rafmacalaba/datause-encoder-data huggingface dataset:
As can be seen from Table 2, these effective tariff rates vary widely across countries. This, in turn, is the result of two factors: the sectoral composition of trade and the carbon intensity of different sectors in different countries. Clearly, a uniformly higher (lower) virtual carbon tax would uniformly boost (shrink) these percentages.

Table 3 shows the effective tariff rate that would be faced by country and sector if the virtual carbon content of traded goods were taxed at $50 per ton of CO2. Average tax rates, as reported in Table 2, give the big picture, but it is at the sector level that the economic impacts of virtual carbon taxes will be felt. The large developing countries highlighted in Table 2 have tradable sectors that would be particularly hard-hit by a tax on virtual carbon – chemicals, ferrous and non-ferrous metals, and other mineral products. ‘Other mining’ products are particularly carbon-intensive in Mexico and South Africa, as are paper products produced in China and India. Tradable sectors such as chemicals, ferrous metals and mineral products are highly carbon intensive in low income countries.

Modeling the wider economic impacts of taxing virtual carbon at the border is beyond the scope of this paper, since this would involve general equilibrium effects. But our analysis gives a strong sense of what would drive these general equilibrium effects (for which, see Mattoo _et al._ (2009a and 2009b).

# **7. Conclusions**

The IO analysis of the flows of virtual carbon in international trade, as presented in the figures and Table 1, is of inherent interest. It shows that the combination of trade volumes, trade composition, and the carbon intensity of production across countries results in very large net flows of virtual carbon from the major developing countries to high income countries. This finding suggests that taxing virtual carbon at the border in high income countries could result in significant effective carbon tariff rates on developing country exports, a suggestion that is borne out in Tables 2 and 3.

Our main finding is that if virtual carbon is taxed at $50 per ton of CO2, a level of tax that has already been experienced in the European ETS, then the effective tariff rates faced by developing country exports is significant, up to 10% of the value of the average export bundle, and two to three times this level for specific tradable sectors. This, combined with our analysis of the border tax literature, suggests that border taxes on virtual carbon will be trade distorting in the sense that the volume and composition of international trade will change as a result, with associated losses in efficiency and welfare, particularly in developing countries.

At least four other perspectives on our findings are possible: First, from the perspective of global climate change policy, the incentive to producers provided by taxing virtual carbon may be welfare-improving if global emissions decline.

Second, from a purely trade perspective, it is clear that _unilateral_ moves to tax virtual carbon at the border would exacerbate trade tensions at a time when the international 15
the mechanism described above is present, then this correction should increase the magnitude of the estimated coefficient on the EPA expenditures variable while the coefficient on the incidence of exports over GDP at the time of the creation of the EPA should be positive.

We pursue two alternative identification strategies that use the mechanisms through which EPAs affect exports to try to address the endogeneity problem. If EPAs have an impact on exports, they are likely to have a larger impact when they are most needed. When would that be? We offer two alternatives mechanisms. First, EPAs might be most effective when market access barriers in the rest of the world are high and need to be circumvented. A positive and statistically significant coefficient in the interaction of ln _MAC_ and ln _Bud/popc_ would allow us to identify this mechanism. One concern with this strategy is that exporters could affect importers trade policies through multilateral and bilateral trade preferences, but our measure of trade policy restriction is mainly driven by technical regulations and sanitary and phitosanitary measures which are rarely and inefficiently addressed in multilateral and bilateral trade agreements (see Kee, Nicita and Olarreaga, 2009). Moreover, these are areas where EPAs can help through on-shore export support services that help domestic firms understand the technical requirements in the rest of the world.

Second, exports of heterogeneous goods, which are more likely to be affected by asymmetric information, a barrier that can be overcome with the assistance of EPAs. For example, EPA services might not be needed to export oil or other commodities. Thus a negative and statistically significant coefficient on the interaction of EPA expenditures with a variable that captures the degree of homogeneity in the export bundle would imply that EPAs are more likely to help when the export bundle of a given country has a larger share of heterogeneous goods. To implement this identification strategy, we follow the approach pioneered by Rajan and Zingales (1998) by estimating (2) at the product level (4 digits of the SITC rev. 2 classification),<sup>10</sup> and add as an explanatory variable Rauch’s (1999) dummy for homogeneous goods. Rauch (1999) classified goods on the basis of whether they were traded in an exchange (organized), had prices listed in trade publications (reference), or were brand name products 10For each country and each four digit SITC product, we use average exports during 2000-2004, and divided per capita as in (2).

11
Other recent analyses of school feeding programs in Burkina Faso have found evidence of nutritional spillover effects for younger siblings (Kazianga, de Walque & Alderman, 2010). Spillover effects would show up in our analysis as a gain in weight-for-age or height-for-age or reductions in anemia for younger children (age 3-5). While we do observe significant increases in height-for-age for boys and girls ages 3-5 in Nhot Ou who were exposed to take-home rations in between-district analyses, this appears to be driven more by the declines in height-for-age among children in the control villages. Similarly, the marginally significant reduction in anemia for younger children in Nhot Ou takeup villages is relative to the increase in anemia prevalence in non-takeup villages in the within-district analysis. Collectively, the nutritional analyses fail to find evidence of positive effect of school feeding on children’s nutritional status.

# **_5.3 Additional analyses_**

Our two difference-in-difference specifications do not yield compelling or consistent evidence of the positive impact of school feeding in this study context. We undertake a set of additional analyses to explore possible sources of null findings.

# 5.3.1 Selective village and household attrition

Eleven villages in the 2006 sample had relocated by 2008 and could not be re-interviewed. Attrition from the village sample by 2008 is significantly associated with the number of households in the village, with larger villages less likely to later relocate (results not shown). This is consistent with the Government of Laos policy to relocate or combine smaller villages to achieve economies of scale in service provision and agricultural development (Evrard & Goudineau, 2004; Baird & Shoemaker, 2005; World Food Programme, 2005). Six of the 11 relocated villages were in Phongsaly district. Given that these villages were also less likely to 21
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- S.R. Schuler, S.M. Hashemi, A.P. Riley, and S. Akhter. Credit programs, patriarchy and men’s violence against women in rural bangladesh. _Social Science & Medicine_ , 43(12): 1729–1742, 1996.

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21
policy experiments make it possible to unpack the contribution of each policy. The experiment relying only on storage policy assumes that India liberalizes its wheat trade, including the use of export subsidies to dispose of excess stocks. Similarly, the trade policy assumption implies no public storage.

In interpreting the results that follow, a word of caution is in order regarding the limitations of rational expectations storage models with respect to what they aim to represent and how well they achieve that aim. First, the only motivation for private storage assumed here is intertemporal arbitrage. While that is an important motive per se, it excludes many other important transactional or precautionary motives that could explain much actual stock-holding behavior (Williams 1986). Given that trade is instantaneous in the model, there is also no reason to accumulate stocks to account for time to ship (Coleman 2009). These motivations are quantitatively important, as evidenced by historical stock data. A purely speculative motive would allow for regular stockouts, while at the world level the stock-to-use ratio never decreases much below 20%. Because of this focus on one motive for storage, the simulated statistics on private stock levels are likely to underestimate real storage. Second, stabilization policies are represented as free of any flaws (except potentially in their design): The policies perfectly follow the decided rules without any delay or discretionary political interventions. Such a perfect representation of India’s wheat policies is likely to overestimate the achievable price stabilization. Third, for the sake of tractability, the model focuses on two main sources of price volatility: production shocks in India and in the RoW. This focus excludes other sources of volatility such as shocks to exchange rates, and prices of oil and other agricultural commodities. Overall, the model aims to capture the most important types of spatial and intertemporal arbitrage, but the inherent complexity of this issue implies that the model should not be considered a forecasting tool but rather a means of quantitatively illustrating the consequences of each policy option.

Table 3 presents the effects of the various instruments of current policy on the asymptotic distribution of the model.<sup>10</sup> Storage and trade policies alone provide similar levels of price stabilization, with storage decreasing the coefficient of variation a little more than trade policy. The ability of storage to achieve such price stability in an open economy comes from the fact that India’s domestic price is often between the fob and cif margins. This assessment is confirmed by the fact that world price volatility is little affected by a storage-only policy. The fundamental difference between the stability provided by trade and storage concerns extreme prices. The storage policy truncates low prices almost perfectly, but it cannot prevent all price spikes and does little to reduce the occurrence of high prices. The explanation for this outcome lies with the rule

> 10 See Annex 3 for details about the simulation method.

22
52

**Figure 20  MWRs as a Function of Gender and Age**

<!-- Start of picture text -->
1.15<br>1.1<br>1.05<br>1<br>0.95 married men<br>single men<br>married women, NO widower pension<br>0.9 married women WITH widower pension<br>single women<br>0.85<br>0.8<br>0.75<br>−10 −8 −6 −4 −2 0 2 4 6 8 10<br>age difference between spouses<br>money’s worth ratio<br><!-- End of picture text -->

Money's worth ratios as a function of gender and the age difference between spouses (horizontal axis; negative numbers mean than that spouse is younger). The figure is drawn for a retirement age of 62, a discount rate of 4 percent and a constant annuity.

Source: Authors’ Calculations

||**Table 14**<br>|**MWRs as a **<br>|**Function **<br>|**of Marit**|**al Status**<br>||
|---|---|---|---|---|---|---|
|Year|Gender|R.A.|C.F.|FIF|SFSO<br>pooled|SFSO<br>marital|
|2004|female pooled|62|0.0720|1.175|1.191||
||female married|62 (65.7)<br>|0.0720|||1.210|
||female married|62 (—)<sup>49</sup>|0.0720|||1.171|
||female single|62|0.0720|||1.143|
||female divorced|62|0.0720|||1.108|
||female widowed|62|0.0720|||1.139|
||male married|65 (61.3)|0.0720|1.120|1.104|1.093|
||male married|65 (55)<br>|0.0720|||1.148|
||male married|65 (—)<sup>50</sup>|0.0720|||0.896|
||male single|65|0.0720|0.959|red 0.904|red 0.805|
||male divorced|65|0.0720|||0.796|
||male widowed|65|0.0720|||0.809|

The computations are based on a constant nominal interest rate of 3.5 percent. The number in parenthesis corresponds to the age of the main claimant’s spouse at retirement, and λ = 0.6 . Mortality rates are updated by improvement rates from SFSO. Source: Authors’ Calculations 49Without survivor benefits 50Without survivor benefits
Similar concerns affect recent OECD-led efforts to construct indicators of public procurement practices.

_Transparently acknowledge margins of error of all governance indicators._ All governance indicators have measurement error and so should be thought of as imperfect proxies for the fundamentals of good governance that we seek to measure and improve across countries. This is not just an abstract statistical point, but rather one of fundamental importance for all users of governance indicators. Wherever possible such margins of error should be explicitly acknowledged, as for example they are in the Worldwide Governance Indicators project. And these margins of error should be taken seriously when using these indicators to monitor progress on governance. At times the lack of disclosure of margins of error is rationalized by suggesting that they would be largely be missed by most readers. Yet our experience with the Worldwide Governance Indicators suggests that this is not the case, with many users recognizing and benefitting from this additional degree of transparency about data limitations.

_Exploit the wealth of currently-available indicators, recognizing that further progress in developing new governance indicators is likely to be incremental._ As we have seen in this survey, a very large number of different governance indicators already exist. Considerable research has been done on the cross-national links between broad measures of governance and broad development outcomes. But much more work needs to be done to exploit the large body of disaggregated measures of governance already in existence. Linking disaggregated indicators to disaggregated outcomes, both across countries and over time, is likely to be an important and exciting area of research over the next several years. And it is likely to have important implications for policymakers.

At the same time, there is also scope for developing new and better indicators of governance to address some of the weaknesses of existing measures that we have flagged in this review. Work to improve such indicators will be important as indicators are increasingly used to monitor the success and failure of governance reform efforts. But given the many challenges of measuring governance, it is also important to recognize that progress in this area over the next several years is likely to be incremental rather than fundamental. In fact, in terms of potential payoff, alongside 33
<!-- Start of picture text --> Years<br>10 = interquartile range<br>8 | ¢ Median<br>6<br>‘<br>2<br>0<br>Club 1 Club 2 Club 3-5<br><!-- End of picture text -->

<!-- Start of picture text --> Log productivity<br>42 @ Interquartile range<br>* i ¢ Median<br>LI<br>° Kaj<br>6<br>Club 1 Club 2 Club 3-5<br><!-- End of picture text --> <!-- Start of picture text --> Index<br>2.0 = Interquartile range<br>1.6 .<br>42 Median<br>0.4<br>0.0<br>-0.4<br>0.8 L > |<br>-1.2<br>Club 1 Club 2 Club 3-5<br><!-- End of picture text -->

<!-- Start of picture text --> Index<br>2 @ Interquartile range<br>4 ¢ Median<br>0 Lo]<br>4 ma<br>-2<br>Club 1 Club 2 Club 3-5<br><!-- End of picture text --> <!-- Start of picture text --> Percent of GDP = Interquartile range<br>100 .<br>¢ Median<br>“80 Bl<br>* |< |<br>20<br>0<br>Club 1 Club 2 Club 3-5<br><!-- End of picture text -->

<!-- Start of picture text --> Percent GDP m Interquartile range<br>30<br>25 ¢ Median<br>20 lo | rs |<br>15<br>10<br>5<br>0<br>Club 1 Club 2 Club 3-5<br><!-- End of picture text -->
<!-- Start of picture text --> 1973<br>Brazil<br>Russia<br>GlobalNorth GlobalSouth China<br>Other<br><!-- End of picture text --> <!-- Start of picture text --> 2008<br>Brazil<br>GlobalNorth GlobalSouth chins,<br>Other<br><!-- End of picture text --> Source: World Bank staff calculations, from Maddison (2009) <!-- Start of picture text --> “og<br>~<br><!-- End of picture text -->
Komives, Whittington, and Wu "Infrastructure Coverage and the Poor: A Global Perspective" is not the same. The surveys may have been carried out in different years and with different sampling procedures.

Such general-purpose surveys typically ignore informal service options such as water vending or the provision of electricity from a private generator. Different surveys may use different definitions of some infrastructure service options.<sup>6</sup> Countries generally report summary statistics that cannot be related to the income of an individual household so that it is impossible to determine how coverage of the poor differs from coverage of other income groups. Moreover, the international agencies that compile coverage statistics for one infrastructure service (e.g., water) rarely coordinate their efforts with other agencies (or even other divisions within the same organization) interested in different infrastructure services, so it is unusual to see comparable coverage statistics reported for multiple infrastructure services.

In this paper we introduce a new data source for global coverage statistics, the World Bank's Living Standards Measurement Study (LSMS) surveys, that addresses some but not all of these limitations. These multi-topic surveys gather extensive socioeconomic and expenditure information from households, as well as limited information on a household's use of selected infrastructure services. The data used in this paper are drawn from LSMS surveys conducted in fifteen countries. The pooled sample includes more than 55,500 households in Asia, the Americas, sub-Saharan Africa, Eastern Europe, and Central Asia. The LSMS surveys enable us to examine coverage for several infrastructure services among different income groups in many different countries using householdlevel data.

The results of our analyses show that all income groups throughout the world have much higher levels of coverage for electricity than for other formal infrastructure services (in-house piped water service, sewer service, and private telephone service). In many countries most households in

> 6 For example, a respondent may be asked, "What is the household's principal water source for drinking and cooking?" Some surveys may use precoded answers that distinguish between in-house connections and yard taps, but others may not.

2
sample.

The estimated drop in catfish shares is consistent with switching out of aquaculture. But it may also be simply the consequence of the decline in catfish prices, regardless of whether households actually switched resources towards other economic activities.<sup>23</sup> To further explore the pattern of switching, we now study if the AD tariffs actually led to full exit from catfish production.

The data show substantial exit from fish farming. In the M4 sample, 145 of the 561 households involved in fish farming in 2002 have stopped the activity by 2004. This implies an exit rate of around 25 percent. Full exit is even stronger in sample M6. Out of 788 catfish farmers in the panel, 224 (almost 30 percent) fully abandoned fish farming. To explore if exit was related to the income shares from catfish, we estimate a model analogous to (6) but using, as dependent variable, a binary indicator that takes a value of 1 for farmers for whom _sh,_ 2004=0. We estimate the regression using a Linear Probability Model, and as before we only include households who were involved in fish farming in 2002. The results are in columns (3) and (4) of Table 7. The pattern of full exit is negatively related to the initial shares, that is, smaller farmers are more likely to abandon all catfish activities than larger farmers. For instance, in sample M4, the probability of exit of the median farmer is 23.4, while it is 19.3 percent for the average farmer and 14.7 percent for high-exposure farmers. Similar patterns emerge in M6. Overall, these findings are consistent with a scenario in which it is easier for farmers who are relatively less involved in catfish farming to exit, even though exit is also observed among households with large values of _sh._ Given this decline in catfish shares, we now look at the changes in income shares from other economic activities among catfish households in our sample. We are interested in the response of other aquaculture activities and other major activities such as agriculture and wage labor (see Table 4 in section 3). For this purpose, we estimate model (6) using changes in different income shares as the dependent variable. While part of the estimated adjustment is just a mechanical response of the computation of the shares (if the share of catfish declines, then other shares will necessarily increase), the analysis can reveal interesting compositional

> 23In addition, if income shares follow a stationary time series process, then the sign of the estimated coefficients is mechanically negative.

27