rafmacalaba/datause-encoder-data huggingface dataset:
|DR Congo Private Sector Development and Competitiveness ProjectSteven R. Dimitriyev|
|---|
|DR Congo Private Sector Development and Competitiveness ProjectSteven R. Dimitriyev|
|DR Congo Private Sector Development and Competitiveness ProjectSteven R. Dimitriyev|
|DR Congo Private Sector Development and Competitiveness ProjectSteven R. Dimitriyev|
|DR Congo Private Sector Development and Competitiveness ProjectSteven R. Dimitriyev|
|DR Congo Private Sector Development and Competitiveness ProjectSteven R. Dimitriyev|
|DR Congo Private Sector Development and Competitiveness ProjectSteven R. Dimitriyev|
|DR Congo Private Sector Development and Competitiveness ProjectSteven R. Dimitriyev|
|DR Congo Private Sector Development and Competitiveness ProjectSteven R. Dimitriyev|
|DR Congo Private Sector Development and Competitiveness ProjectSteven R. Dimitriyev|
|DR Congo Private Sector Development and Competitiveness ProjectSteven R. Dimitriyev|
|DR Congo Private Sector Development and Competitiveness ProjectSteven R. Dimitriyev|
|DR Congo Private Sector Development and Competitiveness ProjectSteven R. Dimitriyev|
|DR Congo Private Sector Development and Competitiveness ProjectSteven R. Dimitriyev|
|DR Congo Private Sector Development and Competitiveness ProjectSteven R. Dimitriyev|
|DR Congo Private Sector Development and Competitiveness ProjectSteven R. Dimitriyev|
|RURAL INVESTMENT AND LOCAL GOVERNANCE PROJECT<br>Janelle Plummer|
|RURAL INVESTMENT AND LOCAL GOVERNANCE PROJECT<br>Janelle Plummer|
|RURAL INVESTMENT AND LOCAL GOVERNANCE PROJECT<br>Janelle Plummer|
|RURAL INVESTMENT AND LOCAL GOVERNANCE PROJECT<br>Janelle Plummer|
|RURAL INVESTMENT AND LOCAL GOVERNANCE PROJECT<br>Janelle Plummer|
|RURAL INVESTMENT AND LOCAL GOVERNANCE PROJECT<br>Janelle Plummer|
|RURAL INVESTMENT AND LOCAL GOVERNANCE PROJECT<br>Janelle Plummer|
|RURAL INVESTMENT AND LOCAL GOVERNANCE PROJECT<br>Janelle Plummer|
|RURAL INVESTMENT AND LOCAL GOVERNANCE PROJECT<br>Janelle Plummer|
|Tuberculosis Control<br>John C. Langenbrunner|
|Tuberculosis Control<br>John C. Langenbrunner|
|Tuberculosis Control<br>John C. Langenbrunner|
|Tuberculosis Control<br>John C. Langenbrunner|
|Tuberculosis Control<br>John C. Langenbrunner|
|Tuberculosis Control<br>John C. Langenbrunner|
|Tuberculosis Control<br>John C. Langenbrunner|
|Tuberculosis Control<br>John C. Langenbrunner|
|Tuberculosis Control<br>John C. Langenbrunner|
|Land Administration Project<br>Charles Annor-Frempong|
|Land Administration Project<br>Charles Annor-Frempong|
|Land Administration Project<br>Charles Annor-Frempong|
|Land Administration Project<br>Charles Annor-Frempong|
|Land Administration Project<br>Charles Annor-Frempong|
|Land Administration Project<br>Charles Annor-Frempong|
|Land Administration Project<br>Charles Annor-Frempong|
|Land Administration Project<br>Charles Annor-Frempong|
|Land Administration Project<br>Charles Annor-Frempong|
|Land Administration Project<br>Charles Annor-Frempong|
|Land Administration Project<br>Charles Annor-Frempong|
|Land Administration Project<br>Charles Annor-Frempong|
|Land Administration Project<br>Charles Annor-Frempong|
27 Appendix 2-Description of the GVAR methodology Appendix 3- Description of the variables, summary of VAR estimates for each country, and coefficient of variation of variables included in each VAR.

Appendix 4- Generalized Impulse Response functions for output. Appendix 5- Shocks, identification: dates and magnitude Appendix 6- Generalized Variance Decomposition for the other variables besides output Appendix 7- Rank correlation exercise Appendix 8- Model Selection Summary

# **References**

Agenor, P.R. , J. Aizenman and A. Hoffmaister (1999) "Contagion, bank lending spreads, and output fluctuations". Mimeo. June.

Ahmed, Shaghil (1999) Sources of economic fluctuations in Latin America and implications for choice of exchange rate regimes. Mimeo. Federal Reserve Board. December.

Blanchard, 0. and D. Quah "The dynamic effects of aggregate demand and supply disturbances", American Economic Review, 79, pp.<sup>65 5</sup> -<sup>6 73</sup> .

Calvo, G..,L. Leiderman and C. Reinhart (1993) "Capital inflows and real exchange rate appreciation in Latin America: The role of external factors", IMF Staff Papers, 40, 1, pp 108-151 Canova, Fabio (1995) "The economics of VAR models", Macroeconometrics: developments, Tensions and Prospects, ed. K. Hoover. Kluwe Academic Publishers.

Cashin, P., Lian, H. and C.J. Mc Dermott (1999) "How persistent are shocks to world commodity prices?", IMF Working Papers.WP/99/80.

Gourieroux, C. and A. Monfort (1997) Time Series and Dynamic Models Camnbridge University Press Haque,N., H. Pesaran, S. Sharma (1999) "Neglected heterogeneity and dynamics in cross-country savings regressions". Mimeo. Trinity College. January Hausman, R. and M. Gavin (1995) Hacia una Economia Menos Volatil BID, Informe Annual.

Hoffmaister, A. and J. Roldos (1997) "Are business cycles different in Asia and Latin America?" IMF Working Paper., January.
91

# **Vietnam**

The Ministry of Finance and the Ministry of Industry and Trade exercise control over prices by adjusting import duties, setting rules for price changes based on the movement of the base price (which is calculated using a formula set forth in Decree 84/2009/NÐ‐CP, subsequently amended in Decree 83/2014/NÐ‐CP), setting ceilings on retail prices, and using transfers to and from a fuel price stabilization fund. Unit levies to finance the fund vary from fuel to fuel, with a typical rate of D 300 per unit (liter or kg). Fuel suppliers can adjust prices as long as the base price changes by less than 3 percent. The period between price adjustments is limited to a minimum of 15 days for price increases. If the base price changes by more than 3 percent but less than 7 percent, the fuel stabilization fund is used to finance 40 percent of the price increase in excess of 3 percent. If the base price changes by more than 7 percent, the government considers options in addition to the use of the stabilization fund. These changes are subject to socioeconomic considerations.

Table 19 shows adjustments made to the levies in 2015. After the first adjustment in February, the levies ranged from D 270 to D 820 per unit. In addition, although not shown, the levy on gasoline blended with 5 percent ethanol was set equal to zero to make it cheaper than gasoline and promote fuel switching from gasoline to the gasoline‐ethanol blend.

**Table 19: Fuel price stabilization fund levies in February 2015**

|Date<br>Unit|Gasoline<br>per liter|Kerosene<br>per liter|Diesel<br>per liter|Fuel oil<br>per kg|Gasoline<br>per liter|Kerosene<br>per liter|Diesel<br>per liter|Fuel oil<br>per kg|
|---|---|---|---|---|---|---|---|---|
|Prior to Feb 5, 2015|D 800|D 800|D 800|D 800|US$0.04|US$0.04|US$0.04|US$0.04|
|Feb 5, 2015|D 340|D 520|D 820|D 270|US$0.02|US$0.02|US$0.04|US$0.01|
|Feb 24, 2015|D 300|D 300|D 300|D 300|US$0.01|US$0.01|US$0.01|US$0.01|
|Oct 19, 2015|D 200|D 300|D 300|D 300|US$0.009|US$0.01|US$0.01|US$0.01|
|Since Nov 3, 2015|D 300|D 300|D 300|D 300|US$0.01|US$0.01|US$0.01|US$0.01|

_Source:_ Ministry of Finance.

The transfers from the fund can be substantially larger, and have not always followed the above rules. For example, despite the fact that the increase in the base price from the previous period for every fuel was more than 12 percent on February 24, 2015, the Ministry of Industry and Trade cited socioeconomic considerations, did not change prices, and allowed large withdrawals from the stabilization fund combined with a reduction in the fund levy for gasoline, kerosene, and diesel (BCT‐TTTN No. 1860). Consequently, large net withdrawals from the fund had to be made on March 11 (Table 20), because by March 10, the difference between the base price (item 10 in the table) and the retail price had widened to reach a deficit of D 3,468 (US$0.16) per liter of 92 RON gasoline (VINPA 2015). The retail prices were increased by 10 percent for gasoline and 4.5 percent for kerosene and diesel on March 11, but these price increases were not sufficient.
Ethiopia’s blend of upward accountability with downward engagement resolves the instrumental dissonance implicit in aid-funded programs to combat inequality in a federal context. In Ethiopia, donors are not undermining federalism because officials’ incentives are not downward-looking, as theory predicts, but rather upward-looking, as the party demands. And yet local inputs do not go missing, but are instead provided by explicit mechanisms of grassroots engagement. A federal system that is notably closed to citizens at the national level has significantly opened at the local level. And local engagement has facilitated rapid improvements and rising equality in primary service provision.

# **References**

Barro, R.J. 1997. _Determinants of Economic Growth: A Cross-Country Empirical Study_ . Cambridge, MA: MIT Press.

- Barro, R.J., F. Caselli,  and J.W. Lee (Eds.). 2013. “Symposium on human capital and economic development: An introduction.” _Journal of Development Economics,_ 104: 181–183.

- Bevan, P., C. Dom, and A. Pankhurst. 2010. “Long-term Perspectives of Development Impacts in Rural Ethiopia: Wide Stage One Final Report.” Mokoro Limited, Oxford.

- Boix, C. 2008. “Economic Roots of Civil Wars and Revolutions in the Contemporary World.” _World Politics_ , 60 (3): 390-437.

- Boix, C. 2015. _Political Order and inequality: Their foundations and their consequences for human welfare_ . Cambridge: Cambridge University Press.

- Boone, P. and J.P. Faguet. 1998. “Multilateral Aid, Politics and Poverty: Past Failures and Future Challenges”, chapter 2 in R. Grant and J. Nijman (eds.). _The Global Crisis in Foreign Aid_ , Syracuse: Syracuse University Press.

Brett, E.A. 2009. _Reconstructing Development Theory_ . Basingstoke: Palgrave Macmillan.

- Burnside, C. and D. Dollar. 2000. “Aid, Policies, and Growth.” _American Economic Review_ , 90 (4): 847-868.

Burnside, C. and D. Dollar. 2004. “Aid, Policies, and Growth: Revisiting the Evidence.” World Bank Policy Research Paper No. O-2834, Washington, DC: World Bank.

23
duration of exposure (Extension Toxicology Network, 2004).<sup>11</sup> The health impairment equation took these factors into account, along with the number of years in business, experience, nutritional status, smoking habits and location. Physical amounts, toxicity and duration of exposure are expected to be positively related to the prevalence of becoming sick, as are the number of years in business and smoking habits. Experience is expected to be negatively correlated with health impairment, while the sign on nutritional status can be in either direction.<sup>12</sup> A traders’ health risk perception is specified as a function of health effects, the degree of highly toxic stock, time in the shop, training and source of information. As perception is defined as whether the trader believes that the pesticides they handle are responsible for short- or long-term health effects (=1), we expect actual health impairment (sickness), pesticide amount, toxicity, time in the shop and training to be positively correlated with risk perception.

Two interaction variables were also added to each equation to capture the joint effect between the toxicity of pesticides and training in safe handling as well as toxicity and the use of protective measures while loading pesticides. The interaction term between toxicity and training may have a positive (negative) sign in the health effects equation if the toxicity (training) effect is larger than the training (toxicity) effect. In the second equation, we expect the interaction terms to be positive as increases in either toxicity or training will increase a traders’ perception of hazardous pesticides. The second interaction term between toxicity and protective measures is expected to be negative in the health equation and positive in the perception equation. Model (4) below summarizes the empirical relationship based on Model (1). Variable definitions are provided in Table 3.

_SICKNESS = f_ ( _PESTAMT_ , _PWHOII_ , _PWHOIII_ , _PWHOU_ , _BUSYEAR_ , _AGE, NUTRTON_ , _SMOKENUM, PIabPIITRN, PIabPIIPROT, District Dummies_ ) _PERCEPTION_ = _f_ ( _SICKNESS_ , _PESTAMT, PWHOIab_ , _PWHOII_ , _PWHOIII_ , _TIMEDAY_ , _TRNGSAFE_ , _PIabPIITRN, PIabPIIPROT, SUPPLIER_ ) (4)

> 11 The route of exposure was not directly proxied in the model.

> 12 The sign would be negative if the respondent had a very low weight to height ratio (i.e. malnourished), and positive if a high ratio (i.e. obese).

12
**_15_**

# **Incidence of Multiple Currency Practices**

21. The incidence of multiple exchange rates among Fund members is shown in Table 2. The Fund's **Annual Report on Exchange Arrangements and Exchange Restrictions has** reported that developing countries have, in general, moved towards reduced reliance on multiple currency practices, or made progress in either simpl;fying multiple currency practices, or in reducing the distortions associated with them. **A** look at Table 2 reveals that virtually all countries with multiple currency practices employ different rates for different types of imports (e.g.; essential imports against payments on invisibles) as well as for exports. Multiple exchange rates for about 20 member countries are shown in the **IFS.**

# **Deviations of** Multiple Rates

22. The existence of a multiple exchange system provides indications that the use of the official rate as a conversion factor is inappropriate. The larger the deviations in the multiple rates relative to the ficial rate, the stronger the reasons for a careful and systematic review of the various rates. The focus on multiple rates should be determined on the basis of the magnitude of the premium as well as the size of the market, i.e., how much economic activity is performed through this channel.

> **"** For a detailed discussion of developments in multiple currency practices, see International Monetary Fund, Annual Report on Exchange Arrangements and Exchange Restrictions, (Washington,D.C.; International Monetary Fund, **1986); pp. 18-20.**
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of goods (and services) and the potentially different effects RTAs can have on them. The increasing trade in intermediate goods has been the most relevant characteristic of this globalization phase since at least the early 1990s. Disentangling the effect of RTAs for these two types of uses can help better understand how trade policy has been working in the region. Table (5) differentiate by sector and type of good. At the world average level, the only significant difference is in services, with the effect being stronger for input services, probably due to the nature of market services that are more used as intermediate consumption rather than final services and RTAs might reduce more trade barriers for the intermediate consummation of services. For RTA signed between a South Asian country and the rest of the world, it is the case across all sectors that the additional positive effect is always stronger for the final consumption of goods and services, in particular for agricultural goods, there is no additional effect with respect to the world average effect. Finally, for intra-regional trade agreements, we see that the additional effect is again stronger for the final consumption of goods and services, and the intermediate consumption for services gets the same effect from RTAs as those of South Asia with third countries. The general result here is that in South Asia trade agreements tend to stimulate more trade in final goods and services.

||Manufa|cturing|Agric|ulture|Ser|vices|
|---|---|---|---|---|---|---|
||(1)<br>Finals|(2)<br>Inputs|(3)<br>Finals|(4)<br>Inputs|(5)<br>Finals|(6)<br>Inputs|
|RTA|0.10<sup>_∗∗∗_</sup>|0.09<sup>_∗∗∗_</sup>|0.08<sup>_∗∗∗_</sup>|0.08<sup>_∗∗∗_</sup>|0.03<sup>_∗∗∗_</sup>|0.05<sup>_∗∗∗_</sup>|
||(11.36)|(11.30)|(8.64)|(5.43)|(3.76)|(7.09)|
|RTA_×_ SAR|0.16<sup>_∗∗∗_</sup>|0.08<sup>_∗∗∗_</sup>|0.06<sup>_∗∗_</sup>|-0.00|0.26<sup>_∗∗∗_</sup>|0.18<sup>_∗∗∗_</sup>|
||(6.97)|(4.84)|(2.45)|(-0.06)|(10.45)|(8.69)|
|RTA_×_ SAR-intra|0.23<sup>_∗∗∗_</sup><br>(5.17)|0.19<sup>_∗∗∗_</sup><br>(5.75)|0.45<sup>_∗∗∗_</sup><br>(9.50)|0.29<sup>_∗∗∗_</sup><br>(4.81)|0.25<sup>_∗∗∗_</sup><br>(6.45)|0.02<br>(0.57)|
|_N_|928746|933660|928746|933660|928746|933660|

**Note:** the variable RTA measures the world average effect of trade agreements. The SAR effect is measured in addition to the average, and SAR-intra is in addition to the SAR effect. _t_ statistics in parentheses. * _p <_ 0.10, ** _p <_ 0.05, *** _p <_ 0.01.

Table 5: South Asia results by type of good Table (5) evaluated bilateral GVC integration using trade in parts and components. This way we can use the structural gravity model. Table (6) uses a different way, sub12
Dollar, David. 2015. “China’s rise as a regional and global power” _Horizons - Journal of_ _International Relations and Sustainable Development_ . Summer, Issue 4:162-172.

Financial Times. 2016. Hudson Lockett and Lucy Hornby. “China routes RMB 5tn into transport infrastructure,” May 11 2016.

International Energy Agency (IEA). 2010. _Projected Costs of Generating Electricity_ , IEA and OECD, Paris.

International Energy Agency (IEA). 2014. _World Energy Outlook 2014_ , IEA, Paris. International Monetary Fund (IMF) (2014), World Economic Outlook, IMF, Washington, DC.

Nielsen, Chris and Mun Ho (eds.). 2013. _Clearer Skies over China: Reconciling Air Quality, Climate and Economic Goals_ , MIT Press, Cambridge, MA.

- Qi, Tianyu, Niven Winchester, Da Zhang, Xiliang Zhang and Valerie Karplus. 2014. The Chinain-Global Energy Model. MIT JPSPGC Report No 262, May.

Sue Wing, Ian, Kathryn Daenzer, Karen Fisher-Vanden and Katharine Calvin. 2011. Phoenix Model Documentation. PNNL Joint Global Change Research Institute. Available at: <u>http://www.globalchange.umd.edu/models/phoenix/</u> Vennemo, Haakon, Jianwu He and Shantong Li. 2014. Macroeconomic Impacts of Carbon Capture and Storage in China. Environmental and Resource Economics, 59(3):455-477.

World Bank and Development Research Center (DRC). 2013. China 2030. The World Bank, Washington DC and Development Research Center of the State Council, People’s Republic of China.

‐30‐
is a trade agreement between the two countries (a bilateral agreement or a belonging to a common regional trade agreement). The variable comes from the CEPII Gravity data set that we have updated until the year 2010. Data on the existence of a bilateral social security agreement has been drawn from _Natlex_ , the database of national labor, social security and related human rights legislation published by the ILO's International Labor Standards Department. Finally, data on life expectancy difference between countries has been built from the World Development Indicators (2012). Appendix B presents the definition of variables and their source, and descriptive statistics are summarized in Appendix C. Our sample includes 195 countries, 52 of which are in Africa, and as many trading partners over the years 1980, 1990, 2000 and 2010.

# **4. RESULTS**

# 4.1. World versus Africa: Migrants as a substitute for weak institutions

We begin our analysis by estimating equation (1) for the entire world. To assess the distinctive effect of African migrants, we add an interaction term indicating whether migrants originated from the African continent or not ( _Migrants_ij*Africa_i_ ). If the trade creation effect is more important for African countries, we expect the coefficient associated with the interaction term _Migrants_ij*Africa_i_ to be positive and statistically significant. The corresponding results are presented in Table 1.

The first column suggests, with the OLS estimator, a mean positive and statistically significant effect of the diaspora from country _i_ established in country _j_ ( _Migrants_ij)_ on exports from _i_ to _j_ , all over the world. This effect appears even larger in the case of migrants originating from Africa since the coefficient of the interaction term for migrants coming from Africa is significantly positive.

Furthermore, all the control variables exhibit the expected sign. The level of GDP of both the source and the origin markets are positively linked with exports. A large distance between country-pairs is associated with a significantly lower value of trade. The contiguity between countries, the share of a common language, the existence of former colonial ties and the existence of free trade agreements are all positively related to trade.

11
results are robust to using the indicator from earlier years (1998–2007) and later years (2009–2012).<sup>20</sup> The correlation between the WGI political instability indicator and the ACD conflict dummy is 0.58, and the correlation between the Transparency International measure and the WGI corruption measure is 0.81. Both correlations are significant at the 1 % level. For the empirical analysis we create lagged three year averages of the two WGI measures.

# **III**

To obtain an estimable equation, we take logs of equation (2), rewrite average costs<sup>_C_</sup><sup><u>(</u></sup><sup>_w_</sup><sup><u>1</u></sup> _q_<sup><u>,</u></sup><sup>_w_</sup><sup><u>2,</u></sup><sup>_<u>q</u>_</sup><sup><u>)</u></sup> as _c_ , denote _α_ + _δβ_<sup>=</sup><sup>_γ_,1</sup><sup>_−_</sup> _α_<sup><u>(</u></sup><sup>_α_</sup> +<sup>+</sup> _β_<sup>_<u>β</u>_</sup><sup><u>)</u></sup> = _φ_ 1, _α_ + _αβ_<sup>=</sup><sup>_φ_2,</sup> _α_ + _<u>ββ</u>_<sup>=</sup><sup>_φ_3, add an error term, and fixed effects</sup> for work activities, time, and region, as well as subscripts for work activities, work types, countries, and time. We thereby obtain for work activity _a_ = 1,..., _A_ , work type _p_ = 1,..., _P_ , country _i_ = 1,..., _N_ , and time _t_ = 1,..., _T_ , where _c_ is the cost per kilometer and _q_ is a dummy variable that is equal to one if the length of the road is above 50 km; we do not have data on the cost of labor and capital for each construction project. Rather than estimating the technological parameters, our controls are selected to proxy for the determinants of factor prices. The cost of capital is going to be a function of access to markets, so we include the distance to the nearest ice-free coast from Nunn and Puga (2012) as a measure of the price of capital and equipment. For about half of the road work activities, we know whether the terrain in which the road works are undertaken is flat, mountainous, hilly, or rolling. We include these as dummy variables,

> 20A popular source for perception-based data on institutions, due to its coverage across countries and time, is the International Crisis Research Group (for example, Alesina and Weder (2002), Fisman and Miguel (2007), Ahmed (2013), Svensson (2005), Wei (2000)). We do not include this measure due to questionable scores as highlighted by Treisman (2007), and the fact that the ICRG measure reflects the _political risk_ associated with corruption, rather than a country’s level of corruption (Graf Lambsdorff 2005); the ICRG website does not provide information on how the scores are constructed.

13