rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 2: Path dependence for contracting with Indian workers**

||Ethnic<br>Indians|non-Ethnic<br>Indians|Difference|
|---|---|---|---|
||(1)|(2)|(3)|
|Share of company contacts selecting India on:||||
|First contract|0.39|0.32|0.07***|
|Second contract, having chosen India on first contract|0.58|0.57|0.01|
|Second contract, having not chosen India on first contract|0.20|0.19|0.01|

Notes: Tabulations consider contracts formed with company contacts located outside of India for whom the name classification algorithm perfectly classifies Indian ethnicity. Third and subsequent contracts are similar to second contracts. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively.

**Table 3: Success dependence for contracting with Indian workers**

||Sample:<br>Hired in<br>India on first<br>contract<br>DV: (0,1)<br>Stay in India<br>on 2nd<br>contract|Sample:<br>Hired in<br>India on first<br>contract<br>DV: (0,1)<br>Continue to<br>use oDesk|Sample:<br>Hired outside<br>of India on<br>first contract<br>DV: (0,1)<br>Switch to<br>India on 2nd<br>contract|
|---|---|---|---|
||(1)|(2)|(3)|
|(0,1) Success on first contract|0.063***<br>(0.013)|0.057***<br>(0.012)|-0.030***<br>(0.007)|
|Probability that hiring contact is of ethnic Indian origin|0.061|-0.011|0.021|
||(0.043)|(0.043)|(0.031)|
|Interaction of success on first contract and|-0.028|0.013|-0.013|
|probability that hiring contact is of ethnic Indian origin|(0.054)|(0.050)|(0.037)|
|Observations|6,615|9,180|14,674|
|Year x job type x country of company contact FE|Yes|Yes|Yes|
|Mean of dependent variable|0.572|0.625|0.197|

Notes: Regressions consider persistence in location choice on second outsourcing contracts formed on oDesk by company contacts. The sample excludes company contacts located in India. Column headers indicate sample composition and dependent variables. The success regressor is a binary variable that takes unit value if the first contract of the company contact garnered a "good" performance rating or higher according to an internal survey or the public feedback score left for the employee. Estimates are unweighted, include fixed effects for year x job type x country of company contact, and report robust standard errors. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively.
their diets or marginally increase their _injera_ consumption. These effects – due to the nature of the cuisine and the _Mirt_ technology - are expected to be very limited, however, causing us to not be overly concerned with leakage.

What factors determine fuelwood and time savings? First, over time – possibly as cooks gain experience - they cook meals more quickly and use less wood. Additional analysis is needed to evaluate whether wood savings stabilize at 31% or if they increase further over time, because it could imply that the 26% average savings is too conservative.

Second, our analysis highlights the importance of regional variation. In relatively forest-rich SNNP Regional State during the CCT we detect fewer wood savings, while gains are observed in Amhara and Oromiya. We are only speculating when we mention forest quality as a potential explanation, however, and additional research is needed. Fuelwood savings are also affected by the quantity of food cooked, characteristics of cooks and the cooking environment. Concerning the treatment variables, we find no systematic effect on fuelwood savings. Finally, our satisfaction survey suggests that people like the _Mirt_ stoves and want to use them. We find that reduced smoke is a particularly important _Mirt_ stove attribute.

What might these findings mean for carbon sequestration and user incentives? In this paper we estimate the fuelwood saved per kilogram of food cooked, which is largely determined by the (fixed) amount of _teff_ flour provided, and we do not know the actual amount of _teff_ flour used in uncontrolled cooking events. Though based on focus group results we believe the amount of _injera_ dough cooked in the CCT to be typical, in reality carbon savings are dependent on the actual quantity of food actual cooked and the cooking frequency; the more food that is typically cooked during each event and the more frequently the _Mirt_ stove is used, the larger will be the savings. We therefore view carbon savings results as indicative and suggest necessary extensions.

Typically, half of wood biomass is estimated to be carbon (Pearson et al, 2007; MacDicken, 1997). If we use the UNFCCC (2012) default percentage of nonrenewable biomass of 88%, each year the _Mirt_ stove could reduce carbon emissions by about 528,000 tons if 9.4 million units were in service. This amount of carbon is equivalent to about 1.938 million tons of CO2, which is the metric used in carbon markets. At the May 19 | P a g e
1

# About AICD and its country reports

This study is a product of the Africa Infrastructure Country Diagnostic (AICD), a project designed to expand the world’s knowledge of physical infrastructure in Africa. AICD provides a baseline against which future improvements in infrastructure services can be measured, making it possible to monitor the results achieved from donor support. It also offers a solid empirical foundation for prioritizing investments and designing policy reforms in Africa’s infrastructure sectors.

The AICD is based on an unprecedented effort to collect detailed economic and technical data on African infrastructure. The project has produced a series of original reports on public expenditure, spending needs, and sector performance in each of the main infrastructure sectors, including energy, information and communication technologies, irrigation, transport, and water and sanitation. _Africa’s Infrastructure— A Time for Transformation_ , published by the World Bank and the Agence Française de Développement in November 2009, synthesized the most significant findings of those reports.

The focus of the AICD country reports is on benchmarking sector performance and quantifying the main financing and efficiency gaps at the country level. These reports are particularly relevant to national policy makers and development partners working on specific countries.

The AICD was commissioned by the Infrastructure Consortium for Africa following the 2005 G8 (Group of Eight) summit at Gleneagles, Scotland, which flagged the importance of scaling up donor finance for infrastructure in support of Africa’s development.

The AICD’s first phase focused on 24 countries that together account for 85 percent of the gross domestic product, population, and infrastructure aid flows of Sub-Saharan Africa. The countries are: Benin, Burkina Faso, Cape Verde, Cameroon, Chad, Côte d'Ivoire, the Democratic Republic of Congo, Ethiopia, Ghana, Kenya, Lesotho, Madagascar, Malawi, Mozambique, Namibia, Niger, Nigeria, Rwanda, Senegal, South Africa, Sudan, Tanzania, Uganda, and Zambia. Under a second phase of the project, coverage was expanded to include as many of the remaining African countries as possible.

Consistent with the genesis of the project, the main focus is on the 48 countries south of the Sahara that face the most severe infrastructure challenges. Some components of the study also cover North African countries so as to provide a broader point of reference. Unless otherwise stated, therefore, the term ―Africa‖ is used throughout this report as a shorthand for ―Sub-Saharan Africa.‖
2SLS estimates are based on the policy instrument, those from the OLS reflect endogenous linkages from mobile use activity.

|**Table 3: OLS: Effect of mobile use and activity on business enterprise**|
|---|

|<br>DV:**Business**|<br>(1)|<br>(2)|<br>(3)|<br>(4)|(5)|
|---|---|---|---|---|---|
|**Enterprise**||||||
|MOBILE use<br>activity|0.086***(0.023)|0.089***(0.023)|0.089***(0.023)|0.094***(0.023)|0.110***(0.028)|
|**1**[Sex = female]||0.016(0.021)|0.021(0.021)|0.026(0.023)|-0.055(0.036)|
|**1**[Married =||0.051**(0.024)|0.060**(0.027)|0.060**(0.024)|0.018(0.034)|
|Yes]||||||
|Mobile X**1**[Sex<br>= female]||||-0.018(0.046)|0.027(0.059)|
|Age in Quantiles<br>Any Education?|No|No|Yes|Yes|Yes<br>Yes|
|Constant|0.189***(0.014)|0.182***(0.030)|0.180***(0.048)|0.179***(0.048)|0.337*(0.198)|
|R–squared (%)|1.07|1.44|1.74|1.74|2.58|
|# of|2,163|2,163|2,163|2,163|2,007|
|Households/OBS||||||
|Fixed Effects|hh+Time|hh+Time|hh+Time|hh+Time|hh+Time|

_Notes_ : Table reports estimates from regressions of “engagement in non-agricultural business enterprises” on mobile use and activity. Columns differ based on the combination of model type and fixed effects included. All standard errors are clustered at the region <u>level and shown in parentheses. Significance levels are indicated by ∗∗∗ 1%, ∗∗ 5%, ∗ 10%. Unit of Analysis:</u> **<u>household-level</u>**

# Main estimates

We begin with results from the OLS. Those pertaining to business enterprises are contained in Table 3. We expect positive relationship between mobile activity and propagation of business enterprises. Different columns correspond to different model specifications. All five specifications presented include household and year fixed effects. Column 1 excludes all control variables, while 2 adds gender and marital status. Column 3 flexibly adds age in quantiles to specification 2. Column 4 replicates 3 but includes an interaction between MOBILE and gender; while column 5 additionally control for whether respondents have some form of education. The coefficient on mobile activity MOBILE is positive and significant at conventional levels in all specifications. The OLS estimates range between 0.086 - 0.110; with an average estimate of about 0.101. An average OLS estimate of 0.101 implies that shifting a household with no mobile use activity to a mobile usage activity environment will result in about 10.1% points increase in the likelihood of engaging in some form of non-agricultural business enterprise. These estimates ‐ 13 ‐
Since Mishra’s (2007) paper, a few other studies that focus on national wage effects have found similar results. Using data drawn from the Canadian, Mexican, and U.S. Censuses, Aydemir and Borjas (2007) conclude that a 10 percent change in labor supply is associated with a 3 to 4 percent change in wages in the opposite direction. In a study of Puerto Rican workers, Borjas (2008) finds that a 10 percent emigration-induced fall in the number of workers in a particular skill group raises the average wage by about 2 percent.

The literature on emigration and wages has thus far focused only on the North American experience with international migration—particularly in Canada, the United States and Mexico. In large part, this has been out of convenience, as close to all migrants from Mexico and Puerto Rico are in the U.S., allowing for empirical analysis drawing on U.S. data. Recent evidence shows that although United States is the largest immigrant recipient (in absolute size) of any country in the world, most of the top emigration source countries (in percent of the population) are outside North America. There is, however, no accumulated empirical evidence on the impact of migration on wages in these other countries. Thus, there is a significant knowledge gap in the emigration literature on countries outside North America.

Examining the impact of emigration empirically is challenging because source countries typically do not maintain data on emigrants. In this paper, we examine for the first time the impact of emigration on wages in Moldova using demographic and labor market information on emigrants documented by a recent, nationally representative survey. Moldova provides an ideal case study, given that Moldova’s emigrants represent 17 percent of its population and about 30 percent of its labor force, placing it at the highest end of the global distribution of emigrants as a share of (source country) labor force.<sup>4</sup> The majority of workers are in Russia, close to a fifth are in Italy, and the rest are in Ukraine and in France and other Western European countries.<sup>5</sup> About half of these emigrants are employed in construction industries abroad, and about a fifth of these workers are

> 4 Mishra (2007) reports that, in a sample of countries, emigrants account for 7 to 27 percent of the source country’s labor force. Moldova’s emigrant share is calculated in percent of the Moldovan work force, including the emigrants themselves.

> 5 See also International Organization for Migration (2007).

3
**Figure 4: Variability in output cycles in selected EMDEs**

_(Long-to-short term variability of GDP per capita at various lags)_ <!-- Start of picture text --> 4.0 ZAF BRA<br>IND BWA<br>3.5 RUS MEX<br>PER IDN<br>3.0<br>TUR<br>2.5<br>2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27<br>K<br>Relative Variances at K<br><!-- End of picture text -->

# Source: Authors’ calculations.

Note: ZAF: South Africa; IND: India; RUS: Russia; PER: Peru; TUR: Turkey; BRA: Brazil; BWA: Botswana; MEX: Mexico; IDN: Indonesia.

Finally, a measure that captures _the cycle is the trend_ is calculated by taking the above indicator (equation 10) and multiplying it by the smoothed (three-year moving average) growth in per-capita GDP: As with the non-commodity output gap, we also compute the non-commodity CITT variable as: Figure 5 provides a sense of how much the commodity cycles in each country contributed to larger growth rate than what would have been observed without movements in real commodity prices. The left panel highlights CITT before the crisis in 2007, the center panel compares CITT and CITT excluding commodity price movements during 2009, and the right panel provides an estimate of the two CITTs in 2016. A 45-degree line divides the charts in two.

Note that the majority of the countries are to the right of the line in 2007 – non-commodity CITT was much lower relative to actual CITT, which supports the hypothesis that commodity prices cycles prolonged the economic cycle. During the financial crisis, countries moved closer to the 45-deggree line, implying that the effects of commodities contributed little to the economic cycle.

8
# **Figure 5**

Forecasted share of RE in 2010 by energy project and according to year of publication Note: US scenarios are depicted as large squares

|10<br>11<br>12<br>13<br>14<br>15||
|---|---|
|0<br>1<br>2<br>3<br>4<br>5<br>6<br>7<br>8<br>9<br>**RE share (%x100)**||
|19|70<br>1975<br>1980<br>1985<br>1990<br>1995<br>2000<br>2005<br>2010|
||Department of the Interior<br>EUOGEP<br>Energy Research and Development Agency<br>European Union / DGTREN<br>Ford Foundation<br>Greenpeace<br>Harvard Business School<br>IEA<br>IIASA<br>IPCC<br>OECD<br>PEW|
||Shell International BV<br>Union of Concerned Scientists<br>WEC<br>WEC and IIASA|
<!-- Start of picture text --> 600<br>400 — Brazil<br>; : Colombia<br>g /\ - : Ethiopia<br>g / \ 7S a “Mexico<br>a / Philippines<br>J © \ j > ~~ South Africa<br>200 4 \ J — Vietnam<br>0<br>2000 2005 2010 2015<br>Year<br><!-- End of picture text -->
- Burniaux, Jean-Marc and Joaquim Oliveira Martins. 2000. Carbon emission leakages: a general equilibrium view. OECD Economics Department Working Papers: 242. Paris: Organization for Economic Cooperation and Development.

- Cacho, O.J. Marshall, G.R. and Milne, M. 2005. Transaction and abatement costs of carbon-sink projects in developing countries. Environment and Development Economics. 10(5): 1-18.

- Cacho, Oscar J. and Leslie Lipper. 2007. Abatement and transaction costs of carbon-sink projects involving smallholders. Fondazione Eni Enrico Mattei Working Paper 32. Milano: Fondazione Eni Enrio Mattei.

- CBD. 2009. Secretariat of the Convention on Biological Diversity. Connecting Biodiversity and Climate Change Mitigation and Adaptation: Report of the Second Ad Hoc Technical Expert Group on Biodiversity and Climate Change. Montreal, Technical Series No. 41.

CDM Climate Research. 2010. Tendances Carbone 51 (October). Arcueil, France.

- Chomitz, Kenneth M. 1999. Evaluating carbon offsets from forestry and energy projects: how do they compare? Policy Research Working Paper Series 2357. World Bank, Washington.

- Conte, Marc N. and Matthew J. Kotchen. 2009. Explaining the price of voluntary carbon offsets. NBER Working Paper 15294. Cambridge, MA: National Bureau of Economic Research.

- Convery, Frank J. and Luke Redmond. 2007. Market and price developments in the European Union Emissions Trading Scheme. Review of Environmental Economics and Policy 1(1), 88-111.

- Cullet, Philippe and Annie-Patricia Kameri-Mbote. 1998. Joint Implementation and forestry projects: conceptual and operational fallacies. International Affairs 74(2), 393-408.

- Daily, G.C. (ed.) (1997.) Nature's Services: Societal Dependence on Natural Ecosystems (Island Press, Washington, DC).

- DEFRA. 2008. Draft Code of Best Practice for Carbon Offset Providers Accreditation requirements and procedures. Department for Environment, Food and Rural Affairs. London.

- den Elzen, Michel G. J. and S. Both. 2002. Modeling emissions trading and abatement costs in FAIR 1.1 case study: the Kyoto Protocol under the Bonn-Marrakesh Agreement. RIVM report 728001021/2002. National Institute of Public Health and the Environment, Bilthoven, the Netherlands.

- Dudek, D.J. and Wienar, J.B. 1996. Joint implementation, transaction costs, and climate change. Paris Organisation for Economic Co-operation and Development,.

EBRD. 2011. The Low Carbon Transition. London: European Bank for Reconstruction and Development.

- EPA. 2007. Inventory of U.S. Greenhouse Gas Emissions and Sinks: Fast Facts 1990-2005. Conversion Factors to Energy Units (Heat Equivalents) Heat Contents and Carbon Content Coefficients of Various Fuel Types. Washington: U.S. Environmental Protection Agency, USEPA #430-R-07-002 (PDF)

- Europa. 2007. Emission Trading Scheme (EU ETS). Available on the internet at: http:\ec.europa.eu.

- FAO. 2000. Land Resource Potential and Constraints at Regional and Country Levels. Rome: Food and Agriculture Organization.

- FAO. 2004. Carbon sequestration in dryland soils, World Soil Resources Report No 102. Rome: Food and Agricultural Organization.

34
policies and FDI have also been identi...ed as important factors for moving up GVCs, based on ...rm-level evidence for China and Bangladesh.<sup>21</sup> Hence, we expect tari¤s and FDI (as well as deep PTAs) to be signi...cant in determining GVC participation. But these determinants may be subject to endogeneity concerns that we address in detail in Section 3.1.

# 2.2.5 Institutional Quality

As discussed in Antràs (2016), what distinguishes GVC trade from traditional trade are the intense ...rm-to-...rm interactions characterized by contracting and specialized products and investment. Weak contract enforcement is thus a signi...cant deterrent not only for traditional trade, but also for GVC trade.<sup>22</sup> Because the performance of a GVC depends on the strength of its weakest link, production delays driven by weak contract enforcement might be particularly harmful in GVCs. In addition, the presence of relationship-speci...c investments (e.g., the customization of products) and the exchange of large ‡ows of intangibles (such as technology, intellectual property and credit) reinforces the potential role of institutional quality as a signi...cant determinant of relational GVC participation. Some emerging evidence ...nds a correlation between a stronger rule of law and stronger GVC integration.<sup>23</sup> We expect institutional quality to be important in determining GVC participation.

# 2.2.6 Connectivity

Transport costs remain, according to surveys of developing country suppliers, the main obstacle to entering, establishing, or upgrading in GVCs.<sup>24</sup> The geographic centrality of a country can attract downstream production stages in GVCs. But geographic centrality is more related to centrality in the transport network than to distance. Thus, logistics and communication infrastructure, port and customs e¢ ciency, and information technology networks could be important for trade in general and especially for GVC trade in particular.

> 21See Kee (2015) and Kee and Tang (2016).

> 22A body of work establishes institutional quality as a comparative advantage factor in determining export patterns: Acemoglu et al. (2007), Levchenko (2007), Nunn (2007), Costinot (2009), and Chor (2010).

> 23See Kowalski et al. (2015).

> 24See OECD and WTO (2013). Hummels and Schaur (2013) ...nd that a day of delay in transit due to di¤erent transport mode choice has a tari¤ equivalent of 0.6 to 2.1 percent, and the most sensitive ‡ows are for a type of GVC trade, i.e., trade in parts and components. Similar magnitudes for the cost of a one-day delay in inland transit are found by Djankov et al. (2010).

9
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