rafmacalaba/datause-encoder-data huggingface dataset:
25

# _Table A-1_ **_: Specific Definitions of indicators  and f data sources_** _(grouped by models)_

# _Panel A - Variables Specific to the Davoodi and Zou Model_

|Vari|able|Definition|Cross-<br>Sect.|Obs.|Mean|Max.|Min.|Std.|Data Source|
|---|---|---|---|---|---|---|---|---|---|
|Dependant<br>(3-year<br>moving<br>average<br>smoothing)|GDP per<br>capita<br>growth|GDP per capita annual growth<br>rate|<br>10|30|0,036|0,074|-0,040|0,026|WDI, World<br>Bank|
|Decentrali-<br>zation<br>(Davoodi<br>and Zou)|Spending<br>ratio net of<br>grants|Ratio of subnational<br>government spending to<br>aggregate government<br>spending,net ofgrants|10|30|0,109|0,213|0,042|0,046|GFS, IMF|
||GNI per<br>capita, Atlas<br>method|<br>GNI per capita in constant<br>US$ (x1000)|10|30|3,891|9,933|1,307|2,361|WDI, World<br>Bank|
|Control<br>Variables<br>(3-year<br>moving|School<br>enrolment|<br>Ratio of students enrolled in<br>secondary school to<br>population of secondary<br>school age|10|30|0,919|1,062|0,793|0,072|WDI, World<br>Bank|
|average<br>smoothing)|Population<br>growth|<sup>Annual population growth rate</sup>|10|30|-0,003|0,002|-0,012|0,004|WDI, World<br>Bank|
||Investment|Gross capital formation : ratio<br>of total investment including<br>inventories over GDP|10|30|0,253|0,327|0,149|0,048|WDI, World<br>Bank|

# _Panel B - Variables Specific to the Oates Model_

|Variable|Definition|Cross-<br>Sect.|Obs.|Mean|Max.|Min.|Std.|Data Source|
|---|---|---|---|---|---|---|---|---|
|Dependent<br>Variable<br>Public<br>sector size|Ratio of aggregate<br>government current<br>expenditures (excluding<br>capital expenditures) over<br>GDP|10|30|0,391|0,463|0,286|0,049|OECD|
|Subnational<br>Revenue<br>ratio<br>Decentraliza|<br>Ratio of subnational<br>governments revenue to<br>aggregate government<br>revenue|10|30|0,188|0,324|0,063|0,065|GFS, IMF|
|tion (Oates) Subnational<br>Spending<br>ratio|<br>Ratio of subnational<br>governments spending to<br>aggregate government<br>spending|10|30|0,162|0,287|0,055|0,063|GFS, IMF|
|Urban<br>population<br>ratio|Ratio of urban population over<br>total population|<br>10|30|0,643|0,747|0,503|0,072|WDI, World<br>Bank|
|GDP per<br>capita<br>Control<br>Variables|GDP per capita in constant<br>1995 US$ (x1000)|10|30|4,043|11,659|1,372|2,757|WDI, World<br>Bank|
|Population|Population (x1000000)|10|30|10,467|38,666|1,387|11,297|<sup>WDI, World</sup><br>Bank|
generation is also a good predictor for CO2 mitigation potential; for instance, in France where nuclear power accounts for almost 80% of power generation, an emission tax even at the relatively high level of US$50/tCO2 reduces emission by only 6.5%, while the same tax rate reduces emissions in the UK, where coal and gas account for the bulk of power generation, by about 28%.

**Table 7: Change in CO2 emissions (%) relative to baseline in 2020**

|**Carbon tax**|**US$10**|**/tCO2**||**US$25**|**/tCO2**||**US$50**|**/tCO2**||
|---|---|---|---|---|---|---|---|---|---|
|**Biofuels subsidy**|**0%**|**25%**|**50%**|**0%**|**25%**|**50%**|**0%**|**25%**|**50%**|
|**World total**|-12.9|-13.0|-13.7|-23.5|-23.7|-23.9|-32.8|-32.9|-33.1|
|**High-income**|-6.9|-7.0|-7.4|-13.7|-13.8|-14.0|-20.6|-20.7|-20.9|
|**Aus-NZ**|-10.9|-10.9|-11.4|-19.6|-19.6|-19.7|-27.2|-27.2|-27.2|
|**Japan**|-6.0|-5.9|-6.0|-9.1|-9.0|-8.9|-12.6|-12.5|-12.4|
|**Canada**|-6.7|-6.8|-7.1|-13.4|-13.5|-13.5|-20.0|-20.1|-20.1|
|**United States**|-7.8|-8.0|-8.7|-15.9|-16.1|-16.4|-23.8|-23.9|-24.2|
|**France**|-1.7|-1.8|-1.9|-3.8|-3.9|-4.0|-6.5|-6.6|-6.6|
|**Germany**|-4.2|-4.4|-4.9|-9.1|-9.3|-9.6|-14.4|-14.6|-14.9|
|**Italy**|-2.5|-2.5|-2.8|-5.7|-5.7|-5.9|-9.6|-9.6|-9.8|
|**Spain**|-2.6|-2.6|-2.7|-5.8|-5.8|-5.9|-9.9|-9.9|-9.9|
|**UK**|-9.7|-9.7|-9.9|-19.1|-19.1|-19.1|-28.0|-28.0|-28.0|
|**Rest of EU & EFTA**|-5.6|-5.6|-5.8|-11.8|-11.8|-11.7|-18.2|-18.2|-18.2|
|**Middle & Low-income**|-15.8|-15.9|-16.8|-28.2|-28.4|-28.6|-38.6|-38.8|-39.0|
|**China**|-22.2|-22.4|-23.5|-38.3|-38.5|-38.8|-50.3|-50.5|-50.7|
|**Indonesia**|-9.3|-9.5|-10.1|-18.1|-18.3|-18.6|-26.2|-26.4|-26.7|
|**Malaysia**|-8.6|-8.9|-9.6|-17.7|-17.8|-18.2|-26.6|-26.8|-27.1|
|**Thailand**|-4.5|-4.6|-4.8|-9.5|-9.6|-9.7|-15.0|-15.1|-15.2|
|**Rest of EAP**|-8.2|-8.1|-8.3|-16.3|-16.3|-16.2|-24.2|-24.1|-24.1|
|**India**|-17.0|-17.2|-18.0|-29.1|-29.2|-29.5|-38.5|-38.6|-38.9|
|**Rest of SA**|-11.4|-11.5|-11.9|-21.8|-21.8|-22.0|-31.9|-31.9|-32.1|
|**Argentina**|-15.9|-16.0|-16.5|-28.2|-28.3|-28.3|-38.2|-38.2|-38.2|
|**Brazil**|-2.9|-4.1|-6.0|-6.4|-7.6|-9.3|-10.5|-11.7|-13.3|
|**Rest of LAC**|-8.0|-8.1|-8.5|-13.5|-13.6|-13.8|-19.4|-19.5|-19.7|
|**Russia**|-12.6|-12.9|-13.9|-24.7|-25.0|-25.5|-36.1|-36.4|-36.8|
|**Rest of ECA**|-10.8|-10.8|-11.2|-21.1|-21.1|-21.2|-30.9|-31.0|-31.0|
|**MENA**|-8.0|-8.0|-8.3|-16.9|-17.0|-17.1|-26.7|-26.8|-26.9|
|**South Africa**|-21.4|-21.7|-22.7|-35.9|-36.1|-36.4|-46.3|-46.5|-46.7|
|**Rest of SSA**|-9.9|-10.2|-10.8|-15.5|-15.7|-16.2|-21.2|-21.4|-21.8|

29
matching grant program in Mauritius, 80 percent of recipients said they would have made the investments in technology transfer even without the matching grants. In many countries the mindset was still very much one of trying to select the “best” firms or the ones most likely to succeed, not necessarily those for whom the grant funding could make a difference between success and failure. We hoped that the process of engagement of World Bank operational staff and governments in the impact evaluation would also help improve project implementation in this respect.

Finally, given that these programs are pervasive throughout Africa, we emphasized that there is a global public good element to the knowledge produced by any impact evaluations – the results would not only influence and inform policy in the country being studied, but also help inform efforts in other countries. For these reasons we were able to raise funding from different sources to help finance the impact evaluations, so that, when approaching these countries to try to implement evaluations, we were coming with some funding in hand to supplement their data collection costs.

# **3.2 Why Use Randomized Experiments as an Evaluation Tool?**

There are a number of private sector development policies in which a randomized evaluation is difficult or near impossible (e.g., changing a national regulation, building new infrastructure). Matching grants on the other hand satisfy a number of conditions that make randomization a possibility: i) they involve selection of individual firms; ii) the numbers of firms involved can be large enough to potentially generate enough statistical power for measuring impacts (provided that appropriate methods are used, see McKenzie, 2011b, 2012); and iii) data on key outcomes may be measured reasonably well through firm surveys.

Furthermore, matching grants are a type of program for which it is hard for non-experimental methods to convincingly deal with selection issues – firms self-select into whether or not to apply for the program, and then government implementation units select which firms receive the grants. An explicit element of both selection stages is likely to be forward-looking behavior on the part of both the government and the firm. Firms that receive support are more likely to be perceived (by the firm and by government) as having faster potential growth in the future than firms that do not apply or do not get selected, even when they are observably similar in terms of current size and recent growth history. As such, matching on observable characteristics and differencing out of firm fixed effects is still unlikely to control for the differences between the firms which participate in the program and those which do not. In some circumstances regression-discontinuity designs may offer an appropriate (local) estimate of the 13
Table C.5: Average Within-firm Variance of Technology Sophistication by Country and Sector

||A|BF MOST||G|BF MOS|T|SS|BF MOS|T|
|---|---|---|---|---|---|---|---|---|---|
||AGRI|MANF|SVC|AGRI|MANF|SVC|AGRI|MANF|SVC|
|Overall|0.54|0.54|0.58|0.48|0.56|0.62|0.54|0.19|0.27|
|Brazil (BR)|1.00|1.01|0.90|1.03|1.08|1.00|0.83|0.31|0.39|
|Vietnam (VT)|0.42|0.40|0.50|0.34|0.42|0.50|0.50|0.09|0.27|
|Senegal (SN)|0.19|0.20|0.34|0.08|0.17|0.35|0.30|0.17|0.15|
|Gap: BR - SN|0.81|0.81|0.56|0.95|0.91|0.65|0.53|0.14|0.24|
|Relative Gap|20%|20%|14%|24%|23%|16%|13%|4%|6%|
||A|BF EXT||G|BF EXT||S|SBF EXT||
||AGRI|MANF|SVC|AGRI|MANF|SVC|AGRI|MANF|SVC|
|Overall|0.71|0.81|0.83|0.75|0.83|0.87|0.55|0.27|0.37|
|Brazil (BR)|0.89|1.09|0.94|1.03|1.15|1.00|0.52|0.36|0.43|
|Vietnam (VT)|0.76|0.69|0.72|0.75|0.66|0.72|0.72|0.13|0.40|
|Senegal (SN)|0.47|0.66|0.84|0.47|0.68|0.88|0.42|0.31|0.29|
|Gap: BR - SN|0.42|0.43|0.10|0.56|0.47|0.12|0.10|0.05|0.14|
|Relative Gap|11%|11%|3%|14%|12%|3%|3%|1%|4%|
||A|BF GAP||G|BF GAP||S|SBF GAP||
||AGRI|MANF|SVC|AGRI|MANF|SVC|AGRI|MANF|SVC|
|Overall|0.61|0.71|0.40|0.67|0.72|0.70|0.47|0.20|0.35|
|Brazil (BR)|0.85|0.85|0.47|0.97|0.85|0.75|0.67|0.28|0.42|
|Vietnam (VT)|0.60|0.72|0.39|0.64|0.70|0.70|0.44|0.11|0.37|
|Senegal (SN)|0.38|0.56|0.35|0.41|0.60|0.65|0.30|0.21|0.25|
|Gap: BR - SN|0.47|0.29|0.12|0.56|0.25|0.10|0.37|0.07|0.17|
|Relative Gap|12%|7%|3%|14%|6%|3%|9%|2%|4%|

Note: AGRI, MANF, and SVC represent agriculture, manufacturing, and services, respectively. Overall is the average of Brazil, Vietnam, and Senegal. For the columns on SSBF, the sample is restricted to the firms having sector-specific technologies. Technology measures are weighted by the sampling weights.

75
<!-- Start of picture text --> Figure 3a: Paris Club, Commercial<br>Bank and IBRD Defaults<br>35<br>'° 35 -5 | _ Paris Club<br>a20 -U- Commercial<br>L. 15 L ^Banks<br>[--<br>.0 10 -- _ZIBRDl<br>O 5<br>CD( CD0 CO (0 to c CD (0 CD CO<br>°° c; en -s (D0 - CX 4<br>year<br><!-- End of picture text -->

Source: Tables A3.2, A2.3 Global Development Finance 1999: Analysis and Summary Tables. Washington DC, The World Bank and FINCR internal records.

Figure 3b shows the number of countries going into default in a given year, which is arguably the phenomenon of most interest. We see the effects of the Debt Crisis of the early 1980's, with the a large number of countries entering into default with Commercial Banks and the Paris Club creditors in 1982 and 1983. The upsurge in IBRD defaults did not occur until 1987-88, five years later, with a second peak in defaults in 1993 caused by the separation of forrner Yugoslavia. We see that to reconcile IBRD default with default to the other creditor groups one needs to carefully consider issues of timing.

_5_
32 Table 3. Effects on Sectoral Value Added of Removing all Merchandise Import Tariffs and Agricultural Subsidies as of 2001 (percent)

|Region:|Primary<br>agricultural<br>value<br>added|All non-<br>agricultural<br>value<br>added|
|---|---|---|
|Sub-Saharan Africa|2.3|2.7|
|South Asia|0.3|-0.1|
|E. Asia (excl. Ja, Ko, Ta)|9.5|5.0|
|Latin America|11.5|-0.2|
|All Developing Countries|5.6|2.0|

Source: Anderson and Valenzuela (2007)
# **_3.1 General Equilibrium Modeling_**

The study by Hertel et al. (2010) is an effort to analyze the impacts of climate change through a more careful modeling of the channels and the heterogeneity of impacts in the context of economic growth. They use disaggregated data on household economic activity (stratified by their primary source of income) within individual countries (15 developing countries) and a general equilibrium global trade model (Global Trade Analysis Project or GTAP) to explore how changes in agricultural productivity will affect poverty in poor countries. Although the extent of heterogeneity allowed by their model is limited, a key feature of their model is that allows different type of households to be affected differently by the prices of agricultural goods depending on whether they are net producers or net consumers of food.<sup>15</sup> They use three scenarios of climate change on agricultural productivity (low, medium, or high productivity) to evaluate the resulting changes by 2030 in: (i) global commodity prices, (ii) national economic welfare and (iii) the incidence on the poverty headcount rate (defined as the portion of a nation’s population living on less than $1 a day). The poverty consequences of a decline in agricultural productivity are evidenced through two channels: _changes in earnings_ and _changes in the real cost of living at the poverty line_ . The impact of a food price rise on earnings depends on the income sources for a given household group (these earnings shares are estimated from household survey data). If earnings rise faster than the cost of living for households at the poverty line in a given stratum, then the poverty headcount falls and vice versa. The responsiveness of the stratum poverty headcount to a given real income shock is determined by the density of the stratum population in the neighborhood of the poverty line, and is also estimated from the household survey data. When combined with information about the distribution of national poverty across socio-economic strata, the authors are able to estimate the change in the national poverty headcount.

> 15 The authors consider seven types of households based on their primary sources of earnings (i.e., where they earn 95% of their income): agricultural self-employed (farm income), non-agricultural (non-agricultural self employed earnings), urban labor (urban households with wage labor income), rural labor (rural households with wage labor income), transfer payment dependent, and two groups of households with non-specialized income sources (urban diverse and rural diverse).

12
Policy Research Working Paper 6457

# **Abstract**

Using data on Israeli closures inside the West Bank, this paper provides new evidence on the labor market effects of conflict-induced restrictions to mobility. To identify the effects, the analysis exploits the fact that the placement of physical barriers by Israel was exogenous to local labor market conditions and uses a measure of conflict intensity to control for the likely spurious correlation between local unrest, labor market conditions, and the placement of barriers. The study finds that these barriers to mobility have a significant negative effect on employment, wages, and days worked per month. The barriers had a positive impact on the number of hours per working day. These effects are driven mainly by checkpoints while other barriers, such as roadblocks and earth mounds, have a much more limited impact. Only a tiny portion of the effects is due to direct restrictions on workers’ mobility, suggesting that these restrictions affect the labor market mainly by depressing firms’ production and labor demand. Despite being an underestimation of the actual effects, the overall costs of the barriers on the West Bank labor market are substantial: in 2007, for example, these costs amounted to 6 percent of gross domestic product. Most of these costs are due to lower wages, thus suggesting that the labor market has adjusted to the restrictions more through prices than quantities.

This paper is a product of the International Trade Department, Poverty Reduction and Economic Management Network. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank. org. The author may be contacted at mcali@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# References

- Abadzi, Helen. 2009. 'Instructional time loss in developing countries: Concepts, measurement, and implications', _World Bank Research Observer_ , 24: 267-90.

- Andrabi, Tahir, Jishnu Das, Asim Ijaz Khwaja, Tara Vishwanath, and Tristan Zajonc. 2007. 'Learning and Educational Achievements in Punjab Schools (LEAPS): Insights to inform the education policy debate', _World Bank, Washington, DC_ .

- Auwarter, Amy E, and Mara S Aruguete. 2008. 'Effects of student gender and socioeconomic status on teacher perceptions', _The Journal of Educational Research_ , 101: 242-46.

- Banerjee, Abhijit, and Esther Duflo. 2006. 'Addressing absence', _The Journal of Economic Perspectives_ , 20: 117-32.

- Banerji, Rukmini. 2000. 'Poverty and primary schooling: field studies from Mumbai and Delhi', _Economic and Political Weekly_ : 795-802.

- Beatty, Amanda, and Lant Pritchett. 2012. "From Schooling Goals to Learning Goals." In. Washington DC: Center for Global Development.

- Bold, Tessa, Deon Filmer, Gayle Martin, Ezequiel Molina, Brian Stacy, Christophe Rockmore, Jakob Svensson, and Waly Wane. 2017. 'Enrollment without Learning: Teacher Effort, Knowledge, and Skill in Primary Schools in Africa', _Journal of Economic Perspectives_ , 31: 185-204.

- Bruns, Barbara, Deon Filmer, and Harry Anthony Patrinos. 2011. _Making schools work: New evidence on accountability reforms_ (World Bank Publications: Washington DC).

- Bruns, Barbara, and Javier Luque. 2014. _Great teachers: How to raise student learning in Latin America and the Caribbean_ (World Bank Publications: Washington DC).

- Chaudhury, Nazmul, Jeffrey Hammer, Michael Kremer, Karthik Muralidharan, and F Halsey Rogers. 2006. 'Missing in action: teacher and health worker absence in developing countries', _Journal of Economic Perspectives_ , 20: 91-116.

DellaVigna, S., & Gentzkow, M. 2010. Persuasion: empirical evidence. Annu. Rev. Econ., 2(1), 643-669.

- De Ree, Joppe, Karthik Muralidharan, Menno Pradhan, and Halsey Rogers. 2015. "Double for nothing? Experimental evidence on the impact of an unconditional teacher salary increase on student performance in Indonesia." In.: National Bureau of Economic Research.

- del Río, M Francisca, and Jaime Balladares. 2010. 'Género y nivel socioeconómico de los niños: Expectativas del docente en formación', _Psykhe (Santiago)_ , 19: 81-90.

- Denzau, Arthur T, and Douglass C North. 1994. 'Shared mental models: ideologies and institutions', _Kyklos_ , 47: 3-31.

- Ferguson, Ronald F. 2003. 'Teachers' perceptions and expectations and the Black-White test score gap', _Urban Education_ , 38: 460-507.

- Fisher, Robert J. 1993. 'Social desirability bias and the validity of indirect questioning', _Journal of consumer research_ , 20: 303-15.

13
create effeciencies and ultimately maximize overall program effects by taking into account subgroup differences.

The aforementioned static and dynamic factors, which affect treatment outcome, can be measured either categorically or continuously. Categorical divisions of the population could be based on some naturally formed social groups (e.g. ethnicity and gender). The impact of a policy may be moderated by these factors where the policy may unevenly affect different groups within the population. For continuous latent variables (e.g. level of SDO), which are measured along a continuum, we could use confirmatory factor analysis (CFA) to study and describe the relationships between a set of observed variables (e.g. based on Likert scale items) and the specific set of continuous latent variables of concern (Fox, 2010; van der Linden, 2016). This is especially helpful when no pre-established indexes are available. This allows us to create a combined measure capturing the level or degree of an unobserved latent characteristic or factor. For example, when there are no reliable indicators that accurately classify individuals in the community with respect to level of SDO, an evaluation team may decide to incorporate certain assessment criteria (measured continuously) and assess the uniformity of these criteria in measuring SDO levels (the unobserved latent variable). Following Bitler et al. (2006), consider two distributions where we have our experimental and control groups, respectively, _F_ 1 and _F_ 0, and define quantile treatment effects (QTE) as ∆ (0), where is the _q_ th quantile of distribution _Ft_ . qq qq qq qq **_Accounting for heterogeneity_** = yy (1) − yy yy (tt) Policy outcome differences within the distribution of the continuous latent variable will indicate whether the outcome effects are stronger for some individuals than for others. Figure 1 shows the cumulative distribution for hypothetical treatment and control groups in an RCT of a given policy.

16
The first component is correlated with firm productivity, _ait_ , with the degree of correlation governed by _γ_ . If _γ <_ 0 , the distortion discourages (encourages) investment by firms with higher (lower) productivity, the empirically relevant case. The opposite is true if _γ >_ 0 . The remaining components of _τit_ are both uncorrelated with _ait_ , but differ in their time-series properties. The term _χi_ is permanent while _εit_ is iid over time. Thus, the severity of factors 2 other than adjustment/information frictions is summarized by three parameters: _γ_ , _σχ_ , which 2 is the cross-sectional variation in the fixed component, and _σε_ , which is the volatility of the iid shocks to the wedge.

These five distinct forces all contribute to dispersion in _arpk_ : adjustment costs, uncertainty and the three components of the wedge, parameterized by _ξ_ , V , _γ_ , _σ_ 2 and _χ σ_ 2. The main _ε_ contribution of David and Venkateswaran (2017) is an empirical strategy to estimate these five parameters from firm-level data on revenues and capital. Specifically, they use a simulated method-of-moments estimator targeting the following five moments from Table 1: the variability and serial correlation of investment growth, the correlation of investment growth with lagged changes in _a_ , the correlation of _arpk_ with _a_ and the cross-sectional dispersion in _arpk_ . We follow their approach and report the resulting parameter estimates in Table 2.<sup>14</sup> The implications for _arpk_ dispersion and aggregate productivity are presented in Table 3 and discussed in detail there.

Table 2: Parameter Estimates

||||O<br>|ther Factors<br>|<br>|
|---|---|---|---|---|---|
||Adjustment Costs|Uncertainty|Correlated|Permanent|Transitory|
||_ξ_|_μ_<br>V/_σ_<br>2|_γ_|_σ_<br>2<br>_χ_|_σ_<br>2<br>_ε_|
|ARG|0_._19|0_._67|_−_0_._79|0_._36|0_._00|
|BRA|0_._12|0_._71|_−_0_._67|0_._42|0_._00|
|CHN|0_._16|0_._63|_−_0_._63|0_._51|0_._00|
|COL|0_._54|0_._61|_−_0_._55|0_._60|0_._01|
|MEX|0_._13|0_._58|_−_0_._82|0_._42|0_._00|
|MYS|0_._83|0_._49|_−_0_._94|0_._18|0_._00|
|TWN|0_._20|0_._58|_−_0_._65|0_._32|0_._00|
|THA|0_._29|0_._61|_−_0_._58|0_._59|0_._00|
|TUR|0_._15|0_._72|_−_0_._61|0_._37|0_._00|
|JPN|2_._05|0_._49|_−_0_._35|0_._32|0_._06|
|USA|1_._38|0_._42|_−_0_._33|0_._29|0_._03|

> 14The estimation also uses standard values for other parameters governing production and demand. We report the values for these parameters in Table 5 in Appendix A.

8