rafmacalaba/datause-encoder-data huggingface dataset:
maximum number of sources is Indonesia, appearing in 17 of 22 sources. Suppose next that we rank sources according to country coverage. If we were to base the aggregate indicator on only the top five data sources by country coverage, and insist on a fully balanced set of countries, we would have only 117 countries appearing in all five indicators, instead of the 207 we do cover for 2006.<sup>14</sup> Moreover, the underlying data sources would not be very diverse, as they would consist exclusively of data provided by commercial business information providers. If we were to include the next five data sources ranked by country coverage, we would have a more diverse set of data providers, including a firm survey, a household survey, and two ratings from public sector data providers. But if we were again to insist on fully balanced coverage we would have only 37 countries appearing in all 10 indicators. We therefore think it much better to report data for as large a set of countries as possible, drawing on as diverse as possible a set of underlying data sources. The fact that not all countries appear in all data sources is a minor inconvenience compared with the very significant benefit of broad country coverage and diversity of underlying data sources for those countries where these sources are available. And in any case we report through our website virtually all of the individual underlying data sources, so that users interested in comparisons based only on common sources can readily do so.

# **3.2  Changes over Time in Governance Using the WGI**

We now turn to the changes over time in our estimates of governance in individual countries. Figure 2 illustrates these changes for two selected governance indicators over the period 1998-2006, Voice and Accountability and Rule of Law.<sup>15</sup> In both panels, we plot the 1998 score on the horizontal axis, and the 2006 score on the vertical axis. We also plot the 45-degree line, so that countries above this line correspond to improvements in governance, while countries below the line correspond to deteriorations in governance. The first feature of this graph is that most countries are clustered quite close to the 45-degree line, indicating that changes in our estimates of

> 14 The average standard error for the 117 countries appearing in five data sources is 0.16, while for the remaining 90 countries it is 0.27 as countries in this latter group necessarily appear in a smaller number of sources.  Nevertheless, even for this group, the aggregate indicators are still considerably more informative than individual indicators.  For Control of Corruption in 2006, the median (across sources) of the standard error of the individual indicators is 0.64, and only one data source has an estimated standard error smaller than 0.27.

15 Throughout this section we emphasize changes over the period 1998-2006 as our aggregate indicators for 1996 are based on significantly fewer data sources than 1998.

18
Table 3: Effects of forecast errors and forecast growth on worker compensation and worker composition

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
|Dep. variable:|log<br>monthly<br>wage|log hourly<br>wage|log<br>monthly<br>base wage|log (1 +<br>overtime<br>pay)|log (1+<br>other pay)|log total<br>hours|share with<br>a degree|person FE|
|Weighted forecast error|0.0090**|0.0085**|0.0060*|0.0328**|0.0210|0.0314***|0.0028|-0.0423**|
||(0.0041)|(0.0041)|(0.0035)|(0.0144)|(0.0186)|(0.0066)|(0.0019)|(0.0167)|
|Weighted forecast growth|0.0033|0.0030|-0.0001|0.0304***|0.0143|0.0108***|0.0002|-0.0131|
||(0.0023)|(0.0024)|(0.0019)|(0.0104)|(0.0130)|(0.0041)|(0.0013)|(0.0105)|
|Period x region FE|Y|Y|Y|Y|Y|Y|Y|Y|
|Period x industry FE|Y|Y|Y|Y|Y|Y|Y|Y|
|Period x export share|Y|Y|Y|Y|Y|Y|Y|Y|
|Firm FE<br>|Y|Y|Y|Y|Y|Y|Y|Y|
|N (obs.)|22199|22199|22199|22199|22199|22199|22199|12631|
|N (firms)<br>|9306|9306|9306|9306|9306|9306|9306|6012|
|Adj. R<sup>2</sup>|0.0210|0.0229|0.0556|0.0222|0.00951|0.0601|0.0231|0.0490|
|RSS|472|471|249|15012|16665|1711|105.3|1546|

Notes: In each column, the dependent variable is the change between the averages of each 3-year period. Standard errors are clustered at firmlevel. *10% level of significance, **5% level of significance, ***1% level of significance.
Policy ReseaRch WoRking PaPeR 4854

# **Abstract**

Competition in public procurement auctions in the water supply and sanitation sector is largely limited. This is partly because of high technical complexity and partly because of auction design flaws. The division of lot contracts is an important policy choice for auctioneers to achieve efficiency. In general, there is a tradeoff between competition in auctions and size of contracts. Larger works could benefit from economies of scale and scope, but large contracts might undermine competition. Using data on public procurement auctions for water and sewage projects in developing countries, this paper shows that bidder entry is crucially endogenous, especially because it is determined by the auctioneer’s bundling and unbundling strategy. If water treatment plant and distribution network works are bundled in a single lot package, competition would be significantly reduced, and this adverse entry effect would in turn raise the public procurement costs of infrastructure. There is no evidence of positive scope economies in the bidder cost structure. It is important to account for the underlying cost structure for designing efficient auction mechanisms.

This paper—a product of the Economics Team, Finance, Economics and Urban Development Department—is part of a larger effort in the department to understand and enhance efficiency and effectiveness in public procurement for infrastructure. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at aiimi@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
<!-- Start of picture text --> Child Sex Ratios (m/f) in<br>Provinces of China and States of India,<br>2000-01<br>Gt yy RYE BEE 1.20 or chove<br>tHyyYyyy “ [7] 110-114<br>% Y / 3 115-1,19<br>~~sca , Gi 3 [J] 1.06 or below<br>=F YY > 4 | a<br>om India “gs Pees be P : ; i i<br>aes i ; Sources: ip orga Censuses ofChina 200),<br>:Gi stall Thcil sxratios aefrcitenaged<br>WTiWe ¢ ‘ This map wosSoyeuseby theMap Lintof Tha Wari Bonk,<br>pileeanear eyed ieee<br>' soa le kr Aa ad i<br>Be<br>es<br><!-- End of picture text -->
# **<mark>Box 1.  Key Causes of Population Decline in Latvia</mark>**

<mark>Between 1990 and 2002 the population of Latvia declined by about 12 percent. This process of depopulation was caused by several factors. After a gradual opening of the border in the early years of transition, external migration became a major factor in the population decline. Between 1990 and 2002, average net migration from Latvia was 15 thousand persons (or about 0.7 percent of population) per year. However, as shown in Figure 4, net migration rates have leveled off since the mid 1990s.</mark> <mark>The rate of natural population increase has also been negative throughout the 1990s. It remains negative today. The rate of natural population decrease (more deaths than births) is estimated at about 12,000 persons or 0.5 percent per annum.</mark> <mark>The decline in population may have negative effects on the labor force in Latvia. One of the most important problems in this context is that people deciding to leave the country are most often of working age. Therefore, external migration could put additional pressure on the remaining population to carry social burdens. The natural decrease of the population may be felt most strongly in the long-term. In 10-20 years time the labor force will not receive sufficient numbers of new entrants to maintain itself. In the first three years of this century we have observed a significant decline in the number of primary school students. This trend will continue for at least ten years, because birth rate have not picked up yet. Despite the gradual increase in average income in Latvia, fertility rates have remained low.</mark>

**Figure 2. Cumulative Natural Increase and Net Migration in Latvia, 1991-2002**

<!-- Start of picture text --> 0<br>-2 0<br>-4 0<br>-6 0<br>-8 0<br>-1 0 0<br>-1 2 0<br>-1 4 0<br>-1 6 0<br>-1 8 0<br>-2 0 0<br>1 9 9 1 1 9 9 2 1 9 9 3 1 9 9 4 1 9 9 5 1 9 9 6 1 9 9 7 1 9 9 8 1 9 9 9 2 0 0 0 2 0 0 1 2 0 0 2<br>N a t u ra l In c re a s e N e t M ig ra t io n<br>thousands<br><!-- End of picture text --> _Source:_ Central Statistical Bureau of Latvia The possibility of a significant loss of workers due to emigration following EU accession should not be underestimated. Preliminary research (Hazans, 2003b; 2003c) shows that (i) the Latvian population seems to be relatively mobile in comparison with other nations; (ii) significant proportions of skilled nonmanual, clerical and service workers, and students would seriously consider the possibility of moving permanently or temporarily to one of the EU-15 countries if this were possible. The OECD suggests that such potential mobility could have positive effects on the Latvian labor market. According to OECD (2003a), EU accession “should encourage a trend of convergence with [the EU-15] in terms of wages and other labor market conditions”.

6
69

**Figure 17: Comparison of 92 RON gasoline retail price in Kazakhstan with FOB price**

<!-- Start of picture text --> 200 1.00<br>150 0.75<br>100 0.50<br>50 Tenge/liter US$/liter FOB US$/liter 0.25<br>0 0.00<br>Jan‐11 Jul‐11 Jan‐12 Jul‐12 Jan‐13 Jul‐13 Jan‐14 Jul‐14 Jan‐15 Jul‐15 Jan‐16<br>US$/liter<br>Tenge/liter<br><!-- End of picture text --> _Source:_ http://stat.gov.kz for Kazakhstan retail prices, industry sources for FOB Singapore. _Note_ : National average of surveyed prices for gasoline with 92 RON in Kazakhstan and 92 RON gasoline FOB prices in Singapore.

For the purpose of ensuring adequate supplies and maintaining stable and relatively low domestic prices, the government has introduced what began as a seasonal ban on exports of petroleum products during the sowing and harvesting seasons. In practice, the “temporary” ban, typically lasting six months, has been renewed repeatedly, resulting in a continuous ban on diesel and other fuel exports since May 2010. The most recent ban was for kerosene, diesel fuel, and other petroleum products outside the Eurasian Economic Union from July 30, 2015 for six month. Exports of gasoline have also been banned during much of the time. While banning exports, the government has also imposed temporary bans on imports of petroleum products from Russia. The most recent ban, in effect from March 5 to April 20, 2015, was prompted by the collapse of the ruble, making Russian imports cheaper and thereby threatening the competitiveness of the domestic refineries ( _Astana Times_ 2015). Earlier, the import bans had more to do with Russian imports being too expensive to enable cost recovery.

The government’s pricing and cross‐border trade policies have often led to regional fuel shortages. In the summer of 2014, for example, gasoline shortages persisted, in part because the ceilings on retail prices did not allow cost recovery, leading to large losses suffered by fuel suppliers. In August, the government raised the price of 92 RON gasoline by about 11 percent, but the gasoline shortages persisted well into September, while the First Deputy Minister of Energy in a press conference reiterated the government’s commitment to subsidizing petroleum products (Godwin Advisory 2014).

In September 2015, the National Bank of Kazakhstan moved to a floating exchange rate regime. Citing greater price volatility of Russian petroleum product imports as a result, the government deregulated the prices of 92 and 93 RON gasoline, while continuing to set maximum prices for 80 RON gasoline and diesel.

# **Malawi**

In June 2012, against the backdrop of supply shortfalls and growing price gaps, the Malawi Energy Regulatory Authority (MERA) resumed implementation of an automatic fuel pricing mechanism, last abandoned in 2004. The mechanism sets price ceilings and calls for price adjustment if landed costs in local currency vary by more than 5 percent. For changes smaller than 5 percent, a price stabilization
Table 4. Share of Occupied Adults per Household

||Ave<br>|rage of sa<br>|mple<br>|(under<br>|Poor<br>US$1.2<br>|5 a day)<br>|(unde<br>|Poor<br>r US$2.5<br>|a day)<br>|
|---|---|---|---|---|---|---|---|---|---|
||Initial<br>period|Final<br>period|Annual<br>change|Initial<br>period|Final<br>period|Annual<br>change|Initial<br>period|Final<br>period|Annual<br>change|
|Argentina,2000–10|48.8|55.7|1.3%|26.3|27.0|0.3%|32.8|34.7|0.6%|
|Brazil,2001–09|60.4|62.7|0.5%|42.8|34.8|-2.6%|50.9|46.7|-1.1%|
|Chile,2000–09|49.4|49.6|0.1%|14.9|6.9|-8.3%|27.4|15.7|-6.0%|
|Colombia,2002–10|55.1|60.5|1.2%|40.2|39.1|-0.3%|46.0|46.2|0.1%|
|Costa Rica,2000–08|53.7|56.9|0.7%|22.5|16.5|-3.8%|31.4|26.3|-2.2%|
|Ecuador,2003–10|61.5|59.4|-0.5%|49.4|50.7|0.4%|53.7|51.7|-0.5%|
|Honduras,1999–2009|63.2|59.6|-0.6%|55.1|41.3|-2.8%|57.7|50.3|-1.4%|
|Panama,2001–09|51.7|59.7|1.8%|45.7|50.3|1.2%|44.6|51.8|1.9%|
|Paraguay,1999–10|61.7|64.1|0.3%|51.2|49.1|-0.4%|53.6|53.8|0.0%|
|Bangladesh,2000–10|<br>46.9|<br>48.2|0.3%|49.7|50.5|0.2%|47.8|49.1|0.3%|
|Ghana,1998–2005|41.4|39.2|-0.8%|38.1|32.0|-2.5%|39.3|35.6|-1.4%|
|Moldova,2001–10|65.3|66.1|0.1%|64.7|76.0|1.8%|64.1|71.0|1.1%|
|Nepal,1996–2003|32.7|33.8|0.5%|32.7|34.0|0.6%|32.7|34.2|0.6%|
|Peru,2004–10|69.4|72.5|0.7%|82.5|78.3|-0.9%|79.1|80.1|0.2%|
|Romania,2001–09|83.2|82.1|-0.2%|79.5|||83.6|74.1|-1.5%|
|Thailand, 2000–09|74.7|74.4|-0.1%|60.6|72.0|1.9%|78.2|71.3|-1.0%|

<mark>Source: Author’s calculations with data from SEDLAC (CEDLAS and the World Bank), RIGA, and National Household Surveys.</mark> 28
expenditure survey from 2010 with the unit record population census data from 2008 using the ELL approach. This poverty map serves as our benchmark. We then ignore the population census data and obtain a second poverty map for Malawi by combining the household expenditure survey data with predictors of poverty derived from publicly available remote sensing data.

An important motivation for using data from Malawi is that we have access to the exact coordinates of the survey PSUs. This enables us to produce the remote-sensingbased poverty map both using the publicly available geographic coordinates (that have been subjected to random offset to protect the anonymity of households) and exact coordinates (that are not publicly available) to investigate whether the random offsets – might undermine the approach and inspect the correspondence.

Our findings are two-fold. First, assessments of the correspondence between the remote-sensing-based poverty estimates and the census-based poverty estimates depend on how the correspondence is evaluated. The two approaches reveal the same patterns in the geography of poverty in Malawi; it is hard to tell the difference in a side-by-side comparison of the two poverty maps. The statistical correlation between the two different small area estimates of poverty is above 0.9, again confirming their close correspondence. However, there are districts for which the two approaches obtain markedly different estimates of poverty. Accordingly, poverty maps obtained using remote sensing data can be expected to do well when the decision maker is interested in estimates or comparisons of poverty between assemblies of areas. Yet, the decision maker may not be served as well by remote-sensing-based estimates when the focus is on estimates for specific small areas. Second, the random offsets embedded in the geographic coordinates of the public use data do not meaningfully alter the estimated geography of poverty. But they may, however, lead to an under-estimation of the standard errors of the small area poverty estimates (i.e. an over-estimation of precision). The reason for this is that the random offsets partially destroy the spatial correlation structure in the data and error term.

The decision to restrict attention to publicly available remote sensing data is a deliberate one. This ensures that the data are available at no cost in any given country, and so the approach can easily be replicated and readily repeated as new survey data become available. By the same token, our exercise arguably provides a conservative assessment, i.e. a lower bound of success as higher resolution remote sensing data that come at a cost will allow for more precise estimates. It should be noted that with the rapid technological advancements in this field, what is costly today will in all likelihood become low-cost or publicly-available in the near future.

The paper is organized as follows. An overview of the two small area estimation 5
13 last step with the usual assumption that the aggregate demand and supply curves have the same slope (that is, _a_ = _b_ = 0.5). The resulting measure can be regarded as the import tax/export subsidy which, if applied uniformly, would give the same loss of welfare as the combinations of measures distorting consumer and producer prices in the import-competing and exportable sub-sectors.

The TRI can be similarly decomposed as follows: where  , _a_ and _b_ are as already defined, _RM_ and _S M_ are _R_ and _S_ from equation (4b and c), and The aggregates in equation (16) are the weighted average levels of distortions to consumer and producer prices in the exportables sub-sector, respectively, with weights<sup>_u_</sup> _i_ and<sup>_v_</sup> _i_ given in equation (4b and c). Importantly, distortions to the exportables sub-sector enter equation (16) as negative values. This is because whilst a lowering of _ri_ (the distortion of the consumer price of good _i)_ or _si_ (the distortion of the producer price of good _i)_ in the import-competing sub-sector reduces the reduction index, a lowering of _ri_ or _si_ in the exportables sub-sector increases it.

These extensions of the TRI and the WRI have precisely the same properties as the indices for the import-competing sector.

# **The World Bank’s Agricultural Distortions database**

The database generated by the World Bank’s Agricultural Distortions project (Anderson and Valenzuela 2008), using a methodology summarized in Anderson et al. (2008), provides a
Policy Research Working Paper 5709

# **Abstract**

Global matrices of bilateral migrant stocks spanning 1960–2000 are presented, disaggregated by gender and based primarily on the foreign-born definition of migrants. More than one thousand census and population register records are combined to construct decennial matrices corresponding to the five census rounds between 1960 and 2000. For the first time, a comprehensive picture of bilateral global migration over the second half of the 20th century emerges. The data reveal that the global migrant stock increased from 92 million in 1960 to 165 million in 2000. Quantitatively, migration between developing countries dominates, constituting half of all international migration in 2000. When the partition of India and the dissolution of the Soviet Union are accounted for, migration between developing countries is remarkably stable over the period. Migration from developing to developed countries is the fastest growing component of international migration in both absolute and relative terms. The United States has remained the most important migrant destination in the world, home to one fifth of the world’s migrants and the top destination for migrants from some 60 sending countries. Migration to Western Europe has come largely from elsewhere in Europe. The oil-rich Persian Gulf countries emerge as important destinations for migrants from the Middle East and North Africa and South and Southeast Asia. Finally, although the global migrant stock is predominantly male, the proportion of female migrants increased noticeably between 1960 and 2000. The number of women rose in every region except South Asia.

This paper is a product of the Trade and Integration Team, Development Research Group. 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://econ.worldbank.org. The author may be contacted at cozden@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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