rafmacalaba/datause-encoder-data huggingface dataset:
written as the sum of �<sup>F</sup> i<sup>andapartthatconstitutespurelydomesticcontent,q</sup> i<sup>D:Likewise,</sup> P<sup>I</sup> Mi<sup>Icanbewrittenasthesumof�D</sup> i<sup>andapartthatconstitutespurelyforeigncontent,</sup> qi<sup>F:</sup> Similar to the concept of a country’s gross domestic product, we de...ne the DVA of a ...rm as the total value of domestic goods and services embodied in the ...rm’s output. In other words, a ...rm’s DVA equals the sum of its pro...ts, wages, rental costs of capital, and both direct or indirect domestic materials purchased:<sup>14</sup> For a processing ...rm that exports all its output and imports some of its intermediate inputs and capital equipment, its export (EXPi) equals its revenue, while its import (IMPi) equals the costs of imported materials, P<sup>I</sup> Mi<sup>I,andimportedcapital,�K</sup> i<sup>.Thus,(1)implies</sup> Equation (3) shows that we may use EXPi � IMPi to measure a processing ...rm’s DVA after adjusting for �<sup>D</sup> i<sup>;�F</sup> i<sup>and�K</sup> i<sup>.ForChina,KWW12andWang,Wei,andZhu(2014)</sup> ...nd that �<sup>D</sup> i<sup>isverycloseto0forprocessingexports.15Moreover,inourcurrentdataset,</sup>

> 14Note that while some ...rms may have foreign capital in its ownership, the returns to this foreign capital as well as its pro...ts are still included in its DVA in exports. This is because the service of these capital is rendered within the country’s borders. Moreover, a ...rm’s DVA contains domestic materials produced by other ...rms and is therefore larger than its own value added by de...nition.

> 15Based on the GTAP Multi-Country IO tables, Koopman, Wang, and Wei (2014) estimate that the domestic content embedded in imported materials accounted for 0.7% of China’s ordinary exports in 2004, and essentially 0 for its processing exports. Using the IO tables from the World Input-Output Database (WIOD), Wang, Wei and Zhu (2014) update these estimates and show that the domestic content embedded

7
Table 1: Factor Loads of noncognitive items (corrected for acquiescence bias)

|**Question's short**<br>**name**|Fact<br>Load 1|Fact<br>Load 2|Fact<br>Load 3|Fact<br>Load 4|Fact<br>Load 5|Fact<br>Load 6|Dominant<br>interpretation|
|---|---|---|---|---|---|---|---|
|cesd17|**0.60**|‐0.03|0.03|0.03|‐0.02|0.04||
|cesd6|**0.60**|0.01|‐0.04|0.01|0.08|0.05||
|cesd15|**0.59**|0.02|0.10|0.00|‐0.10|‐0.06||
|cesd14|**0.59**|‐0.03|‐0.02|‐0.02|0.05|0.02||
|cesd10|**0.58**|0.00|‐0.01|‐0.04|‐0.01|‐0.06||
|cesd1|**0.58**|‐0.01|‐0.10|0.03|0.06|‐0.03||
|cesd3|**0.57**|0.03|0.07|‐0.01|‐0.07|0.01||
|cesd18|**0.55**|0.01|0.02|‐0.02|‐0.06|‐0.06||
|cesd11|**0.54**|0.01|‐0.02|0.02|0.01|0.08||
|cesd19|**0.53**|‐0.02|0.09|0.00|‐0.03|‐0.02|CESD|
|cesd2|**0.51**|‐0.08|‐0.02|0.00|0.12|0.07||
|cesd20|**0.48**|‐0.04|0.00|0.09|0.00|0.02||
|cesd13|**0.47**|0.03|‐0.03|‐0.03|‐0.08|‐0.02||
|cesd9|**0.41**|0.13|‐0.20|0.01|0.15|0.09||
|cesd5|**0.39**|0.02|‐0.15|0.03|0.04|‐0.09||
|cesd12|**0.35**|‐0.04|0.07|‐0.07|‐0.06|0.26||
|cesd7|**0.27**|0.08|‐0.18|0.07|0.06|‐0.10||
|patience1|**0.11**|0.04|‐0.07|‐0.01|0.11|‐0.03||
|BF_C1|0.03|**0.51**|0.00|0.20|‐0.03|‐0.01||
|BF_C5|0.03|**0.49**|0.17|0.19|0.07|‐0.05||
|BF_C3|‐0.01|**0.48**|0.00|‐0.12|0.16|0.06||
|BF_C4|‐0.03|**0.45**|0.03|‐0.11|0.05|0.12||
|BF_A4|0.10|**0.45**|0.01|‐0.20|0.00|‐0.18||
|BF_N1|0.14|**0.41**|0.08|‐0.08|0.02|0.13||
|BF_C6|0.12|**0.39**|0.23|0.15|0.06|‐0.03||
|tenac2|‐0.06|**0.39**|‐0.13|0.11|‐0.06|0.07||
|tenac1|‐0.06|**0.36**|0.09|0.06|0.10|0.06|Conscientiousnes|
|BF_O1|‐0.04|**0.31**|0.06|0.27|‐0.05|0.21|Tit|
|BF_N2|0.14|**0.30**|0.00|0.10|0.01|0.23|enacy|
|tenac3|0.10|**0.30**|0.16|0.13|0.07|0.08||
|BF_E4|0.03|**0.28**|0.26|0.17|0.10|‐0.09||
|metacog2|0.09|**0.27**|0.16|0.20|0.15|0.06||
|LOC1|‐0.09|**0.24**|‐0.05|‐0.04|0.20|0.04||
|selfesteem1|0.08|**0.23**|0.18|0.10|0.06|0.14||
|tenac4|‐0.03|**0.18**|‐0.02|0.08|0.08|0.11||
|riskav3|‐0.08|**0.03**|‐0.07|0.02|‐0.02|0.00||
|LOC4|0.01|0.05|**0.46**|‐0.03|‐0.04|0.02||
|LOC3|0.10|‐0.10|**0.43**|0.15|0.17|0.05||
|BF_A1|0.02|0.30|**0.40**|‐0.26|‐0.18|‐0.02||
|LOC2|0.10|0.14|**0.38**|0.10|0.05|0.10|Locus of Control|
|BF_O4|0.02|0.16|**0.37**|0.18|0.02|0.14||
|metacog3|0.08|0.22|**0.37**|0.12|0.18|‐0.03|Metacognitive|
|metacog1|0.09|0.24|**0.35**|0.04|0.17|‐0.09|Oenness|
|BF_E2|0.10|0.09|**0.34**|0.17|0.02|0.13|p|
|BF_O2|0.05|0.18|**0.34**|0.19|0.06|0.07||
|BF_A2|0.03|0.26|**0.33**|0.12|‐0.04|‐0.13||

41
**TABLE 15: ROAD FREIGHT PRICES IN CENTRAL AMERICA** **<u>(FIRM MEANS BY COUNTRY IN US CENTS PER TON-KILOMETER)</u>**

|**Country**|**Mean**|**Min**|**Max**|**Avg markup**<br>**over ADC**|
|---|---|---|---|---|
|**Costa Rica**|10.9|2.9|38.2|6.1|
|**El Salvador**|15.6|4.5|53.9|8.3|
|**Guatemala**|16.4|4.1|88.9|11.0|
|**Honduras**|15.7*|3.6|114.0|9.3|
|**Nicaragua**|11.3|3.3|81.2|6.1|
|**Panama**|33.2**|2.9|111.0|27.0|
|**TOTAL**|16.9|2.9|114|11.0|

Notes: Means are simple firm means, calculated from frequency-weighted route-firm prices*Difference with Costa Rica statistically significant at 5 percent level **Difference with all other countries significant at 1% level.

**TABLE 16: COSTS AND PRICES ON NATIONAL AND INTERNATIONAL ROUTES (US CENTS PER TON-KILOMETER)**

||National|Routes||Internatio|nal Routes||
|---|---|---|---|---|---|---|
|Country of Firm|**ADC**|**Price**|**Markup**|**ADC**|**Price**|**Markup**|
|**Costa Rica**|3.4|24.9|21.5|5.2|8.3|3.1|
|**El Salvador**|11.8|26.0|14.2|6.8|13.5|6.7|
|**Guatemala**|5.5|22.1|16.6|5.4|8.9|3.5|
|**Honduras**|7.3|22.5|15.2|4.9|10.7|5.9|
|**Nicaragua**|7.1|17.5|10.4|4.0|7.3|3.3|
|**Panama**|6.0|62.0|56.0|8.3*|7.8|-0.5|
|Total|6.4|35.0|28.6|5.4|9.9|4.5|

*Sample size of 4 firms only. Note:  Since _ADC_ is only available at the firm level, _ADC_ for national versus international “routes” are computed for firms with more or less than 50 percent of business on such routes.  Average firm prices are route-level prices for national routes versus international routes weighted by ton-kilometers served.

# 3. ANALYSIS OF TRUCKING COSTS AND PRICES

Given the nature of the data, our empirical analysis is based on a single snapshot of market outcomes. The market is not static – firms enter, exit, invest, and choose routes and clients on an ongoing basis given perceived opportunities for future profit. However, because recent entry is minimal – only 5 firms in the sample are less than two years old, we can assume that the current market structure and firm capacity primarily reflect entry and investment decisions made in prior years. Conditional on these prior decisions, we estimate short run costs and firm pricing behavior. We first provide a simple theoretical motivation for the empirical model.

## **Theory and Empirical Model**

We suppose that firms, indexed by _i_ , simultaneously choose route-specific prices ��� where routes are indexed by _r_ , and total output, �� _,_ as the sum of quantities on each route, _qir,_ to maximize the discounted sum of current and expected future profits, given the market structure, ��, where �� in the current period can be written as: 19
## THE INDUSTRIAL POLLUTION PROJECTION SYSTEM

# by

Hemamala Hettige* Paul Martin Manjula Singh David Wheeler ‘The authors are, respectively, Economist, Environment, Infrastructure and Agriculture Division (PRDEI), Policy Research Dept., World Bank; Consultant, Environment Unit, EA3, World Bank; Ph.D. Candidate, Boston University; and Principal Economist, PRDEI, World Bank The research reported in this paper was undertaken in collaboration with the Center for Economic Studies, U.S. Bureau of the Census. Our thanks to the VS Environmental Protection Agency for providing the industrial pollution data and to Angela Williams for invaluable assistance with preparation of final text and tables.

Please address all correspondence to Mala Yettige, PRDEI FAX: (202) 522-3230 INTERNET: HHETTIGE@WORLDBANK .ORG
registration (including internet, phone, and kiosks) require over 30 percent less time to start a business, and that costs are reduced by 50 percent, as measured by the Doing Business Report (Figure 8).

**Figure 8: Impact of business registration modernization**

<!-- Start of picture text --> 50 47<br>With out e‐registration<br>45 With e‐registration<br>39<br>40<br>35 32<br>30<br>24<br>25<br>20<br>15<br>10<br>5<br>0<br>Duration (days) Cost (% GNI per capita)<br><!-- End of picture text --> Source: 2008 World Bank Group Entrepreneurship Survey and Doing Business 2009.

# **5. Conclusions**

With new topics and broader coverage of developing countries, the 2008 World Bank Group Entrepreneurship Survey and future surveys will continue to support a deeper and more comprehensive understanding of conditions that can encourage entrepreneurship, as a policy tool to measure the impact of policy reforms to create new firms and stimulate economic growth.

The WBGES findings show that those countries with the highest business entry rates provide entrepreneurs with a stable political climate, good governance, modernized business registries, reduced red tape, and simplified business legal forms. Therefore, policy makers and governments should take these variables into account to better design, implement, monitor and evaluate policies and programs aimed to develop the private sector.

18
-49 -

# ANNEx 3. BASIC EARNINGs **FUNeCoNs**

# Argentina 1989

|||Ge|nder|Se|ctor|
|---|---|---|---|---|---|
|Variable|Entire<br>Sample|Males|Females|Private|Public|
|Years of Schooling (S)|0.103|0.107|0.112|0.010|0.089|
|Experience (EX)|0.045|0.052|0.039|0.046|0.048|
|Experience-squared (EX2)|-0.0006|-0.0007|-0.0006|-0.0007|-0.0007|
|Log Hours (H) Worked|0.613|0.355|0.590|0.38t|0.647|
|Constant Term|4.856|5.843|4.719|5.767|4.847|
|R<sup>2</sup>|0.35|0.32|0.40|0.26|0.35|
|N|4,760|2,965|1,795|2,463|1,097|
|Mens||||||
|Earnings (Y)<br>v|7,456|8,563|5,629|7,413|8,463.|
|S|9.1|8.7|9.8|8.7|11.0|
|EX|22|23|21|22|21|
|H|42|46|36|45|39|
|Var (Ln Y)|_0.656_|0.546|0.696|0.557|0.610|

Source: _Encuesta Permanente de Hogares (EPH), CEPAL._ Notes: The earnings variable (Y) is in australes per month. H is the weekly hours worked.

All coefficients are statistically significant at the 5% level or better.
**Table 1: Key Data Sources for Manpower Planning**

# Data Source

# Type of Data Purpose

|National Household<br>Survey|Population<br>Labor Force Activity<br>Employment<br>Unemployment<br>Incomes and Wages<br>Education and Training<br>Other Demographic Charac.|Rate of Return<br>Studies<br>Wage and<br>Employment Trends<br>Labor Market<br>Analysis|
|---|---|---|
|National Establishment<br>Survey|Employment<br>Industry<br>Earnings<br>Firm Size<br>Value Added|Wage and<br>Employment Trends<br>Productivity and<br>Labor Market<br>Analysis|
|Social Insurance<br>Administrative Data|Employment<br>Unemployment<br>Earnings<br>Industry|Wage and<br>Employment Trends|
|Tracer Studies|Employment<br>Unemployment<br>Earnings<br>Occupation|Rate of Return<br>Studies (benefits)|
|Cost Studies|Capital Costs<br>Recurrent Costs<br>Enrollments<br>Training Capacity|Rate of Return<br>Studies (costs)|

26
# TABLE OF CONTENTS

||Page|
|---|---|
|I. WHAT IS THE PROBLEM? ..<br>.......................|1|
|A. OBJECTIVE AND APPROAC.......<br>...................|......<br>4|
|B. SCOPE ........................<br>o. .<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.....|. 5|
|II. APPROACHES<br>TO A SOLUTION||
|A. CRITERIA TO EVALUATE POLICY OPTIONS..<br>.......|6|
|B. DEFINING INSTRUMENTS.........<br>....<br>.......<br>.......<br>. . . ....|9|
|C. LINKING INSTRUMENTS TO TARGETS..............<br>.<br>..........<br>.<br>.....|13|
|1. Emissions........<br>.........|13|
|2. Beyond Emissions<br>.<br>....|19|
|3.Modeling<br>Congestiong<br>e<br>s<br>t<br>in.......<br>...|23|
|III. MODELING<br>THE POLLUTION-TRANSPORT<br>LINK:<br>LESSONS FROM THE LITERATEURE...<br>.<br>..<br>.<br>.....<br>E..|26|
|A. STYLIZED FACTS AND MODELING IMPLICATiONS.........|26|
|B. AN "IDEAL" MODEL OF TRANSPORTATION DEMA<br>D<br>.<br>......|28|
|C. MODELS IN THE LITERATURE...<br>...<br>......<br>...|30|
|Wheaton<br>(1982).<br>.....<br>....<br>.<br>. .........<br>...........|32|
|Wharton<br>(17977)<br>....<br>....<br>...............<br>.|34|
|Train (CARS Model) (1990).<br>.....<br>.<br>...............<br>.<br>McConnell and Straszheim(1982)..................|35<br>39|
|Kim(1991.............................<br>Ingram(1951...........................|42<br>43|
|Cameron(1911........................|47|
|Deaton(197<br>.........................|50|
|D. MORE ON SIMPLIFIEDMODELSHQ<br>DELS.............................|52|
|1. ElasticityModlo..........<br>e.............<br>2. Elasticities<br>of Gasoline<br>demand<br>..|53<br>55|
|3. Sketch Planning Models<br>o<br>dels...........<br>.<br>.|56|
|IV. CONCLUSIONS.<br>. . . ..............<br>................<br>....|57|
|References||
individual treatment results in negative point estimates on the level of employment, and an effect which is significantly different from the group treatment at the 10 percent significance level.

Columns 3 onwards of Table 5 use formal employment data from the PILA. Column 3 first documents that treatment had no significant impact on firm survival. Columns 4 through 7 use the same time period as we have firm data for – January 2013 to December 2017 – to enable comparison to our firm data results and because this is the period over which we can examine other outcomes. Columns 4 and 5 show that when we consider the employment levels of surviving firms, the group treatment firms are significantly larger in size, with similar magnitudes as found with the firm data. In contrast, the individual treatment has smaller impacts on employment, which are not significantly different from zero post-treatment, and which are significantly different from the group treatment when using the inverse hyperbolic sine. Columns 6 and 7 consider unconditional employment as the outcome, coding employment as zero once firms die. The point estimates still suggest a 4 worker (9 percent) increase in employment after the group treatment, but the standard errors are larger, and these impacts are no longer statistically significantly different from zero, or from the individual treatment. Finally, columns 8 and 9 add in the 2018 data, and examine the extent which treatment effects vary with time since treatment. The group treatment has positive treatment effects in all three years post-intervention, and we cannot reject equality of treatment effects over time.

Figure 4 shows the distribution of changes in employment between 2013 and 2017 by treatment status. The control and individual interventions have similar distributions, with changes centered on zero. In contrast, the group treatment has a much smaller peak and more positive mass to the right of zero, indicating firms which expanded. However, the figure also shows long tails, coming from the small number of large firms which closed down – these long tails are what cause the standard errors to increase in the unconditional estimates. Figure A8.2 shows positive quantile treatment effects of the group treatment on the change in employment, which are significant around the 60<sup>th</sup> and 70<sup>th</sup> quantiles, but very wide confidence intervals at the bottom reflecting this couple of firms with large drops in employment.

An interesting question is whether the increased employment in the group treated firms comes from and whether it changes the composition of the labor force in these firms. We were able to use anonymized worker-level data to examine these questions in more detail (see also Appendix 8). A 21
**A5 Mexico**

**Table A5: Regional disparity trends** in **Mexico**

|**Year**|Max/Min<br>(MMR)|Simple<br>coefficient<br>of variation<br>(CVu)|Weighted<br>coefficient<br>of variation<br>(CVw)|Relative<br>mean<br>Deviation<br>(Rw)|Unweighte<br>gini index<br>_(Gu_|d Weighted<br>gini index<br>_(Gw)_|Theil<br>index (T)|
|---|---|---|---|---|---|---|---|
|1993|5.591|0.473|0.572|0.414|0.251|0.302|0.137|
|1994|5.583|0.471|0.573|0.417|0.250|0.301|0.137|
|1995|5.479|0.461|0.559|0.413|0.246|0.295|0.130|
|1996|5.618|0.464|0.561|0.415|0.249|0.297|0.131|
|1997|5.793|0.473|0.571|0.422|_0.253_|0.301|0.136|
|1998|5.874|0.469|0.566<br>_|0421|0.251|0.300|0.134|

**Figure A5: Regional disparity trends** in Maexico

<!-- Start of picture text --> 0.700<br>0.800<br>0.500 0 .n '--" , f _ . _<br>Y.V '* . r @-~---t -;.-<br>X 48- 'a ' ...........- S______________________ -Z .......... ,<br>*0.400<br>Eo -4 ° ' ' '<br>0. 32 00 0<br>0.000 . - -.<br>1993 1994 1995 1996 1997 1998<br>Year<br>-<br>Simple coefficient of variation (CVu) --Weighted coefficient of variation (Cvw) - Relative mean Deviation (Rw)<br>+Unweighted gini index (Gu) Weighted gini index (Gw) Theil index (T)<br><!-- End of picture text --> 39
<!-- Start of picture text --> 500000 457172 463582<br>450000 411213 420671<br>400000<br>w<br>3 350000<br>2 300000<br>‘S 250000<br>32. 76093 95074 88486 89260<br>E 150000<br>z<br>100000<br>50000<br>0<br>2017-2018 2018-2019 2019-2020 2020-21<br>m= Number of Male owners (single and joint)<br>mg Number of Female owners (single and joint)<br><!-- End of picture text --> <!-- Start of picture text --> a|<br>rs<br>rr<br><!-- End of picture text -->
to explode. Although exact figures for post 2001 are not available, it is clear that borrowing volumes have continued to rise rapidly thereafter. By 2001, more than 60% of the cities in China had infrastructure loans from banks or outstanding loan applications. The total banking-sector debt of local governmental units for urban infrastructure stood at RMB 74.2 billion in 2001, up 23 fold in just a decade. Use of the banking sector to finance urban infrastructure investment was encouraged by national policy, which identified the urban infrastructure sector as a top priority for lending by banks, all of which were then owned by the State.

Figure 7: Share of Urban Infrastructure Investment Financed by Loans <!-- Start of picture text --> 800 35<br>30<br>600 25<br>20<br>400<br>15<br>200 10<br>5<br>0 0<br>1 2 3 4 5 6 7 8 9 10 11<br>Domestic loans proportion<br><!-- End of picture text --> Source : Authors calculations Central government borrowing and on-lending to local

# governments for infrastructure investment

Bank borrowing has not been the only the type of debt financing. The national government also issued so-called infrastructure bonds, and passed on the 39
an important commodity exporter, exports of energy and minerals would decline by more than 23 percent. Similarly, South Asia is expected to lose competitiveness in textiles and clothing with a decline in exports of 7 percent. Exports of China and the United States decline less strongly in relative terms, on average by 3.9 percent and 7.9 percent relative to the baseline by 2020 but there are sectors which are more affected such agricultural exports which decline by 17 percent in China and 20 percent in the United States.

Results of Scenario 2 are consistent with those reported by Bouet and Laborde (2010) who estimate a similar decline in world trade of 9.9 percent and welfare loss of US$353 billion. Their simulations, however, do not include an increase in the cost of traded services. Furthermore, results in Bouet and Laborde (2010) are based on global production and trade data from the year 2007 which reflects the structure of the world economy before the financial crises.<sup>9</sup>

# **5.3 The costs of protectionism: global impact**

The summary of the global impact of the simulations is presented in Table 8. Results show that a coordinated global withdrawal of tariff commitments from existing unilateral preferential schemes, as well as bilateral and regional trade agreements coupled with a 3 percent increase in the cost of traded services would translate in worldwide welfare losses that amount to 0.3 percent or US$211 billion relative to the baseline by 2020. Similarly, consumers would be hurt by a decline in real consumption by close to US$148 billion. The impact on global trade would be much more pronounced with a decline of close to 2.1 percent or more than US$606 billion relative to the baseline after three years.

Second, a worldwide increase in tariffs up to the legally allowed bound rates coupled with a 3 percent increase in the cost of traded services would translate into global welfare losses of 0.8 percent or US$634 billion relative to the baseline by 2020. Global household consumption would decrease by 0.9 percent corresponding to a loss of nearly US$445 billion. The distortion to trade flows would be significant with decline by 9 percent or more than US$2.6 trillion relative to the baseline after three years.

If these protectionist measures were to stay in place for longer than the assumed three real income effects are to further deteriorate before years, likely negligibly rebounding after 2025 (Figure 3). Reversal to bound tariffs is expected to translate into strong negative

> 9Bouet and Laborde (2010) also use a different modelling framework then the one used in the current paper namely the MIRAGE model. For more information on the MIRAGE model see Decreux and Valin (2007).

24
**_Energy demand models for policy_** **_<u>formulation</u>_**

# **4.1.1 Primary energy demand forecasting**

In the past, when data availability was restricted, especially in developing countries, and access to computing facilities was limited, aggregated studies were common using reduced form specifications (see Dahl (1994a) for a long list of such studies). Dahl (1994a) suggests that although models are found to test per capita energy and/or total energy consumption in the reduced form versions with or without dynamic elements, “aggregation can cause heteroscedasticity when the population varies across the sample.” [See Box 7 for further explanation.]

# **Box 7: Difference between the total and per capita specifications of energy demand**

Consider a simple log-linear demand specification with price and income as dependent variables as shown in Eq. 1 Eq. 1: lnQ = α + βlnP + γlnY, where Q is the total energy demand, P is the price of energy and Y is the GDP of the country.

The per-capita specification can be written as Eq.2.

Eq.2: ln(Q/pop) = δ + εln(P) + ζln(Y/pop) Where pop represents population. Equation 2 can be rewritten as Eq. 3: lnQ = δ + εln(P) + ζln(Y) + (1-ζ)ln(pop) Comparing Eq. 1 with Eq. 3, it becomes clear that the two specifications are equivalent, if the income elasticity is equal to 1. When the income elasticity is different from one, the two specifications are not equivalent because of the last term in Eq. 3. When ζ>1, the last term in Eq. 3 is negative and when ζ<1, the last term is positive. This would affect the income elasticity estimation and the forecast.

Source: Dahl (1994a).

Westoby and Pearce (1984) present a brief history of the evolution of the single equation energy demand estimation. They report that the initial attempts were either to establish a linear relationship between output and energy adjusted for calorific content or to study linear relationships between energy and income. Subsequently, more variables, including 43
9.132 Mean of the dependent variable

**Dependent Variable: Sensitivity analysis: LN Total cost of tax compliance (-40% across the board)**

||Coeff.|Std. Error|Sig.|
|---|---|---|---|
|(Constant)|5.939|0.377|0.0%|
|LN full-time employees|0.169|0.039|0.0%|
|LN of q55 after imputed values were introduced (imputed "no<br>answer")|0.144|0.030|0.0%|
|dummy for full in-house (0) and full outsourcing|0.302|0.106|0.4%|
|Guateng provincial dummy|0.372|0.086|0.0%|
|Manufacturing|-0.243|0.119|4.1%|
|PAYE|0.529|0.140|0.0%|
|Adj. R2|0.256|||
|Mean of the dependent variable|9.007|||

**Dependent Variable: Sensitivity analysis: LN Total cost of tax compliance (-50% across the board)**

||Coeff.|Std. Error|Sig.|
|---|---|---|---|
|(Constant)|5.754|0.377|0.0%|
|LN full-time employees|0.168|0.039|0.0%|
|LN of q55 after imputed values were introduced (imputed "no|0.144|0.030|0.0%|
|answer")||||
|dummy for full in-house (0) and full outsourcing|0.477|0.106|0.0%|
|Guateng provincial dummy|0.374|0.086|0.0%|
|Manufacturing|-0.244|0.119|4.0%|
|PAYE|0.525|0.140|0.0%|
|Adj. R2|0.260|||
|Mean of the dependent variable|8.860|||

48