Investments on Productivity Growth in Developing Countries: Evidence from Cameroon.‖ Paper presented at the African Economic Conference in Addis Ababa.

- www.uneca.org/aec/documents/Arsene%20Honore%20Gideon%20Nkama.pdf.

- Rosnes, Orvika, and Haakon Vennemo. 2009. ―Powering Up: Costing Power Infrastructure Spending Needs in Sub-Saharan Africa.‖ AICD Background Paper 5, Africa Region, World Bank, Washington, DC.

- USAID (United States Agency for International Development). 2010. ―Cameroon Country Profile. Property Rights and Resource Governance.‖ Washington, DC.

- World Bank. 2008. ―Republic of Cameroon: Energy Sector Development Project.‖ Project Appraisal Document as of May 29, World Bank, Washington, DC.

- ———. 2009c. ―Enterprise Surveys for Cameroon 2009.‖ Washington, DC: World Bank.www.enterprisesurveys.org/ExploreEconomies/?economyid=34&year=2009.

- ———. 2011b. _Getting Electricity—Database_ . Washington, DC: World Bank.

- World Bank and IFC (International Finance Corporation). 2010. ―Project Appraisal Document for the Development of the Kribi Power Development Company.‖ World Bank and IFC, Washington, DC, February 23.

# Transport

- Bofinger, Heinrich C. 2009. ―An Unsteady Course: Growth and Challenges in Africa’s Air Transport Industry.‖ AICD Background Paper 16, Africa Region, World Bank, Washington, DC.

- Briceño-Garmendia, Cecilia, and Nataliya Pushak. 2010. ―The Republic of Congo’s Infrastructure: A Continental Perspective.‖ AICD Country Report, Africa Region, World Bank, Washington, DC.

- Bullock, Richard. 2009. ―Off Track: Sub-Saharan African Railways.‖ AICD Background Paper 17, Africa Region, World Bank, Washington, DC.

50
heterogeneous goods.<sup>4</sup> Recent empirical studies have fitted a variety of augmented gravity models to international trade data. Frankel (1997) tests for the effects of a common border, per capita GDP, a common language, and membership in regional trading arrangements, as well as economic scale and distance. Rose (2000) augments Frankel’s model by introducing colonial ties, exchange rate volatility and a common currency. Soloaga and Winters (2001) provide a further control for effective distance, by introducing a measure of generalized remoteness from all potential trade partners. Carillo and Li (2002) include the effects of a common border and trade association membership in a gravity-model analysis of Latin American trade. In an econometric analysis of trade links among seven of eight West African Economic and Monetary Union (WAEMU) countries<sup>5</sup> and other states, Coulibaly and Fontagné (2004) augment distance with measures of transport infrastructure quality: percent of roads that are paved and quality of border-station customs services.<sup>6</sup> Most of the cited econometric studies employ a log-transformation of the gravity model: (2) log _Tij_ = _k_ 0 + α _i_ log _Ei_ + α _i_ log _M j_ + β log _qij_ +γ log _dij_ + ε _ij_ Estimation techniques vary with the cross-sectional and time series properties of the data. The econometric results of Carillo and Li for Latin America and Coulibaly and Fontagné for West Africa are of particular interest for this paper, because both focus on developing regions. In addition, Coulibaly and Fontagné control for West African road infrastructure

> 4  For summaries of the relevant literature, see Yamarik and Ghosh (2005) and Feenstra, Markusen and Rose (2001).

> 5  The WAEMU countries are Togo, Benin, Cote d’Ivoire, Guinea Bissau, Senegal, Mali, Burkina Faso and Niger. Guinea-Bissau was excluded from the Coulibaly- Fontagné study because its foreign trade data were not available.

> 6  For other recent empirical work on intra-African trade, see Kirkpatrick and Watanabe (2005), Chauvin and Gaulier (2002), Longo and Sekkat (2004) and Musila (2005).

7
Finally, we consider the ability of industrial policy to promote cooperation in solving global externalities. The reduction in abatement cost achieved by the application of green industrial policy might increase with the number of other countries that adopt this policy. For example, there may be increasing returns to scale, or network effects arising from this policy. In this setting, even if green industrial policy does promote cooperation, it is likely to do so to a limited extent.

# 2 Current green industrial policies

There has been a surge in renewable energy investment over the last several years, spurred by more active renewables policy across the globe.<sup>2</sup> Currently at least 119 countries have some sort of renewable energy policy in place at the national level, more than double the number of countries with such policies in 2005. Solar photovoltaics (PV), wind, solar water heating systems and biofuels have been growing at average rates ranging from 15% to nearly 50% annually. Approximately half of the new electric capacity added in 2010 came from renewable sources. Total investment in renewable energy reached $211 billion in 2010, a 32% increase from the previous year. Renewable energy, including hydropower, geothermal and biomass, provided approximately 16% of global energy use in 2009.

Developing countries have recently overtaken developed countries in renewable energy investment. For the second year in a row, China was the global leader in new investment in renewables, attracting $49 billion in 2010, more than two thirds of investment for developing countries. The United States ranked second in investment in the renewables sector, followed by Germany.

Mandatory blending requirements are probably the most influential policy support for biofuels. Other important policy tools include direct subsidies, tax incentives, infrastructure development, low-cost financing, and R&D support. Mandatory blending laws exist in 31 countries at the national level and 29 states and/or provinces. They take the form of either nationwide usage targets, blending standards on all fuels, or simply a required option at the gas station. Biofuel mandates work by ensuring producers that there will be a minimal level of demand for their products. In the absence of subsidies,

> 2Much of the data from this section is taken from the Renewable Energy Policy Network for the 21st Century, REN21 (2009, 2011).

8
These findings are _consistent with_ an interpretation, based on the LLI fieldwork, that the _desa_ government organizations are used as a mechanism of social control. More participation in these groups allows for more effective control of decision making and does not represent a broadening of information, voice, and participation beyond those directly involved. However, the data are _not compelling_ for this interpretation as we have no way of technically pinning down the direction of causation responsible for the 23 observed empirical associations These empirical findings raise three important issues that relate both specifically to Indonesia and to literatures on social capital, decentralization and local governance, and project design more generally.

In the Indonesian context there are both issues of project design and of the reform of governance structures. There is a growing, empirically founded, consensus that projects that provide local services are more effective when they incorporate the intended beneficiaries in the project24 . But details matter: how "participation" is structured and through what intermediary organizations makes a difference. Isham and Kahkoinen 1999 compared project success in water supply between two types of projects carried out in the same region of Indonesia: the Village Infrastructure Project (VIP) gave the _desa_ legislative council (LKMD) final choice of design while the Water Supply and Sanitation in Low Income Communities (WSSLIC) project facilitated participation through water user associations. Although the WSSLIC user groups may have been predicted as more

> 23 The difficulty is that to do the procedure of "instrumental variables" one needs valid instruments and we have not found a valid and informative instrument for "village less HH" social activity. We attempted using lagged social activities from the 1996 survey as an instrument but, perhaps surprisingly, the power of the instrument in the first stage was too low and the standard errors on the "social" terms grew very large.

35
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low income economies also fall below the thres ~~hold. Whereas countries such as Niger~~ ia and Botswana are troubled by extremely high export concentration, China and Nicaragua have reached levels of diversification that fall clearly below the threshold. The question then arises: can developing countries that choose to pursue a diversification path do so expediently, so that they quickly fall below the diversification threshold?

The answer is yes, and this is clearly illustr ~~a~~ ted in Figure 4, which follows the path of six developing countries toward lower levels of diversification over the sample period, using the 5 product measure. Countries such as Nicaragua, Kenya, or Colombia had ~~very concentrated export baskets~~ at the beginn ~~ing of our sample period, yet successfu~~ lly diversified to levels close to or below the threshold value we have identified. This illustrates that diversification—as a means of deriving larger benefits from trade openness ~~and at the same time shielding t~~ he economy a ~~gainst global shocks—is indeed a feasib~~ le and realistic policy goal.

<!-- Start of picture text --> 0.9 Colombia South Africa<br>Kenya Nicaragua<br>0.8 Jordan Mexico<br>0.7<br>0.6<br>0.5<br>Threshold<br>0.4<br>0.3<br>0.2<br>1981-85 1986-90 1991-95 1996-00 2000-05<br>Source: Authors' calculations<br>Export concentration<br><!-- End of picture text --> Figure 4: Example diversification paths for a selection of six developing countries between 1981–2005, and their relation to the threshold level of (5 product) diversification (maroon line). Nicaragua, Kenya, and Colombia are countries that behan with very concentrated export baskets, but which were successful in diversifying to levels close to or below the threshold diversification value, where they would benefit from reduced volatility.

# **4 Robustness Checks**

We perform a sequence of robustness checks to ensure the stability of our results. These are: (a) the inclusion of additional controls (to the benchmark reported in Table 3) that have been identified by the literature as potential additional influences on volatility; and (b) subsample analysis of the benchmark specifications with the selective exclusion of specific time intervals and country types. In the interest of space, we report results pertaining to only two product diversification indicators—product Herfindahl and 5 product—noting 22
commerce can help accelerate consumption growth and narrow the regional gaps in e-commerce development, which in turn can lead to a more inclusive spatial development pattern.

Due to data limitations, our study cannot separate total household consumption into online consumption and offline consumption, and therefore cannot examine the effects of e- commerce development on these two types of consumption separately. Studies on whether e- commerce results in additional offline consumption or online consumption substitutes for offline consumption will be interesting future research topics. Of equal if not more importance, studies on how e-commerce contributes to entrepreneurship and employment, and how this varies across sectors, regions, and individuals of different characteristics, will be crucial to understanding the role of e-commerce in the production and income aspects. In what areas and to what extent e- commerce adds to or substitutes for traditional offline business, and as a result, the net effect on the economy remains an important question for more empirical studies.

# **References**

Agriculture and Agri-Food Canada (2017), E-grocery market in China,

- http://www.agr.gc.ca/eng/industry-markets-and-trade/international-agri-food-marketintelligence/asia/market-intelligence/e-grocery-market-in-china/?id=1504037238257.

- Baye, Michael R., and John Morgan, “Price Dispersion in the Lab and on the Internet: Theory and Evidence,” _The RAND Journal of Economics_ 35, no. 3 (2004): 449.

- Baye, Michael R., John Morgan, and Patrick Scholten, “Price Dispersion in the Small and in the Large: Evidence From an Internet Price Comparison Site,” _Journal of Industrial Economics_ 52, no. 4 (2004): 463–496.

- Blum, Bernardo S., and Avi Goldfarb, “Does the Internet Defy the Law of Gravity?,” _Journal of International Economics_ 70, no. 2 (2006): 384–405.

- Brown, Jennifer R., Tanjim Hossain, and John Morgan, “Shrouded Attributes and Information Suppression: Evidence from the Field,” _The Quarterly Journal of Economics_ 125, no. 2 (2010): 859–876.

- Brown, Jennifer R., and Austan Goolsbee, “Does the Internet Make Markets More

- Competitive?,” _Journal of Political Economy_ 110, no. 3 (June 2002): 481–507.

- Brynjolfsson, Erik, Yu Hu, and Mohammad S. Rahman, “Battle of the Retail Channels: How Product Selection and Geography Drive Cross-Channel Competition,” _Management Science_ 55, no. 11 (2009): 1755–1765.

- Cavallo, Alberto, “Are Online and Offline Prices Similar? Evidence from Large Multi-Channel Retailers,” _American Economic Review_ 107, no. 1 (2017): 283–303.

- Choi, Changkyu, “The Effect of the Internet on Service Trade,” _Economics Letters_ 109, no. 2 (2010): 102–104.

- Clay, Karen, Ramayya Krishnan, Eric Wolff, and Danny Fernandes, “Retail Strategies on the Web: Price and Non-Price Competition in the Online Book Industry,” _The Journal of Industrial Economics_ 50 (2002): 351–367.

·· 25
<!-- Start of picture text --> me Distribution of log LP (standardized by country)<br>~ / s.<br>2 H \<br>=" / \<br>zZSn // \\<br>= weose<----"" Se<br>4 -2 () 2 4<br>Log Ip<br><!-- End of picture text --> <!-- Start of picture text --> 2<br>Distribution of log sales (standardized by country)<br>< : -_<br>8 / \<br>ge i \<br>8aN: 7i \X<br>= ———---- ~~~~~~<br>4 -2 ) 2 4<br>Log sales<br><!-- End of picture text --> <!-- Start of picture text --> Distribution of log no. of working people including owners<br>(standardized by country)<br>©<br>ae<br>E<br>2<br>><br>2<br>Sw a<br>2 la<br>o es a<br>-2 0 2 4 6 8<br>Log empt<br><!-- End of picture text -->

||No. of<br>firms|Lab<br>abor<br>pr<br>p<br>|ductivity<br>oductivi<br>|sal<br>ale<br>|s<br>|i<br>Exporting<br>share (%)|W.<br>age pe<br>Be p<br>|k<br>r worker<br>|Profit<br>rofit<br>pe<br>p<br>|rowner<br>|No. of <br>wor<br>|fulltime<br>kers<br>|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|||mean|median|mean|median||mean|median|mean|median|mean|median|
|Overall|||||||||||||
|informal|2377|652|83|1,195|172|3.4|67|43|881|a7|0.43|0|
|formal|892|1,344|188|3,795|515|9.2|150|57|2,435|234|1.55|0|
|Total|3269|e41|104|1,903|242|5|107|47|1,307|114|0.74|0|
|Topdecile b|y productivity||||||||||||
|informal|140|4,317|2,094|9,322|2,753|5.7|116|55|7,492|2,460|0.37|0|
|formal|142|6,314|2,427|16,861|5,875|15.5|401|57|14,630|3,919|1.59|0|
|Total|282|5,571|2,400|13,118|3,810|10.6|297|55|11,086|3,069|0.98|0|
|Top decile b|y sales||||||||||||
|informal<br>|144<br>|4,540<br>|1,568<br>|10,671<br>|3,392<br>|3.5<br>|90<br>|43<br>|7,842<br>|2,092<br>|0.79<br>|0<br>|
|formal|157|5,430|1,805|17,189|7,613|15.9|293|63|11,405|3,820|3.07|1|
|Total|301|5,019|1,640|14,071|4,597|10|213|57|9,760|3,043|1.98|1|