rafmacalaba/data-use-mentions-extended huggingface dataset:
for heatingand cooling?|
||2.4.1|re_2_4_1|Is there an assessment of thepotential role for renewables in transport includings and electrification?|
||2.4.2|re_2_4_2|Is there a specific target for renewables in transport?|
|Planning for renewable|2.5.1|re_2_5_1|Does the renewableplan or strategyestimate the amount of investment necessaryto meet the RE target?|
|energy expansion (PE)|2.5.2|re_2_5_2|Is there an institution responsible for tracking progress in renewable energydevelopment?|
||2.5.3|re_2_5_3|Is there any periodic reportingmechanism for renewable energy progress?|
||2.5.4|re_2_5_4|Is there a mechanism for adjustingtheplan based on reportingof renewable energydeployment?|
||2.5.5|re_2_5_5|Is currentpolicyenvironment conducive to renewable energydeployment?|
||2.6.1|re_2_6_1|Isgeneration and transmissionplanningintegrated?|
||2.6.2|re_2_6_2|Isplanningfor dispatch included in thegeneration and transmissionplan?|
||2.6.3|re_2_6_3|Is thegenerationplan based on aprobabilistic approach?|
||2.6.4|re_2_6_4|Does the current transmissionplanningconsider renewable energyscale-up?|
||2.7.1|re_2_7_1|Does the government endorse and use the solar/wind resource maps and data applicable to their country that are available through the Global Solar<br>Atlas / Global Wind Atlas, or have they published some other solar/wind resource mapthat conforms to bestpractice in the last fiveyears?|
||2.7.2|re_2_7_2|Has the countrycarried outgeospatialplanningorproduced zoning guidance to inform the commercial development of the RE resource?|
||2.7.3|re_2_7_3|Has the geospatial planning or zoning guidance been carried out according to best practice by i) being undertaken as part of a strategic environmental<br>and social assessment or equivalentprocess; and ii)bymakingthe outputspublicallyavailable?|
|
Table B.5a: Comparison of income inequality from the literature, Gini index and mean log deviation

|||Th|ispaper|||Milan|ovic(202|1)||World|Bank(20|16)|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|||M|LD|||M|LD|||M|LD||
||Gini||Between-|No. of|Gini||Between-|No. of|Gini||Between-|No. of|
||index|<br>Total|country|countries|index|<br>Total|<br>country|countries|index|<br>Total|country|countries|
|1988|||||||||69.7|101.4|80.0|73|
|1993|69.7|98.7|73.7|218|||||69.3|98.3|75.9|102|
|1998|69.3|96.7|72.2|218|||||68.6|94.0|73.5|106|
|2003|68.4|92.6|70.4|218|||||68.7|93.9|72.3|135|
|2008|66.3|85.5|67.8|218|66.4|91.0|61.9|136|66.6|86.3|69.8|136|
|2013|63.0|75.3|65.6|218|61.
In line with Corsetti et al. (2021), we extend equation (1) to study whether the effect of financial deepening varies based on country-level institutional characteristics: Finally, we explore whether financial deepening affects CO2 emissions per dollar of GDP differently in countries with initially higher CO2 emissions intensity than in countries with initially lower CO2 intensity and whether the conditioning characteristics explored in equation (2) affect countries heterogeneously. To do so, we further interact the interaction terms from equation (2) with dummy variables for above/below average CO2 emissions per dollar of GDP. In other words, we expand equation (2) by including triple interaction terms. We measure countries’ initial CO2 intensity of production as their average level of CO2 emissions per dollar of GDP over the previous three years.<sup>8</sup>

> 8 We do so because we have an unbalanced panel dataset and for many countries in our sample data on CO2 emissions per dollar of GPD are not available for the first year of our sample (i.e., 1990). If we used 1990 data to identify countries with above/below sample average level of CO2 emissions intensity, our sample of countries would be significantly reduced. Similarly, if we used the first year that a country’s CO2 emissions per dollar of GDP data become available as the initial CO2 intensity, we would not be able to identify above/below sample average as we would be comparing data across different years.

11
26

# References

# Centro Latinoamericano de Demografia (Celade). 1987. 'Population projections.' Computer printouts. Santiago, Chile.

- Coale, Ansley J., and Paul Demeny (with Barbara Vaughn). 1983. Regional Model Life Tables and Stable Populations. 2nd ed. New York: Academic Press.

- Eurostat. 1987. DemograRhic Statistics. Luxembourg: Statistical Office of the European Communities.

- Monnier, Alain. 1987. "La conjoncture demographique: L'Europe et les pays developpes d'outre-mer,n Population 42(4-5):719-736.

- Unesco. 1977. Statistics of Educational Attainment and Literacy. 19451NA. Statistical Reports and Studies, no. 22. Paris: Unesco.

- United Nations. Department of International Economic and Social Affairs. 1984. Pooulation and Vital Statistics Report: 1984 SRecial SuppRement. New York: United Nations.

- United Nations. Department of International Economic and Social Affairs. 1986. World Population Prosoects: Estimates and Proiections as Assessed in 1984. New York: United Nations.

- United Nations. Dep&rtment of International Economic and Social Affairs. Forthcoming. World Population Prospects: 1988. New York: United Nations.

- United Nations. Department of International Economic and Social Affairs. Various years PoRulation and Vital Statistics Report. New York: United Nations.

- United States, Bureau of the Census. 1985. World PoRulation: Recent Demographic Estimates for the Countries and Regions of the World. Washington, D.C.: U. S. Government Printing Office.

- World Bank. 1988. World Development ReDort. New York: Oxford University Press.

- Zachariah, K. C., and My T. Vu. 1988. World Population Projections: 1987-88 Edition. Baltimore and London: The Johns Hopkins University Press.
graduates among DMO staff (source: WB 2020 survey)

- _DM Legal framework:_ presence of legal requirements to produce debt statistics, strategy and/or annual borrowing plan (source: WB 2020 survey)

The matrix Zij adds a set of controls including categorical variables that capture the country’s income level, debt sustainability risk as rated by WB/IMF’s LIC-DSA (low, moderate, high, or in debt distress), status of fragile and conflict-afflicted country as defined by the WB,<sup>12</sup> and participation in the Highly Indebted Poor Countries (HIPC) initiative. Even though country fixed effects to address endogeneity could not be added, sub regional fixed effects ( δj ) have been included to partially deal with this issue.<sup>13</sup> Table 1 presents the results from estimating the linear model in equation (1). The model in column 1 shows that using a standard DRMS significantly contributes to improvements in debt transparency. Comparing an off-the-shelf DRMS to a non-standardized system has shown that both CS-DRMS/Meridian and DMFAS facilitate production of debt statistics, the former being more robust. LIDCs benefit from

> 11 Regional patterns are relevant since debt disclosure requirements could be enforced by regional institutions (e.g. Central Bank of West African States or Eastern Caribbean Central Bank).

> 12 <u>https://www.worldbank.org/en/topic/fragilityconflictviolence/brief/harmonized-list-of-fragile-situations</u> 13 Subregions are defined as follows: Central America and Caribbean (8), Eastern Europe and Central Asia (4), East Asia and Pacific (18), South Asia (6); Sub-Saharan Africa non-CEMAC (23); Sub-Saharan Africa CEMAC (4), West Africa non-WAEMU (9); West-Africa WAEMU (7).
022)|
|_N_|58,323|58,323|58,323|

Notes: This table reports estimates of the eects of trade agreements on trade ows over the period 1990-2011. The dependent variable is nominal trade ows. The estimator is PPML. All estimates are obtained with three-year interval data and three-way xed eects, including exporter-time, importer-time, and directional pair xed eects. In addition, all specications include a full set of time-varying border variables. The estimates of the border dummies and all xed eects, including the constant, are omitted for brevity. The standard errors in all specications are clustered by country pair. The dierence between the three columns are in the set of trade agreement variables. Specically, column (1) reports the average PTA eect across all agreements in the sample. Column (2) adds the eects of DTAs. Finally, in addition to PTAs and DTAs, column (3) introduces a continuous variable for DTA depth. The estimate on _DEPTHij,t_ in column (3) is 0 _._ 00047 . See text for further details.

21
subsidiary of a foreign company it is likely that the firm is part of a global supply chain; 3) productivity—foreign-owned firms are more productive and hence they are more likely to export.<sup>14</sup> Amongst the variables of interest, only ISO certification and the technological marketing tools are consistently positive and significant across all specifications. Firms that have ISO certification are 22% more likely to be exporters than firms that are not ISO certified. There is a close relationship of firms that have ISO certification and those that are able to export and therefore ISO certification can provide an important avenue for policy implementation. Having a website, on the other hand, increases the likelihood that the firm exports by 11%, whereas the use of email increases the likelihood of exporting by 9%.<sup>15</sup> The significance and high coefficient on email is probably picking up on the effect of the firm having access to the Internet.

The Enterprise Survey also includes the percentage of total sales from exports for each firm. This variable allows for the exploration of the importance of signaling and technological tools in the export intensity of each firm. For easier interpretation I use _ln(1 + % total sales from exports)_ as the dependent variable in the estimation of the tobit model. Table 7 presents the marginal effects conditional on the firm being an exporter. As with the probability of exporting, the productivity and size of the firm positively affect its export intensity. Foreign owned exporting firms sell 33% more in foreign

> 14 See Ferro (2010) for a more detailed discussion of the impacts of foreign ownership on productivity. 15 It is important to mention that out of the 17,535 firms that have their own website, 16,962 also use email, even though the correlation between the two variables is 0.57. Therefore, any positive marginal effect of using a website is in addition to that of using email. I tested for the inclusion of a variable that
beliefs. The results further indicate that, as expected, higher education and lower household income drive more women into the labor force. The positive relation between reservation wages for women and labor force participation is interesting, indicating possibly that women only enter the work force once their expectations of higher wages are met, or that working women have higher reservation wages. Column 2 shows that the reservation wage calculation is different and lower for non-working women who want to work compared to those who do not.<sup>11</sup>

**Table 8: Determinants of whether women work**

||(1)<br>Workingfemale|(2)<br>Want to work|
|---|---|---|
|Education|0.0404***|0.0225***|
||[0.00455]|[0.00637]|
|Log HH Income (minus female|||
|income)|‐0.144***|‐0.0713**|
||[0.0222]|[0.0350]|
|Reservation wage for female|0.000601***|‐0.000995***|
||[0.000123]|[0.000240]|
|PB index|0.0317**|0.115***|
||[0.0148]|[0.0193]|
|SE index|0.0398***|0.00720|
||[0.0140]|[0.0192]|
|SN index|‐0.00501|‐0.0476**|
||[0.0144]|[0.0188]|
|Observations|949|685|
|Adjusted R‐squared|0.167|0.085|

Note: *** denotes significance at 1%, ** at 5%, and
3

# **I. Introduction**

This paper is motivated by the consensus view that the accession to the European Union (EU) of Central and Eastern European (CEE) countries will heavily influence the policies they implement and that, in turn, this might have important consequences for economic growth. The accession process is realistically expected to take several years, but the ties with the European Union are likely to grow increasingly stronger in a number of crucial areas ranging from institutional and political coordination to resource flows.

Key among the many aspects related to EU accession is the per capita income gap that exists between even the most developed CEEs and the present EU member countries. As Table 1 highlights, CEE countries are very far from the EU average income per capita level . This gap is likely to affect the EU-CEE accession negotiations, and its gradual bridging is a necessary condition for a sustainable single market, particularly with regard to labor mobility. This will in tur require that the average growth rate in CEE countries be higher (substantially higher) than the EU average.

The growth literature suggests that countries which have similar "environments" converge in incomes per capita. However, what is relevant for the population of CEE countries is not only the expectation of convergence taking place but, more importantly, that such convergence occurs as rapidly as feasible. Rapid convergence is also important from the perspective of current EU members. If the European Community is to financially support the reduction in income disparities between present and future members, then an estimate regarding the time frame for this convergence to occur might be extremely helpful for financial and budgetary planning purposes. Moreover, identifying the forces that enhance convergence may have a catalytic role in defining the policy requirements for EU accession.

I The GNP per capita estimates in purchasing power parity terms (PPP) show that CEE countries are closer to the EU average than if we use current exchange rate GDP per capita data.
# **I. Introduction**

In recent years, there has been a re‐thinking of the concept of “skills” and how it relates to economic success. While hundreds of papers have estimated the returns to education using “years of schooling” as a proxy for understanding the impact of skills acquired on labor market success, more recent work (Hanushek and Woessmann 2008) argues that this previous research suffers from measurement error.<sup>1</sup> Instead, there is a growing effort to more accurately measure a range of skills and to correlate them with individual and aggregate economic success variables. In this strain of literature, economists have relied on works from behavioral and personality psychologists that refine the concept of “skills” to encompass “cognitive” skills and “non‐cognitive” skills (see for example Almlund, 2011). The former refers to all forms of knowing and awareness such as perceiving, conceiving, remembering, reasoning, judging, imagining, and problem solving” (APA, 2006), while the latter is roughly defined as personality traits and socio‐emotional behaviors that guide the way people feel, behave and react to different stimulate (Borgans et al., 2008).

The emerging literature confirms that higher measures of cognitive skills are associated with higher wages, even when holding constant years of education. For example, studies that use longitudinal data from the US to fit models of labor force variables of young adults and cognitive high school test scores, find that a one standard deviation increase in a mathematics test score in 12th grade increases annual earnings by 10‐15 percent during their mid‐twenties to early thirties (Mulligan 1999, Murnane et al. 2000, Lazear 2003). Similar results are found for the UK (McIntosh and Vignoles 2001) and Canada (Finnie and Meng 2001). Using literacy scores (to proxy cognitive skill measures) and labor force behaviors from the International Adult Literacy Survey (IALS), Hanushek and Zhang (2009)
Sophal, C., T. Saravy, and S. Acharya. 2001. "Land Tenure in Cambodia: A data update." Working Paper 19. Phnom Penh: Cambodia Development Resource Insititute.

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32
of wage labor activities into self-employment activities in agriculture as peace was restored. Similarly, since the consumption data for 2011 and 2012 are not reliable and it is then when conflict declined the most, it is not possible to capture the effect on this welfare measure.

Thus far, all of the regression results presented have relied on a monetary dependent variable and so prices have both been part of the construction of the dependent variable and the explanatory variable included in the analysis. As a robustness check on the findings, we conduct a similar exercise using non-monetary measures of welfare that are correlated with consumption: z-scores (standard scores) of weight for age and weight for height among children less than 5 years of age in the household. The results are presented in Table 11. These data were only collected from 2010 onwards and only collected for children, making the sample size available for these regressions much smaller. For this reason only one price—the price of beans—is considered. Although the results are not consistently significant across specifications, they do show that weight for height is positively impacted by rainfall, and that weight for age is positively affected by higher prices, as suggested by the regressions on income and consumption.

Overall the results show that while income diversification is an effective strategy to manage some sources of risk—such as weather—the amount of diversification undertaken by Ugandan households is not able to fully insure total income and consumption from fluctuations in the external environment. These findings suggest that it is desirable for households to be more fully insured against shocks than they currently are.

**Table 9: Impact of weather,** **<u>prices and peace on income and consumption</u>**

||(1)<br>Crop<br>income|(2)<br>Livestock<br>income|(3)<br>Agricultural<br>wage<br>income|(4)<br> <br>Non-<br
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Diggle, P.J. _et al._ (2013) ‘Spatial and Spatio-Temporal Log-Gaussian Cox Processes: Extending the Geostatistical Paradigm’, _Statistical Science_ , 28(4), pp. 542–563. doi:10.1214/13-STS441. van Doremalen, N. _et al._ (2020) ‘Aerosol and Surface Stability of SARS-CoV-2 as Compared with SARS-CoV-1’, _New England Journal of Medicine_ , 382(16), pp. 1564–1567. doi:10.1056/NEJMc2004973.

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Elmore, R. _et al._ (2020
4 Specifically, we make use of a new variant of a model of the global economy known as GTAP-AGR (Keeney and Hertel 2005) to provide real farm income effects of moving to free trade by developing countries versus by high-income countries, and in agriculture as compared with non-agricultural sectors. We also make use of the latest GTAP database (Version 6), which has the virtue of including not only reciprocal but also non-reciprocal preferential tariffs, the latter providing exporters in many lowincome countries with duty-free access to protected high-income country markets. This allows us to take into account the fact that such a reform may cause a decline in the international terms of trade for those developing countries that are enjoying preferential access to agricultural markets of high-income countries (in addition to those that are net food importers because their comparative advantage is in other sectors such as laborintensive manufacturing).

The paper begins with an examination of current distortions, the emphasis being mainly on import tariffs since they are later shown to be far more important than agricultural subsidies. This is followed by a description of GTAP-AGR model of the global economy to be used to analyze the consequences of removing those distortions. The key results of the simulations are then presented, distinguishing between the impacts of policies of high-income and developing countries, of agricultural and nonagricultural (including services) policies and, within agriculture, of the different policy instruments and the different commodity programs. The paper concludes by highlighting the key messages and suggestion priority areas for further research.

**Key distortions in global markets**
short samples. When it comes to consumption, they often having only one year of seasonal data, rendering it impossible to discern regular seasonal patterns from (idiosyncratic) intra‐ annual variation. This paper revisits this agenda, methodologically and empirically, taking 19 years of food price data over the past two decades and three waves of nationally representative panel data on household consumption over the past decade from Tanzania. Three insights emerge.

First, methodologically, the paper has shown that the most widely used technique of estimating the seasonal gap either from a dummy variables regression or by averaging deviations from a moving average trend can give upwardly biased estimates of the seasonal gap when implemented on samples covering less than 20 years. The bias results when non‐systematic high or low prices are mistakenly taken as identifying seasonal peaks or troughs. This problem is particularly severe for crops in which the seasonal pattern is poorly defined. The alternative, trigonometrically based procedure for estimating seasonal factors on short data samples proposed here, gives more reliable results.

Second, the current neglect of price seasonality is premature. Wholesale maize prices are estimated to be 27 percent higher than those during the troughs (on average across the 20 wholesale markets). The seasonal gap is about half as large for rice (15 percent), which is partly irrigated and more widely traded. Seasonality explains 7 to 13 percent of overall price volatility (for rice and maize respectively), underscoring that many other sources of domestic food price volatility remain. Nonetheless, seasonal variation is 2.5 to three times larger than on the international reference markets (SAFEX for white maize and Bangkok for rice). This suggests substantial scope for improved access to secure storage and other measures to reduce seasonality, even more so when targeted to those markets displaying the highest seasonal gaps. Across markets in the country, they vary between 13 and 46 percent for maize and between 3 and 22 percent for rice, indicating substantial heterogeneity in food price seasonality across the country.

Third, there appears price‐induced