rafmacalaba/datause-encoder-data huggingface dataset:
# **Table S8. Relationship between the SDG Index Overall Score from the 2023 Sustainable Development Report and SPI scores, 2016-2022**

||**Model 1**|**Model 2**|**Model 3**|**Model 4**|**Model 5**|**Model 6**|
|---|---|---|---|---|---|---|
|Overall SPI Score|0.540***|0.024*|0.027**||||
||(0.03)|(0.01)|(0.01)||||
|SPI Pillar 1 Score (Data use)||||0.062|0.002|0.002|
|||||(0.04)|(0.00)|(0.00)|
|SPI Pillar 2 Score (Data services)||||0.015|-0.001|0.002|
|||||(0.02)|(0.00)|(0.00)|
|SPI Pillar 3 Score (Data products)||||-0.060**|0.009|0.007|
|||||(0.03)|(0.01)|(0.01)|
|SPI Pillar 4 Score (Data sources)||||0.275***|0.004|0.000|
|||||(0.03)|(0.01)|(0.01)|
|SPI Pillar 5 Score (Data infrastructure)||||<br>0.133***|<br>0.013***|<br>0.014***|
|||||(0.02)|(0.00)|(0.00)|
|Log GDP per capita (constant 2015 US$)|||3.071***|||<br>3.126***|
||||(0.86)|||(0.86)|
|Trade (% of GDP)|||-0.008*|||-0.007|
||||(0.00)|||(0.00)|
|Agriculture, forestry, fishing value added (% of GDP)|||<br>-0.004|||<br>-0.011|
||||(0.04)|||(0.04)|
|Manufacturing value added (% of GDP)|||-0.037|||-0.033|
||||(0.03)|||(0.02)|
|School Enrollment, Primary (% gross)|||<br>0.025**|||<br>0.024**|
||||(0.01)|||(0.01)|
|Year 2017||0.601***|<br>0.546***||0.624***|<br>0.547***|
|||(0.05)|(0.05)||(0.07)|(0.06)|
|Year 2018||<br>0.876***|<br>0.778***||<br>0.955***|<br>0.812***|
|||(0.08)|(0.08)||(0.10)|(0.09)|
|Year 2019||<br>1.267***|<br>1.113***||<br>1.321***|<br>1.120***|
|||(0.09)|(0.09)||(0.10)|(0.10)|
|Year 2020||<br>1.432***|<br>1.393***||<br>1.456***|<br>1.410***|
|||(0.11)|(0.09)||(0.15)|(0.13)|
|Year 2021||<br>1.685***|<br>1.570***||<br>1.608***|<br>1.509***|
|||(0.14)|(0.12)||(0.24)|(0.21)|
|Year 2022||<br>1.797***|<br>1.665***||<br>1.718***|<br>1.587***|
|||(0.15)|(0.15)||(0.25)|(0.23)|
|Constant|31.029***|||42.661***|||
||(2.60)|||(4.03)|||
|Sigmau||5.67|4.77||5.00|4.65|
|_<br>Sigmae||0.73|0.69||0.71|0.66|
|_<br>R2|0.646|0.997|0.997|0.745|0.997|0.997|
|Country FE|No|Yes|Yes|No|Yes|Yes|
|No of countries|146|146|146|146|146|146|
|No of observations<br>**Note**: * p<0.10, ** p<0.05, *** p<0.01. Standard error<br>Development Indicators (WDI) and SPI. In cases where d<br>the nearest available value.|1001<br>s are clustered<br>ata are missin|1001<br>at the count<br>g for a partic|1001<br>ry level. Dat<br>ular covariat|1001<br>a are from t<br>e, the data are|1001<br>he World B<br>imputed fo|1001<br>ank's World<br>rward using|

57
state B interviewee, that side payments are apparently the norm to enable processing of transfers, is telling: _“When one wanted a transfer to be posted close to the place of spouses’ work place . . . one had to go through the same institutionalized informal system. Using spouse criterion_<sup>_15_</sup> _was only a means to gain the attention of higher ups. Once the application is accepted by the department on ‘spousal’ grounds, doctors would still spend some money to get it approved.”_ State A follows a formal transfer policy that defines the eligibility criteria, minimum and maximum tenure, and also involves “counseling” in assigning posts (after transfer approval). A minority of respondents (37 percent) considered the “transfer policy” clear-cut. Parallel systems appear to prevail as much as in state B where formal tenure rules are nearly absent According to the perception survey, twothirds of State A respondents agree that financial arrangements can be used to secure a desired place of posting through temporary transfers known as deputations. Reflecting on application of transfer policies in state A, one physician opined: _“Lack of strict adherence to the policy has resulted in people finding loopholes in the policy and tweaking it to suit their convenience.”_ If tenure rules and other criteria, where they exist, are not key criteria for determining one’s duration in a specific post, what does? The qualitative research suggests three factors: political and community connections, willingness and ability to pay, and demand for specific posts.

Prolonged tenure facilitates the cultivation of deeper political and community relations. By virtue of a long stay and developing a support network among local leaders and politicians, MOs can enjoy long tenures in desirable posts. Local community leaders and politicians may try to retain doctors in their areas which they view as high performers. However, MOs may be transferred at any time if they fall out of favor with local politicians and administrators.

12
**Plc,Research, and External Affairs**

<!-- Start of picture text --> Internallonal Trade<br><!-- End of picture text --> WPS 760 This paper- a product of the International Trade Di sision, International Economics Department- is part of a larger effort in PRE to improve the quality and consistency of statistics needed to analyze developing countries' trade performance, industrialization, andgrowth. Copies are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Jean Jacobson, room S7-037,extension 33710 (21 pages).

Economists often stress the importance of increasing the production and exports of manufactures in developing countries, but national and international agencies are inconsistent about how they define "manufactures." The definition of "manufactures" used in compiling production data for industrial and developing countries is far broader than the definition used for trade statistics. This limits the analytical utility of output and trade data for studies using, say, apparent consumption or impon penetration ratios.

International agencies also use different definitions of manufactures when compiling trade statistics. For some countries, these definitional changes produce major differences in the value and share of manufactures exports and imports.

Yeats assesses the analytical implications of six different definitions by comparing results when each is used to tabulate exporLs of the "manufactures" of 72 developing countries.

He shows that the different cefinitions produce a discrepancy of $60 billion in estimates of developing country exports.

Sensitivity tests show that five or six products - especially refined petroleum (SITC 332) - and some processed food products are responsible for the main discrepancies. These items are includ. 'in the UNIDO (trade) and most agencies' output definitions but are excluded from the trade definition used by UNCTAD, GATT, and the World Bank.

Clearly, international organizations must resolve these inconsistencies in definitions of output and trade statistics.

The PRE Working Paper Series disseminates the findings of work under way in the Bank's Policy, Research, and Extemal AffairsComplex. An objective of the series is to get these findings out quickly, even if presentations are less than fully polished. The findings, interpretations, and conclusions in these papers do not necessarily represent official Bank policy.

Produced by the PRE Dissemination Center
- 20 -

they should be used more widely so that their possible risks might be evaluated by proper case-control and long-term follow-up studies. The two positions are deadlocked, and this has considerably intensified the debate about injectables. Scientists have questioned the suitability of animal trials administering 50 times the human dose for 10 years (the regimen to which monkeys which developed endometrial tumours were subjected) for assessing possible risk to humans.

There is also concern about their mode of administration. The objection is that, since an injection remains contraceptively active for three months, it could be misused by being given to women without their fully informed choice or consent. !t is argued, however, that the drug should not be blamed for its misuse; that counselling about possible sideeffects must be comprehensive, and that these objections are not scientifically valid. Nevertheless, it is interesting that these objections, concerned with mode of administration, are the 'mirror image' of a claimed major advantage of injectables, that one injection allows a women to make a personal decision which confers contraceptive protection for three months.

# Abortion

Abortion is an emotional subject and has been receiving a great deal of attention. Reverberations from the 'right-to-life' versus 'pro-choice' debate in the United States and the United Kingdom are spreading around the world. While most of the world's people live in countries where abortion is legal and easy to obtain (in populous countries like China or India, for example), many developing countries, particularly in Africa and Latin America, retain restrictive abortion laws. The ethical arguments about abortion are complex, although often presented simplistically. Often debated is a woman's 'right' to control over her body and to refuse to carry to term a pregnancy she does not want. However, not often considered is the ethics of withholding the benefits of a technology which is less hazardous than carrying a pregnancy to term. Nineteenth-century anti-abortion laws were generally designed to save women from the dangerous, and often experimental, surgical procedures of the time. However, this reason is no longer valid, and today the effect of applying anti-abortion laws is to increase rather than reduce risk to women's lives and health.

Abortions are sought and obtained, of course, whether they are legal or illegal. Wealthy women, who can afford private doctors or travel to countries where abortion is legal and safe, can get abortions almost free of risk; but poor women often pay for abortions with their health, their future fertility and possibly their lives. Such discrimination against the poor cannot be accepted as moral or ethical under any circumstances,
**Figure 19: Estimated reduction in volatility if determinants were brought at OECD levels**

<!-- Start of picture text --> Middle Income Non-OECD<br><!-- End of picture text --> <!-- Start of picture text --> 20.7%<br>14.6%<br>11.1%<br>7.7%<br>5.8%<br>0.7%<br>Institutional Volatility of Volatility of Trade Volatility of Volatility of<br>Quality Inflation Gov. Openness TOT Foreign<br>Spending Growth<br>Low Income<br>27.2%<br>20.8%<br>11.1%<br>6.2%<br>3.1%<br>Institutional Volatility of Volatility of Gov. Trade Openness Volatility of TOT<br>Quality Inflation Spending<br><!-- End of picture text --> Middle income Non-OECD: average of China, Indonesia, Thailand, Malaysia and the Philippines vs. average OECD levels in 2000-05. Low income non-OECD: average of Lao PDR, Myanmar, Papua New Guinea, Solomon Islands, and Vietnam. The bars represent the estimates’ standard deviation. Source: Author’s own calculations using data from WDI, IMF and Polity IV.

Determinants that reduce overall volatility are also found to reduce the likelihood of major crises. Our estimations show that improved monetary and fiscal policies, for instance, reduce both “regular” and crisis volatility (Table A.1), hence further improvements would likely help preventing the likelihood of transmission of major crises events. In addition, it also appears that for crisis volatility improvements in the regulation and supervision of the Banking sector would reduce the likelihood of a major crisis (Figure 20), something not observed for “regular” volatility. Given the nature of the current crisis, and strains in the Banking sector that some countries such as Cambodia are currently facing, it is vital to rapidly improve prudential supervision and create fast response plans for handling banking 25
||Tabl<br>Share<br>industr|e 3a(co<br>of employ<br>ywho are|ntinued)<br>ees in<br>women|Share of<br>female-o|female emp<br>wned establ|loyees in<br>ishments|Share of<br>male-ow|male emp<br>ned establ|loyees in<br>ishments|
|---|---|---|---|---|---|---|---|---|---|
||2001|2006|2010|2001|2006|2010|2001|2006|2010|
|**15** Food products and beverages|0.254|0.273|0.227|0.736|0.790|0.808|0.791|0.789|0.840|
|**16** Tobacco products|0.686|0.759|0.809|0.957|0.961|0.981|0.596|0.575|0.661|
|**17** Textiles|0.413|0.432|0.438|0.895|0.929|0.942|0.710|0.746|0.798|
|**18** Wearing apparel; dressing and dyeing of fur|0.277|0.382|0.434|0.928|0.961|0.964|0.910|0.922|0.910|
|**19** Leather tanning; luggage, handbags, footwear|0.098|0.090|0.133|0.641|0.590|0.928|0.927|0.939|0.920|
|**20** Wood and wood products; straw and plating articles|0.311|0.348|0.226|0.794|0.921|0.867|0.765|0.790|0.857|
|**21** Paper and paper products|0.372|0.653|0.311|0.811|0.956|0.865|0.788|0.845|0.842|
|**22** Publishing, printing and media reproduction|0.083|0.124|0.114|0.349|0.571|0.414|0.937|0.917|0.919|
|**23** Coke, refined petroleum and nuclear fuel|0.097|0.093|0.254|0.471|0.383|0.695|0.911|0.934|0.778|
|**24** Chemicals and chemical products|0.558|0.729|0.622|0.931|0.981|0.973|0.780|0.623|0.669|
|**25** Rubber and plastic products|0.212|0.247|0.192|0.735|0.608|0.943|0.878|0.813|0.922|
|**26** Other non-metallic mineral products|0.332|0.268|0.278|0.598|0.618|0.506|0.678|0.745|0.736|
|**27** Basic metals|0.033|0.057|0.137|0.170|0.215|0.850|0.971|0.955|0.952|
|**28** Fabricated metal products, except machinery|0.070|0.046|0.033|0.274|0.252|0.507|0.936|0.962|0.975|
|**29** Machinery and equipment, n.e.c.|0.047|0.033|0.033|0.172|0.254|0.198|0.958|0.973|0.969|
|**30** Office, accounting and computing machinery|0.126|0.206|0.102|.|.|.|0.874|0.794|0.898|
|**31** Electrical machinery and apparatus, n.e.c.|0.157|0.098|0.092|0.528|0.532|0.671|0.860|0.928|0.921|
|**32** Radio, television, and comm. equipment|0.116|0.053|0.016|0.633|0.038|0.414|0.917|0.942|0.988|
|**33** Medical, precision and optical instruments, watches|0.073|0.068|0.062|0.597|0.291|0.194|0.946|0.945|0.946|
|**34** Motor vehicles, trailers and semi-trailers|0.028|0.029|0.093|0.116|0.553|0.568|0.974|0.982|0.932|
|**35** Other transport equipment|0.021|0.027|0.039|0.311|0.030|0.548|0.982|0.976|0.988|
|**36** Furniture, manufacturing n.e.c.|0.179|0.128|0.219|0.890|0.876|0.933|0.913|0.946|0.899|
|Nationwide|0.321|0.363|0.337|0.876|0.916|0.934|0.795|0.817|0.859|

Notes: See Table 1.
# Table IV.2

## Finncial Characteristice of Private Family Planntig Delivery Experiteate

||||S|ubsidization||
|---|---|---|---|---|---|
|Title<br>of Project,<br>Country|COntraceptive<br>Donated|Company Subsidy|Donor Subsidy<br>for Services|Client<br>PaysFree|Policy and astimates<br>of<br>Costl/Rvenaues<br>or Budget|
|India:<br>Fam ly Planin<br>Programm,<br>TVS<br>LUC"|Uso|Tes|no|me|No Informtto,|
|Tata Cbemlcals,<br>mithpur|No|Tr|so|so|nomfor_ti"o|
|Tata Idumtries<br>Ltd. family<br>Plasning<br>ProSrm|we|Yee|no|**TS**(Cr4iiamally<br>highe<br>I<br>employs"<br>paid<br>fee)|is lmfeatiom|
|Oodrej & Boye.<br> <br>Hanufacturieg<br>Co. Ltd.,<br>Sobay|*.|**Ys**|MD|No|Ubs itfo.tia|
|F*mily Planning<br>Is Te Rotates|s".by "A of<br>Iadia|**Tes**|Us|Us|soinformatio|
|misiuca<br>Spinning<br>**and**<br>Weaving Hills|Uo<br>s|es, full<br>subsidy<br>for all<br>but oral<br>contraceptlive|No|as. I.SO/cycle<br>orals|Rs. 2,375 per motb<br>for physlcian<br>fees|
|NISR Spinning<br>*<br>Weaving Company<br>Hospital Family<br>Planning Research<br>Centre|No Information|Yea|No information|No Information|soitformation|
|India:<br>Delhi Cloth I<br>General Mills<br>Co., Ltd.|Yes, by FPA|Yes|No|No|No Informatlon|
|Gajarat Refinery<br>(Indian<br>Oil Corp..<br>Baroda|No|Yes|No|No|No lnformation|
# **I. The Developing Country Urban Productivity Puzzle**

There is overwhelming evidence of the economic benefits of agglomeration, arising from ‘sharing’ of factors of production, improved ‘matching’ of firms and workers, and externalities related to knowledge spillovers (see, for example, Duranton and Puga, 2004). The canonical measure of such agglomeration effects is the elasticity of wages with respect to density and the estimates are large: 0.043 in the United States (Chauvin et al., 2017); 0.03 in France (Combes et al., 2008); and 0.025 in Spain (de la Roca and Puga, 2012).

Recent findings suggest that these effects are even larger in developing countries: 0.1 to 0.19 in China (Combes et al., 2013; Chauvin et al., 2017); 0.12 in India (Chauvin et al., 2017); and 0.17 in Africa (Henderson et al., 2019)- that is, a given increase in density increases wages by four times more in Africa than in the US. This poses something of a puzzle given that other sources of information suggest that developing country cities are not so much extraordinarily productive, as just crowded and even dysfunctional. Satellite and geographic information system (GIS) data covering large cities across the Sub-Saharan Africa region suggest that they are sprawling and disconnected – a far cry from the dense packing of educated workers sharing ideas in advanced country cities such as Chicago historically (McMillen 2003), or today’s Silicon Valley. As an example, land markets often function poorly with overlapping property rights regimes, suboptimal and ineffective zoning regulations, and limited supporting infrastructure, including transport—all of which hinder efficient concentration and raise costs (Lall, Lebrand, and Soppelsa 2021). Further, if Glaeser and Resseger (2010) found that areas in the US with below average human capital showed no agglomeration benefits because of limited benefits of pooling differentiated labor and knowledge spillovers, we might expect even fewer benefits in skillpoor developing countries.

This paper contributes to a resolution of this puzzle. It first conceptually disaggregates the canonical wage elasticity into component parts and shows that increasing productivity is only one possible driver – rising urban costs are as well – but also that the data employed to date is not capable of distinguishing between them. We then study four countries ranging across regions and the development spectrum – in order of income, Ethiopia, Indonesia, Colombia and Chile – four of the very few countries whose nationally representative establishment level databases permit separating plant prices from physical output, linkable to a standard set of geographical covariates, which have substantial subnational variation, and which are available for use. This allows estimating physical productivity or efficiency (TFPQ), which should capture the effects of better matching and externalities, and marginal costs, thereby showing how they contribute to the counterintuitive wage elasticity estimates.

2
expenditure), the head having attended a private school and living in an urban area. Furthermore, the higher the level of education of the head of family, the higher the probability that the child is enrolled in a private school.

The results from the two samples (all children in school and children receiving an education subsidy) are very similar to one another. This is evident from inspecting the coefficient estimates from the OLS and probit (where coefficients measures the marginal effects).

Table 13: Determinants of school choice, all children benefiting from subsidies: Dependent <u>variable: probability of attending private school</u>

|Variable**|OLS<br>Coefficient ( t-value)|Probit<br>Marginal Prob*(z-value)|
|---|---|---|
|Total Family Expenditure|2.68e-08 (3.43)|1.89e-08 (2.90)|
|Christian (omitted: other)|0.016 (0.76)|0.019 (0.86)|
|Urban area (omitted: rural)|0.081 (3.43)|0.094 (3.92)|
|Head attended private school)|0.202 (4.74)|0.170 (3.81)|
|(omitted: head attended public)|||
|Education of head: Tertiary|0.123 (2.55)|0.097 (2.08)|
|(omitted: less than tertiary)|||
|Constant|0.015 (0.69)|-|
|N<br>|973|973|
|R-sq adjusted (or Pseudo R<sup>2</sup>)|0.088|0.102|
|Observed/Predicted prob.||observed: 0.137<br>predicted: 0.115|

Source: Own calculations using the 1998 Enquête Niveau de Vie des Ménages

* dF/dX is for discrete change of dummy variable from 0 to 1; **  The sample of children receiving subsidies was too small to support the creation of an education cost proxy

One question that can be raised in relation to the above estimated models has to do with sample selection bias; that is the possibility that there may be variables which could influence the decision to attend private school, but which may also affect education outcomes (such as distance to school and religion). In detecting sample selection bias and correcting for it, a 24
<!-- Start of picture text --> 10.2<br>10<br>ya<br>ia 98 _<br>4sH - --""7<br>& oe<br>2 96 5<br>4aanto} —_—_i<br>94<br>9.2<br>9<br>2017,Rural 2018, Rural ~—-2019,Rural 2017,Urban 2018, Urban 2019,Urban<br>— Reweighted CPHS ——PLFS --- Reported CPHS<br><!-- End of picture text --> <!-- Start of picture text --> 6<br>weer<br>i<br>58 a<br>»ap=a 56 < =<br>c o<br>54ey a“<br>5.2<br>5<br>2017,Rural 2018, Rural ~—«-2019,Rural 2017,Urban 2018, Urban -2019,Urban<br>—Reweighted CPHS ——PLFS --- Reported CPHS<br><!-- End of picture text -->
v 19 exogenous fbrecasts for LIBOR, while the exchange rate<sup>is adjusted to reflect the differential</sup> between the future inflation rates which are assumed<sup>for Kenya and the rest of the world.</sup> Accordingly, the exchange rate (K£1US$) moves from 1.202<sup>in 1991 to 1.581 in 1993 and 1.856</sup> in 1996. The real domestic loan interest rate is 11.2 percent in 1991,<sup>10.5 percent in 1993 and</sup> 10.3 percent in 1996. The corresponding domestic inflation<sup>target is 9.5 percent, 6.0 percent and</sup> S.0 percent. The complete set of input and output tables<sup>of the model fori e base projections,</sup> including the flow of funds consistency check, is at<sup>Annex IV.</sup> 4.3 In this base scenario, the current account deficit falls from<sup>8.4 percent of GDP</sup> (excluding grants) in 1991 to 8.3 percent of GDP (excluding<sup>grants) in 1996. Exports (goods</sup> and nonfactor services) and imports (goods and nonfactor<sup>services) are 21.1 percent of GDP and</sup> 28.9 percent of GDP respectively in 1991 compared with<sup>23.7 percent of GDP and 31.4 percent</sup> of GDP in 1996. 6/ In dollar terms, the external financing<sup>gap moves from US$19.8 million</sup> in 1991 to US$159.5 million by the end of the period (Table<sup>I). Meanwhile, government revenue</sup> and expenditure reach 26.2 percent of GDP (excluding<sup>grants) and 31.4 percent of GDP</sup> respectively in 1996, from 19.8 percent (excluding grants)<sup>and 32.2 percent of GDP in 1991.</sup> The residual public sector borrowing requirement is 27.8<sup>million Kenya pounds in 1991, 97.8</sup> million Kenya pounds in 1993 and 450.7 million Kenya<sup>pounds in 1996.</sup> f lThroughout, exports and imports refer to goods and nonflactor services.