rafmacalaba/datause-encoder-data huggingface dataset:
**_Energy demand models for policy_** **_<u>formulation</u>_**

Hoffman, K. and D. W. Jorgenson, 1977, Economic and technological models for evaluation of energy policy, Bell Journal of Economics, 8, pp. 444-66.

Hogan, W. W. and A. S. Manne, 1979, Energy-Economy interactions: the fable of the elephant and the rabbit, Advances in Economics of energy and resources, Vol. 1, pp. 7-26.

- Holtedal, P. and F. L. Joutz, 2004, Residential electricity demand in Taiwan, Energy Economics, 26, pp. 201-24.

Hondroyiannis, G., 2004, Estimating residential demand for electricity in Greece, Energy Economics, 26, pp. 319-334.

Houthakker, H. S., 1951, Some calculations on electricity demand in Great Britain, Journal of the Royal Statistical Society. Series A (General), Vol. 114, No. 3 (1951), pp. 359-371.

Hughes, J. E., C. R. Knittel and D. Sperling, 2008, Evidence of a shift in the short-run price elasticity of gasoline demand, Energy Journal, 29(1), pp. 113-134.

Hunt, L. C. and N. Manning, 1989, Energy price and income elasticities of demand: Some estimates for the UK using the cointegration procedure, Scottish Journal of Political Economy, 36(2), pp. 183-93.

Hunt, L. C. and E. L. Lynk, 1992, Industrial energy demand in the UK: a cointegration approach, in D. Hawdon (ed.), Energy Demand: Evidence and Expectations, Academic Press, London.

Hunt, L. C. and Y. Ninomiya, 2005, Primary energy demand in Japan: an empirical analysis of long-term trends and future CO2 emissions, Energy Policy, 1409-24.

109
Absolute convergence has not been supported by the data, leading to the notion of conditional convergence, that is, once the differences in steady-states across countries are controlled for, poorer countries will grow faster. Hence, one might still hold out hope that in a world of liberalized capital flows, capital would flow from rich to poor countries, augmenting their capital base and spurring growth. However, empirical evidence is to the contrary. Even though capital flows to middle income countries increased rapidly during the 1990s, “..from less than $40 billion per year over the period 1983-1990 to an average of about $200 billion a year” 8during the first half of the 1990s—the lion’s share of capital flows are among rich countries, not from rich to developing countries. Developing countries’ share in global private capital flows was just 12 percent in 1991, and fell to 8 percent by 2000 (see Figure 1).<sup>9</sup>

**Figure 1.  Share of Developing Countries in Global Capital Flows**

<!-- Start of picture text --> per cent<br>40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>1991 1992 1993 1994 1995 1996 1997 1998 1999 2000<br>in global total private capital flows in global capital market flows<br>in global FDI flows<br><!-- End of picture text --> One explanation for this unexpected pattern of capital flows is that the assumed diminishing marginal returns to capital posited by neoclassical growth theory may not obtain in practice. This is the core idea of endogenous growth theory, namely, that technological progress may be linked to capital accumulation, and that human capital should also be included, both of which favor rich countries. The upshot is that the income gap between rich and poor countries may not narrow. This is a simplistic statement of a complex debate about economic growth. The point is that many factors, economic and non-economic, could intervene to lower returns to capital in poorer countries below those in richer ones, a theme that is pursued below.

# _Higher Capital Flows: Enhanced Growth versus Enhanced Vulnerability_

The observation that rich-to-poor capital flows are minor compared to those among rich countries has spawned a number of explanations, which are summarized in Box 2. The explanations center on the idea that risk-adjusted returns in the poorer countries may actually be lower than those in the richer countries because of institutions, incentives and productivity.

> 8 Keynote address by the General Manager of the Bank for International Settlements, Mr. Andrew Crockett, to the 33<sup>rd</sup> Seacen Governors’ Conference in Bali on 13/2/98.

> 9 Global Development Finance (2001).

7
- 20 -

The table shows an even larger variation between countrLes ln fuel mix than ia the case for the O.3.C.D. Thli reflects .n large part the varlatlons Ln the endowment of natural resources. The oll producers not surprLiLngly uae large amounts of oll and gas, whlle those countrLes whlch do not produce oll use relatlvely little. CountrLes where there **li** ample materlal for bLomais, notably ln South AmorLca, use thLs to a great extent. The variatlons due to endowments may exaggerate the extent to whlch fuel substltutLon li economlcally feasLble wi.thLn a country for likely varlatlons in domestlc prLces. There will aido be very substantial variations ln the LndustrLal composltLon between theae countrLes whlch accounts for some of the varLatLons ln fuel mix. Nevertheless, they do lndlcate that at a technleal level lt is possLble to use a wlde range of fuel mix. Thli Ln turn suggests that there is subst&ntLal scope to lnduce such substLtutLon.

The domlnant use of hydro power ln certain countrles and oll in others is, as noted above, related to the domestLc avallabillty of the various fuels. ThLi is Ln effect related to the prLces (or potentlal prices) of the varLous fuels c.l.f. at the burner tip. If hydro capaclty has been Lnstalled then lts prlce can be so low (even allowing for the capltal cost element) that the price of any lmported fuel would be uncompetltive. As well as the general world f.o.b. cos0U- other fuels would need to add on the domestLc transport coat margin and associated capital cost where the infrastructure did not already exist (e.g. the costs of a terminal and pipeline for oil or gas). The effective hydro price could be very much lower than the potential oil cost so that a small change in the price of oil relative to hydro would bring no shift in fuel use. Fuel Usl in POMwr Gner-ation Outside the O.B.C.D. Data are also available for the fuels used ln power generatlon ln the non O.B.C.D. countries. For these countries nuclear is unimportant and hydro is **so** dependent on natural resources that the availability of figures for thermal
grow larger by integrating vertically. Vertical integration replaces less frequently repeated supplier relationships with more frequently repeated employee relationships. We might, then, expect vertical integration to be decreasing in the level of generalized trust, and hence a negative correlation between generalized trust and firm size. We leave it to the data to decide the issue.

The size of the market in which the firms sell may also affect the size of firms. One rationale for a connection between firm size and market size is suggested by Rosenstein-Rodan (1943) and Murphy, Shleifer and Vishny (1989). They propose that goods may be produced by either craft or mass production technologies. Smaller markets may not be of sufficient size to support mass production, and the larger firms it brings. The association should be strongest for sectors in which there are large fixed costs of entry. In most of the sectors we examine, however, fixed entry costs appear to be very low even in the largest markets in Mexico (McKenzie and Woodruff, 2003).

Firm size may also be related to market size measured by income. One of the implications of the Lucas (1978) model is that firm size will be increasing in per capita income. As incomes increase, wage rates do as well. So long as the elasticity of substitution in production between capital and labor is less than unity, wage rates increase faster than the return to entrepreneurship for the marginal entrepreneur. Hence, the level of entrepreneurial talent representing the cutoff between self employment and wage work increases. The lowest ability entrepreneurs, who manage the smallest firms, shift to wage work, resulting in larger overall firm size. Whatever the underlying cause, KRZ (2002) find a very strong positive relationship between market size and firm size in European data. We include variables measuring the total employment in the sector and state, the log 16
resource poor and resource rich countries. Section 3 presents the empirical model applied to MENA, and section 4 discusses data sources and variable construction. Section 5 presents the empirical results and section 6 concludes.

# **2. Trade agreements between resource poor and resource rich countries: An analytical setup**<sup>**5**</sup>

Let us assume a three-country world with two countries which are natural resource abundant and form a preferential trade agreement. One should expect little trade creation from such an agreement if the two countries have a comparative advantage in the same natural resource. Indeed, there is no reason for these countries to trade and therefore little trade creation or trade diversion should be expected from such an agreement. On the other hand if the countries are abundant in different natural resources, then trade creation can be expected, and this will be accompanied of little trade diversion. Thus, the first prediction for regional integration among natural resource abundant countries is that this should be accompanied of no trade diversion and mild levels of trade creation.

If the preferential trade agreement on the other hand is signed by a natural resource abundant country and a natural resource poor country with a small but developing manufacturing sector, then the introduction of tariff preferences will probably lead to some trade creation in the resource poor country, as it will be able to import more natural resources from the resource rich country<sup>6</sup> . There is little scope for the resource poor country to suffer from trade diversion if the resource abundant country is specialized in the natural resource good. On the other hand, the resource rich country may suffer from a significant amount of trade diversion as the resource poor country benefiting from the preferential access can increase its exports to the

> 5 This section draws heavily from Venables (2011) and WTO (2010).

> 6 As surprising as it could appear, resource poor countries in MENA for instance applied tariffs (13 percent) on imports of resources commodities such as oil from resource abundant countries of the region (GCC) before PAFTA implementation. See the Appendix.

5
|**Reports module**|Generates a wide range of reports (standard and customized) for internal requirements as<br>well as for Debt statistics, and international reporting such as World Bank reports (DRS<br>etc.).<br>The module provides also techniques for data control. It is aimed at debt managers<br>seeking to set up quality control procedures for use with databases and reports (data<br>validation reports).|
|---|---|
|**Data validation**<br>**module**|An automated and coordinated workflow for data verification among different users.<br>When data entry is done, notifications are sent to validators to check and confirm the<br>data recorded.|
|**Analysis**<br>**module**|Designed to calculate debt ratios, financial indicators, undertake sensitivity analysis, debt<br>portfolio analysis, generate projections based on debt real outstanding, calculate the<br>present value of a debt portfolio.|
|**Security**|Highly structured yet flexible system to administer users and user groups and to control<br>and/or restrict access rights to the software modules and functions|
|**Auditing**|Extensive tracking facility of all database operations carried out by system users|
|**Technical**<br>**characteristics**|DMFAS 6 is web-enabled,it can easily be interfaced with other integrated financial management<br>systems using web services. The software is currently available in 3 languages (English, French,<br>Spanish) and can be easily customized in other languages. Documentation and online-help are also<br>available in the 3 languages.|
|**Documentation**|Extensive User Guidesand On-help are available in the 3 languages.|

# **<u>WALL STREET SUITE FOR DEBT MANAGEMENT</u>**<sup>**19**</sup>

|**Wall Street Suite**<br>|**for Debt Management**<br>|
|---|---|
|**Debt recording**|Information on all government debt held in a single database: Bond issuance, non-<br>marketable concessionaryfinance, portfolio allocation,debt servicing profile|
|**Active debt**<br>**management**|Calculation of risk figures: interest rate and foreign currency hedging strategies, portfolio<br>rebalancing,what-if scenario analysis,debt measured at market value|
|**Integrated debt**<br>**and cash**<br>**management**|Integrated forecasting of revenues and expenditure: sophisticated risk figures,<br>performance benchmarking, credit risk management|
|**Total balance**|Asset and liability management of consolidated government balance sheet: “Cost at Risk”|
|**sheet**<br>**management**|testing of debt strategies, contingent liability management, provision of single treasury<br>account,investment management,treasuryservice center|

> 19 One commercial solution as an example. From Wallstreet suite for debt management brochure.

39
**Table 3:** Fixed Effects Estimation with all Finance<sup>Variables</sup>

# Dependent Variable: STANDARD DEVIATIONS OF<sup>GDP, CONSUMPTION, AND INVESTMENT GROWTH</sup> (Annual Rates)

||_GDP_|_C_|_I_|
|---|---|---|---|
||(1)<br>|(2)<br>|(3)<br>|
|_BANKt-_<br>_1_|-6.88*<br>(2.91)<br>|-7.23*<br>(2.94)<br>|-9.81**<br>(5.44)<br>|
|_LLYt-I_|5.26<br>(5.69)|7.38<br>(5.62)|7.59<br>(8.96)<br>|
|_PRIVATEt-_<br>_1_|-1.54*<br>(.583)<br>|-1.49*<br>(.539)<br>|-. 354<br>(1.66)<br>|
|_PRIVYt- 1_|1.25<br>(2.73)|2.78<br>(2.55)<br>|9.18**<br>(5.40)<br>|
|_GROWTHt_|.216<br>(.209)<br>|.270<br>(.309)<br>|.085<br>(.124)<br>|
|_MEANt- 1_|.153<br>(.222)<br>_<br>|.254<br>(.379)<br>|-. 403<br>(.617)<br>|
|_INFMEANt_|- .031<br>(.046)|- .026<br>(.049)|.004<br>(.080)<br>|
|_INFSTDEVt_|.057**<br>(.031)|.068**<br>(.035)<br>|.038<br>(.075)<br>|
|_FXVOLt_|12.1*<br>(5.12)|10.5*<br>(4.45)<br>|18.8*<br>(6.90)<br>|
|_No. of obs._|190|190|189<br>|
|_J_R2_|56__|.61|.68|

Note: Country-specific and time-specific fixed effects estimate.<sup>Heteroskedasticity-corrected</sup> standard errors are in parentheses. *, ** respectively<sup>denote significance at the 5 per-</sup> cent and 10 percent levels. The variables _GOVMEAN, GOVSTDEV, POLITY, OPEN, OPEN * FXVOL_ are included but not shown.
regressions for this exercise are estimated without household fixed effects<sup>11</sup> . Second, since the number of households in the sample experiencing shocks is low, the 2002 and 2005 means of per capita expenditures in Azteca and non-Azteca municipalities are noisy, especially for the control group. With these drawbacks noted, there is some evidence suggesting that households from Azteca municipalities were better able to increase their consumption during bad economic times after Azteca opened branches. The OLS DID estimates suggest that informal households in Azteca municipalities increased their percapita expenditure by Mx $9,339 and Mx $8,463 if experiencing illness of a household member or failure of the family business (panel B of table 7). As seen from panel B of table 8, formal households from Azteca municipalities experienced no significant change relative to formal households from other municipalities.

# **3.4 Robustness Check**

According to the data, informal households from municipalities with an Azteca branch experienced various changes on their saving and consumption patterns once the branch opened. However, a concern with these findings is that informal households from municipalities where Azteca entered were in a different trend from households in other municipalities, and would have actually experienced these results even in the absence of this bank. One possible way to rule out this possibility would be to examine whether the trends in saving and consumption patterns between treated and control households were similar in the periods before Azteca opened. As the first wave of MxFLS was in 2002, this test is not feasible. However, an alternative robustness check is to analyze the change in saving and consumption patterns from 2002 to 2005 of households from municipalities that would get an Azteca until 2006 or later. If informal households from municipalities selected by Azteca have different trends to other households, and this is driving the results, then we should expect the saving and consumption patterns of these households to be changing even in the absence of Azteca. If the entrance of Azteca is altering households’ behavior, then households from municipalities where Azteca entered in later periods must not have experienced substantial changes.

Tables 9 and 10 report the DID coefficients of equation (1) on the saving and consumption outcomes of informal households using two different treatments. The first treatment corresponds to the standard group of informal households from municipalities with an Azteca by 2005 (Column 1). The alternative treatment is composed of informal households from municipalities where Azteca 11Since less than 5% of all households experienced shocks both before and after Azteca opened its branches, the household fixed effects DID estimates are positive but not significant, due to the lack of within-household variation.

13
# **#6: Cost recovery levels have increased on average, but progress has been very uneven, with over half of the case studies experiencing a decline compared to the pre-reform period**

46. This section compares pre-reform cost recovery benchmarks from the late 1980s and early 1990s with cost recovery for 2010-2017 to understand the impact of reforms. As laid out in Section 3, the prereform benchmarks are based on a comparison of actual pre-reform tariffs and pre-reform estimates of long-run marginal costs.

47. The pre-reform cost recovery benchmarks can be understood as a _counterfactual of today’s cost recovery levels if real tariffs remained the same and actual costs materialized exactly as anticipated in estimates of LRMC._ Comparing these benchmarks to actual cost recovery levels in 2010-17 allows to draw conclusions about (a) the change in electricity tariffs in real terms compared to the pre-reform period; (b) if actual costs are now higher or lower than expected in the LRMC estimates from the late 1980s and 1990s; and (c) if the combination of changes in real tariffs compared to the pre-reform actuals and changes in real costs compared to pre-reform estimates of LRMC led to a net increase or decrease in cost recovery compared to the counterfactuals.

48. A comparison of the findings presented under Observation #1 with pre-reform cost-recovery levels (see Figure 11) suggests that there has been convergence in full-cost recovery levels across countries. As shown in Figure 12, the largest improvements were made by countries that had low cost recovery in the pre-reform period, and vice versa (see Figure 11). The largest improvements in cost recovery were observed in ECA (Ukraine +92 percentage points; Tajikistan +43 percentage points) and LAC (Colombia +49; Peru +22), with SAR and EAP seeing, on average, relatively little change compared to the pre-reform period.

**Figure 11: Pre-Reform Levels of Full-Cost Recovery (C3 approximation, %)**

<!-- Start of picture text --> Philippines<br>Morocco<br>Kenya<br>Dominican Republic<br>Pakistan<br>Senegal<br>Tanzania<br>Peru<br>Vietnam<br>Colombia<br>India - Rajasthan<br>India - Odisha<br>India - Andhra Pradesh<br>Uganda<br>Tajikistan<br>Egypt, Arab Rep.<br>Ukraine<br>0% 20% 40% 60% 80% 100% 120% 140%<br><!-- End of picture text --> _Source: World Bank staff._ 25
5 inthropOmeLric measures. Behrnman and Deolalikar (1988) provide an extensive and careful review of these studies. They conclude that at<sup>the household</sup> level, it is difficult to document that higher income improves<sup>health.5</sup> This result may reflect the high correlation of income with<sup>other variables that</sup> are generally included in such analyses, such as education;<sup>and the much</sup> greater tendency of such variables as education<sup>to reflect past behavior and</sup> investments which heevily influence current health. Or it may<sup>be, as seemsto</sup> be true at the national level, that other factors (most nutably,<sup>mother's</sup> education<sup>6</sup> ) are more important determinants of the health of household members. Behrman and Deolalikar also conclude that there<sup>is little evidence</sup> that higher prices, be they for health services, for health<sup>goods such as</sup> drugs, or for food (and thus nutrition, an important input<sup>to good health),</sup> contribute to poor health.

Thus the two variables that economists might assume would influence healthi, income and prices, appear to explain little at the household level. Why these poor results? It may be that community-level variables, such a. climate, exposure to disease and the quality of health co--'-- available matter more than household income and local prices (measured<sup>independent of quality).</sup> Or it may be that results are poor because many studies are<sup>plagued by</sup> 5 Thomas et. al. (1990) do find that the predicted logarithm of per capita expenditures, a proxy for income, does enhance child survival, controlling<sup>for</sup> other factors including parents' education. They use a large data<sup>set from</sup> Brazil, where the range in income is probably greater than in many household<sup>data</sup> sets.

6 Even the importance of mother's education has been questioned. See Wolfe and Behrman (1987).
that the results are not very sensitive to the procedure of selection of workers who formalize (randomly selected individuals in our static scenarios and individuals with the highest probability of informal employment according to the behavioral models). Therefore, the behavioral model confirms the robustness of the results of static simulations.

**Table 7: Simulated changes in indicators of income distribution and fiscal budget, behavioral simulation**

||no random component (1%<br>formalization)|with random component<br>(6.2%formalization)|
|---|---|---|
|Income of 1st decile, %|0.8|0.7|
|Income of the formalized, %||-11.7|
|Income total, %|0.9|0.8|
|Absolute poverty rate of the formalized, p.p.|0.0|9.3|
|Absolute poverty rate, p.p.|-0.2|-0.1|
|Gini index, p.p.|0.0|0.0|
|Total budget balance, %ofGDP|-0.6|-0.5|

Source: Authors’ calculations using RUSMOD for 2017 policy year and 2016 RLMS-HSE data.

If the economic incidence of SIC falls on workers, all deciles and age groups benefit from the tax maneuver and the formalization that it drives. As shown in Figure 11, on average, per capita household consumable income increases by 0.8 percent. The highest gain goes to the 8th and 9th decile – they get an additional 1.3-1.4 percent of consumable income, while the lowest effect is observed in the middle of the income distribution (0.4 percent for the 5th decile) and at the top (0.5 percent for the 10th decile). The reform has the highest positive welfare impact for families with working age adults and with 1-2 children. Families with 3 or more children and lone parents have the smallest positive effect, while pensioner households are the only group that is losing because they suffer from VAT increase and do not benefit from SIC reduction. Those who were formal before the reform gain more than other groups of workers. Their per capita consumable income increases by 2.2 percent. At the same time, for those who formalize the net loss amounts to 11.7 percent of their consumable income. The effect for those who are not working (pensioners) is almost neutral.

28