rafmacalaba/datause-encoder-data huggingface dataset:
(columns I and II, Table 6). This is still a conservative assumption for future skilled labor supply growth, comparable to the least optimistic projections of IASSA’s labor projections (KC et al., 2010).<sup>16</sup> The impacts of the pipeline effect on the skill share of the labor supply can thus be substantial depending on the benchmark year educational attainment rates of the cohorts entering the working age population and the rate of the growth of the working age population. For example, skilled workers account for 67 percent of the total employed in 2007 in South Africa, and this share rises to 75 percent by 2030 when the pipeline effect is considered. In contrast, high-income economies like the USA have virtually no improvements in the skilled labor shares of their labor supply, since skilled labor already accounts for more than 80 percent of the total labor supply. When even current-year educational attainment rates are allowed to interact with demographic change, there can be a substantial improvement in the number of skilled workers (columns I and II, Table 6).

**Table 6: Skilled workers as a share of total labor supply in 2030 under alternate scenarios (%)**

||_Baseline_|_Skilled Pipeline_|_Education Improvement_|
|---|---|---|---|
||I|II|III|
|Burkina Faso|3.7|4.4|13.8|
|Botswana|58.9|70.6|89.2|
|Côte d’Ivoire|38.7|39.8|65.0|
|Cameroon|20.9|24.3|40.3|
|Ethiopia|36.5|41.4|73.5|
|Ghana|37.5|40.7|55.7|
|Kenya|35.9|38.2|57.6|
|Madagascar|18.8|20.4|46.7|
|Mozambique|1.5|2.0|6.4|
|Malawi|18.8|20.4|43.1|
|Nigeria|34.0|39.2|58.3|
|Rwanda|18.8|20.4|46.3|
|Senegal|11.9|12.7|32.7|
|Tanzania|7.0|7.1|22.7|
|Uganda|20.1|22.0|42.5|
|Rest of SSA|23.9|24.5|48.6|
|South Africa|66.6|74.8|89.0|
|Zambia|19.4|19.8|39.2|
|Zimbabwe|18.8|20.4|37.2|
|SSA|25.3|27.5|48.8|

Note: Zimbabwe is not covered in the I2D2 database, and so the skilled labor supply with improvements in educational attainment cannot be estimated. The skilled labor supply is thus assumed to be the same as that of the Sub-Saharan Africa as a whole.

Source: Author’s estimates and simulation results The pipeline scenario cannot account for possible improvements in educational attainment that countries may experience, especially in countries with very low attainment rates. Estimates from the Barro and Lee (2013) database show that such improvements in educational attainment have occurred before in the region – in Botswana (1980-2000), Ghana (1960-1980), South Africa (1985-2005), and

> 16 This scenario incorporates the pipeline effect of the share of skilled workers in the 25-29 years old age cohort. As far as this age cohort is more educated that the older cohorts, which is an observed fact for developing African countries, the share of skilled workers increase under the assumption that new generations have at least the same level of education as the one observed for the 24-29 age cohort.

22
35

# Notes

1. It should be emphasized at the outset, that this paper is not meant to be an exhaustive survey of the litewature. For recent surveys of the literature on "political business cycles" the reader is refered to Alesina (1988) and Nordhaus (1989). For a survey of the literature on the political economy of development in LDCs, see Roubini (1990). For a survey of the theoretical contrbutions in the "new political macroeconomics", with emphasis on fiscal policy see Persson and Tabellini (1990) and Alesina and Tabellini (1992). For a survey of the traditional Public Choice literature see Mueller (1989).

2. See Akerlof and Yellen (1985a,b) for applications of "near rational models" to product and labor markets.

3. This section is largely based upon Sections 1 and 2 of Alesina and Roubini (1990).

4. Whether inflation starts to increase before the election or only after the election, depends upon the exact specification of the model. See Lindbeck (1976) for a discussion of this point. 5. The United States is quite a good case for this theory. For a general theoretical and empirical model of macroeconomic outcomes and elections in the US see Alesina, Londgregan and Rosenthal (1990).

6. Alvarez, Garret and Lange (1991) have investigated the role of labor organizons in a 'pardsan' model of macroeconomic policy.

7. See Roubini (1990) for a recent survey of this literature.

8. See Grossman (1991) for an interesting formalization of the probability of successful insurections against dictators.
<!-- Start of picture text --> 25<br>225<br>20<br>53<br>x6<br>&<br>===<==<=<<<br>io<br>65<br>55Le5LeLe m2oeoe 0.0<br>1 2 2 4 5 6 Total<br><!-- End of picture text --> <!-- Start of picture text --> 50<br>461<br>45<br>403535<br>30<br>aFa225aB&20Fa225aB&20225aB&20aB&20B&20&2020 229<br>S17 2s<br>99<br>2<br>°<br>3 | oF0707 0.0 00<br>1 2 3 4 5 6 Total<br><!-- End of picture text -->

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**Table 2:** Selected economic indicators, 2004-2008

||2004|2005|2006|2007|2008<br>(prelim.)|
|---|---|---|---|---|---|
|||_Annual pe_|_rcentage chan_|_ge_||
|Real GDP|5.3|7.2|7.3|7.9|8.5|
|Inflation (CPI average)|12.0|9.0|8.9|9.1|15.4|
|Exports, f.o.b. (U.S. dollars)|55.3|28.7|16.9|20.1|25.3|
|Imports, f.o.b. (U.S. dollars)|13.1|28.2|26.2|30.2|42.3|
|Export volume|26.9|14.4|12.7|6.2|13.0|
|Import volume|6.7|26.1|27.0|31.0|24.9|
|Terms of trade|15.5|10.7|4.3|13.7|-2.6|
|||_Perc_|_ent of GDP_|||
|**Balance of Payments**||||||
|Current account balance||||||
|Including official transfers|-1.9|-1.1|-7.4|-4.9|-6.9|
|Excluding official transfers|-15.9|-15.6|-16.1|-15.5|-18.1|
|Gross official reserves (months of imports)|5.9|6.2|5.6|5.2|5.3|
|External debt stock (end of year)|84.9|63.0|16.9|16.8|15.4|
|**Public Finance**||||||
|Total revenue (excl. grants)|12.9|13.5|13.3|13.6|14.2|
|Total expenditure|24.1|25.6|24.5|24.9|27.1|
|Fiscal deficit (excl. grants)|-11.2|-12.0|-11.1|-11.3|-13.0|
|Fiscal deficit (incl. grants)|-0.2|0.6|-0.4|-1.5|0.1|
|_Memorandum items_||||||
|Exchange rate RwF/US$ (average)|575|556|552|547|552|
|GDP per capita (USD, constant prices)|170|185|197|210|222|
|Nominal GDP(million USD)|1,981|2,398|2,833|3,412|4,230|

_Source:_ IMF.

These challenges are recognized by the Economic Development and Poverty Reduction Strategy (EDPRS), which is based on the foundation laid by Rwanda’s first Poverty Reduction Strategy Paper (PRSP), covering the period 2002-2006, and focused primarily on the sectors that would yield immediate growth benefits and build the human resource and infrastructure base. The EDPRS refocused Rwanda’s priority on growth and human development, with emphasis on decentralization, and a greater role for the private sector. The EDPRS provides the medium-term strategy for the next five years (2008-2012) in the context of achieving the Millennium Development Goals in line with Rwanda’s Vision 2020, which states the objective that Rwanda reach middle-income status by 2020. More specifically, the review of the current situation as well as the recent socio-economic performance led to the following objectives for the EDPRS: increase economic growth, slow down population growth, tackle extreme poverty and ensure greater efficiency in poverty reduction.

**Table 3:** MDG progress in Rwanda

|**MDG indicator**|**2000**|**2006**|**Target 2015**|**Target 2020**|
|---|---|---|---|---|
|MDG 1: Poverty headcount (% of population)|60|57|30|30|
|MDG 2: Primary school completion rate<br>(gross, % of relevant age group)|22|52|100|100|
|MDG 4: Under-5 mortality rate<br>(per 1,000 births)|196|152|50|50|
|MDG 5: Maternal mortality rate<br>(per 100,000 births)|1071|750<sup>a</sup>|268|200|
|MDG 7a: Access to safe drinking water<br>(% of population)|64|64<sup>a</sup>|82|100|
|MDG 7b: Access to improved sanitation facilities<br>(% ofpopulation)|37<sup>b,c</sup>|38<sup>b</sup>|69|n/a|

_Source:_ EDPRS, September 2007 and Millennium Development Goals – Rwanda Country Report 2007;<sup>a</sup> data from 2005,<sup>b</sup> World Bank estimates,<sup>c</sup> data from 1990.

5
84

**Figure 23: Oil fund balance in Thailand**

<!-- Start of picture text --> 1,500<br>1,000<br>500<br>0<br>‐500<br>‐1,000<br>‐1,500<br>‐2,000<br>‐2,500<br>Jan‐01 Jan‐03 Jan‐05 Jan‐07 Jan‐09 Jan‐11 Jan‐13 Jan‐15<br>US$ million<br><!-- End of picture text --> _Source_ : World Bank staff calculations based on data at http://www.eppo.go.th/info/8prices_stat.htm.

There are three sources of LPG in Thailand: imports, refineries, and gas separation plants. Until February 2015, the price of LPG at the gate of gas separation plants was set at US$333 per tonne. During this period of price freeze, Saudi Aramco contract prices for LPG rose to as high as about US$1,200 per tonne in March 2012 and December 2013.

In July 2011, in preparation for ending the policy of fixing the ex‐plant price of LPG at US$333 per tonne and removing the subsidy, the government moved to a two‐tier pricing system for LPG: low price for households and higher price for industrial users. In February 2013, automotive LPG was added as a third price category, and in September 2013, a fourth category—low‐income households—was added as the government began to raise prices for other households. In December 2014, the prices charged to the first three categories were equalized, and in February 2015, all categories except that for low‐income households were merged into one and no longer had access to subsidized fixed ex‐gate prices. However, through frequent adjustments of the Oil Fund levy, the retail prices changed infrequently in 2015, and in November and December 2015, the Oil Fund was again subsidizing LPG. The history of the LPG pricing reform is provided in Figure 24.

**Figure 24: History of differential LPG pricing in Bangkok, Thailand**

<!-- Start of picture text --> 30<br>25<br>20<br>15 Low‐income households Cooking LPG<br>Industry Automotive<br>10<br>Jan‐11 Jul‐11 Jan‐12 Jul‐12 Jan‐13 Jul‐13 Jan‐14 Jul‐14 Jan‐15 Jul‐15<br>B/kg<br><!-- End of picture text --> _Source_ : http://www.eppo.go.th/info/8prices_stat.htm.
<!-- Start of picture text --> 2006<br>6 ates<br>if<br>4 fe * x<br>a \<br>Hf S nN<br>2<br>&<br>0 So —<br>-2<br>4 5 6 7 :) 9 10<br>Aggregate Consumption<br>—— Control<br>—---- Treatment<br><!-- End of picture text --> <!-- Start of picture text --> 3s 2<br>a<br>0<br>-2<br>4 5 Aggregate6 Consumption7 _ 8 9 10<br><!-- End of picture text --> <!-- Start of picture text --> 2011<br>, “si<br>6 ish<br>f<br>é<br>4 q<br>i ~<br>== i sNy<br>S 2 ‘\<br>a YX \ oe<br>0 =<br>-2<br>4 5 6 7 8 9 10<br>Aggregate Consumption<br>—— Control<br>-—---- Treatment<br><!-- End of picture text --> <!-- Start of picture text --> 15 ]I<br>itq<br>q<br>A !<br>3<br>5!<br>2 05 4<br>1<br>a i<br>-.05 iti<br>i!i<br>-1 f<br>4 5 Aggregate6 Consumption7 _ 8 9 10<br>——— Difference-Difference<br><!-- End of picture text --> <!-- Start of picture text --> 2006<br>6 zis ‘<br>1 ‘<br>4 hq Ss N<br>Il<br>q<br>a i 1 $<br>ec ! “SN<br>0 hb !q Sey, == -_—____-<br>qlI1<br>-2 f<br>4 5 6 7 _ 8 9 10<br>Aggregate Consumption<br>—— Control<br>—---- Treatment<br><!-- End of picture text --> <!-- Start of picture text --> 4<br>3s 2<br>a<br>0<br>-2<br>4 5 6 7 8 9 10<br>Aggregate Consumption<br><!-- End of picture text --> <!-- Start of picture text --> 2011<br>| \<br>LWI<br>‘<br>6 ae<br>q ‘<br>1 ;<br>24a ?i H y<br>FE i \<br>2 ie efi 11q y x 2 \<br>0oa qi =<br>-2 4<br>4 5 6 qT 8 9 10<br>Aggregate Consumption<br>—— Control<br>—---- Treatment<br><!-- End of picture text -->

<!-- Start of picture text --> A qt<br>f<br>3<br>5 .05 H<br>2 q<br>£ qi<br>2 q<br>a f<br>qI<br>-.05 iu1<br>H<br>4 5 Aggregate6 Consumption7 _ 8 9 10<br>——— Difference-Difference<br><!-- End of picture text -->
We note that the high growth rate attained during 2004-08 reflected in robust growth rates in all domestic sectors (Figure 16). In contrast, several sectors have lagged in the last decade. Economic growth has been increasingly driven by consumption (private and public) since 2009, while two important engines of growth, private investment and exports, have continued to underperform. This trend is particularly concerning as investments and exports are not just important direct sources of growth and productivity, they also determine the technological capability, as well as the competitiveness of a country’s production structure. We infer that, in an accounting sense, sustaining a growth rate higher than that indicated by the trend growth rate of 7.0-7.5 percent, will require contributions from all domestic sectors. Besides, at a time, when the economy is fairly open, it will be difficult to sustain such levels of growth only with the support of domestic factors, and will require support from the global economy.

**Figure 16: Growth during 2004-08 built on robust growth across domestic and external sectors**

16 A: Average growth across uses during and 16 B: Average growth across sectors during and after the boom years of 2004–08 after the boom years of 2004–08 <!-- Start of picture text --> 25<br>20.1 14 12.5<br>18.7<br>20 17.8<br>12<br>9.7 10.0<br>9.2<br>15 10 8.7<br>7.7<br>8.8 8 7.1 6.8<br>10 7.1 7.27.4 6.7 5.4<br>5.1 5.8 6 4.9 4.8<br>5 4 3.0<br>0 2<br>0<br>2004-08 2009-17<br>2004-08 2009-17<br>GDP Exports Imports GVA Services<br>Consumption Capital Formation Agriculture Manufacturing Construction<br>Other Industries<br><!-- End of picture text --> Note _:_ Central Statistics Office data. Data are in constant Indian rupees (INR). Years are fiscal years.

32
# **References**

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Andersson, M., O. Hall, N. Olén, and A. Tolonen (2015). ‘Does Large-Scale Gold Mining Reduce Agricultural Growth? Case Studies from Burkina Faso, Ghana, Mali and Tanzania’. Unpublished paper.

Andres, A.R., and C. Ramlogan-Dobson (2011). ‘Is Corruption Really Bad for Inequality? Evidence from Latin America’. _Journal of Development Studies_ , 47: 959–76.

Angrist, J.D., and A.D. Kugler (2008). ‘Rural Windfall or a New Resource Curse? Coca, Income, and Civil Conflict in Colombia’. _Review of Economics and Statistics_ , 90(2): 191–215.

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18
Table 7: Counterfactual 4): 10% bilateral decrease in US-China trade costs Counterfactual 4A: Baseline calibration with _τ_ = 1 _._ 5

|I|mporter-exporter|With cr|oss-border|No|
|---|---|---|---|---|
||pair|fragm|etation|fragmentation|
|||_All trade_|_Final goods_|_Final goods_|
|∆log_πni_|USA-CHN|0.138|0.151|0.119|
||CHN-USA|0.087|0.120|0.090|
|Trade cost elasticities|USA-CHN|4.399|4.828|3.893|
||CHN-USA|3.609|4.071|3.962|

Counterfactual 4B: Starting from twice lower trade costs, _τ_ = 1 _._ 25

||Importer-exporter|With cr|oss-border|No|
|---|---|---|---|---|
||pair|fragm|etation|fragmentation|
|||_All trade_|_Final goods_|_Final goods_|
|∆log_πni_|USA-CHN|0.095|0.101|0.066|
||CHN-USA|0.053|0.066|0.040|
|Trade cost elasticities|USA-CHN|5.273|5.601|3.794|
||CHN-USA|4.265|3.997|3.895|

of trade costs. In each exercise we track two outcomes in our model (total trade and trade in final goods alone), and track final goods trade in the model with no trade in intermediates. We report the percentage change in trade flows in each direction, and a trade elasticity measured as ∆log _π_<sup>_π_</sup> _<u>nn</u>_<sup>_<u>ni</u>_We hold wages fixed in these simulations because this partial elasticity is conceptually</sup> ∆log _τni_<sup>.</sup> closer to empirical estimates of the trade elasticity than its general equilibrium counterpart.

In the baseline calibration we find somewhat larger trade responses in final goods when fragmentation is allowed. This is consistent qualitatively with the Proposition 4, which demonstrated magnification of the final goods trade elasticity in a two-country version of the model. When we look at trade responses using trade that includes intermediates, the change in the calculated trade elasticity is ambiguous. These results show that the qualitative predictions of magnification in the theory have relatively little quantitative importance at existing levels of implied trade costs.

We impose lower levels of trade costs ( _τ_ = 1 _._ 25), recalibrate as before and again simulate reductions in _τ_ . In this case, magnification through fragmentation is quantitatively more important. In both directions the elasticity of total trade and trade in final goods to changing trade costs are larger in the fragmentation model than in the model without fragmentation.

50
**_Energy demand models for policy_** **_<u>formulation</u>_**

that is lower than the predicted outcome. The balance will then be supplied from renewable energies. The Optimal Renewable Energy Mathematical (OREM) model then selects the renewable energy technology options by minimizing the cost/ efficiency ratio subject to social acceptance, reliability, demand and potential constraints. 38 renewable energy options for various end-uses have been considered to generate realistic distribution of renewable energy use. Similar modeling efforts are reported in Suganthi and Williams (2000).

Paga and Birol (1994) used a log-linear relationship to estimate aggregate oil demand in 8 developing countries using data for the 1971-1991 period. They then projected the oil demand for 2000.

Pokharel (2007) has developed static log-linear Cobb-Douglas functions for Nepal for different fuels (8 fuels) and energy consuming sectors (5 sectors). Energy demand has been estimated using economic variables (such as GDP, prices) and demographic variables (population) and the estimation was based on data for the period 1988-2002. The estimated relationships were used to forecast demand for 2007 and 2012. Although the actual data for 2007 is not yet available, it appears that the reliability of the forecasts made here is not very high. In the case of fuel wood, the demand appears to be underestimated as the IEA data for combustible renewable energies for 2005 is much higher than the wood fuel forecast.

Sharma et al (2002) have developed econometric models for forecasting electricity, coal and petroleum products for the Indian State of Kerala. Electricity demand was estimated at the sector level while for other products only aggregate product demand functions were estimated. Single equation models for each sector/ product using OLS was estimated but corrected for auto-correlation. Demand forecasts were presented for four periods, 2005/06, 2010/11, 2015/16, and 2021/21. Unlike other studies where forecasting of independent variables is summarily presented, this study contains a detailed documentation of this aspect.

48