rafmacalaba/datause-encoder-data huggingface dataset:
consider imports of parts and components do we see the sharp distinction between the two countries that might be expected given the discussions on global production sharing. These accounted for 31 percent of China’s merchandise imports, as against only 12 percent in India.

On the export side, the two countries differ substantially in the importance of final goods in their exports. While 61 percent of China’s non-fuel exports are classified as final goods, only 40 percent of India’s exports are final goods, with 52 percent intermediate manufactured goods, and 8 percent non-fuel primary products.

Between 1992 and 2004, the major change evident is the dramatic increase of China’s trade in parts and components. In 1992, these accounted for only 15 percent of non-fuel imports, but this share rose to 31 percent by 2004. By contrast, in India, this share declined from 15 to 12 percent. While discussions of China’s role in production networks tend to focus on China’s role as an importer of components, it is notable that there has also been a substantial increase in the importance of parts and components in China’s exports, with this share rising from 5 to 15 percent. By contrast, in India, this share rose from 5 to just 6 percent of total non-fuel exports. These data are consistent with the widespread perception that India remains much less integrated than China in global production networks, despite the existence of Indian policies to allow duty-free access to imported components for use in the production of exports (World Bank 2004).

**Table 1. Composition of Non-fuel Imports and Exports by Broad Economic Classification**

||China<br>Imports|Exports|India<br>Imports|Exports|
|---|---|---|---|---|
|2004|||||
|Nonfuel Primary Inputs|10|1|16|8|
|Intermediate inputs|63|38|60|52|
|Final Goods|28|61|25|40|
|Total|100|100|100|100|
|_Parts/components_|_31_|_17_|_12_|_6_|
|1992|||||
|Nonfuel Primary Inputs|8|6|30|6|
|Intermediate inputs|61|30|55|47|
|Final Goods|31|65|15|47|
|Total|100|100|100|100|
|_Parts/components_|_15_|_5_|_15_|_5_|

Source: UN COMTRADE statistics from the World Bank WITS system.

7
# 3. Data

## 3.1 The Sample

The sources of firm-level data we employed are the World Bank Enterprise Surveys (WBES) and the COVID-19 Follow-up Enterprise Surveys, both conducted by the World Bank. WBES offers an expansive array of economic data on 171,000 firms in 148 countries, and the data has been widely used in economics, finance, international business, and management research. Since the outbreak of COVID-19 pandemic, to measure the pandemic’s impact on the firms, the Followup Enterprise Surveys re-interviews those firms that had been interviewed in WBES in selected countries. The follow-up surveys contain information on sales changes, business operations, labor adjustments, access to finance, expectations about the future, and support from the government in response to COVID-19. We combine the WBES data with COVID-19 Follow-up Enterprise Surveys data. The former provides us with the pre-determined firm characteristics which are employed as independent or control variables; the latter provides seven indicators measuring firm responses during the pandemic.

By April 2021, the Follow-up Enterprise Survey has been implemented in 41 countries, and firms in 26 countries out of them have been re-contacted in the second round. The relevant WBES of these 41 countries have been posted in different years during 2016-2019. We choose to use the data of the latest survey, which means that for firms having been interviewed in two rounds, we only keep the second-round survey data and for firms that have only one round survey, we just use the data. The reason is that the COVID-19 pandemic is still ongoing, and the surveys add a few new interview questions over time, thus using the latest survey could help to examine the impact of COVID-19 more accurately. We also use the first-round survey to check the robustness and compare the results with the results of the second-round survey to explore the dynamic of firm responses during COVID-19.

We further collect country-level data from various sources. GDP per capita is from the World Development Indicators database (WDI). The stringency of government policy to contain COVID19 is from Oxford COVID-19 Government Response Tracker (OxCGRT) database, which provides information on the government policy responses to the COVID-19 pandemic across more 9
# **Figure 11. Share of Workers within Employment types and welfare level with the job characteristics**

<!-- Start of picture text --> Unpaid workers LE-SE Informal Wage employees<br>1.0 1.0 1.0<br>0.8 0.8 0.8<br>0.6 0.6 0.6<br>0.4 0.4 0.4<br>0.2 0.2 0.2<br>0.0 0.0 0.0<br>Agric Indust Serv Other Low- Med- High- Agric Indust Serv Other Low- Med- High- Agric Indust Serv Other Low- Med- High-<br>skilled skilled skilled skilled skilled skilled skilled skilled skilled<br>Bottom 40% Top 60% Bottom 40% Top 60% Bottom 40% Top 60%<br><!-- End of picture text --> Source: Own elaboration based on GMD. Notes: Cross-country averages using population weights. The number of countries used for each variable is the same as the one indicated in previous figure. Up to secondary incomplete education is classified as "low education" and secondary complete or college education is classified as "high education".

**Table 2. Determinants of Urban Informal Types of Workers**

||Unpaid<br>|workers<br>|Low-educ<br>empl<br>|ated Self-<br>oyed<br>|Inform<br>empl<br>|al Wage<br>oyees<br>|
|---|---|---|---|---|---|---|
|VARIABLES|(1)|(2)|(1)|(2)|(1)|(2)|
|25-54 years old<br>55 years or older|-0.192<br>[0.008]***<br>-0.192<br>[0.008]***|-0.222<br>[0.006]***<br>-0.247<br>[0.006]***|0.110<br>[0.007]***<br>0.189<br>[0.010]***|0.140<br>[0.006]***<br>0.212<br>[0.008]***|-0.013<br>[0.007]*<br>-0.090<br>[0.009]***|-0.004<br>[0.007]<br>-0.065<br>[0.007]***|
|Education - Primary<br>Education - Primary complete,<br>Secondary incomplete|-0.014<br>[0.004]***<br>-0.042<br>[0.004]***|-0.003<br>[0.005]<br>-0.008<br>[0.004]*|-0.026<br>[0.007]***<br>-0.118<br>[0.006]***|-0.008<br>[0.007]<br>-0.129<br>[0.006]***|0.031<br>[0.006]***<br>0.061<br>[0.005]***|0.015<br>[0.006]**<br>0.041<br>[0.006]***|
|Education - Secondary complete|-0.054<br>[0.006]***|-0.018<br>[0.005]***|-0.613<br>[0.009]***|-0.463<br>[0.006]***|0.027<br>[0.009]***|-0.002<br>[0.008]|
|Education - Tertiary|-0.064<br>[0.004]***|-0.018<br>[0.005]***|-0.600<br>[0.006]***|-0.485<br>[0.007]***|-0.033<br>[0.007]***|-0.028<br>[0.008]***|
|Female|0.004<br>[0.004]|0.023<br>[0.003]***|0.105<br>[0.005]***|0.131<br>[0.004]***|-0.057<br>[0.005]***|-0.090<br>[0.004]***|
|Spouse<br>Other relative or not|0.065<br>[0.004]***<br>0.181<br>[0.006]***|0.059<br>[0.004]***<br>0.193<br>[0.005]***|0.017<br>[0.007]**<br>-0.146<br>[0.007]***|0.013<br>[0.006]**<br>-0.180<br>[0.006]***|-0.076<br>[0.006]***<br>0.052<br>[0.007]***|-0.050<br>[0.005]***<br>0.049<br>[0.006]***|
|Industry||-0.227<br>[0.006]***||-0.149<br>[0.009]***||0.333<br>[0.009]***|

26
_Kompas_ (2015b) ‘PGRI: Moratorium CPNS Berdampak Pada Krisis Guru’, 5 July, available at: <u>http://edukasi.kompas.com/read/2015/07/05/14184321/PGRI.Moratorium.CPNS.Berdampak. pada.Krisis.Guru, downloaded 16 September 2015.</u> Lampost.co (2013) ‘Wapres: Perbaiki Cara Rekrutmen PNS Dengan Obyektif’, available at: <u>http://lampost.co/berita/wapres-perbaiki-cara-rekrutmen-pns-dengan-obyektif, accessed 17</u> September 2015.

Leftwich, A. (2010) ‘Beyond Institutions: Rethinking the Role of Leaders, Elites and Coalitions in the Institutional Formation of Developmental States and Strategies,’ _Forum for Development Studies_ , 37 (1), pp.93-111.

Leslie, S. and J. Strudwick (2015) _Education Partnership Annual Partnership Performance Report: Management Response_ , available at: <u>https://dfat.gov.au/aboutus/publications/Documents/2014-indonesia-education-annual-parthership-performancereport-management-response.pdf, accessed 21 September 2015.</u> Liputan6.com (2012) ‘Gugat Uji Kompetensi Guru ke MA’, available at: <u>http://m.liputan6.com/news/read/429859/gugat-uji-kompetensi-guru-ke-ma, accessed 14</u> September 2015 Manggiasih, B. (2013) ‘Rekrutmen PNS Pintu Masuk Korupsi’, _Tempo.co_ , 4 December, available at: http://nasional.tempo.co/read/news/2013/12/04/173534672/rekrutmen-pns-pintu- <u>masuk-korupsi, accessed 17 September 2015.</u> McKenzie, P. _et al_ (2014) _Study on Teacher Absenteeism in Indonesia 2014_ , Jakarta: ACDP.

McKinsey and Co (2012) _The Archipelago Economy: Unleashing Indonesia’s Potential_ , McKinsey and Co.

McLeod, R. (2000) ‘Soeharto’s Indonesia: A Better Class of Corruption’, _Agenda_ , 7 (2), pp.99112.

Mulkeen, A. (2010) _Teachers in Anglophone Africa: Issues in Teacher Supply, Training, and Management_ , Washington DC: World Bank.

Mustakim (2015) ‘Retno Listyarti, Guru Pejuang Pendidikan’, available at: <u>http://m.news.viva.co.id/news/read/615290-retno-listyarti--guru-pejuang-pendidikan/2,</u> accessed 14 November 2015.

Padmawati (2010) _Kajian Yuridis Status Hukum Tenaga Guru Honorer Pemerintah Kota Surakarta Pada Dinas Pendidikan Pemuda dan Olahraga Kota Surakarta Menurut UndangUndang Nomor 43 Tahun 1999 Tentang Pokok-Pokok Kepegawaian_ , Skripsi, Fakultas Hukum, Universitas Sebelas Maret Surakarta.

Pahlevi, R. (2012) ‘SKB 5 Menteri Rugikan Guru’, _Inilah.com_ , available at: <u>http://m.inilah.com/news/detail/1863163/skb-5-menteri-rugikan-guru, accessed 22 September</u> 2015.

PGRI (2015) _Teachers Problems and its Solution_ , position paper.

54
## **Source** : GTAP 9 dataset.

# **Table 10: Value Added by Region and Sector in the Benchmark Data**

(In billions of US dollars)

|||||||**Region**<br>||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
|||**Australia-**|**Canada**|**China**|**Japan**|**Mexico-**|**Low**|**Middle**|**OECD **|**Philippines**|**USA**|
|||**New**||||**Peru-**|**Income**|**Income**|nec|||
|||**Zealand**||||**Chile**|nec|nec||||
|**Sector**|**Sector**|||||||||||
||**Classification**|||||||||||
|**aggregate CRTS**||**880**|**1005**|**3377**|**3297**|**842**|**442**|**9527**|**9421**|**123**|**8984**|
|**aggregate IRTS**||**571**|**664**|**3640**|**2414**|**676**|**214**|**5653**|**9687**|**93**|**6247**|
|Agriculture|CRTS|35|20|541|50|50|93|1153|316|24|148|
|Business Services|IRTS|420|402|1335|1352|310|106|2761|5037|38|3776|
|Utilities|CRTS|29|38|110|103|20|15|391|397|5|299|
|Food|IRTS|34|40|177|154|79|36|582|612|15|283|
|Natural Resources|CRTS|127|108|384|18|99|107|2159|287|10|273|
|Other Manufactuing|IRTS|54|107|958|481|137|19|901|1969|23|1158|
|Other Services|CRTS|689|839|2342|3125|672|226|5824|8421|84|8264|
|Refined Resource Products|IRTS|57|107|933|405|130|35|1177|1803|13|930|
|Textiles and Apparel|IRTS|5|8|236|21|19|17|234|267|4|100|

Source: GTAP 9 dataset.

45
- 1.78 -

M. Growth In Popuation & Income wih No Grin ImporX **Cocoa** Ikyn Prce - 160 (1987 oedis) <!-- Start of picture text --> Cocoa Cocoa Acrea Labor Utllzaon Labor Sources<br>Prod'n (000s Acrs (Mililors/Manrdays) M<br>You (000. MT) Trad. Hybrid Annual Pow. Total Family Casual<br>1968 228.0 757.9 1365.2 296.3 284.0 580.3 562.8 17.5<br>1960 231.3 623.0 1421.2 310.9 280.0 590.9 576.0 14.9<br>19o 246.5 466.0 1424.1 325.6 276.8 602.4 589.2 13.2<br>1991 264.0 312.6 1426.1 340.4 276.2 616.6 604.2 12.4<br>1992 279.9 183.4 1459.2 355.1 281.1 636.2 622.8 13.4<br>1903 291.7 102.3 1555.5 360.5 290.5 651.0 637.2 13.8<br>1994 297.2 58.2 1685.5 366.6 302.0 667.6 653.3 14 5<br>1995 299.1 30.9 1819.6 371.3 312.5 683.8 669.0 14.8<br>1906 303.5 36.4 1928.4 376.6 320.6 697.2 683.0 14.2<br>=ao7 315.5 36.4 1962.2 362.1 324.4 706.5 694.2 12.3<br><!-- End of picture text -->

|Y u|Crop**A**<br>(0009<br>A<br>l|**creage**<br>AMs)<br>Peron.|Famr<br>(B13o11 of<br>Concept I|Income<br> 1967Codis)<br> <br>Concept2|
|---|---|---|---|---|
|198<br>1980<br>1990<br>1991<br>1992<br>1993<br>1994<br>**1905**<br>**19-6**<br>1997|5125<br>**5366**<br>**5670**<br>5853<br>6o09<br>6162<br>6269<br>**6355**<br>6442<br>6528|3970<br>4011<br>3996<br>3975<br>3965<br>4073<br>4209<br>**4366**<br>4514<br>4626|174.0<br>192.5<br>210.6<br>231.4<br>245.6<br>263.9<br>281.0<br>297.1<br>313.1<br>331.9|231.6<br>250.8<br>260.8<br>291.9<br>380.1<br>3276<br>346.6<br>364.3<br>381 5<br>401.0|

to cocoa farmers are In place early In the decade. Note also, the growth In inputs to **annual** crops Is restrahed by the Increase In grain kmports to 0.8 mIllIon tons by 1997.

Experkuents **II** and III are really a matched pair, differing only In whether or not grain huports are allowed. They will be discussed together sInce all of the differences are due to the reubrement that domestic food demand be met from domestic production In the no-food grains **hkorts** case. Thib requirement forces additional land and labor Into food production. Since cocoa production at a fixed real price Is an alternative, resources can be bid away from cocoa use only by IncreasIng factor returns and therefore food commodity prices (which are now totally nontraded). The results In 1997
_Table 3. Impact of Change in Exports on Skill Premium_

||(1)|(2)|(3)|
|---|---|---|---|
|Skill*∆Export Index|-.023***<br>|-.023***<br>||
||(.0067)<br>|(.0080)<br>||
|∆Export Index|.027**|.022**||
||(.012)|(.0089)||
|Skill*∆Export US Index|||-.045***<br>|
||||(.014)|
|∆Export US Index|||.032**<br>|
||||(.014)|
|Skill status|.29***|.21***|.22***|
||(.033)|(.033)|(.034)|
|∆Skilled/unskilled status|.080<br>|.023<br>|.023<br>|
||(.065)|(.068)|(.068)|
|∆Marital status|-.12|-.12*|-.12*|
||(.070)|(.064)|(.064)|
|Initial wage in log|-.33***|-.41***|-.41***|
||(.022)|(.029)|(.029)|
|Female||-.12***<br>|-.12***<br>|
|||(.026)|(.026)|
|Minority||-.016|-.018|
|||(.087)<br>|(.088)<br>|
|Education||.026***<br>|.026***<br>|
|||(.0037)|(.0036)|
|Potential Experience||-.00023|-.00024|
|||(.0013)|(.0013)|
|Urban||.040|.039|
|||(.030)<br>|(.030)<br>|
|Agriculture share 2002<sup>a</sup>||-.36|-.33|
|||(.22)|(.22)|
|Industry share 2002<sup>a</sup>||-.053|.082|
|||(.43)|(.43)|
|Construction share 2002<sup>a</sup>||.53<br>|.41<br>|
|||(.74)|(.72)|
|Government share 2002<sup>a</sup>||-1.32|-1.35|
|||(1.16)|(1.17)|
|Constant|4.44***|<br>5.63***|<br>5.58***|
||(.29)|(.44)|(.43)|
|Observations|1698|1698|1698|
|R<sup>2</sup>|.173|.228|.228|

_Note_ : The dependent variable is the log difference of monthly wage ( _∆Ln W_ in Equation (5)). The standard errors in parentheses are based on heteroskedasticity-consistent estimates of the variance-covariance matrix and corrected for within province correlation (clustering). *, **, *** indicate that the coefficients are significant at the 10, 5, and 1 percent level respectively.

The classification between skilled and unskilled workers is based on the status in 2002. a The share of workers employed in the service sector is the omitted category.

47
43 Table I leturns to Zducation by **lea** and Sehoel LOvel: 195-1385

|Country<br>(year otdata)|lstimation<br>Hetbod e|Pri<br>**male**|mary<br>YJoalc|Jeenad<br>hale|ergy|1gb<br> <br>Male|:r<br>_tooaL*_<br>**Female**|
|---|---|---|---|---|---|---|---|
|Uaited<br>States<br>(1949)|S|17.9|5.6|14.0|13.0|3.7|4.2|
|Colobia,<br>Desoto<br>(1965)|IS|10.2|*11|$4.4|**1.39**|**4.5**|5.3<br>|
|memany<br>(aa 1330)|b|***0o**||***.**||4.6|5.0|
|Crecee<br>(ea<br>1390)|b|*****|*|3*0|5.0|*||
|Renya<br>(ea1030)|I|21.7|7.1|23.0|13.5|*** ***|*****|
|Malaysia<br>**(Ca**1360)|b|*.4|3.3|12.3|11.4|10.7<br>'|.*|
|NewZealand<br>**(Ca**1960)|b|*****|*****|10.4|25.3|13.4|S3.5|
|IrasiL<br>(1950)|b|17.3|33.6||||.|
|Australia<br>(ca 1060)||-|.|..|.|21.1|21.2|
|France<br>(1976)|b|***1**|*****|14.|16.2|20.0|12.7|
|United Kingdom<br>(1971)|b|**..**|..|10.0|<br>0.0|3.0|12.0|
|**Japan**<br>(1330)|I|*****|..|||5.7|5.6|
|South Korea<br>(1971)|b|*****|*****|13.7|16.9|15.7|22.9|
|Taiwan<br>(1032)|b|0.4|16.0|*****||*****||
|Puerto<br>Rica<br>(1050)|b|29.5|18.4|27.3|40.4|21.9|0.0|
|Andra Pradesh,<br>India (1977)|it<br>I|O.9<br>7.2|1l.3<br>.3|3.7<br>6.3|11.9<br> <br>2.41|6.2<br>5.5|5.9<br>5.5|
|IvoryCoast<br>(1365)|SS|10.3|51|17.0|33.7|21.1|13.6|

ts8tieation Hothods: I - Include partitipatiom rate io la.r torgc te d*flate returns, depressing tcmal **returns** diaproportsioately; SS - estimate **wage** rate relationahip wtthout labor totsc ad*uatmnt. **lee** tezt **for d4o9selsois.** blctimation method not known.

N Not teportod.

sourcoe: Psaohbopous. 1973 (TSble 4.5), 1395 (table A-2). except<sup>torIndia</sup> (Tilak. 1907: Table 6.5). Average return; Ivory Coast (Van doe easg and Vijvorberg 1037: Appondix 2); and Colombia (Schultz. 1366:<sup>Tablo7)hourLy</sup> Wase.
# **2d. Quality**

The literature utilizes two ways of measuring the degree of quality of court judgments. First, by measuring the extent to which the judgment meets a certain number of features and predefined indicators (conformity with requirements); and second, by measuring the gap between the expectations that court users had before using the courts and the assessment made following their use (conformity with expectations). The usual indicator on the former is consistency and predictability of judgments, i.e., whether judgments follow precedents (Contini and Carnevali 2010). The most often-used empirical measure of the latter is the probability that the first-instance judgment gets overturned on appeal (for example, Calabrese 2013; Coviello et al., 2014). Increasingly, however, surveys of user experience are also the basis for judicial quality assessments (Mbassi et al 2019).

A study using data from Nepal assesses the determinants of the quantity–quality tradeoff. It finds that in Nepal judicial staffing exhibits a robustly positive effect on court output. Quality increases with the qualification of judges and can be seen in fewer reversals on appeal. The study does not find evidence implying that increasing court output would decrease adjudicatory quality (Grajzl and Shikha 2020). Similar results showing the importance of the quality of first instance rulings on reducing appeal rates were found in a study using Greek data (Mitsopoulos and Pelagidis 2007).

Research using European data finds that the most straightforward way to improve the judiciary involves the dedication of additional resources, and that these resources would be best devoted to increasing judicial pay (Cross and Donelson 2010). The pivotal role of judicial pay on quality also emerges from an analysis of the Mexican judiciary, which found that low judicial salaries left the best-trained and most capable young law graduates inclined to pursue careers in private practice. Consequently, lawyers with uncompetitive institutional pedigrees and undistinguished records of professional experience tended to pursue careers on the bench. This observation is corroborated, in part, by the findings of 1985 and 1993 judicial surveys that an average of 93.15% of Mexico’s federal judges and magistrates graduated from what are generally 21
Appendix C thus suggest that our regressions estimates in Table 3 in the main text are not biased by not accounting for the price of paper.<sup>17</sup> Finally, we present regression results using computer penetration as an additional covariate (Appendix D). The results on the computer penetration variable are surprising and do not suggest that computer use increase the consumption of paper as might be expected. However, we note that ITU discontinued the production of statistics on computer penetration because of the problems associated with measuring it adequately.<sup>18</sup>

# **9. Conclusion**

Using a panel of country-level data we have found that Internet adoption decreases aggregate paper consumption. The effects appear to be driven by the paper categories that are more likely to be displaced by the Internet: the Internet reduces the consumption of the paper category used for newspapers and the Printing and Writing paper category that is used in the production of magazines and books. As expected, the Household and Sanitary paper category is not affected by the increase in Internet use. Our results also show that the reduction in paper consumption caused by the Internet is large in size and can thus have important positive environmental implications.

We believe our results have important policy implications. A large amount of attention and resources are currently directed to regulations trying to curb the

> 17 We do not include the price of paper as a covariate in the regressions in Table 3 because prices and quantities are simultaneously determined.

> 18 We also tried using other empirical strategies such as random trends models in order to account for idiosyncratic trends. Unfortunately, our study period is not long enough to account for idiosyncratic trends – the standard errors increase substantially.

23
**Table 13: Ease of paying taxes: Time spent on tax compliance**

||Hours spent per<br>year by a model<br>firm on<br>compliance of<br>all business<br>taxes<br>(Ranking among<br>181 countries in<br>brackets)|Of which: Hours<br>spent on labor<br>taxes|
|---|---|---|
|Estonia|81(17)|34|
|Lithuania|166 (58)|76|
|Croatia|196 (68)|96|
|Romania|202(74)|110|
|Slovenia|260 (101)|96|
|Latvia|279 (117)|165|
|Slovakia|325 (129)|120|
|Hungary|330(132)|203|
|Poland|418 (151)|228|
|Bulgaria|616 (167)|288|
|Czech Republic|930(174)|420|
|Selected other countries|||
|Switzerland|63 (9)|40|
|New Zealand|70 (10)|30|
|United Kingdom|105 (24)|45|
|Australia|107 (25)|18|
|Sweden|122 (34)|36|
|France|132 (40)|80|
|Denmark|135 (41)|70|
|Austria|170 (59)|55|
|United States|187 (65)|55|
|Germany|196 (68)|123|

_Source: World Bank, Paying Taxes 2009 - The global picture._ 40
- 31 -

Table 3.5. Average Cost of Own Water Supply by Size of Own Water Production

|OwnWater<br>p0tion<br>(1000<br>|_Niger_<br>(per9|_ia_<br> llon)|OmnWater<br>Production<br>(1000|Indonis<br>(per cublc|<br>mater)|That ln<br>(per cubi.l|d<br>_eter)|
|---|---|---|---|---|---|---|---|
|Gallons)|Naira|USS|Cibec|Rupiah|uUSS|0aht|US|
|I - 99|2.7024 a/|0.676|1- 9|2961.85|1.481|<br>107.455|4.298|
|100- 499|0.3245|0.081|10- 49|452.34|0.226|67.359|2.694|
|500- 999|0.3588|0.090|<br>S0- 99|104.58|0.052|15.506_m_|0.620|
|1000 - 4999|0.1110|0.028|100- 499|111.24|0.056|12.554|0.502|
|5000 - 9999|0.0326|||||__A_|ME|
|Att<br>||-0.3900<br>||0.096S|<br>A<br>All21.04||0.861|59.931|2.397|
|NLub rof<br>Observ tices|||Numberof<br>Obseorvations|174<br>||||

NOTE: U') The exchuue rates were 4 Nalra/S In 1987; 2000 Ruplah/t and 25 Sdht/S In 1992.

- (2) The aurralzed values of borehole (or artesian wells) were calculated at an Interest rate of 10 percent per yer.

- : (3) From Appendix Table E9, F9, and _09._ The cell man **m_y** not be representative becoaue of the mttl r of observations In the cell.

Source: World Sank Nigeria Infrastructure Project Establishaent Survey 1988. World Sank Research Project (RPO 676-71) EstablIshmnt Survey for Indonsr and Thelawnd 1992.
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