rafmacalaba/datause-encoder-data huggingface dataset:
Figure 2: The Importance of GVC Trade in World Trade <!-- Start of picture text --> 55<br>50<br>45<br>40<br>35<br>30<br>Sources: WDR 2020 team, using data from Eora26 database; Borin and<br>Mancini (2019); and Johnson and Noguera (2017). See appendix A for a<br>description of the databases used in this Report.<br>1970 1975 1980 1985 1990 1995 2000 2005 2010 2015<br>GVC share of global trade (%)<br><!-- End of picture text --> these exports encompass two broad types of GVC trade. On the one hand, GVC trade includes transactions in which a country’s exports embody value added that it has previously imported from abroad. This type of GVC participation is often referred to as backward GVC participation. On the other hand, GVC trade also comprises transactions in which a country’s exports are not fully absorbed in the importing country, and instead are embodied in the importing country’s exports to third countries. The latter form of GVC participation is often dubbed forward GVC participation. As Figure 2 indicates, the overall share of GVC trade in total world trade grew very significantly in the 1990s and early 2000s, but it appears to have stagnated or even declined in the last 10 years. Still, about one-half of world trade appears to be related to global value chains.

The two components of GVC participation, backward and forward, can also easily be computed at the country and even country-sectoral level. When doing so, it becomes clear that the expansion of GVC activity has occurred in an uneven way in the world. On the one hand, there are regions in the world (such as Europe and East Asia) that are deeply involved in GVCs, while GVC participation is much smaller in other parts of the world, most notably Latin America and Africa. On the other hand, even when countries actively participate in GVCs, the sectoral composition of these GVC flows are also quite diverse. Some countries have largely specialized in agricultural GVCs (such as Ethiopia) or in the natural resource segments of GVCs (such as Chile or Norway). Other countries are largely involved in the manufacturing segments of GVCs, with relatively less developed economies (such as Tanzania) specializing in low-tech (or limited) manufacturing, and more developed economies (such as Mexico, Slovakia or China) participating in high-tech (or advanced) manufacturing processes. In addition, it is useful to distinguish a subset of countries (e.g., India) that have largely specialized i <mark>n</mark> the services embodied in GVCs, and a small set of very advanced economies (e.g., United State <mark>s</mark> , Germany or Japan) playing a significant role in the provision of 7
bags), or stocks of PPE (only on whether a least one pair of disposal latex gloves or one gown/apron is available, etc.).<sup>25</sup> In sum, no major survey exists, at present, that collects adequate information on facility pandemic preparedness, which reflects a clear blind spot in standard measurement tools.

# Innovations to further the measurement agenda

To address these gaps, the SDI Team and partners from within and outside of the World Bank have undertaken several innovative lines of work. First, in the immediate term, the data from the health SDI surveys done to date (in nine countries in Sub-Saharan Africa) can serve as sample frames for rapid phone surveys to collect additional information on COVID-19 preparedness. Not only does the SDI collect contact information for facilities (allowing for rapid re-contacting of the sample), but also, only a limited set of phone survey questions would be required to supplement the broad set of items from the SDI to generate a comprehensive view of pandemic preparedness.

Second, for the longer term, pandemic preparedness experts from the GPs and the SDI Team have developed a novel pandemic preparedness/disaster response module for inclusion in future SDIs. This module is visible in Appendix 6 and aims to fill the gaps described above in the current tools. Specifically, the module includes a more comprehensive set of questions on infrastructure, equipment, and supplies needed in the case of a pandemic (including PPE and infection control items), in addition to questions on preparedness plans/guidelines, drills, and trainings. An earlier version of this module was piloted in Bhutan (in preparation for that country’s first health SDI) in January. The team welcomes feedback on this module (please see Appendix 6 for additional details). In addition, future iterations of the SDI will include questions for health care providers (based on the Brief Resilience Scale<sup>26</sup> ) to assess their ability to cope with external shocks, such as natural disasters and pandemics.

> 25 SARA Reference Manual

> <u>https://www.who.int/healthinfo/systems/SARA_Reference_Manual_Chapter2.pdf?ua=1</u>

> 26 <mark>Smith, B.W., Dalen, J., Wiggins, K.</mark> _<mark>et al.</mark>_ <mark>The brief resilience scale: Assessing the ability to bounce back.</mark> _<mark>Int. J. Behav. Med.</mark>_ **<mark>15,</mark>** <mark>194–200 (2008). https://doi.org/10.1080/10705500802222972</mark>

22
**Table 4.** Spatial difference-in-difference estimation—Dependent variable: Nighttime radiance

||Pre-Post(2|013,2019)|All Years(2|012–2020)|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|**Post × Treat (Admin. 2)**|**0.213**<sup>*******</sup>||**0.048**<sup>*****</sup>||
||**(0.000)**||**(0.061)**||
|**Post × Treat (Admin. 1)**||**0.110**<sup>*******</sup>||**0.025**<sup>*****</sup>|
|||**(0.000)**||**(0.052)**|
|Post|0.018<sup>***</sup>|0.016<sup>***</sup>|||
||(0.000)|(0.000)|||
|Treat (Admin. 2)|1.592<sup>***</sup><br>(0.000)||||
|Treat (Admin. 1)||0.800<sup>***</sup><br>(0.000)|||
|Year FE|||yes|yes|
|Grid Cell FE|||yes|yes|
|Observations|1,560|1,560|7,020|7,020|
|R-squared (within)|0.150|0.089|0.073|0.072|
|R-squared(overall)|0.313|0.103|0.030|0.007|

_Notes_ : Difference-in-difference regressions at the grid-cell level with robust standard errors. The dependent variable is average masked nighttime radiance in each cell (where the size of each grid cell is 100 square kilometers). * p < 0.10, ** p < 0.05, *** p < 0.01. p-values in parentheses. The administrative level 1 corresponds to districts; the administrative level 2 corresponds to subdistricts or cities.

_Source:_ Authors’ analysis using data from Global VIIRS (Visible Infrared Imaging Radiometer Suite) Nighttime Lights, Annual VNL 2 Composite.

# **VI. Conclusion**

This paper studies the impacts of a large-scale development intervention aimed at improving energy efficiency in Malawi, using a DiD approach based on a combination of remote-sensing (satellite) data and data from national household surveys. The estimation strategy takes advantage of the geographical variation in the implementation of different project components, which allows us to identify treatment and control groups to construct a counterfactual.

We find that a combination of demand-side and supply-side interventions was associated with a statistically significant increase in electricity access, a decrease in the reported frequency of blackouts, and a switch from traditional fuels to electricity as the main source of energy for lighting (albeit not for cooking). These results are based both on evidence obtained from national household survey data and from the fact that nighttime radiance (as measured by satellite images) increased relatively more in grid cells belonging to areas with project components than in non-project areas. The results are consistent with existing evidence in the economic literature of an (often overlooked) synergy between EE and energy access, according to which improvements in efficiency constitute a channel to also expand access to electricity.

Overall, our results support the view that energy efficiency projects in developing countries can have significant impacts on relevant outcomes that are measurable both from national household survey data and from remote-sensing data from space. At the same time, we stress that the analysis presented in this paper focuses on a specific project where the external validity remains generally unclear. More empirical research to investigate the impacts of energy efficiency projects, and guide policy decisions, is clearly warranted.

14
- 66 -

This selection of estimated elasticities of inter fuel substitution shows that there certainly is no agreement in the values obtained. However this result needs careful interpretation. A key feature of all the models used is that (deliberately) tbey move away from the restrictiveness of a constant elasticity of substitution specification to one in which it can vary. Since in most models it varies with the level of fuel shares it will be very sensitive to relative prices. In this view there is no value in comparing elasticities from different studies or for different industries or countries unless they are evaluated at the same fuel shares. Where different functional forms have been fitted to the erame data and evaluated at the same fuel shares, then the results of Mountain and Hsaio suggest that there may be a robustness in the elasticities which could allow the structural parameters of the model to be used to calculate elasticities at other fuel shares and in other situations.

Despite the substantial amount of econometric work that has been undertaken on estimating fuel elasticities certain limitations for their potential use in a wider content are very apparents

- (i) the studies are largely concentrated on the most industrialized countries and even when comparative values have been derived, as by Griffin and Pindyck, this has been on the basis of a single set of parameters evaluated at the fuel shares of the individual countries. No formal testing for differences between countries at the same fuel shares has been attempted;

- (Li) much of the econometric work has used data which does not allow for the post oil shock period. There has apparently been no attempt to test for structural stability of the estimated models between (say) the pre and post 1980 period. Given that major movements in relative fuel prices may have taken place outside the estimation period of many models, it is not ideal to use elasticities based solely on earlier data;

- (iii) rather little is known about the comparative behavior of different industries. However the work of Mountain and Hsaio suggests that, for Canada at least, the elasticities may be rather similar so that working with aggregate industry data may not suffer too greatly from potential bias caused by shifts in the relative importance of the various components.

- (iv) very little attention has been paid to specific modelling of dynamic adjustment. The cross section work of Griffin by constraining all countries to lie on the same cost function can be seen as focussing on those long term differences which distinguish one country from another, and hence the values obtained may be more representative of long term elasticities
# **Potential Gains and Losses of Biofuel Production in Argentina: A Computable General Equilibrium Analysis**

Omar O. Chisari, Carlos A. Romero and Govinda Timilsina<sup>1</sup>

**Key Words** : Biofuels, Energy Economics, General Equilibrium Modeling, Argentina

**JEL Codes** :  Q42, Q43, Q48

**Sector** :  Energy

> 1 Respectively from Universidad Argentina de la Empresa and CONICET, Universidad Argentina de la Empresa, and The World Bank, Washington, DC. Juan Pablo Vila Martínez and Javier Maquieyra provided research assistance. The views and findings presented here should not be attributed to the World Bank. We thank John Nash and Henry Chen for their valuable comments. We acknowledge the Knowledge for Change Program (KCP) Trust Fund for the financial support.
Loayza and Ranciere (2006) use the PMG estimator on a sample of 75 countries and annual data over the period 1960 - 2000 and find a positive long-run relationship between financial development and growth, while the mean short-run coefficient on current financial development enters negatively.<sup>19</sup> Using the Hausman test that compares the MG with the PMG model, they cannot reject the hypothesis that the long-run coefficients on finance are the same in a cross-country panel growth regression. This is also evidence that the assumption that β _i_ = β in the cross-country estimations discussed so far is a valid one, as long as the focus is on the long-term relationship between financial development and economic growth.

# **4. Time-series approach**

The use of higher-frequency data, often limited to one or a few countries, and the concept of causality, are the main differences between the time series approach and the crosscountry approach discussed in the previous section. First, the time-series approach relies on higher-frequency data, mostly yearly, to gain econometric power, while the crosscountry approach typically utilizes multi-year averages.<sup>20</sup> Further, the time-series approach relaxes the somewhat restrictive assumption of the finance - growth relationship being the same across countries – i.e. β _I_ = β – and allows country heterogeneity of the finance-growth relationship; most studies therefore focus their analysis on a few countries with long time-series data. The time-series approach also directly addresses biases introduced by the persistence and potential unit root behavior of financial development, as we will see in the following.

Second, and more importantly, different causality concepts underlie the two approaches. The time-series approach relies on the concept of Granger causality, as first developed by Granger (1969). A time series _X_ is said to Granger-cause _Y_ if, controlling for lagged _Y_ values, lagged _X_ values provide statistically significant information about the current value of _Y_ . Granger causality tests are tests of forecast capacity; i.e. to what extent does one series contain information about the other series? Unlike the cross-

> 19 This negative short-run coefficient is consistent with the finding of the banking crisis literature.  See for example, Demirguc-Kunt and Detragiache (1999).

> 20 It is important to note, however, that the power of such high-frequency tests depends on the span of the time series rather than the number of observations.

17
**<u>Table 5 Health care expenditure by region, among those who seek care</u>**

||(mo|**Alb**<br>nthlyavera|**ania**<br>ges in New|Lek)|**Bos**<br>(m|**nia and**<br>onthlyav|**Herzegov**<br>erages in|**ina***<br>KM)|**M**<br>(mon|**onteneg**<br>thly avera<br>Euro)|**ro**<br>ges in|(mo|**Ser**<br>nthlyavera|**bia**<br> ges in Dina|rs)|(mon|**Kosov**<br>thly aver<br>DEM)|**o**<br>ages in|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||**_Tirana_**|**_Other_**<br>**_urban_**|**_Rural_**|**_ALL_**|**_City_**|**_Sub-_**<br>**_urban_**|**_Rural_**|**_ALL_**|**_Urban_**|<br>**_Rural_**|**_ALL_**|**_Belgrade_**|**_Other_**<br>**_urban_**|**_Rural_**|**_ALL_**|**_Urban_**|**_Rural_**|**_ALL_**|
|Exp. on<br>public<br>providers<br>|2117.26|2078.43|2438.18|2289.65|5.23|6.61|9.56|6.68|10.90|10.91|10.90|527.23|600.97|742.79|648.05|35.45|30.80|33.67|
|Exp. on<br>private|4201.27|2106.41|1706.20|2025.59|17.20|10.64|12.23|14.76|10.77|6.39|9.80|1694.30|3583.94|2420.24|2738.76|58.77|47.84|55.10|
|providers|||||||||||||||||||
|Exp. for<br>health abroad|_Na_|_Na_|_Na_|_Na_|_na_|_Na_|_na_|_na_|_na_|_Na_|_na_|145.83|16.40|62.54|62.59|_na_|_Na_|_na_|
|TOT exp. for<br>out-patient<br>visits|<br>2507.37|2294.00|2547.52|2467.92|7.72|7.06|11.51|8.61|10.85|9.23|10.50|848.79|930.36|1084.39|975.74|45.07|37.38|42.17|
|Exp for in-<br>patient visits<br>(hospital)|4630.09|2074.24|2489.24|2598.41|7.65|26.69|11.97|12.82|2.65|7.96|4.37|1003.89|301.80|292.22|435.68|39.03|23.53|33.04|
|Exp. for<br>medicines w.p|593.30|458.87|418.92|449.88|4.98|3.55|3.09|4.18|5.73|6.10|5.86|432.87|347.33|420.47|392.63|13.11|11.49|12.45|
|Exp. for<br>dentist|466.50|415.52|276.92|350.18|4.70|3.33|6.55|4.90|19.39|22.38|20.22|2500.40|1583.45|1121.87|1613.74|_na_|_Na_|_na_|
|**_By typology:_**|||||||||||||||||||
|General exp.|1446.66|1179.42|1164.29|1207.35|_na_|_na_|_na_|_na_|11.10|10.98|11.07|1038.30|900.70|876.22|922.86|37.12|28.51|33.81|
|Informal exp.|62.94|153.09|155.45|141.25|_na_|_na_|_na_|_na_|_na_|_na_|_na_|16.91|39.36|12.63|23.19|0.50|0.82|0.62|
|Transportation<br>exp.|50.47|41.88|134.65|95.09|_na_|_na_|_na_|_na_|5.62|4.19|5.24|89.20|91.19|247.98|157.15|7.42|6.49|7.07|
|TOT health<br>exp.|1542.60|1337.46|1388.64|1395.01|9.70|10.08|11.61|10.31|10.85|10.69|10.80|1090.93|967.84|1052.58|1026.53|43.44|34.24|39.90|

Note: *These are imputed figures as the Bosnia and Herzegovina questionnaire on health expenses is based on the previous 14 months.
Table 8: Impacts on household wealth indices

||(1)<br>**Full**|(2)<br>(3)<br>**2011-2012**<br>**2013-2014**|
|---|---|---|
|||**Panel A: Wealth indices**|
|Housing index|-0.015<br>|-0.029<br>-0.009<br><br>|
||(0.017)|(0.037)<br>(0.019)|
|Control Mean|0.654|0.679<br>0.638|
|Assets index|0.003|0.003<br>0.004|
||(0.019)<br>|(0.044)<br>(0.022)<br><br>|
|Control Mean|0.366|0.393<br>0.347|
|Food index|-0.008|-0.029<br>-0.003|
||(0.022)|(0.040)<br>(0.029)|
|Control Mean|0.535|0.562<br>0.517|
|Observations|8413|3466<br>4947|
|||**Panel B: Other wealth components**|
|Had livestock last 12m|-0.029<br>|-0.018<br>-0.039<br><br>|
||(0.040)|(0.072)<br>(0.052)|
|Control Mean|0.418|0.461<br>0.389|
|Household member migrated|-0.035*|-0.031<br>-0.039*|
||(0.019)|(0.045)<br>(0.022)|
|Control Mean|0.080|0.094<br>0.071|
|Credit constrained|0.001|0.009<br>0.027|
||(0.031)|(0.056)<br>(0.040)|
|Control Mean|0.531|0.505<br>0.549|
|Sold smth to cover unexpected cost|0.016|-0.001<br>0.013|
||(0.030)|(0.051)<br>(0.040)|
|Control Mean|0.378|0.386<br>0.373|
|Observations|8413|3466<br>4947|
|||**Panel C: Education**|
|Fraction 6-11 year olds in pri. sch.|0.005|-0.032<br>0.052|
||(0.031)|(0.050)<br>(0.041)|
|Control Mean|0.837|0.837<br>0.837|
|Observations|2152|875<br>1277|
|Fraction 12-15 year olds in sec. sch.|0.059|-0.069<br>0.110*|
||(0.053)|(0.095)<br>(0.063)|
|Control Mean|0.606|0.632<br>0.590|
|Observations|1978|798<br>1180|
|Fraction 16-18 year olds in high-sch.|0.090|0.148<br>0.115*|
||(0.055)|(0.113)<br>(0.069)|
|Control Mean|0.385|0.361<br>0.400|
|Observations|1766|691<br>1075|
|Fraction 19-25 year olds in univ.|0.002|-0.043<br>0.039|
||(0.033)|(0.065)<br>(0.040)|
|Control Mean<br>Observations|0.136<br>2514|0.137<br>0.135<br>1043<br>1471|

> 1 Regressions include the threshold dummy variable, the first and second order polynomials of the de-meaned point score and their interactions with the threshold dummy, as well as state fixed effects. Standard errors clustered at the community level are presented in parentheses. *** p _<_ .01; ** p _<_ .05;40 * p _<_ .10
functions of competency, such as regression coefficients, requires the Monte-Carlo method. For this reason, the PISA dataset includes five random draws for each subject area from the posterior distribution; these random draws are called _plausible values_ and they are used to compute a country’s official “score” in PISA. For further details see OECD (2002, pp99–108).

In addition to these measures of competency, three of PISA’s background variables are used: student’s gender, grade level, and household wealth. Student’s gender and grade level are based on responses to the respective questions in the student background questionnaire; household wealth, alternatively, is an index created by PISA and based on students’ responses to questions about household possessions. To construct the index, a type of Rasche model is used where, instead of estimating the difficulty of a test item, the “expense” of a household possession is estimated. Possessions are assumed to be more expensive if they decrease the probability of a student’s household owning one and cheaper if otherwise. The wealth index is the magnitude of effect of a particular student’s household on the probability of owning a possession that maximizes the probability of owning the possessions actually owned by the household given the Rasche-estimated expensiveness of the possessions. This method is explained in more detail in OECD (2002: 217-49). One limitation of this index is it does not represent the monetary value of household assets directly. This may distort the comparability between two households since two households might have the same possessions used in the calculation of the index but one may be monetarily much wealthier. Consequently, our measure of wealth is 11
Our empirical analysis uses individual level employment data from the Household Expenditure and Income Survey (HIES, 2000) of Bangladesh. With high incidence of rural poverty and an ever dwindling supply of agricultural land, growth in nonfarm activities is perhaps the best hope for development in this densely populated country.<sup>6</sup> Bangladesh lies at the con fl uence of three of the largest rivers in Asia, Ganges-Brahmaputra-Meghna, which have virtually sliced the country into four distinct and poorly integrated geographical regions. These regions di ff er considerably in terms of their agricultural potential and infrastructure development, which make Bangladesh particularly suitable for our analysis.

In our empirical speci fi cation, farm linkages are captured by a crop suitability index developed by agricultural scientists. The use of this crop suitability index, determined by inherent land and water quality as well as climatic condition of a locality, helps to avoid the potential endogeneity problems that arise when farm income/productivity/employment is used as a regressor. The urban linkage is proxied by an index of access to urban centers which is measured by the minimum arc distance from the surveyed primary sampling units to either of the two main cities - Dhaka and Chittagong.<sup>7</sup> This distance variable thus represents access to national and international markets (Dhaka being the capital city and main airport, and Chittagong, the main port city). It should be noted that this distance measure does not depend on the actual placement of transport infrastructure. Use of this access variable in the estimation helps to avoid the potential endogeneity of more commonly utilized urban access regressors such as travel times.

Because of the heterogeneity of non-farm activities, we also make the distinction between

> 6 More than half of Bangladesh’s nearly 100 million rural residents still live in poverty. With nearly 1000 people per square kilometer, population density in Bangladesh is among the highest in the world. Non-farm activities have already assumed an important role in the rural economy, accounting for nearly 42 percent of total rural employment and more than half of rural household income (World Bank, 2004).

> 7 This distance is estimated using the Haversine formula. In addition to this urban access variable, the regression analysis also controls for proximity to smaller towns, which is found to be not important.

4