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44
**<mark>Table C.5: Degree of homogeneity of classes</mark>**

||Count|Share|
|---|---|---|
|Relations with suppliers|||
|Pure bilateralism|8|67%|
|Bilateralism with private support|12|100%|
|Bilateralism with legal support|8|67%|
|Strong comprehensive governance|10|83%|
|Relations with customers|||
|Pure bilateralism|12|100%|
|Bilateralism with private support|12|100%|
|Bilateralism with weak support|8|67%|
|Weak comprehensive governance|10|83%|

**<mark>Table C.6a: Degree of separation of classes, relations with suppliers</mark>**

||AvePP�|OCC�|mcaP�|class<br>membership<br>probabilities<br>(π��)<br>95%<br>the|CI of<br> π��|
|---|---|---|---|---|---|
|Pure bilateralism|0.963|13.730|0.665|0.657<br>0.609|0.705|
|Bilateralism with private support|0.883|37.990|0.159|0.166<br>0.123|0.209|
|Bilateralism with legal support|0.940|81.934|0.159|0.160<br>0.127|0.194|
|Strong comprehensive governance|0.977|10016.817|0.016|0.017<br>0.008|0.025|
|Rule-of-thumb minimum|0.7|5||||

Note: See Appendix C.2 for definitions of the statistical measures.

**<mark>Table C.6b: Degree of separation of classes, relations with customers</mark>**

||AvePP�|OCC�|mcaP�|class<br>membership<br>probabilities<br>(π��)<br>95%<br>the|CI of<br> π��|
|---|---|---|---|---|---|
|Pure bilateralism|0.915|8.310|0.576|0.565<br>0.503|0.626|
|Bilateralism with private support|0.874|21.833|0.241|0.242<br>0.184|0.299|
|Bilateralism with weak support|0.810|25.188|0.136|0.145<br>0.096|0.193|
|Weak comprehensive governance|0.965|3412.999|0.047|0.050<br>0.034|0.065|
|Rule-of-thumb minimum|0.7|5||||

Note: See Appendix C.2 for definitions of the statistical measures.

Appendixes, page 22
# **Table 2.  Armenia’s Energy Balance**

_(1,000s tons of oil equivalent –ktoe)_

||Percent of Supply<br>/Consumption|Percent of<br>Imports|
|---|---|---|
|_Sources_|||
|**Total Primary Energy Supply (TPES)**|2,844<br>100||
|**Internal sources**|825<br>29||
|**Imports**|2,126<br>75|100|
|**Petroleum**|426<br>15|19|
|**Gas**|1,664<br>60|80|
|**Electricity**|34<br>1|1|
|**Exports**|(49)<br>(2)||
|**Gas**|(21)||
|**Electricity**|(28)||
|**International Aviation Bunkers**|(58)<br>(2)||
|_Uses_|||
|**TPES**|2,844||
|**Energy Conversion and Distribution**|(839)||
|**Losses**|||
|**Total Final Consumption (TFC)**|2,005<br>100||
|**industry sector**|860<br>43||
|**transport**|188<br>9||
|**agriculture**|60<br>3||
|**Commercial and Public Services**|71<br>4||
|**residential**|579<br>29||
|**other**|247<br>12||

_Source:_ International Energy Agency, _Energy Statistics, 2007_ Between 2002 and 2008, both import and consumption of gas have more than doubled. Household (residential) consumption has led the growth in demand for imported gas. Household consumption of gas increased from only 102 million m<sup>3</sup> in 2002 by nearly six-fold to about 590 5
countries, giving a line of $1.23 a day ($37.41 per month). With the help of the World Bank’s ICP team we also recalculated the official P3s for consumption using the set of basic headings chosen by DD. Column (1) reproduces the estimates from Table 6, while the column (2) gives the corresponding estimates for the full sample of countries using P3s calibrated to the 102 basic headings used by DD. Columns (3) and (4) gives the corresponding results to (1) and (2) using the 60 country sub-sample used by DD. Columns (5)-(7) given our estimates of the poverty measures using the P4s from DD, for each of their three methods. We give (populationweighted) aggregate results for the sample countries.<sup>46</sup> It can be seen from Table 10 that the switch from 110 to 102 basic headings reduces the aggregate poverty measures by about three percentage points, while switching from the 115 country sample to the 60 country sample has the opposite effect, adding three points. The pure effect of switching from P3 to P4 is indicated by comparing column (4) with columns (5)-(7). This change has only a small impact using the EKS method (for either the Fischer or Törnqvist indices) though it has a slightly larger effect using the CPD method.

On balance, the aggregate poverty count turns out to be quite similar between the P4s and our main estimates using standard P3s on the full sample. If one assumes that the countries without household surveys have the regional average poverty rate then the Fisher P4 gives a count of 1402 million for the number of poor, while the CPD and Törnqvist P4s give counts of 1454 and 1359 respectively, as compared to 1377 million using standard P3s. The regional profile is also fairly robust, the main difference being lower poverty rates in EECA using P4s, although the poor representation of EECA countries in the 60-country sample sued by DD is clearly playing a role here. The reduction in coverage of consumption items makes a bigger difference, with a higher poverty count in the aggregate (28% for these 60 countries using the standard PPP, versus 25% using the PPP excluding housing), due mainly to higher poverty rates in East and South Asia when all 110 basic headings for consumption are included.

The trends over time are also very similar (Figure 5). This is not surprising given that, following the usual practice of doing the PPP conversion at only the benchmark year and then using national data sources over time, the real growth rates and distributions at country level are unaffected.

> 46 Note that this is a slightly different aggregation method to our earlier results, which assumed that the sample was representative at regional level. That is clearly not plausible for the 60-country sample used by DD. We have re-calculated the aggregates for the 115 country sample under the same basis as for the 60-country sample.

30
Changes in the entry and exit of firms affect _TFP_ through the consequent change in the number of firms and through the demand for labor that goes into entry and _L_ . fixed operation costs, which determines _p_ Changes in the firms’ portfolio of products also affect output through changes in _Lp_ , because of the labor-intensive nature of the fixed costs of supplying goods. Furthermore, it affects _TFP_ by shaping the distribution of products across firms of different productivities, controlled by _eλ_ ( _ω_ ).

# **3 Quantitative Analysis**

We turn now to the quantitative evaluation of the model. As emphasized throughout the paper, we are interested in measuring the welfare gains from liberalizations that eliminate misallocation frictions. We seek to provide a quantitative answer accompanied with an understanding of how the various forces at work in the model contribute to shaping the gains.

## **3.1 Calibration**

We must choose parameter values for the elasticity of substitutions _σ_ and _ρ_ , the subjective discount factor of the household _β_ , the size of the labor force _L_ , and the set of parameters governing the process of firm dynamics, entry and exit: entry and fixed operation costs _fe_ and _fc_ , the size and probability of the jump in the binomial process _h_ and _α_ , and the exogenous exit rate _δ_ . Furthermore, we must specify values for the fixed cost of supplying varieties to the market, _f p_ , as well as the shape parameter of the distribution of product attributes _η_ . We calibrate these parameters working with the undistorted stationary allocation of the model, taking the United States as empirical target. Table 1 summarizes the parameter values.

The strategy of the calibration is as follows. For the elasticity of substitution, we set _ρ_ = _σ_ = 3, which lies in the middle of estimates of substitutability found in the trade and industrial organization literature, and is the value chosen by Hsieh and Klenow (2009) in their measurement of misallocation in the United States, China, and India<sup>9</sup> . Since our assumptions imply no heterogeneity of prices across products,

> 9See Broda and Weinstein (2006) for a range of estimates of the elasticity of substitution for US

16
employed. The unemployment rate among the young population is almost twice as high as compared with the entire working age population.

The disparity in outcomes between men and women is also telling. An estimated 11 percent of working age women are employed as compared to 71 percent for men, the latter itself being low by international standards. The gender dimension in unemployment is significant as well. The unemployment rate among women aged 15-64 years is twice as high as that of men (23 percent versus 12 percent). Education levels among women appear lower as well with the exception of tertiary education, although the tertiary education population is a small fraction of the overall population (less than five percent for both women and men). Thus, from the descriptive statistics, it appears that the youth are objectively deprived compared to the non-youth, and that women are objectively deprived compared to men.

**Table 5: Descriptive statistics**

||<br>|**Entire popu**<br>**(aged 15-**<br>|**lation**<br>**64)**<br>|**Youth popula**<br>**(aged 15-2**<br> <br>|**tion**<br>**9)**<br>|
|---|---|---|---|---|---|
||**All**|**Males**|**Females**|**All**<br>**Males**|**Females**|
|Observations|6451|3112|3339|2429<br>1169|1260|
|Population share|100|48.78|51.22|100<br>47.76|52.24|
|Share employed|40.2|70.6|11.3|33.8<br>59.7|10.1|
|Share not employed|59.8|29.4|88.7|66.2<br>40.3|89.9|
|Unemployment rate|13.9|12.1|23.2|23.9<br>21.3|35.5|
|Education shares||||||
|No education|42.3|29.8|54.2|24.2<br>14.1|33.5|
|Primary education|29.3|35.1|23.7|37.8<br>40.9|35.0|
|Low secondary education|15.1|18.7|11.7|24.0<br>29.4|19.0|
|Secondary education|9.0|11.5|6.6|9.9<br>11.6|8.3|
|Tertiary education|4.3|4.8|3.8|4.1<br>4.0|4.2|
|Total|100|100|100|100<br>100|100|
|Married population share|56.4|53.8|58.9|25.4<br>13.5|36.4|
|Average Age|35.7|36.2|35.2|22.6<br>22.8|22.4|
|Urban population share|53.3|53.5|53.1|46.3<br>48.0|44.7|

Source: MHYS 2009-2010. Note: For population currently not in school To construct the labor group variable ( ) used for the first step of the two-steps model proposed, the labor force was split into two categories: individuals who were employed at the time of the survey were classified to be in the ‘good’ category ( ), while those who were unemployed or were out of the labor force altogether were classified to be in the ‘bad’ category ( ). This choice is justified by the fact that in a country with a large informal sector, a large rural population and a history of long-term unemployment, unemployment alone is not a good indicator of labor market malaise. This is also witnessed by the fact that in Morocco the reduction in the unemployment rate of the last ten years has been accompanied by a reduction in labor force participation.

To estimate the predicted values of the good state (i.e., employment) we used education levels, marital status, age and location related variables. We are therefore assuming that these are the main variables 15
Policy Research Working Paper 10690

# **Abstract**

On current trends, the future of global poverty reduction will be determined by Sub-Saharan Africa. Yet even during Sub-Saharan Africa’s period of high economic growth— roughly corresponding to the first decade and a half of the 2000s—the extent to which this growth translated into improved living standards for African households was hotly debated. This paper revisits the issue of Sub-Saharan Africa’s relatively low growth elasticity of poverty using a sample of 575 successive and comparable growth spells between 1981 and 2021. The findings confirm that, even controlling for initial differences in poverty, income levels, and inequality, Sub-Saharan Africa consistently had a significantly lower growth elasticity of poverty relative to other regions over this period. The lower growth elasticity of poverty, which has remained unchanged over time, is due to a lower passthrough between growth in gross domestic product per capita (or growth in household final consumption expenditure as measured by national accounts) and growth in household consumption expenditures as measured from surveys. Given the low passthrough of economic growth to households, Africa thus needs higher rates of economic growth than its peer countries in other regions to achieve equal rates of poverty reduction. Given the challenge of achieving this in the current global economic environment, success in reducing global poverty will require a focused effort to strengthen the effect of aggregate economic growth on household welfare in Sub-Saharan Africa. The results suggest that this will require (i) improved provision of basic education services and basic infrastructure, (ii) faster structural transformation, and (iii) a decrease in the occurrence and persistence of violent conflicts.

This paper is a product of the Poverty and Equity Global Practice. 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://www.worldbank.org/prwp. The authors may be contacted at tbundervoet@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
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37
Table 4 Developing Countries' Tariffs (in %)

|COtlNTRY<br>V|YEAR]|BOUND|APPLIED|SD|CV|MARGIN|% UNBOUND|
|---|---|---|---|---|---|---|---|
|Argentina|1997|35|14|2.1|0.2|22|7_|
|Bangladesh|1996|.<br>84|29|15.0|0.5|54|<br>._|
|:,<br>nin*<br>|1998<br>|_114_<br>|13<br>|6.4<br>|0.49<br>|101<br>|<br>|
|1solivia|1995|40|IC<br>|0.1|0.0|30|<br>0<br>|
|Brazil|1996|32|12|2.9|0.2|20|<br>0|
|Caineroon<br>|1994|80<br>|21|4.7<br>|0.2<br>|59<br>|<br>._<br> <br>|
|Chile|1996|25|1|0.2|0.0|14|<br>T|
|Colombia|1996|52|13|3.4|0.3|39|<br>.0|
|Costa Rica|1995|44|12|5.5|0.4|32|<br>0|
|C6te d'lvoire|1994|13|21|0.3|0.0|-7||
|Cyprus|1996|43|15|10.4|0.7|28|<br>.j|
|Dominican Rep.|1994|40|20|5.0|0.2|20|0|
|Egypt|1993|48|32|16.2|0.5|15|_<br>|
|El Salvador|,|38|10|7.6|0.8|28|.<br>0|
|Fiji*|7|40|12|...|...|28|<br>48|
|Ghana|I<br>993|78|17<br>|4.0<br>|0.2<br>|61<br>|<br>|
|Hong Kong, China|1999|0|0<br>|0.0|0.0|0<br>|<br>:52<br> <br>|
|-India*|1997|54|35|...|...|1.|9<br>._7|
|,idonesia*|a49|38|0|...|...|29|<br>6|
|.,enya|!944|93|36|7.6|0.2|57||
|Korea|1996|26|I|57.1|3.9|1 1||
|Malaysia|1996|19|.<br>9|14.4|1.7|10|<br>21|
|Mauritius*|1996|70|29|_|...|41|<br>2<br>|
|Mexico|1996|49|14|..|.|35|<br>0|
|Morocco|1995|42|25|13.1|0.5|17|<br>0|
|Nigeria*|1999|117|<br>24|...|...|94||
|Pakistan|1996|69|69|16.3|0.2|0|<br>70|
|Paraguay|1996|35|1|3.4|0.3|24|_|
|Peru|1993|32|19|2.3|0.1|13|<br>0|
|Philip:'.-|1996|28|30|.<br>10.1|0.3|-2|<br>-to|
|Senega!|1989|17|12|...|...|5|<br>F2|
|Singapor.|1995|9|0|...|...|8|<br>i4|
|South Atrica|1993|22|16|9.7|0.6|6|<br>2|
|Sri Lanka|1995|50|24|8.0|0.3|26|<br>73|
|Thailand|1995|29|25|8.9|0.3|4|6_|
|Tunisia|1995|69|31|7.5|0.2|38|<br>17|
|Turkey|1995|30|1|4.8|0.4|19|<br>f|
|UJganda|1996|62|17|4.7|0.3|45|<br>75|
|Uruauav*|1999|31|12|7.3|0.6|19|<br>(|
|Veniezuela|1995|39|14|2.7|0.2|25|<br>0|
|Zambia|1996|101|16i|4.0|0.3|85|<br>._|
|Zimbabwe|1994|123|17<br>|6.4|0.4|106|<br>9|
|Averaoe||49|19|8.0|0.5|30|<br>._|
|Average_||38|14|4.3|0.3|24||

Explanation: Average tor 100% bound only BOUND simple average bound rate at the end ot implementation of URA APPLIED simple average applied rate (latest year available) SD standard deviation for applied tariff lines CV coefficient of variation SD divided by the APPLIED tariff °% lINBOUND proportion of total tariff lines unbound NIARGIN difference between the average bound and appiied rates _Sourcc. I'TO. IDB; *H'TO. TPR. **Finger el. al., 1996, import weighted_ 12
effects associated with a tax increase raise the volume of taxed goods consumed or supplied (see Triest 1990). However, comparison of MWC<sup>C</sup> and MWC<sup>U</sup> does indicate the reduction in the marginal welfare cost of taxation implied when an MMCF estimate is used in place of MCF in equation (6).

Table 1. Estimates of the Marginal Welfare Cost of US Income/Labor taxes.

|Author/Year|MWC<sup>C</sup>|MWC<sup>U</sup>|% Reduction using<br>MWC<sup>U</sup>|
|---|---|---|---|
|Stuart (1984)|0.212|0.076|64|
|Ballard (1990)|0.197<sup>a</sup>|0.030|85|
|Fullerton (1991)|0.25|0.07|72|
|Feldstein (1999)|0.57<sup>b</sup>|0.27|53|

Notes: MWC<sup>C</sup> is defined as MCF -1. MWC<sup>U</sup> =MMCF -1. (a) Based on 85 percent compensation. (b) Calculated by transforming the definitions of MCF and MMCF into elasticity format such that MMCF= 1/[1-(η – ηY){t/(1-t)}] where η is the elasticity of supply of taxable income; ηY is the income elasticity of the supply of taxable income; and t the average marginal tax rate applied to market wages, p*. The values for t= 0.259; η=1.04; and ηY= -0.40 were taken from Feldstein (1999, <u>pp 675;677;679). To obtain the compensated MCF, the ηY term was omitted.</u> In Table 1, the estimates based on Stuart (1984) and Ballard (1990) are those recalculated for consistency by Snow and Warren (1996, p301). The estimates attributed to Fullerton (1991, p303) are from his Table 1. All of these estimates are based on traditional values of response parameters, such as the compensated labor supply elasticity of 0.2 and the uncompensated labor supply elasticity of zero used in Fullerton (1991, p306).

The estimates in the final row of Table 1 were derived for a small across-theboard increase in the income tax rate on income in all tax brackets (including the currently tax-exempt low income bracket) using the average marginal tax rate (0.259) and the estimates of substitution (1.04) and income (-0.40) elasticities relative to the 21