rafmacalaba/datause-encoder-data huggingface dataset:
As may be expected, the results are clearer and stronger<sup>for the high premium</sup> countries. Money stock has the expected sign for<sup>all high premium countries and</sup> is significant for most. The coefficients obtained for the level of international reserves are negative for nine of the thirteen of the<sup>high premium</sup> countries and is statistically significant at the 5 percent level for most. The level of international reserves is not statistically significant<sup>for the cases</sup> where the coefficient has a positive value, with Ghana being the sole exception. The coefficient obtained for the term representing<sup>deviations from equilibrium</sup> of the real exchange rates has the expected<sup>sign for all the high premium</sup> countries andis statistically significant for the majority.<sup>The R' and the</sup> Durbin-Watson statistic are quite good, though the presence<sup>of the lagged</sup> dependent variable term does bias the Durbin-Watson towards<sup>a value of 2. The</sup> Dickey-Fuller as well as the Augmented Dickey-Fuller tests<sup>strongly support the</sup> hypothesis of cointegration, with the exception of Venezuela.<sup>However, the</sup> period under consideration for Venezuela is extremely short<sup>(six years) and the</sup> weakness of the Dickey-Fuller test statistic is to be<sup>expected.</sup> The results are not quite so strong for the moderate<sup>premium countries.</sup> Excluding Pakistan, the coefficient for the money stock has the<sup>expected sign for</sup> all the moderate premium countries in the _sarple._ The money<sup>stock is positive</sup> though not significant for India, Kenya, Mexico and Uruguay;<sup>it is negative but</sup> not statistically significant for Pakistan. The coefficient<sup>for international</sup> reserves has the expected sign for six of the moderate<sup>premium countries,</sup> Pakistan and Mexico being the exceptions. However,<sup>the coefficient for</sup> international reserves is not significant for either country; neither<sup>is it</sup> statistically significant for India or Kenya, even though<sup>it has the expected</sup> sign. The results for the real exchange rate term<sup>are more mixed, but the</sup> coefficient has the expected sign for every case it is statistically significant. Cointegration is supported for most of the countries, but<sup>the result of the</sup> augmented Dickey-Fuller test is ambiguous for Kenya, Turkey<sup>and Uruguay.</sup> The error-correction equation is a flexible specification<sup>of the short run</sup> dynamics. The residual from the cointegration equation measures<sup>the 'error',</sup>
# 1. INTRODUCllON

1. It has been more than ten years since China embarked on a program of econonic refonm aimed at rapid economic development through greatly enlarged participation in world trade. As a result, China's economy has been one of the fastest growing economies in the world with an associated improvement in the standard of living. However, according to the estimate based on the World Bank Atlas approach in _World Development Report,_ the dollar Gross Domestic Product (GDP) per capita of China has remained in the range of $300-$370 during 1980-91 (World Bank, 1992).

2 The question ofthe true standard of living and productive potential of China is very important, not only for providing a measure of China's potential but also for assessing Chinese growth perfonmance. As China becomes a significant force in the world, economists have become increasingly interested in estimating the size of its economy. The purpose of this paper is to present a detailed bilateral companison of GDP between China and the United States wnth 1986 as a reference date, usig the purchasing power parity (PPP)' approach formulated by the United Nations Internaonal Comparison Program (ICP). The findings in the study include the following: a) Chia's per capita GDP in 1986 international dollars<sup>2</sup> (1$) is between I$770 and I$S1,044 depending on the different assumptions made in the comparison of the compansonThe term PPP refers here to the purchasing power of currency, and not to the purchasing power parity theory (see, Kravis, 1984, pp. 2).

2 The term "International Dolls" (I), which has by definition the same purchasing power over total GDP as the U.S. dolar in a given year (see World Bank (1992), pp. 299-300), is used here to signiif that all estimates of GDP in this study are based on the ICP-type approach. These figures, therefore, can be compared with ICPbased GDP estimates, but not with GDPs converted at exchange rates. It should of course be noted that the term "1$" has been used in ICP's Geary-Khamis multilateral comparisons rather in bilatral comparisons, as here.

1
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**Figure 9**

# **Kernel densities of (lagged) external debt over GDP by occurrence of a current account reversal**

_Panel A: raw data_ <!-- Start of picture text --> 1988 1996<br>0 200 400 600 800 0 100 200 300 400<br>External debt/GDP External debt/GDP<br>.01 .01<br>.008 .008<br>.006 .006<br>Density Density<br>.004 .004<br>.002 .002<br>0 0<br><!-- End of picture text --> _Panel B: net of current account balance, country dummies and year dummies_ <!-- Start of picture text --> 1988 1996<br>-.5 0 .5 -.5 0 .5<br>External debt/GDP External debt/GDP<br>2<br>3<br>1.5<br>2<br>1<br>Density Density<br>1<br>.5<br>0 0<br><!-- End of picture text --> solid line: reversals short-dashed line: no reversals
# **A.3 Parameterizing the electricity sub-model**

The parameters of the electricity sub-model are based on three main sources of data – the Input-Output table, the Electric Power Industry Statistics,<sup>21</sup> and the International Energy Agency (2010) “Projected Costs of Generating Electricity”.

The elasticities are summarized in Table A2

# **A.4 Parameters, exogenous variables and data sources**

The key input into the model is the Social Accounting Matrix (SAM) for 2010. This traces the flow of commodities and payments among the producers, household, government and rest of the world. The SAM is assembled from the 2007 benchmark input output table.<sup>22</sup> A summary of this SAM is given in Figure A4, the actual matrix used is disaggregated to the 33 sectors and commodities. From this we derive the labor and capital incomes, the tax revenues for each type of tax, the expenditures on specific commodities by the household, government and foreign sectors, and government payments of all types in equation A76.

These payments are combined with employment and capital input data to give the compensation rates for labor and capital for each sector. The estimates for employment and capital stocks by sector are taken from a productivity study of China (Cao, Ho, Jorgenson, Ren, Sun and Yue 2009) that supplements the official data with labor force surveys. The various tax and subsidy rates are not statutory rates but are implied average rates derived by dividing revenues by the related denominator – value of industy output, capital income, total value added, and imports.

The exogenous variables in the model include total population, working age population, saving rates, dividend payout rates, government taxes and deficits, world prices for traded goods, current account deficits, rate of productivity growth, rate of improvement in capital and labor quality, and work force participation. These variables may, of course, be endogenous (i.e. they interact among each other) but we ignore this and specify them independently. Our assumptions for these exogenous drivers are summarized in Table A3.

The assumption that affects the growth rate the most is the household savings rate, _st_ . Our assumption is to have _st_ beginning at the observed 41.2% for 2010 and gradually falling to 22% in 2020 and 15% in 2050. National private savings is household savings plus the retained earnings of enterprises.

> 21 电力工业统计资料汇编 .

> 22 The 2010 input‐output table is given in NBS (2014). The benchmark IO table for 2007 is derived from detailed enterprise data, the 2010 IO table is extrapolated by the NBS using simpler aggregated data.

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More generally, application of any exchange rate such as customarily used in deriving _Atlas-type_ GNP per capita measures encounters formidable difficulties because of the notable distortions in the PSU price structure, and the resultant extreme disparities from world relative prices.

# b. Attemp **t** o Estimate Multiple Exchange Rates

As a rule, problems arise for the _Atlas_ method when official market interventions drive a sizable wedge between the price of foreign currency (the conversion factor) and domestic prices. Since governments are wont to intervene in foreign currency markets, procedures have been developed to deal with the usual source of 'egregiously' distorted conversion factors, where an over-valued currency is protected by exchange and payments restrictions (see Hte 1990); and the first line of defense for the _Atlas_ method is to construct a trade-weighted average of the multiple exchange rates generated, implicitly if not explicitly, by the restrictive practices. These procedures presume that domestic prices are market-determined, which clearly isn't the case in planned economies like the FSU of 1990. Nonetheless, an attempt was made to follow this approach.

Experimentation with what this study calls foreign trade price differentials (FTs), which in principle should link the commercial to the official rate, suggests a GNP per capita figure of about $3800 for the FSU. These meastires are probably closer to the mark than the official exchange rate, but still too problematic to rely on for _Atlas_ purposes.

An HPE's commercial exchange rate is usually said to differ from its official rate because of the operations of foreign trade organizations (FTOs) and related institutional arrangements that intervened between nonresidents and domestic producers or consumers. Briefly, FTOs were government monopolies for purchasing imports from, and selling exports to, nonresidents. They transacted with domestic economic agents in rubles at prces in line with those set by the plan for domestic agents regardless of the ruble proceeds generated by exports or the ruble outlays required to obtain imports. Such interventions between foreign prices and domestic prices of traded goods are referred to here, generically, as foreign trade price differentials (FTMs).

FSU national accounts included foreign trade at internal prices, meaning after adjustment by FTDs.<sup>7</sup> On that basis, some analysts derive an imputed dollar commercial rate for earlier years,<sup>8</sup> which may remain at about three times the official rate or vary in line with the gap between US and FSU inflation rates. Given past use of internal prices for traded goods, in national accounts, it is arguable that there was no depreciation, just an acknowledgement of the average price for foreign goods, after FTDs, that was implicit in national accounts all along.

> 7The ONP data used for Table 1 include extra-Union exports and imports at foreign prices, i.e., foreign currency prices converted at the official devisa rate.

> 8See, for example, CSO of Estonia, 1991. The same back.casting logic was used in JSSE.

6
# **Table 4: Effect of the Islamic Inheritance Rule on Child-Level Outcomes**

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||||Depend<br>|ent Variable:<br> <br>|||
||Number o|f Older Siblin|gs Ever Bor|n<br>Rel|ative Order o|f Birth|
|Boy|-0.019<br>(0.024)|||-0.028<br>(0.021)|||
|Muslim* At least one brother at|-1.92|-1.60*|-2.19|-0.82|-0.51|-1.04|
|child birth|(1.05)|(0.79)|(1.24)|(0.55)|(0.42)|(0.70)|
|Muslim * At least one brother at|-0.33**|-0.38**|-0.27*|-0.19**|-0.18*|-0.19*|
|child birth* Child birth after 1994|(0.11)|(0.13)|(0.13)|(0.064)|(0.089)|(0.088)|
|Muslim* At least one brother at|1.49|1.10|1.81|0.53|0.12|0.87|
|childbearing|(1.06)|(0.81)|(1.25)|(0.55)|(0.44)|(0.72)|
|Muslim * At least one brother at|0.91***|0.99***|0.82*|0.54***|0.56**|0.51*|
|childbearing * Childbearing after 1994|(0.26)|(0.28)|(0.35)|(0.16)|(0.20)|(0.22)|
|Mean, dependent variable|2.38|2.36|2.40|1.90|1.88|1.92|
|R-squared|0.53|0.53|0.54|0.49|0.48|0.50|
|Observations|20090|10538|9552|20090|10538|9552|
|Sample|All<br>children|Boys|Girls|All<br>children|Boys|Girls|

* p<0.05,  ** p<0.01,  *** p<0.001.

Note: OLS estimates of interactions between indicators of Muslim religion, having at least one surviving brother and whether birth occurred in post-compilation period, evaluated at both the time the couple started childbearing and the time of child birth. Parentheses contain standard errors clustered at the PSU-level. Regressions use survey weights. Baseline sample excludes children born to mixed-religion and polygamous couples. Columns (1) and (4) report estimated coefficients for the entire sample of children conditional on child gender. Columns (2) and (5) report estimates for a sample of boys only; and columns (3) and (6) for a sample of girls only. All regressions include fixed effects for the men’s total number of siblings at the time the couple started childbearing and at the time of child birth, the year of first child birth, the year of birth of the child, the women’s age, and area and region of residence. Other controls included are men and women’s educational attainment and indicator of household wealth. Data summary is provided in Appendix 1.

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where _Ti_ takes on the values of one or zero and the error term now takes the form ε _i_ = _Ti_ ( ν _iT_ −ν _iC_ ) +ν _iC_ . If we may assume that the error term (the “latent effects”) has zero mean conditional on the _X_ covariates and treatment – a reasonable assumption if there is even partial randomization – then we have _E_ ( ν _iT_ | _X_ , _T_ = _t_ ) = _E_ ( ν _iC_ | _X_ , _T_ = _t_ ) = ,0 _t_ = ,1,0 and we can get consistent estimates of the average treatment effects by applying ordinary least squares (OLS) to Eqn. 6, and noting that

_G ATE_ = _E_ [ α _T_ − α _C_ + _X i_ ( β _T_ − β _C_ )]. (7) If we are also willing to assume (more problematically) that β<sup>T</sup> =β<sup>C</sup> then we obtain the _common-impact model,_ where the average treatment effect reduces to α _T_ − α _C_ .

In what we report below we mainly emphasize the results of propensity score matching, but we include the results of the common-impact model both as a robustness check, and as legitimate estimates in their own right.

# **Choosing a Counterfactual**

In all impact evaluations one is trying to compare the actual impact with a counterfactual, where the latter represents our view of what would have happened in the absence of the project. In the case of SFD projects, a number of possible comparisons are possible, because of the presence of non-SFD interventions by ministries and NGOs. The situation is illustrated in Figure 2, and suggests the following useful comparisons:

- (i) **A vs. B.** Any given village either has an SFD intervention or it does not.  This comparison of A vs. B provides one possible baseline.  The implicit assumption here is that in the absence of an SFD intervention, there is still a substantial probability that there would have been some other intervention – most villages without an SFD project have an intervention by some other organization.  If, in this comparison, SFD villages (A) perform better than non-SFD villages (B), then it is largely because the SFD projects are more effective than non-SFD projects.  This is a relatively high bar, and probably provides a lower bound to the impact of the SFD.

- (ii) **D vs. F.** This compares villages that have just an SFD project with villages that have no interventions at all.  It is the relevant comparison if, in the absence of an SFD project, there would have been no other project.  In that sense it measures the pure effect of the SFD.  However, it implicitly assumes that, in the absence of an SFD project, there would have be no comparable

_Impact Evaluation: Egyptian Social Fund for Development_ _Version of June 29, 2009_ _Page 11 of 36_
TANZANIA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 13. Existing financing flows by country typology, average annual 2001-06**

<!-- Start of picture text --> Sub-Saharan Africa<br>SADC<br>middle-income countries<br>resourch-rich countries<br>nonfragile low-income countries<br>fragile low-income countries<br>South Africa<br>Kenya<br>Ethiopia<br>Tanzania<br>Uganda<br>Congo, Dem. Rep.<br>Cape Verde<br>0 2 4 6 8 10 12<br>% of GDP<br>capital expenditure operations and maintenance<br><!-- End of picture text --> _Source:_ Foster and Briceño-Garmendia 2009.

Investment in power and ICT is lower in Tanzania than in other nonfragile low-income countries. There is considerable specialization in sources of investment finance across sectors: the private sector is the predominant source of investment finance for ICT; the private sector and ODA are equally important for the power sector; and ODA is the primary source of funding for the transport and WSS sectors (figure 14). This pattern is typical of the peer group, although Tanzania has less non-OECD funding than many of its peers and the power sector is more reliant on private investment than in other low-income countries.

27
often points to market failure and may warrant government interventions.<sup>14</sup> Tables (6), (7), (8) and (9) present the estimates of the speed of adjustment to world shocks, separated for adjustments that imply an increase in the domestic price (upward adjustment), and those that imply a decrease in the domestic price (downward adjustment), for chicken markets, sugar markets, rice markets and cooking oil markets respectively when we use consider the period 1991m1 − 2007m12, along with statistical significance, and the confidence interval in which these adjustment parameters fall.

Figures (1), (2), (3) and (4) help to visualize these results, by showing how much of a world price shock is corrected every period when it implies a domestic price increase (green line), how much when it implies a domestic price decrease (red line), and how much on average (blue line), for chicken, sugar, rice and cooking oil markets respectively. The pattern in all figures is that the line that shows the adjustment upwards is systematically above that one showing the downward adjustment. In other words, it takes longer for firms to adjust their prices downwards, following a world price decrease, than it takes them to adjust their prices upwards, following a world price increase. This is likely to imply some degree of market power in the markets for chicken, sugar, cooking oil and rice in Bolivia. With the exception of the rice market of Cochabamba (where the difference is not significant), we systematically find that the adjustment upwards is faster than the adjustment downwards. In addition, the speed of adjustment upwards is always significant at 10% (pvalue< 0.1), whereas the speed of adjustment downwards is insignificant.<sup>15</sup> Once we incorporate 2008 in the analysis, the results are distorted. Though the 14 Although systematic, these results are weak, and should be interpreted with caution, as they are “blurry”, from the point of view of statistical significance. Also, if there are adjustment costs associated with pricing decisions, and these are asymmetric, asymmetric price transmission may not reflect imperfect competition, or imply any transfer of welfare. It is however difficult to find convincing arguments for these asymmetric adjustment costs.

15 Note that the adjustments in Cochabamba to world prices of chicken and rice are not significant. The same happens for El Alto’s adjustment to world prices of soya oil. We include them in the figures, anyway, for illustrative purposes.

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# Should marginal abatement costs differ across sectors? The effect of low-carbon capital accumulation

Adrien Vogt-Schilb<sup>1,</sup><sup>_∗_</sup> , Guy Meunier<sup>2</sup> , St ́ephane Hallegatte<sup>3</sup>

> _1CIRED, Nogent-sur-Marne, France._

> _2INRA–UR1303 ALISS, Ivry-Sur-Seine, France._

> _3The World Bank, Sustainable Development Network, Washington D.C., USA_

_Keywords:_ climate change mitigation; carbon price; path dependence; sectoral policies; optimal timing; inertia; levelized costs _JEL classification:_ L98, O21, O25, Q48, Q51, Q54, Q58

## **1. Introduction**

Many countries have set ambitious targets to reduce their Greenhouse Gas (GHG) emissions. To do so, most of them rely on several policy instruments. The European Union, for instance, has implemented an emission trading system, feed-in tariffs and portfolio standards in favor of renewable power, and different energy efficiency standards on new passenger vehicles, buildings, home appliances and industrial motors. These sectoral policies are often designed to spur investments that will have long-lasting effects on emissions, but they have been criticized because they result in different Marginal Abatement Costs (MACs) in different sectors.

Existing analytical assessments conclude that differentiating MACs is a second-best policy, justified if multiple market failures cannot be corrected independently (Lipsey and Lancaster, 1956). For instance, if governments cannot use tariffs to discriminate imports from countries where no environmental policies are applied, it is optimal to differentiate the carbon tax between traded and non-traded sectors (Hoel, 1996). A government should tax emissions from households at a higher rate than emissions from the production sector, if labor supply exerts market power (Richter and Schneider, 2003). Also, many abatement options involve new technologies with increasing returns or learning-bydoing (LBD) and knowledge spillovers. This “twin-market failure” in the area of green innovation can be addressed optimally by combining a carbon price with a subsidy on technologies subjected to LBD and an R&D subsidy (e.g., Jaffe et al., 2005; Fischer and Newell, 2008; Grimaud and Lafforgue, 2008; Gerlagh et al., 2009; Acemoglu et al., 2012). But if the only available instrument is a carbon tax, higher carbon prices are justified in sectors with larger learning effects (Rosendahl, 2004).

> _∗_ Corresponding author _Email addresses:_ `vogt@centre-cired.fr` (Adrien Vogt-Schilb ), `guy.meunier@ivry.inra.fr` (Guy Meunier), `shallegatte@worldbank.org` (St ́ephane Hallegatte)

_World Bank Policy Research Working Paper (2013)_ _April 19, 2013_