rafmacalaba/datause-encoder-data huggingface dataset:
much less likely to invest, which is consistent with their lower capital intensity.

Tables 7A and 7B present growth matrices of rural and urban manufacturing firms. Since our samples are selected on the basis of their current size, it would be unwise to use these matrices to examine the probability of a firm of a certain size at start-up, with, say, one worker, growing into a firm of a given size, say in between 10 and 20 employees. However, we can use the transition matrices to evaluate how many employees firms a firm of a given current size employed when it started.<sup>22</sup> Of course, it is important to keep in mind that these results, as well as those for the subsequent growth regressions, are conditional on firm survival. The matrices confirm that rural firms are stagnant, while there is a substantial amount of mobility across size categories in urban areas. In particular, the results reveal that a minority of currently medium- and large-sized firms started off as small firms, which indicates that small firms are capable of escaping their initial size category in urban areas, though the very smallest firms, one person enterprises, are seemingly least likely to do so. By contrast, all rural enterprises have remained small.

To identify which firms are most likely to grow, we estimate basic growth regressions using information on the age of the firm and its size at start-up. Obviously, the cross-sectional nature of our data does not enable us to analyze dynamics in detail, and we should consequently be careful in interpreting the results. Following Sleuwagen and Goedhuys (2002), growth is modeled as a function of the age of the firm, _at,_ the size of the firm at startup, _S0_ , other enterprise characteristics _E_ , and investment climate characteristics _IC_ : )e<sup>ββEEE+ββIIC</sup> . To allow for non-linear impacts of size and firm-age on growth, as well GG= tt tt gg(SS , aa

> 22 The informal firm survey was designed to exclude firms employing more than 10 employees. Consequently, the most successful microenterprises are left out of the survey. On the other hand, the large manufacturing firm survey was intended to exclude firms with fewer than 10 employees, thus excluding the most unsuccessful enterprises. Using information on size at start-up to inform questions about the probability of a firm of a given size ending up in a certain size category is thus likely to yield misleading answers.  Since we pool the data across different samples, the resulting biases might partially offset each other. However, given that the underlying population proportions are unknown, it is difficult to gage the magnitude and the sign of the bias. Fortunately, we can still use the information to ask whether firms which are currently large (small) started small (large), since by conditioning on current size, the sample selection bias should be controlled for.

-20-
providing a novel method to account for possible heterogeneous e¤ects of NTMs across trading partners while employing econometric techniques in the estimation of AVEs that relate to the literature on the estimation of gravity models. Unlike previous papers, the AVE estimates of this paper are highly detailed and disaggregated at the HS 6-digit level and vary by importer and exporter. Such detailed variations could be useful for studies evaluating the impact of speci...c trade policy changes.

The paper proceeds as follows. Section 2 provides theoretical motivations to de...ne trade discrepancies. Section 3 provides a data description for the paper. Section 4 presents the empirical model to estimate the AVEs of NTMs. Section 5 presents the results on the estimation of AVEs. Section 6 shows the empirical evidence for NTM-induced trade fraud, while robustness checks are presented in Section 7. Section 8 concludes.

# 2 Theoretical Motivations

This section provides a simple theoretical model built on Ferrantino, Liu and Wang (2012), which followed Swenson (2001). Those two studies emphasize the importance of transfer pricing, export taxes such as VAT and capital controls in a¤ecting the under-reporting behavior of importers and exporters from China and the US. Di¤erent from the analysis of those two papers, we focus on a wider range of importing and exporting countries with di¤erent levels of development and institutions, where transfer pricing, VAT and capital controls may not be important. Instead, we emphasize the roles of tari¤s and NTMs in inducing importers and exporters to under-report the quantity and value of trade di¤erently, which leads to the observed trade discrepancies in the o¢ cial statistics. Similarly to the previous models, traders weigh the bene...ts of under-reporting, which are the tari¤s saved, against the costs of under-reporting, which are the potential penalties, to choose the optimal 6
where  _h_ is the share of total household income that is accrue to profits from farming activities. Hence, in the short term, higher food prices will benefit net producers of agricultural goods (  _h_   _h_ ) and hurt net consumers of agricultural products (  _h_  _h_ ) . Equations (2) and (3) assume that production and consumption patterns remain constant after the change in prices (Deaton, 1989) and therefore these results should be complemented with a medium- to long-term analysis.

# 4.2 Simulation Results

The simulations presented here make use of the Global Income Distribution Dynamics (GIDD) dataset that has been recently developed at the World Bank. The GIDD dataset consists of 73 detailed household surveys for low and middle-income countries, 21 of which include information on food expenditure by household.<sup>7</sup> Together, this dataset covers 63 percent of the population in the developing world--the major missing country being China. The majority of the surveys (54) use per capita consumption as the welfare indicator, while the remaining surveys--all but one for countries in Latin America-include only per capita income as a measure of household welfare. The welfare measures are expressed in 2005 PPP prices for consistency with the $1.25 and $2.5 a day poverty lines recently developed in Chen and Ravallion (2008).<sup>8</sup> All the ex-ante poverty simulations presented in this section capture the _ceteris-paribus_ effects of changes in _relative_ food prices observed between January 2005 and December 2007 (see Figure 3). The results presented here differ from Ivanic and Martin’s (2008) estimates in several ways: (1) the country coverage is substantially different, (2) while Ivanic and Martin’s (2008) focus on the poverty effects of changes in 7 food items, we assess the poverty of changes in prices of the total food basket, (3) Ivanic and Martin’s (2008) use the changes in international prices of their 7 food items as the price shock whereas we use the _domestic_ change in the food CPI _relative_ to the non-food CPI.

## **4.2.1 Loss in Urban Household Purchasing Power**

As it is clear from equation (3), the share of total household budget that is spent on food,  _h_ , is an important element determining the deterioration in purchasing power originated from an increase in food prices. For some countries, this information is readily available from household surveys, however, in several cases one has to estimate or impute this value. In 21 out of the total 73 countries included in the GIDD’s sample, household-level information on total food expenditure was available. Using the information for these 21

> 7 See Table 9 in Annex II for a complete country list. A complete description of the dataset is available at http://www.worldbank.org/gidd

> 8 Most of the household surveys in the GIDD are for years between 2000 and 2005. When the GIDD dataset did not include the newest household survey available from the World Bank’s PovCal, the GIDD’s survey mean income (or consumption) was modified so that the extreme poverty headcount matched the latest information available from PovCal.

8
# Table 6: **Regression Results** – Debt Relief and Sources of Credit

The table reports the effect of debt relief on the percentage of credit obtained from formal and informal sector lenders. Within a panel, each column reports results from a separate regression. The dependent variable is the change in the percentage of credit obtained from banks and informal lenders, respectively. Loans from _Banks_ include commercial and cooperative banks, Loans from _Informal Lenders_ include moneylenders, shopkeepers, traders, friends and relatives. All controls and fixed effects are as defined in Table 5. Robust standard errors, in brackets, clustered by bank and district. + p _<_ 0.10 ** p _<_ 0.05 *** p _<_ 0.01.

||Model 1||Model 2||Model 3||Model 4||
|---|---|---|---|---|---|---|---|---|
||Banks|Informal<br>lenders|Banks|Informal<br>lenders|Banks|Informal<br>lenders|Banks|Informal<br>lenders|
|_Panel A: Full Sample_|(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|100% Relief|-10.03***|6.19***|-7.94***|5.27***|-8.04***|5.30***|-8.01***|5.38***|
||[2.48]|[1.47]|[2.47]|[1.46]|[2.63]|[1.44]|[2.47]|[1.46]|
|Observations|2,692|2,690|2,692|2,690|2,552|2,550|2,692|2,690|
|R-squared|0.2|0.12|0.19|0.11|0.2|0.12|0.19|0.11|
|_Panel B: Robustness Sampl_<br>Audited accounts|_es_||||||||
|100% Relief|-9.72**|7.22**|-7.35**|5.97**|-7.77***|6.26***|-7.43**|6.00**|
||[2.93]|[2.36]|[2.95]|[2.38]|[2.73]|[2.20]|[2.96]|[2.39]|
|Observations|1,721|1,720|1,721|1,720|1,633|1,632|1,721|1,720|
|R-squared|0.21|0.15|0.2|0.14|0.21|0.14|0.2|0.14|
|Reduced bandwidth sample|||||||||
|100% Relief|-8.95**|6.22**|-7.27***|5.03***|-7.20***|4.40**|-7.47***|5.19***|
||[2.78]|[1.87]|[2.67]|[1.87]|[2.67]|[1.79]|[2.69]|[1.91]|
|Observations|1,994|1,992|1,994|1,992|1,877|1,875|1,994|1,992|
|R-squared|0.2|0.12|0.18|0.11|0.19|0.12|0.18|0.11|
|Interviewer effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Bank*district effects|No|No|Yes|Yes|Yes|Yes|Yes|Yes|
|Respondent controls|No|No|No|No|Yes|Yes|No|No|
|Local linear control fctn|No|No|Yes|Yes|Yes|Yes|No|No|
|Quadratic control fctn|No|No|No|No|No|No|Yes|Yes|

37
**Table S:** Effects **of Educational** Expansion and Curricular Content<sup>on Economic Growth,</sup> **1960-1985** (Less-developed countries only)

(Unstandardized regression coefficients<sup>are reported; dependent variable is per capita RGDP 1985)</sup>

||Equadon:|(A)|(B)|(C)|
|---|---|---|---|---|
|Ave|r. Ann. Instruct. Time (Total)||.00006||
|Average|Mathematics|||-.00078|
|Annual|Science|||.00210**|
|Instructional|Language|||.00015|
|Tune in|Arts/Music|||.00156*|
|Each|All Social Studies|||_-.00055_|
|Curricular|Moral/Religion|||.00083|
|Topic|Physical Ed.|||-.00180*|
|Atiune 1:|Pre-Vocational Ed.|||-.00094**|
|Educational|Primary<br>Education|.0043**|.0043**|.0031**|
|EnrolLment<br>Rates:|Secondary<br>Education|.0058*|.0057*|.0105**|
|Control|Lagged<br>Dependent Var.|.66**|.67**|.66**|
|Variables:|Mining/Oil<br>Exporters|.10*|.10*|.07|
||Export Commod.<br> <br>Concentration|-.004**|-.004**|-.003**|
||Total Fertility<br>Rate (1965)|.044|.045|.034|
||Adjusted R<sup>2</sup>|.839|.835|.866|
||Constant|.79|.71|.82|
||Number of Cases|43|43|43|

* Unstandardized regression coefficient<sup>is at least 1.5 times its standarderror</sup>

** Unstandardized regression<sup>coefficient is at least 2.0 times its standarderror</sup>
**Table 3.2: Cross-country Comparison of the Simple Welfare, Poverty, and Inequality** **<u>Measures</u>**

||**Low-income, cons**<br>|**umption**<br>|**Middle-incom**<br>|**e, income**|**High-i **|**ncome, income**<br>|
|---|---|---|---|---|---|---|
||**Burkina Faso**|**Mali**|**Colombia**|**Peru**|**France**|**United States**|
||**2018**|**2018**|**2019**|**2019**|**2019**|**2019**|
|Panel A|**z= $2.15 per**|**day**|**z= $6.85 p**|**er day**|**z=**|**$25 per day**|
|_W_|0.82|0.66|1.14|0.92|0.58|0.93|
|Mean|4.85|4.77|15.6|13.9|58.9|83.6|
|_I_|1.85|1.47|2.59|1.86|1.36|3.09|
|Panel B|||||||
|_C_|1.15|1.05|1.47|1.26|1.04|1.46|
|_P_|0.15|0.05|0.47|0.26|0.04|0.46|
|_FGT0_|0.31|0.17|0.35|0.29|0.10|0.11|

**Notes:** This table reports the estimates of W(y, z), mean, and I(y, y̅) in Panel A and C(y, z), P(y , z), and the poverty headcount ratio (FGT0) in Panel B. W, C, P, and FGT0 are estimated at the $2.15, $6.85, and $25 per person per day thresholds in 2017 $PPP for the low-, middle-, and high-income groups respectively. Mean is expressed in 2017 $PPP per person per day. Consumption is used to measure welfare in Burkina Faso and Mali while income is used in other countries.

# **4 The Prosperity Gap: a New Measure of Shared Prosperity**

In this section of the paper, we present a policy application of our new welfare measures. We show how our new welfare measure W can be used as a natural measure of “shared prosperity”, the promotion of which is one of the two main institutional goals of the World Bank. We first provide background on the concept of shared prosperity as it is understood at the World Bank. We then interpret W as a global “prosperity gap” in incomes relative to a prosperity standard set at $25 per day, roughly equal to the median poverty line among high-income countries. We show how the prosperity gap varies across countries and over time, highlighting the benefits of it being both distribution sensitive and subgroup decomposable. For the benefit of readers who are primarily interested in the prosperity gap and are less interested in the technical properties of our new welfare measures, this section is designed to be self-contained and may be read separately from the rest of the paper.

## **4.1 Background on Shared Prosperity**

Since 2013, the World Bank has articulated its twin goals as “ _ending extreme poverty and boosting shared prosperity_ ” (World Bank, 2015). Extreme poverty is measured as the share of the world’s population living below the international poverty line. This line is currently set at $2.15 per day in 2017 $PPP, corresponding to the median national poverty line across low-income countries. The concept of “shared prosperity” lacks an accepted precise definition but is clearly intended to capture improvements in a distribution-sensitive welfare measure. This can be seen in the following explanation of the concept: 26
<!-- Start of picture text --> Pension Fund Annual Balance (10 bn. Yuan)<br><!-- End of picture text --> <!-- Start of picture text --> 60.<br>5 0 - - - - -- - - -- -- - - - - -- --- - - - - --- - - - - - - - - - - - - - - - - -- - - - -- - - - -- - - -<br>40 --- 1 .~~ Exp. 4.1 1<br>Fu 12. P1 - Exp. 4.2 eriments-4.1 -- 4.<br>-<br>Exp 4.3<br><!-- End of picture text --> Figure13.Chgein DPinexperiments4.1 .. . 43 . . . . 1 I Figure 12. Pension fund annual balarice in experiments 4.1 - 4.3 <!-- Start of picture text --> Percentage change of GDP with baseline<br><!-- End of picture text --> <!-- Start of picture text --> 44 55 -- - - - - - - - - - - -_ -- - - - - - - - - - - - - -_- - - - - - - - - - - - - - - - - - - - - - - - - - - - -- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -<br>35 -------- Ex .4 ---------------------- ---- --- -- -- -- ------- -----------<br>I ~~~~~~Exp. 4.2| ,<br>3 t-- ------ + Exp. 4.3 --------------~~~~~~~~ ~<br>2 - - - - - - - - - - - - - - - - - - - - - - - - - . - - - -- - - - - - - - - - - - - - - - - - - - - - - - - - - - - -<br>0-5 - --- --- - ---- - - - - - ---- -- - - - - - - - - - -- - - - - - - - - - -<br><!-- End of picture text -->

~~~ ~~~~~ **~** ~~~~~ Figure 13. Change in GDP in experiments 4.1 - 4.3 41
# **Table 1 Share** **<u>of SADC trade in SADC country imports</u>**

||1980|1985|1990|1995|1999|2003|2008|Proporti<br>on from<br>SACU<br>2003|<br>Proportio<br>n<br>from<br>SACU<br>2008|
|---|---|---|---|---|---|---|---|---|---|
|Angola|0|0.6|0.8|7.1|10||6.5||99.9|
|DRC|0.4|1.6|1.1|18.1|31.5||42.8||3.3|
|Malawi|36.7|53|24.8|49.2|64.4|57.5|58.3|65|46.4|
|Mauritius|14.5|4.2|9.9|11.3|11.2|13.2|9.9|97|84.8|
|Mozambique|3.7|5|7.6|55.5|58.6|39.5|38.0|97|94.7|
|SACU|0.1|1.8|1.8|2.1|1.9|2.7|5.6||0.0|
|Tanzania|0.7|0.7|1.3|13.9|13.3|15|11.5|66|90.4|
|Zambia|1.2|10.9|7.9|49.1|65.5|65|59.1|95|74.1|
|Zimbabwe|8.3|31.7|33.1|51.2|51.2|56.1|76.1|94|91.8|
|Seychelles|||||||10.7||55.6|
|Madagascar|||||||9.3||72.1|
|Intra-SADC|1.6|4.7|5.1|9.9|10.2||12.3|90|80.3<sup>a</sup>|
|excl. Angola,|DRC,Ma|dagascar|and Seyc|helles||10.6|12.3||78.8|

Source: Development Network Africa (2007) and Chauvin, S. and G. Gaulier (2002) updated to 2008 using HS6-digit data obtained from UNComtrade. Note: Intra-SACU trade is excluded. 2002 values used for Zimbabwe in 2003.

a. 80.3 percent of SADC (excluding SACU countries) imports from the region are sourced from SACU.

# **Table 2:** **<u>Share of SADC trade in SADC country exports</u>**

|||||||||Propo<br>on<br>SACU|rti<br>to<br> <br>Proportio<br>n<br>to<br>SACU|
|---|---|---|---|---|---|---|---|---|---|
||1980|1985|1990|1995|1999|2003|2008|2003|2008|
|Angola|0.03|0|0.01|0.03|0.7||1.8||99.9|
|DRC|0.05|0.03|0.1|6|0.3||6.7||3.3|
|Malawi|12.4|15.4|1.6|17.2|16.9|20.1|21.8|74|50.4|
|Mauritius|1.4|0.1|1.2|1.4|1.4|2.1|11.3|76|32.8|
|Mozambique|1.1|0.3|0.2|32.1|17.41|24.6|17.2|74|65.4|
|SACU|0.7|2.8|2.5|10.7|11.5|9.7|10.5|||
|Tanzania|5.2|0.1|0.5|1.4|7.4|9.4|17.2|45|55.7|
|Zambia|0.9|3.1|0.8|3.8|7.8|40.6|20.0|50|54.1|
|Zimbabwe|1.3|25|30.7|31.7|28|30.5|64.4|79|81.2|
|Seychelles|||||||1.2|||
|Madagascar|||||||3.1|||
|Intra-SADC|0.9|3.4|3.1|9.9|10||6.1|68|72.8<sup>a</sup>|
|excl. Angola,|DRC,Ma|dagascar|and Seyc|helles||10.6|12.1||62.9|

Source: Source: Updated table from Development Network Africa (2007) and Chauvin, S. and G. Gaulier (2002). Note: Intra-SACU trade is excluded. 2002 values used for Zimbabwe in 2003. a. 72.8 percent of SADC (excluding SACU countries) exports to the region are sold to SACU.

Further insights on the regionalization of SADC trade are provided by the gravity model. These estimates (Column 3 of Table 9 in Appendix 1) reveal that SADC trade is regionalized: intra-SADC trade is relatively high in relation to what intra-regional incomes and distance would predict. For example, the coefficient on the intra-SADC dummy is significant and at 0.976 suggests that SADC countries trade more than double what would be implied by the gravity model benchmark. Additional specifications indicated that these results are robust to the exclusion of South Africa. The results are consistent with those of earlier research for SSA by Foroutan and Pritchett (1993).<sup>3</sup>

> 3 See also Subramanian and Tamirisa (2003) and Yang and Gupta (2008).

8
**Table 13. Difference between true IOO and median estimated IOO in percentage terms: Enhanced inequality scenario**

||||**Excluded**|**circumstanc**|**e**<br>**’**|**’**|
|---|---|---|---|---|---|---|
|**Observed**<br>**population**<br>**scenarios**|**None**<br>**(1)**|**Female**<br>**(2)**|**Urban**<br>**(3)**|**Region**<br>**(4)**|**Fathers**<br>**education**<br>**(5)**|**Mothers**<br>**education**<br>**(6)**|
||||True IO s<br>|hare = 0.78<br>|<br>||
|all|0.00|‐39.39|‐20.56|‐40.60|‐6.61|‐23.73|
|Top 1% truncated|‐2.51|‐43.63|‐24.69|‐41.52|‐9.41|‐28.00|
|Top5% truncated|‐9.66|‐55.95|‐36.58|‐36.95|‐17.47|‐40.10|
||||True IO s<br>|hare = 0.73<br>|<br>||
|all|0.00|‐39.47|‐20.66|‐40.68|‐6.73|‐23.83|
|Top 1% truncated|‐3.29|‐44.12|‐25.31|‐41.89|‐10.14|‐28.59|
|Top5% truncated|‐11.72|‐56.96|‐37.96|‐38.58|‐19.34|‐41.37|
||||True IO s|hare = 0.67|||
|all|0.00|‐39.28|‐20.43|‐40.50|‐6.46|‐23.60|
|Top 1% truncated|‐3.59|‐44.38|‐25.57|‐41.95|‐10.42|‐28.84|
|Top5% truncated|‐13.08|‐57.60|‐38.81|‐39.66|‐20.54|‐42.16|
||||True IO s<br>|hare = 0.59<br>|<br>||
|all|0.00|‐39.47|‐20.66|‐40.68|‐6.75|‐23.84|
|Top 1% truncated|‐4.96|‐44.12|‐25.31|‐41.89|‐10.14|‐28.59|
|Top5% truncated|‐16.00|‐56.96|‐37.96|‐38.58|‐19.34|‐41.37|
||||True IO s|hare = 0.44|||
|all|0.00|‐39.38|‐20.57|‐40.62|‐6.64|‐23.73|
|Top 1% truncated|‐6.80|‐46.46|‐28.21|‐43.05|‐13.40|‐31.32|
|Top5% truncated|‐19.81|‐60.07|‐42.70|‐45.07|‐26.42|‐45.60|
||||True IO s|hare = 0.32|||
|all|0.00|‐39.33|‐20.46|‐40.55|‐6.54|‐23.66|
|Top 1% truncated|‐8.28|‐47.31|‐29.38|‐43.57|‐14.81|‐32.41|
|Top5% truncated|‐22.25|‐60.33|‐43.71|‐47.25|‐28.51|‐46.41|

Notes: Results based on 1,000 simulations. Enhanced inequality scenario refers to data‐generating process described by equation (3), modified as described in robustness checks section.

31
32 Table 8. Population and Percentage Distribution by Geographic Region and Income Group, 1985*2100

|||Pop|ulation|(miLt|ions)|||Percen|tage<br>of|world p|opulat|ion|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Region|1985|1990|<br>2000|2025|2050|2100|1985|1990|2000|2025|<br>2050|2100|
|World|4,843|5,282|6,205|8,262|9,639|10,560|<br>100.0|100.0|100.0|100.0|100.0|100.0|
|Less developed (a)|3,665|4,069|4,938|6,925|8,311|9,226|75.7|77.0|79.6|83.8|86.2|87.4|
|More developed (a)|1,179|1,213|1,267|1,337|1,328|1,332|24.3|23.0|20.4|16.2|13.8|12.6|
|Africa|560|<br>652|<br>872|1,508|2,046|2,469|11.6|12.3|14.1|18.3|21.2|23.4|
|East Africa|231|<br>270|<br>365|<br>644|<br>885|1,078|4.8|<br>5.1|<br>5.9|7.8|<br>9.2|<br>10.2|
|West Africa|204|<br>239|<br>326|<br>585|<br>810|<br>994|4.2|<br>4.5|<br>5.3|<br>7.1|<br>8.4|<br>9.4|
|North Africa|125|<br>143|<br>181|<br>279|<br>356|<br>404|2.6|<br>2.7|<br>2.9|<br>3.4|<br>3.7|<br>3.8|
|America|667|<br>721|<br>825|1,040|1,150|1,205|13.8|13.7|13.3|12.6|11.9|11.4|
|Latin America and Caribbea|n 399|<br>442|<br>530|<br>721|<br>835|<br>894|8.2|<br>8.4|<br>8.5|<br>8.7|<br>8.7|<br>8.5|
|Northern America|268|279|295|320|315|311|5.5|5.3|4.8|<br>3.9|<br>3.3|<br>2.9|
|Asia|2,821|3,093|3,657|4,815|5,540|5,967|58.2|58.6|58.9|58.3|57.5|56.5|
|East and Southeast<br>Asia|1,648|1,779|2,050|2,535|2,765|2,879|34.0|33.7|33.0|30.7|28.7|27.3|
|South Asia|1,058|1,182|1,437|2,021|2,449|2,722|21.8|22.4|23.2|24.5|25.4|25.8|
|Southwest<br>Asia|115|<br>132|<br>170|<br>259|<br>325|<br>367|2.4|<br>2.5|<br>2.7|<br>3.1|<br>3.4|3.5|
|Europe and U.S.S.R.|771|789|821|864|865|879|15.9|14.9|13.2|10.5|9.0|8.3|
|Oceania|24|26|29|35|38|40|0.5|0.5|0.5|0.4|0.4|0.4|
|Income group (b)|||||||||||||
|Low|2,447|2,703|3,250|4,481|5,341|5,927|50.5|51.2|52.4|54.2|55.4|56.1|
|Lower-middle|680|<br>766|<br>953|1,411|1,749|1,959|14.0|14.5|15.4|17.1|18.1|18.6|
|Upper-middle|592|<br>649|<br>769|1,031|1,194|1,287|12.2|12.3|12.4|12.5|12.4|12.2|
|High (oil exporters)|19|<br>23|<br>32|<br>54|<br>70|<br>80|0.4|<br>0.4|<br>0.5|<br>0.7|<br>0.7|<br>0.8|
|Industrial<br>market|741|<br>760|<br>787|<br>807|<br>774|<br>754|15.3|14.4|12.7|<br>9.8|<br>8.0|<br>7.1|
|Nonreporting<br>normwiter|364|<br>381|<br>414|<br>478|<br>517|<br>552|7.5|<br>7.2|<br>6.7|<br>5.8|<br>5.4|<br>5.2|

(a) "More developed"t comprises Europe, U.S.S.R., Northern America (United States ard Canada), AustraLia, New Zealand, and Japan. "Less developed" comprises the rest of the world.

(b) Criteria for income groups are given in World Bank 1988. See listing in TabLe 15 of this paper.