rafmacalaba/datause-encoder-data huggingface dataset:
distance between surveyed villages was well balanced for each person. This ensured that no enumerators felt they were traveling significantly more than their colleagues.<sup>5</sup> As is common in data collections, there were times when some enumerators could not complete all of the surveys, and so a team leader had to conduct the survey. In other cases, the enumerator decided to leave the survey team. As the team leader was not randomly selected and enumerators who left the teams usually completed only a small number of surveys, surveys were dropped done if they were done by enumerators who conducted fewer than 70 interviews<sup>6</sup> . This reduced the sample from 8403 interviews to 6895 and the enumerators from 90 to 47.

Information was collected on the enumerators who participated in the survey operations. This information included demographics (age, gender, whether their home is in an urban or rural area), education level, previous work experience as enumerator, and behavioral and psychological traits. The enumerator survey was administered after enumerators were selected to be part of the survey team, was voluntary, and was covered under the Institutional Review Board (IRB) for the main project.

Since enumerators were randomly assigned to villages within teams, enumerator data was used to validate the randomization by conducting a _within-team_ balance test on the pairing of respondent and enumerator demographics. In practice, the project regressed a set of the respondent’s characteristics (age, gender, marital status, if she can read, if she can write, and education level) on enumerators’ characteristics (gender, age, education

> 5 Note that nearness and farness are not about the location of the village, but the location of the survey team. That is, how far a village is from where the surveyors were staying the previous night. The teams did not always stay in one location or in the main town within a region, but moved continuously.

> 6 Seventy interviews is the 10th percentile of the distribution of the number of interviews by enumerator. The study also shows later that the results are not sensitive to this selection.

8
Table 2. Fiscal Multiplier Estimates Conditioning on government debt

|Study|Sample|Methodology|Government debt|
|---|---|---|---|
||||High<br>Low|
|Our results 1/<br>|33 countries,<br>1980-2014|IPVAR|0-0.5<br>0.6-0.9|
|Ilzetzki, Mendoza, Vegh (2013) 2/|44 countries,<br>1960-2007|SVAR|-3<br>insignificant|
|Nickell and Tudyka (2014) 2/|17 EU countries,<br>1970-2010|IPVAR|<0<br>1.2|
|Auerbach and Gorodnichenko<br>(2017) 3/|25 OECD<br>countries, 2003-<br>17|LPM|<0<br>>0|
|Conditioning on business cycles 4/||||
|Study|Sample|Methodology|Recession<br>Expansion|
|Favero, Giavazzi and Perego (2011)<br>5/|US, 1980-2009|GVAR|"close to 2"|
|<br>Bachmann and Sims (2012)|US, 1960-2011|VAR|2.13-3.35<br>1.04-1.1|
|Auerbach and Gorodnichenko<br>(2012)|US, 1947-2008|LPM|1.12-3.85<br>0.17-3.02|
|Auerbach and Gorodnichenko<br>(2013)|OECD, 1985-<br>2008|LPM|3.5<br>0|
|<br>Blanchard and Leigh (2013) 6/|26 European<br>countries, 2009-<br>12|Panel<br>regression|"Substantially<br>above 1"<br>...|
|Candelon and Lieb(2013)|US, 1968-2010|TVAR|1-2.4<br>0.5|
|Unconditional 7/||||
|Study|Sample|Methodology|Average|
|Blanchard and Perotti (2002)|US, 1947-97|VAR|0.9-2.67|
|Gali, Lopez-Salido and Valles (2007)|US, 1954-2003|DSGE|1.74-3.5|
|Perotti (2008)|US, AUS, CAN,<br>UK, 1926-2003|VAR|0 (UK, Canada) -3.1 (US)|
|Fatas and Mihov (2009)|Not applicable|Survey|>1|
|Mountford and Uhlig (2009)|US, 1955-2000|VAR|0.65|
|Perotti (2011)|US 1939-2008|SVAR, EVAR|"in the neighbourhood of 1"|
|Ramey (2011)|US|Survey|0.6-1.8|
|Auerbach and Gorodnichenko<br>(2012)|US, 1947-2008|LPM|1-2.12|
|<br>Bachmann and Sims (2012)|US, 1960-2011|VAR|0.89-0.88|
|Alesina, Favero and Giavazzi (2015)|16 OECD<br>countries, 1978-<br>2009|SUR|negative to positive|

Note: IPVAR stands for interacted panel vector autoregression, SVAR for structural vector autoregression,VAR for structural vector autoregression, TVAR for threshold vector autoregression, LPM for local projections model, EVAR stands for expectations-augmented vector autoregression.

1/ High government debt is defined as government debt above 60 percent of GDP, low government debt is the remainder. Fiscal instrument is government consumption 2/ High government debt is defined as debt in the 90th percentile of the sample (92 percent of GDP). High government debt is defined as debt in the 10th percentile of the sample (17 percent of GDP). Fiscal instrument is government consumption.

3/ Explicit multiplier estimates not available. Table shows longterm impulse response of real GDP. Fiscal instrument is government spending (consumption and investment).

4/ Fiscal instrument is government spending, except for Auerbach and Gorodnichenko (2012) who use a wide range of fiscal instruments.

26
- 2 -

# **I. INTRODUCTION**

Africa has great potential for agriculture. Together with agribusiness, it is estimated that agriculture currently generates $31 billion or nearly half of the GDP of the region. This is projected to continue growing to $1 trillion by 2030 (World Bank 2013). However, the potential has not been fully explored yet. For instance, the ratios of potential to actual agricultural outputs are estimated at 1.5 for cassava, 1.9 for rice, 2.7 for maize and 5 for wheat in West Africa (World Bank 2012). Importantly, from the agro-ecological point of view, Africa can feed itself if proper inputs, such as improved seeds and fertilizer, are used. But Africa is currently importing considerable quantities from outside the region.<sup>1</sup> Given rapid urbanization and strong population growth, Africa needs to produce more agricultural products.

There are a number of constraints: Africa’s agriculture is mostly small-scale subsistence farming. Irrigation and fertilizer are among the most important missing inputs (e.g., GyimahBrempong, 1987; Bravo-Ortega and Lederman, 2004; Xu et al., 2009; Dillon, 2011). Access to markets is also important, for farmers to take advantage of advanced technologies and market opportunities. In Africa, rural accessibility is particularly limited.<sup>2</sup> Transport accessibility — regardless of mode — has a crucial role to play to reduce input prices and boost agricultural production (Khandker, Bakht and Koolwal, 2009; Donaldson, 2010). A growing literature also suggests the importance of access to information and communication technology to obtain market information (Kiiza and Pederson, 2012, Zanello 2012).

Spatial techniques are becoming an increasingly popular tool to identify potential economic opportunities and possible bottlenecks in unlocking them. On the agriculture side, there are several important international initiatives to examine current land use and potential crop

> 1 Africa imported $15 billion of cereals in 2008, out of which only 5 percent originated from the African region (World Bank 2012).

> 2 In Africa, rural accessibility measured by the proportion of the rural residents within a 2-km walking distance from an all-weather road is less than 30 percent (Gwilliam 2011).
significantly lower carbon prices (IPCC 2007b). The modeling results, which suggest a significant role for new technologies, are reflected in a set of policy proposal aimed at funding global research. We take up this topic in the section below. The same models are used to measure any adverse impacts of economic growth resulting from Annex B emission reductions. For comparison purposes, the costs are often expressed in terms of reduced GDP. Model predictions of 2050 GDP reductions reviewed by the IPCC (2007b) associated with stabilization around 550 ppm range from near zero to 4 percent.<sup>46</sup> The numeric models have also been relied upon to provide estimates of the potential benefits of the Kyoto Protocol’s flexibility mechanisms. As Springer (2003) notes in his review, a common finding is that the costs of reaching greenhouse reduction goals are greatly reduced by rules that allowed spatial and temporal flexibility. By way of example, early model results by Bernstein, Montgomery and Rutherford (1999) suggest that flexible trading rules could reduce the price of carbon permits – which can be seen as the marginal cost of emission reductions – by a factor of seven in the European Union and by a factor of sixteen in Japan.<sup>47</sup> Early results indicating the importance of trade and project investment countries have held up with time; however, recent modeling efforts have illustrated how the cost-savings from the flexibility mechanisms depends on the stringency of emission reduction targets. For example, under scenarios consistent with earlier assumptions, den Elzen and Both (2002) estimate that the Kyoto flexibility mechanisms reduce the overall cost of meeting the first commitment period targets by 40 percent. However, the authors also show that the withdrawal of the United States from the agreement greatly reduces the need for the provisions; predicted trade under the provisions is cut in half as aggregate abatement levels drop significantly without demand for offsets from the US.

Global averages of the costs of mitigation mask differences in the distributional effects of climate change. As Mendelsohn, Dinar and Williams (2006) note, early models of climate change suggested that the effects of climate change would fall uniformly among the rich and poor. More recent results suggest that this is not the case and that the world’s poor will be disproportionately affected. The study suggests this result comes about primarily because of where the poor live. In the already mentioned paper by Mendelsohn, Dinar and Williams, the authors develop sectoral response functions and country specific measures of geography, population and income to develop country-

> 46 Sathaye et al. (2007) provide a survey of alternative welfare indicators.

> 47See Nordhaus and Boyer (2000), Springer (2003), Weyant (2004), Sands (2004) and Working Group III’s contribution to the Fourth IPCC Assessment (IPCC 2007) for additional reviews of modeling approaches and results. Painuly (2001) reviews numerical models that address project-based investments in developing countries; Muller and Mestelman (1998) review related laboratory-based experiments.

24
School records in PNG were almost non-existent, with no school magazines or boards with the names of the Duxes and Proxime Accesits to provide a record of who the top students were. Records were also not kept in any systematically accessible way by the Ministry of Education. The only formal sampling frame came from the Office of Higher Education (OHE), which allocates slots and scholarships for tertiary study. The OHE provided the names of the 264 students who had achieved a 4.0 GPA in their Grade 12 national examinations during 1995-98 and 2000-2004. On average only 0.7% of Grade 12 exam entrants in these years achieved this perfect GPA. These 264 students had come from 30 different secondary schools, with 32% of them from the National High Schools. We therefore created a sampling frame of these 4.0 GPA students and also any Duxes or Proxime Accesits who were not on the OHE list but who were from the top two-thirds of schools supplying the 4.0 GPA group. This gave a potential sampling frame of 624 if the two groups were mutually exclusive and 376 if all of the 4.0 GPA students were also a Dux or Proxime Accesit.<sup>9</sup> For 1976-1994 our sample frame consists of the Duxes and Proxime Accesits from the National High Schools. This gives a potential sample frame of 152 individuals over this period. However, due to a lack of school records, we were only able to identify the names of 93 of these individuals by asking former students and teachers at these schools, and through radio and television advertisements.

# **2.3 Tracking and Surveying**

The second phase consisted of attempting to track down the list of names, and administering the survey to them. We designed a comprehensive survey intended to collect information on many topics needed to measure both the determinants and consequences of highly skilled emigration. The survey contained separate modules for current migrants, return migrants, and never migrated individuals, and averaged just over one hour to complete. All of the New Zealand top students answered the survey online. A mixture of online surveys, in person surveys in New Zealand, Tonga, PNG, and phone

> 9 This is comprised of a potential 360 Duxes and Proxime Accesits from the top 20 schools plus 16 of the 4.0 GPA students who were from the lower third of schools supplying at least one 4.0 GPA student.

- 10 -
The changes that stand out the most are for the low-income countries, where there has been an significant increase in the stringency of capital regulations and the overall index of capital regulation, a slight decline in official supervisory powers, but a substantial jump in prompt corrective action powers.<sup>21</sup> Interestingly, low-income country supervisors’ discretion to forbear actually increased somewhat – in other words, they have the ability to take prompt corrective action powers, but are not as constrained as they were previously. The one supervisory variable that was found by BCL to have some positive impact on the development of the financial sector and on limiting the occurrence of banking crises, namely requirements that banks diversify their risks, actually decreased in all the countries, including in the low-income countries, where small economic size and insufficient diversification could be a critical issue. Thus it would appear all the more important for authorities to try to strengthen market discipline. Unfortunately, according to the survey, the private monitoring index has decreased somewhat. Part of this decline reflects some spreading of deposit insurance (the absence of which raises this index).

Table 6. _Trends in Bank Regulation and Supervision_ <u>Surveys carried out in 1998-99 (Survey I) and 2003 (Survey II)</u>

|Means||High<br>Income|Upper<br>Middle|Lower<br>Middle|Low<br>Income|
|---|---|---|---|---|---|
|Overall Caital Strinenc|_Survey I_|**3.92**|3.29|**3.12**|3.11|
|p gy|_Survey II_|3.79|**3.45**|3.09|**4.40**|
|il li d|_Survey I_|**5.61**|5.05|4.42|4.47|
|Capta Reguaton Inex|_Survey II_|5.45|**5.23**|**4.70**|**5.60**|
|Official Supervisory Power|_Survey I_|**10.79**|**11.33**|**11.19**|**11.37**|
||_Survey II_|10.39|10.36|10.48|10.90|
|P Ci Ai|_Survey I_|1.49|2.71|1.85|2.47|
|rompt orrectve cton|_Survey II_|**1.63**|**3.00**|**2.39**|**4.10**|
|Supervisory Forbearance|_Survey I_|**1.87**|1.29|**1.38**|**1.72**|
|Discretion|_Survey II_|1.75|**1.55**|1.17|1.40|
|Liuidit / Diversification Index|_Survey I_|**1.92**|**2.10**|**2.00**|**1.68**|
|qy|_Survey II_|1.55|1.55|1.13|1.20|
|Pit Miti Id|_Survey I_|**7.08**|**7.10**|**6.73**|**5.47**|
|rvae onorng nex|_Survey II_|6.87|6.36|6.04|5.40|

Note: In each case, the higher value as between the two surveys is shown in **bold** . Countries are classified by income level into High, Upper-middle, Lower-middle and Low income using standard World Development Indicators classifications, 2003.

Source: World Bank Regulation and Supervision Survey 21 Official supervisory powers, as disaggregated by Barth, Caprio, and Levine (2001) cover a broad array of powers, some related to the ability to implement prompt corrective action, others at supervisors’ ability to force restructuring and reorganization, or to declare insolvency.

28
|Agriculture Census and Information System|Matthew A. McMahon|
|---|---|
|Agriculture Census and Information System|Matthew A. McMahon|
|Agriculture Census and Information System|Matthew A. McMahon|
|Agriculture Census and Information System|Matthew A. McMahon|
|Agriculture Census and Information System|Matthew A. McMahon|
|Agriculture Census and Information System|Matthew A. McMahon|
|Agriculture Census and Information System|Matthew A. McMahon|
|Modernization of the State Technical Assistance Project|Marcelo A. Romero|
|Modernization of the State Technical Assistance Project|Marcelo A. Romero|
|Modernization of the State Technical Assistance Project|Marcelo A. Romero|
|Modernization of the State Technical Assistance Project|Marcelo A. Romero|
|Modernization of the State Technical Assistance Project|Marcelo A. Romero|
|Modernization of the State Technical Assistance Project|Marcelo A. Romero|
|Modernization of the State Technical Assistance Project|Marcelo A. Romero|
|Urban Development Project(02)||
|Telecommunications Project(03)||
|Agricultural and Vocational TrainingProject||
|Education Project(04)||
|Education Project(04)||
|Education Project(04)||
|Education Project(04)||
|Public Sector Modernization Technical Assistance Project|Alberto Leyton|
|Public Sector Modernization Technical Assistance Project|Alberto Leyton|
|Public Sector Modernization Technical Assistance Project|Alberto Leyton|
|Public Sector Modernization Technical Assistance Project|Alberto Leyton|
|Public Sector Modernization Technical Assistance Project|Alberto Leyton|
|Public Sector Modernization Technical Assistance Project|Alberto Leyton|
|Public Sector Modernization Technical Assistance Project|Alberto Leyton|
|Public Sector Modernization Technical Assistance Project|Alberto Leyton|
|Public Sector Modernization Technical Assistance Project|Alberto Leyton|
|Public Sector Modernization Technical Assistance Project|Alberto Leyton|
|Public Sector Modernization Technical Assistance Project|Alberto Leyton|
|Public Sector Modernization Technical Assistance Project|Alberto Leyton|
|Public Sector Modernization Technical Assistance Project|Alberto Leyton|
|Public Sector Modernization Technical Assistance Project|Alberto Leyton|
|Public Sector Modernization Technical Assistance Project|Alberto Leyton|
|Structural Adjustment Loan Project(02)|Gloria M. Grandolini|
|Structural Adjustment Loan Project(02)|Gloria M. Grandolini|
|Structural Adjustment Loan Project||
|Structural Adjustment Loan Project||
|Structural Adjustment Loan Project||
|AGRICULTURAL SECTOR REFORM AND INVESTMENT PROJECT|Norman BentleyPiccioni|
|AGRICULTURAL SECTOR REFORM AND INVESTMENT PROJECT|Norman BentleyPiccioni|
|AGRICULTURAL SECTOR REFORM AND INVESTMENT PROJECT|Norman BentleyPiccioni|
|AGRICULTURAL SECTOR REFORM AND INVESTMENT PROJECT|Norman BentleyPiccioni|
|AGRICULTURAL SECTOR REFORM AND INVESTMENT PROJECT|Norman BentleyPiccioni|
|AGRICULTURAL SECTOR REFORM AND INVESTMENT PROJECT|Norman BentleyPiccioni|
# **List of References**

- Addison, Tony, and Almas Heshmati. _The New Global Determinants of FDI Flows to Develping Countries: The Importance of ICT and Democratization._ Discussion Paper No. 2003/45, WIDER, 2003.

- Agarwal, Jamuna P. "Determinants of Foreign Direct Investment: A Survey." _Weltwirtschaftliches Archiv._ 116, no. 4 (December 1980): 739-773.

- Aggarwal, Aradhna. _Regional Economic Integration and FDI in South Asia: Prospects and Problems._ Working Paper, India Council for Researh on International Economic Relations, 2008.

- Aguiar, Mark, and Gita Gopinath. "Fire-Sale Foreign Direct Investment and Liquidity Crises." _The Review of Economics and Statistics_ 87, no. 3 (August 2005): 439-452.

- Al Nasser, Omar M., and Xavier Garza Gomez. "Do Well-Functioning Financial Systems A_ect the FDI Flows to Latin America?" _International Research Journal of Finance and Economics_ 29 (2009): 60-75.

- Anyanwu, John C. "Why Does Foreign Direct Investment Go Where It Goes?: New Evidence From African Countries." _Annals of Economics and Finance_ 13, no. 2 (2012): 433-470.

- Asiedu, Elizabeth. "On the Determinants of Foreign Direct Investment to Developing Countries: Is Africa Different?" _World development_ 30, no. 1 (2002): 107-119.

- Barrell, Ray, and Nigel Pain. "Domestic Institutions, Agglomerations, and Foreign Direct Investment in Europe." _European Economic Review_ 43 (1999): 925-934.

- Barro, Robert J, and Xavier Sala-i-Martin. "Convergence." _Journal of Political Economy_ 100 (April 1992): 223-251.

- —. _Economic Growth._ 2nd ed. Cambridge, Mass.: MIT Press, 2003.

- Baumol, William J. "Productivity growth, convergence, and welfare: What the long-run data show." _American Economic Review_ 76 (1986): 1072-1085.

- Beck, Thorsten, Aslı Demirgüç-Kunt, and Ross E. Levine. "A new database on financial development and structure." _The World Bank Economic Review_ (The World Bank Economic Review) 14 (2000): 597-605.

- Bertrand, Olivier, Katariina N Hakkala, and Pehr-Johan Norbäck. "Should Countries Block Foreign Takeovers of R&D Champions and Promote Greenfield Entry." _Canadian Journal of Economics_ 45, no. 3 (2012): 10831124.

- Bitzer, J, and H Görg. "Foreign Direct Investment, Competition and Industry Performance." _World Economy_ 32 (2009): 221–233.

- Blalock, Garrick, and Paul J Gertler. "Learning from exporting revisited in a less developed setting." _Journal of Development Economics_ (Elsevier) 75, no. 2 (December 2004): 397-416.

- Blonigen, Bruce A, and Miao G Wang. "Inappropriate Pooling of Wealthy and Poor Countries in Empirical FDI Studies." Chap. 9 in _Does Foreign Direct Investment Promote Development?_ , by Teodore H Moran, Edward M Graham and Magnus Blomström. Peterson Institute of International Economics, 2005.

37
|**Annex A: L**|**ist of variables, data description and**|**sources**|
|---|---|---|
|Variable|Definition|Source|
|Coefficient of Variation|Ratio of standard deviation over mean for each<br>kind of flows|Ratha, Mohapatra and X<br>2008|
|Country Size|Country area in square kilometers, Country<br>population|World Bank, 2008, WDI|
|Credit|Percentage of domestic credit provided by banks|World Bank, 2008, WDI|
|Electric power<br>consumption|Electric power consumption (kWh per capita)|World Bank, 2008, WDI|
|Expenditure on Physical<br>Infrastructure|Expenditure on transport, telecommunications,<br>power and water and sanitation as percent of GDP|Appedix A, Connecting<br>Asia a New Framework<br>Infrastructure, Calderon|
|Financial services|Financial services cover financial intermediary<br>and auxiliary services (except those of insurance<br>enterprises and pension funds) conducted between<br>residents and non-residents.|Balance of<br>Payments,2008.IMF|
|Goods Exports|Goods exports refer to all movable goods<br>(including nonmonnetory gold) involved in a<br>change of ownership from residents to<br>nonresidents.Forms of processed goods, repairs on<br>goods, and goods procured in ports by carriers.|Balance of<br>Payments,2008.IMF|
|Goods and Services<br>Export Share|region’s export as a share of the world’s export of<br>goods and services.|Balance of<br>Payments,2008.IMF|
|Insurance Services|insurance services cover the provision of various<br>types of insurance to non-residents by Trade in<br>services by service-category and country resident<br>insurance enterprises,and vice versa.|Balance of<br>Payments,2008.IMF|
|Improved sanitation|Improved sanitation facilities (% of population|World Bank, 2008, WDI|
|Investment|Gross fixed capital formation as a ratio of GDP|World Bank, 2008, WDI|
|IT services|Information Technology services (ITS) like<br>custom application development,application o<br>NASSCOM,2008.Packaged software installation<br>and support,network infrastructure<br>management,IT training and education,network<br>consulting and integration.||
|IT and IT enabled Services|IT and IT-enabled services is measured by adding<br>the Computer and information services plus<br>miscellaneous business services category in the<br>Balance of payments_._|Balance of<br>Payments,2008.IMF|
|||World Bank, 2008,WDI<br>ii|
|Industrial Labor<br>Productivity|Value added by industry (at constant 2000 US$)<br>divided by total employment in the industrial<br>sector|Svasubramanum,2007<br>India Balance of Paymen<br>2008. IMF.<br>Bosworth,Collins,2007 f<br>China|
|Life Expectancy|Life expectancy at birth|World Bank, 2008, WDI|
|Log GDP per capita|Natural log of GDP per capita,which is measured<br>in constant 2000 US$|Planning Commision of<br>2007 for subnational Wo<br>Bank, 2008,WDI|
|ManufacturingGrowth|Growth in manufacturingvalue added|World Bank,2008,WDI|

39