rafmacalaba/datause-encoder-data huggingface dataset:
increase the competitiveness in other markets. Finally, this channel is particularly important for firms located in Europe and Central Asia that expand their exports to non-BRI countries.

_Table 5: Changes in Trade Among BRI Countries_

||from BRI to BRI|East Asia<br>and<br>Pacific|Europe<br>and<br>Central<br>Asia|Middle<br>East and<br>North<br>Africa|South<br>Asia|Sub-<br>Saharan<br>Africa|non-BRI<br>Area|
|---|---|---|---|---|---|---|---|
||East Asia and Pacific|5.88|8.63|10.98|0.75|-4.05|9.86|
|ers|Europe and Central Asia|0.27|9.59|13.69|0.29|23.82|18.35|
|port|Middle East and North Africa|-1.76|37.87|3.76|25.90|8.21|8.59|
|Ex|South Asia|5.98|13.86|8.52|1.12|-1.45|5.65|
||Sub-Saharan Africa|16.95|22.37|11.00|17.43|-0.28|15.03|

Complementary policies that promote trade facilitation and reduce preferential tariffs among BRI economies would boost their exports. A reduction in border delays would magnify the effects of BRI transportation projects on exports from BRI economies by a factor of three (Figure 8, Panel A). Specifically, if in addition to an improved infrastructure network, border delays were reduced by half, BRI economies could experience export growth of 28.1 percent. This effect is not surprising given the high delays at the border in many BRI economies. Indeed, Panels B and C show that the largest effects would be for low income economies and for Central Asian countries that tend to experience larger border delays. The impact of infrastructure projects could be magnified by a reduction in tariffs among all BRI economies which would create more trade especially among participating countries. Not surprisingly, regions with higher tariffs, such as South Asia, would experience larger trade effects under this policy scenario.

30
<!-- Start of picture text --> Production<br>DISASTER<br>Baseline production<br>(no disaster)<br>om © ‘ame am 6 Gmc aw 6<br>"<br>uAK<br><!-- End of picture text --> <!-- Start of picture text --> Actual production<br>(with disaster)<br>v<br>Time when 95%<br>of damages are<br>repaired<br><!-- End of picture text -->
**GMM System Model:** Since there is the possibility of endogeneity between infrastructure and its determinants, the study uses a panel dynamic model in the form of a GMM-system model given its superiority over the difference GMM estimator to identify long-run determinants of infrastructure development. Studies suggest that the estimation in first differences has a large bias and low precision in finite samples mainly due to the persistency of the series (Blundell and Bond, 1998; Blundell, Bond, and Windmeijer, 2000). In the presence of persistency of series, the lagged instruments are less correlated with current first-differences used in the firstdifferences estimation and so they turn out to be weak instruments. Alternative to this, Blundell and Bond (1998) propose to estimate a system of equations augmented with additional instruments in the form of level lag. Blundell and Bond (1998) show that the system GMM estimator performs better than the DIF GMM estimator because the instruments in the level model remain good predictors for the endogenous variables even when the series is very persistent.

**Panel Unit Root and Cointegration Results:** The result analysis starts by examining the stationary properties of the relevant variables by using the CIPS panel unit root test proposed by Pesaran (2007). The tests are conducted using two specifications: ‘intercept’ and ‘intercept with time trend’. The results are presented in Table B1. The results suggest that most of the variables (except FINFRA, and CRE) are non-stationary in their levels or I (1) process.

**Table B1: Results of CIPS Panel Unit Root Test**

|Variables|At L|evel|First<br>difference|Conclusion|Pesaran CD<br>Test|
|---|---|---|---|---|---|
||Intercept|Intercept<br>with trend|intercept|||
|PINFRA|-1.66|-2.46|-2.62*|Non-stationary|-47.45**|
|SINFRA|-1.94|-2.35|-2.61*|Non-stationary|-23.76**|
|FINFRA|-3.05*||-3.75**|stationary|-45.64**|
|INV|-1.43|-1.54|-2.75*|Non-stationary|-35.99**|
|PGSDP|-2.27|-2.67|-4.10**|Non-stationary|-47.22**|
|GSDP|-2.09|-2.47|-4.2**|Non-stationary|-44.22**|
|CEXP|-1.64|-1.97|-2.60*|Non-stationary|-27.07**|
|UR|0.14|-1.99|-1.94*|Non-stationary|-47.66**|
|Debt|-1.65|-1.87|-3.57**|Non-stationary|-31.08**|
|PDEN|1.83|1.70|-3.29**|Non-stationary|-47.90**|
|CRE|-2.45*|||stationary|-44.45**|

_Notes: “** and *” indicates rejection of null of unit root at 1% and 5 levels respectively._ The panel cointegration test is conducted using two specifications: ‘intercept’ and ‘intercept with time trend and AIC criteria is used to select the lead and lag orders for the cointegration test. The results of the panel cointegration test are provided in Table B2. The results reveal that 35
points as illustrated in Figure 3: discrepancies between budgeted and disbursed funds at the central level; differences in outflows from one level to inflows at the subsequent level; differences between inflows and outflows within a specific level; and leakages across multiple levels in the chain of budget allocations (Savedoff 2008).

**FIGURE 3. OVERVIEW OF POTENTIAL LEAKAGE POINTS**

<!-- Start of picture text --> Level and Recorded Information Potential Leakage<br>Budgeted Policy Changes<br>Level 1 Authorized Diversion, Theft, Administration<br>Disbursed<br>Diversion<br>Inflow<br>Diversion, Theft, Administration<br>Level 2<br>Outflow<br>Diversion<br>Inflow<br>Level 3 Diversion, Theft, Administration<br>Outflow<br>Diversion<br>Inflow<br>Facility Absenteeism, Theft<br>Outputs<br><!-- End of picture text --> Source: Savedoff (2008).

Evidence from PETS illustrates the extent of leakages in the health sector and provides tentative leakage patterns (Table 5). Certain types of funding seem to be more susceptible to leakages: discretionary as opposed to rule-based, in-kind (e.g. medical supplies and drugs) compared to budgets for those items, and non-wage versus wage funds, mainly because rules are more transparent, set clear parameters, and can be more easily monitored and audited (Gauthier 2006; Savedoff 2008).

**TABLE 5: BUDGET LEAKAGES OF NON-WAGE FUNDS IN HEALTH IN** **<u>SELECTED COUNTRIES, 1999-2001</u>**

||**Expenditure program**|**Leakage (%)**|
|---|---|---|
|Ghana (2000)|Discretionary non-wage funds|80|
|Uganda (2000)|Drugs and supplies|70|
|Tanzania (1999)|Non-wage funds|41|
|Ghana (2000)|Wage funds|20|

Source: Lindelow, Kushnarova, and Kaiser (2006).

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ppm would require global emissions to be around 25 percent below the current (i.e., 2005) levels. All the models presented in Grubb et al (2006) find that the stabilization of atmospheric CO2 concentrations at 450 ppm level require global CO2 trend to be reversed by 2025 at the latest and CO2 emissions in 2050 would be 8 to 32 percent below from the 2000 level.

# **5. Reductions in CO2 Emissions under the Stabilization Scenarios**

The percentage reduction of CO2 emissions in different countries/regions from their 1990 levels under both scenarios are presented in Table 4. Under Scenario 1, global CO2 emissions in 2020 and 2030 would be 46 percent and 52 percent higher than thier1990 levels. While Annex I countries’ total emissions in 2020 and 2030 would be respectively, 21 percent and 26 percent lower from their 1990 levels, non-Annex countries’ total emissions would be two times higher from their 1990 levels in 2020 and 2.4 times higher in 2030. Under Scenario 2, global CO2 emissions in 2030 would be 10 percent lower than that under Scenario 1 (i.e., 42 percent above the 1990 levels); nonAnnex I countries’ total emissions in 2030 would still be twice as high as that of 1990 levels.

**Table 4: Percentage Reduction of GHG Emissions from their 1990 Level**

|Country/Region||Percentage|Changein|CO2Emi|ssions||
|---|---|---|---|---|---|---|
|||Scenario1|||Scenario2||
||2010|2020|2030|2010|2020|2030|

-23-
BURKINA FASO’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE This emphasis on increasing power trade brings benefits on many fronts. From the cost perspective, it is well documented that costs per kilowatt-hour (kWh) of power produced by SONABEL are just short of $35 cents, among the highest in Africa, mostly driven by fuel prices. Even with high tariffs, the current level of power costs call for subsidies and represent enormous fiscal costs on the order of $50 million per year. Imported electricity has been negotiated for at 7 cents/kWh, less than 20

**Figure 17. Prevailing tariffs are in line with operational costs; total costs should fall in the medium term**

<!-- Start of picture text --> 40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>Existing Average Existing Levelized Average Existing Cost of<br>cost tariff operating cost WB revenue capital cost Imported<br>cost study electricity<br>$ cents/kWh<br><!-- End of picture text --> _Source:_ Adapted from Rosnes and Vennemo (2009) and Briceño-Garmendia and Shkaratan (2010). _Note:_ LMRC = long-run marginal costs.

percent of the cost of domestic production (figure 17). The combination of domestically produced thermal power and cheap imported hydropower will reduce average costs, making current tariffs sufficient to recover costs in the medium term.

Power trade is already bringing additional benefits in terms of reliability and power security, reducing the reliance on a single supplier. More importantly, it diversifies the generation base. Burkina’s hydroelectric potential is limited; as a predominantly thermal power producer, the country relies on imported oil (about 400,000–450,000 tonnes annually). However, the full development of West Africa thermal capacity is some years away to materialize in its entirety.

From the demand side, despite the evident need to extend the access and availability of electricity, firms with electricity connections report good satisfaction levels compared to other West African countries. The percentage of sales lost due to power outages is comparable to what is found in other LICs, and the percentage of firms relying on their own generation is well below the Sub-Saharan African average according to the most recent enterprise survey (table 12).

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|**Country**|**Total inequality**<br>_MLD_(_X_)_×_100|**Between-type inequality**<br>_MLD_(<sup>�</sup>_XB_)_×_100|**Within-type inequality**<br>[_MLD_(_X_)_−MLD_(<sup>�</sup>_XB_)]_×_100|**Relative within-type inequality**<br>_IR_(<sup>�</sup>_XW_)|
|---|---|---|---|---|
|Chad|0.341|0.023|0.318|93.23|
|Colombia|0.114|0.028|0.086|75.79|
|Cote d’lvoire|0.205|0.026|0.179|87.49|
|Egypt|0.351|0.028|0.323|92.05|
|Ethiopia|0.259|0.025|0.234|90.38|
|Guinea|0.271|0.023|0.248|91.45|
|Haiti|0.171|0.026|0.145|84.61|
|Honduras|0.127|0.027|0.100|78.69|
|Jordon|0.134|0.022|0.112|83.46|
|Kenya|0.261|0.025|0.236|90.48|
|Lesotho|0.234|0.022|0.212|90.50|
|Liberia|0.246|0.026|0.220|89.35|
|Morocco|0.305|0.029|0.276|90.39|
|Mozambique|0.266|0.024|0.242|90.91|
|Niger|0.301|0.022|0.279|92.59|
|Peru|0.132|0.028|0.104|78.84|
|Rwanda|0.192|0.024|0.167|87.25|
|Tanzania|0.200|0.024|0.176|87.77|
|Turkey|0.162|0.026|0.136|83.78|
|Uganda|0.225|0.026|0.198|88.26|

Note: I multiply each number in the first three columns of results by 100 for readability. Source: Authors’ calculations based on Demographic Household Surveys.

Given that _IR_ ( _X_<sup>�</sup> _W_ ) also varies considerably across countries, we can also conclude that lower bound estimates of inequality of opportunity are not comparable. To be comparable we should obtain a high correlation cofficient between _MLD_ ( _X_ ) and _MLD_ ( _X_<sup>�</sup> _B_ ), but the correlation is -0.17. To further show the implications of this, Figure 1 compares the ordinal _MLD_ ( _X_ ) country-rankings and the ordinal countryrankings for _MLD_ ( _X_<sup>�</sup> _B_ ), both from most to least. The results show that there are considerable re-rankings.

# **4 Conclusion**

Several development reports and research papers have incorporated the IOp dimension when addressing development and social issues. Nonetheless, my results suggest that the empirical applications that use lower bound estimates of IOp can be misleading. On the one hand, lower bound estimates can underestimate substantially the actual value of IOp, demanding too little redistribution to equalize inequalities due to circumstances. On the other hand, measurement error can vary considerably across samples, thus making the traditional cross-country comparisons unreliable. These conclusions derive from the empirical advantage of using an outcome variable for which we can confidently assume effort plays no role, so that we are able to identify the size of measurement error that comes from the empirical impossibility of including all circumstances in the analysis.

6
14 over the period 1976-85.14 The data show that export and import shares increase dramatically in all the three industry grouping, but more so in the tradable industries. The share of exports in the output of exportable industries increases from less than 4 percent in the 1970s to over 42 percent in 1985. Similarly, the share of imports in total domestic sales of the importable industries increases from its lowest level in 1979, 21 percent, to over 40 percent irh 1985.15

- 14.In the actual computation of the shares as well in subsequent regression analysis, industry 322 was omitted because of inconsistent trade figures. More precisely, during 1980-85, the value of exports exceeded the value of total output by as much as 100 percent towards the end of the sample period. This outcome may be explained by at least two factors: a) Trade data include all firms whereas output data exclude firms with less than 10 employees until 1982 and with less than 25 employees thereafter. Because in textile and clothing industry small establishments are common, output figures which exclude such establishments underestimate the true value of industry's output. b) The policy of subsidizing exports is well known to have induced a certain degree of overinvoicing of exports, especially in the textile and clothing industry.

- 15.Due to differing coverage of firms of trade and output data, export and import shares may be slightly exaggerated, but the trend is reliable. See footnote 14.
**Table 3: Impact of the intervention on the adoption of business practices**

||Basic T|raining|Enhanced|Training|N|
|---|---|---|---|---|---|
|Outcomes|_OLS_|_Probit_<br>_marginal_<br>_effect_|_OLS_|_Probit_<br>_marginal_<br>_effect_||
|Has a budget|0.0215|0.0216|0.0980**|0.101**|680|
||(0.0450)|(0.0443)|(0.0445)|(0.0440)||
|Has a business plan|-0.0184|-0.0193|0.0993**|0.103**|676|
||(0.0433)|(0.0456)|(0.0458)|(0.0456)||
|Pays herself a wage|0.00355|0.00296|0.149***|0.155***|682|
||(0.0408)|(0.0447)|(0.0452)|(0.0474)||
|Bulk purchases with others|-0.0332|-0.0327|0.0756*|0.0771*|700|
||(0.0391)|(0.0406)|(0.0428)|(0.0433)||
|Differentiates via neater premises|-0.0107|-0.0120|0.0955**|0.0998**|700|
||(0.0407)|(0.0440)|(0.0444)|(0.0467)||
|Differentiates via packaging|0.00751|0.0133|0.0883**|0.0907**|700|
||(0.0302)|(0.0331)|(0.0346)|(0.0368)||
|Registered with Brela|0.0165|0.0275|0.140***|0.178***|692|
||(0.0338)|(0.0461)|(0.0386)|(0.0502)||
|Has a TIN number|0.00981|0.0190|0.0826**|0.125**|694|
||(0.0314)|(0.0478)|(0.0367)|(0.0532)||
|Has a license|-0.0210|-0.0259|0.0843**|0.117**|693|
||(0.0328)|(0.0484)|(0.0365)|(0.0519)||

Notes: All effects measured using ANCOVA specifications. All regressions also control for stratification dummies (geographical district, sector of activity, whether participants received a business gift). Robust standard errors in parentheses. *, **, and *** indicate significance at the 10, 5, and 1 % levels, respectively

**Table 4: Average impact of the intervention on business outcomes, earnings and household assets**

|Type of<br>training|Revenu<br>Typical<br>month (ln)|e from main a<br>Previous<br>month (ln)|ctivity<br>Previous<br>year (ln)|Profit from m<br>Previous<br>month (ln)|ain activity<br>Previous<br>year (ln)|Monthly<br>earnings<br>(ln)|Value of<br>household<br>assets(ln)|
|---|---|---|---|---|---|---|---|
|Basic|-0.339*|-0.186|0.0106|-0.332|0.0106|0.0880|0.0196|
||(0.177)|(0.437)|(0.289)|(0.409)|(0.289)|(0.164)|(0.0745)|
|Enhanced|-0.0966|-0.161|-0.0371|-0.307|-0.0371|0.0703|-0.0302|
||(0.171)|(0.454)|(0.317)|(0.430)|(0.317)|(0.194)|(0.118)|
|N|678|680|645|679|645|692|700|
|R squared|0.063|0.105|0.022|0.112|0.022|0.047|0.190|

Notes: All effects measured using ANCOVA specifications. Monthly earnings are earnings from all sources (including secondary activities). All regressions also control for stratification dummies (geographical district, sector of activity, whether participants received a business gift).

Robust standard errors in parentheses. *, **, and *** indicate significance at the 10, 5, and 1 % levels, respectively 22
all these countries appear to have been affected by the global financial crisis of 2008. FDI inflows into other Middle Eastern countries are the most erratic over the period. A comparison of the left and right side panels of Figure 1 suggests that countries with the largest increases in political instability also suffered substantial reductions in greenfield FDI inflows.

# _2.3 Economic Variables_

When examining the relationship between investment and political instability it is obviously important to account for economic factors. Unfortunately, high frequency data on economic variables for MENA countries are scarce. We draw on two sources to compile quarterly data on inflation, industrial production, and exchange rates. Inflation, measured as the quarterly change in the consumer price index, was derived from national statistical offices, and in some cases the Economist Intelligence Unit database (EIU). The high-frequency database of the Middle East and North Africa Department of the International Monetary Fund (IMF) was the source for the industrial production and nominal exchange rate data. In those cases when no industrial production data are available, we used quarterly export data from the IMF Direction of Trade Statistics database. Descriptive statistics of the variables included in the models are provided in Table A3 in the Appendix A3.

# **3.  Estimation and Results**

# _3.1 Estimation Issues_

One challenge in isolating the effect of political instability on greenfield FDI is that political instability and deteriorating macroeconomic performance often go hand in hand and may in fact aggravate each other. Moreover, there is a possibility of reverse causality, with reductions in greenfield FDI exacerbating political unrest. One solution to this problem would be to 13
is more one that highlights the general importance of ensuring that markets are contestable and entry barriers are removed – which is much more likely to be the case if reforms are implemented on a nondiscriminatory basis.

# **5 Models of Services Liberalization**

## **5.1 Pioneering work**

Early CGE models of multilateral liberalization focused on trade in goods, motivated by the Tokyo Round of multilateral trade negotiations (Deardorff and Stern, 1981, 1982; Whalley, 1982). These pioneering efforts treated services as nontradables. One of the first attempts to move beyond trade in goods and consider services was an application of the Deardorff and Stern Michigan model to assess the possible impact of the Uruguay Round as it related to services. Building on early barrier estimates by Hoekman (1996), Brown, Deardorff and Stern (1996) and Brown et al. (1996) revised the Michigan model to allow for services trade. Confronting problems now well known (and outlined above), but less appreciated at the time, extending the model required the Michigan team to estimate services trade flows from the swiss-cheese of aggregated and bilateral trade data (conceptually a data cube, but full of holes). In a procedure similar to the one followed in future extensions of the GTAP database, Brown et al. (1996) used a RAS-type routine to estimate the full bilateral trade matrix. Given Hoekman’s assessment that the Uruguay Round did not actually yield any actual liberalization, the analysis of Brown et al. is more of an estimate of what might be accomplished in future rounds. In this context, they model reductions in tariff equivalents of policies restricting trade in services, and conclude that future services liberalization had the potential to yield gains comparable to past accomplishments under the GATT in goods, measured in terms of GDP and trade volumes. More recent estimates suggest that the tariff equivalents grounded in earlier counts of the number of nontariff barriers (NTBs), like those from Hoekman, overstate actual price impacts of barriers to services trade. At the same time, because the barriers were modeled as tariff-equivalents rather than as NTBs with associated dead weight costs, these early analyses probably also understated the potential welfare gains from liberalization.

An important weakness of the early Michigan modeling of trade in services was that FDI was not incorporated into the analysis. In a pioneering contribution to the applied CGE literature, Petri (1997) developed a model that included FDI as well as cross-border trade in services. FDI in the Petri model gives rise to affiliates (foreign-owned plants) that differ from domestic firms in the same sector by using inputs ”imported” from the parent company as well as domestic factors of production. By assuming that consumer demand is differentiated both by place of production (along Armington lines) and nationality of ownership of 21
Figure 1 Trends in Agricultural Productivity Maize, Mixed Beans, Millet, Sorghum, Sunflower, and Groundnuts <!-- Start of picture text --> mixed beans millet<br>7.5 7.5<br>7<br>7<br>6.5<br>6.5<br>6<br>5.5 6<br>1997 1998 1999 2000 2001 2002 1997 1998 1999 2000 2001 2002<br>year year<br>sorghum sunflower<br>7.5 7.5<br>7<br>7<br>6.5<br>6.5<br>6<br>6 5.5<br>1997 1998 1999 2000 2001 2002 1997 1998 1999 2000 2001 2002<br>year year<br>groundnuts<br>7.5<br>7<br>6.5<br>6<br>5.5<br>1997 1998 1999 2000 2001 2002<br>year<br><!-- End of picture text --> Note: The graphs compare the trend in maize productivity with the trends in productivity in alternative crops. Starting at the top-left, the panels represent the cases of Mixed Beans, Millet, Sorghum, Sunflower, and Groundnuts, respectively. In each panel, the solid line represents the trends in maize productivity and the broken line, the trend in the productivity in the alternative crops.

39