rafmacalaba/datause-encoder-data huggingface dataset:
female-male wage data to get a good measure of the overall gender wage gap structure _within_ a country. The distribution of occupations by country shows that there are femalemale wage data for 66 occupations in at least 20 countries, and for 18 occupations in at least 30 countries. This means that we can contrast the gender wage gap in these occupations around the world.

Panel C shows the various ways in which countries report female-male wages. Many countries report wage rates from employer surveys or collective agreements or legislated pay schedules. Others report earnings, some from household surveys but mostly from employer surveys. In total about half the observations are reported in wage rates, and the other half in earnings. Most give statistics in the form of means<sup>8</sup> , but some report minimum wage rates, maximum wage rates, prevailing wages, or medium wages. The period to which the pay refers also varies. The most common period is the month, followed by the week and hour, but some countries report daily, annual or forthnightly rates for some occupations.

How can we make valid comparisons of the gender wage gap across countries, occupations, and years if the data are available in such a heterogeneous format? In Freeman and Oostendorp (2000, 2002) a standardization procedure was developed to put the Inquiry data into a form that researchers can readily use, transforming each observation, however reported, into a standard rate based on the most common form of data in the Inquiry - monthly average wages for male workers.<sup>9</sup> Here we use a similar procedure to standardize the gender wage _differentials_ across countries, occupations, and years. We choose to standardize all gender wage differentials into average hourly earnings differentials, because earnings are a better measure of total compensation and only slightly less frequently reported than wages in our data set. Instead of standardizing the gender wage differentials, it would also be possible to standardize the wages for female and male workers first, and then use the standardized female and male wages to analyze the standardized gender wage differentials (or more accurately “the gender

> 8 In a few cases the wages are in the form of ranges.  We found the midpoint of the range and report it as the wage for the category.

> 9 The standardization procedure involves two steps. In the first step dimensional analysis is used to convert annual, weekly, and forthnightly wages into monthly wages while hours of work data (if available) are used to convert hourly and daily wages into monthly wages. In the second step a regression analysis is applied to correct for the remaining differences in pay, averaging, and period concepts.

9
**Table 34. Mean comparison with raw and Rasch Scores using 60 wpm cutpoint (U-Mann Whitney test)**

|||**Raw scores**|||**Rasch Scor**|**es**|
|---|---|---|---|---|---|---|
||**Obs**|**Rank Sum**<br>**(mean)**|**Expected**<br>**Rank Sum**|**Obs**|**Rank Sum**<br>**(mean)**|**Expected**<br>**Rank Sum**|
|Under 60 wpm|273|53,775 (198)|64,974|273|53,938.5 (197)|64,974|
|Over 60 wpm|202|59,275 (293)|48,076|202|59,111.5 (293)|48,076|
|Combined|475|11,3050 (238)|113,050|475|113,050|113,050|
|Unadjusted variance|||2187458|||2187458|
|Adjustment for ties|||-23551|||-11.88|
|Adjusted variance|||2163907|||2187446|
|z|||-7.502|||-7.572|
|Prob>|z||||0.0000|||0.0000|

**Table 35. Mean comparison with raw and Rasch Scores using 60 wpm cutpoint (U-Mann Whitney test)**

|||**Raw scores**<br>|<br>||**Rasch Scor**<br>|**es**<br>|
|---|---|---|---|---|---|---|
||**Obs**|**Rank Sum**<br>**(mean)**|**Expected**<br>**Rank Sum**|**Obs**|**Rank Sum**<br>**(mean)**|**Expected**<br>**Rank Sum**|
|Under 60 wpm|361|76,284 (211)|85,918|361|36,766 (211)|85,918|
|Over 60 wpm|114|36,766 (323)|27,132|114|76,314 (322)|27,132|
|Combined|475|11,3050 (238)|113,050|475|11,3050 (238)|113,050|
|Unadjusted variance|||1632442.00|||1632442.00|
|Adjustment for ties|||17575.49|||-8.87|
|Adjusted variance|||1614866.51|||1632433.13|
|z|||-7.581|||-7.517|
|Prob>|z||||0.0000|||0.0000|

83
# Policy Research Working Paper Series

|Title|Author|Date|Contact<br>for paper|
|---|---|---|---|
|WPS1 019 How Effective are Directed Credk<br>Policies in the United States?<br>A Literature Survey|Anita M. Schwarz|November<br>1992|M. Raggambi<br>37664|
|WPS1020 Another Look at Population<br>and<br>and Global Warming|Nancy Birdsall|November<br>1992|S. Rothschild<br>37460|
|WPS1021 Measuring<br>Welfare Changes<br>from<br>Commodity<br>Price Stabilizatio**n**<br>i<br>Small Open Economies|Jonathan R. Coleman<br>Chris Jones|November<br>1992|D. Gustafson<br>33714|
|WPS1022 A New Approach<br>to Evaluating<br>Trade<br>Policy|James E. Anderson<br>J. Peter Neary|November<br>1992|M. T. Sanchez<br>33731|
|WPS1023 Tariff Index Theory|James E. Anderson|November<br>1992|M. T. Sanchez<br>33731|
|WPS1024 An Exact Approach for Evaluating<br>the Benefits from Technological<br>Change|Will Martin<br>Julian M. Alston|November<br>1992|D. Gustafson<br>33714|
|WPS1025 Openness<br>and Economic<br>Performance<br>inSub-Saharan<br>Africa:<br>Evidence<br>from Time-Series<br>Cross-<br>Country Analysis|Kazi M. Matin<br>|November<br>1992|D. Ballantyne<br>38004|
|WPS1026 Financial Liberalization<br>and<br>Adjustment<br>inChile and New Zealand|Paul D. McNelis<br>Klaus Schmidt-Hebbel|November<br>1992|A. Marahon<br>31450|
|WPS1027 Lessons from Bank Privatization<br>In Mexico|Guillermo Bames|November<br>1992|W. Pitayatonakarn<br>37664|
|WPS1028 Socioeconomic<br>and Ethnic<br>Determinants<br>of Grade Repetition<br>in Bolivia and Guatemala|Harry Anthony Patrinos<br>George Psacharopoulo|November<br>1992<br>s|L. Longo<br>39244|
|WPS1029 Controlling<br>Tropical Deforestation:<br>An Analysis of Alternative<br>Policies|Robert T. Deacon|November<br>1992|P. Pender<br>37851|
|WPS1030 Measuring<br>the Effects of Urban<br>Transportation<br>Policies on the<br>Environment:<br>A Survey of Models|Alan J. Krupnick|November<br>1992|P. Pender<br>37851|
|WPS1031 Measuring<br>the Possibilities<br>of<br>Interfuel Substitution|Robert Bacon|November<br>1992|P. Pender<br>37851|
|WPS1032 East Asia and Pacific Region,<br>South Asia Region Population<br>Projections,<br>1992-93 Edition|Eduard Bos<br>My T. Vu<br>Anr. Levin|November<br>1992|0. Nadora<br>31091.|
_Prices of internet services remained high in Uganda as of 2010._ Reduction in prices would boost Uganda’s already relatively high Internet

### _Source:_ AICD.

_Note:_ EAC = East African Community.

— = Not <u>available.</u> penetration and foster the development of ICT services and IT-enabled exports, helping the country reduce the gap with higher performing peer countries (figure 24a). Fixed broadband pricing has shown little traction and digital subscriber line (DSL) tariffs remain high given the limited number of wired telephone lines. High-speed wireless networks on the other hand offer more attractive tariffs and will drive Uganda’s Internet progress.<sup>3</sup> Prices are expected to fall following Uganda’s connection to the EASSy undersea cable

## **Figure 24. Uganda’s Internet market, 2008-2009**

### **a. Internet service trends, users, 2000–09**

### **b. Internet service trends, COMESA, 2008–09**

<!-- Start of picture text --> 25 1600<br>9.0 70<br>8.0 1400<br>60 20<br>7.0 1200<br>50<br>6.0 15 1000<br>5.0 40 800<br>4.0 30 10 600<br>3.0 400<br>20 5<br>2.0 200<br>10<br>1.0<br>0 0<br>0.0 0<br>Internet users (per 100 people)<br>Internet users (per 100 people)<br>International Internet bandwidth (bits per second per person) International Internet bandwidth (bits per second per person)<br>Internet users<br>Internet users<br>International internet bandwidth<br>International internet bandwidth<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 Ethiopia Madagascar Rwanda Zambia Sudan Swaziland Uganda Kenya Mauritius<br><!-- End of picture text -->

### _Source:_ AICD.

_Note:_ COMESA = Common Market for Eastern and Southern Africa.

# Financing Uganda’s infrastructure

To meet its most pressing infrastructure needs and catch up with developing countries in other parts of the world, Uganda needs to expand its infrastructure assets in key areas (table 15). The targets outlined below are purely illustrative, but they represent a level of aspiration that is not unreasonable. Developed in a standardized way across African countries, they allow for cross-country comparisons of the affordability of meeting the targets, which can be modified or delayed as needed to achieve financial balance.

> 3 For example the UCC reported that in June 2009, mobile accounted for 92 percent of all Internet subscriptions. See UCC (2009).

41
# **_2. Using GPS can help understand policy externalities and spillovers_**

The spatial proximity of one household to another may be directly of interest, particularly for understanding the interactions between actions taken by different households, the role of social networks, and the potential spillovers from policies which treat some households and not others.

One example of interactions between households is the possibility that they learn from one another’s actions. Conley and Udry (2005) study learning in the context of the decision to adopt pineapple in Ghana, and of how much fertilizer to apply to it. They note that the classic identification problem here is that the fact that a farmer is more likely to adopt a new technology soon after his neighbors have done so might just be a consequence of some unobserved variable that is spatially correlated - such as soil types, pests or topographic features – rather than the result of genuine learning. They therefore use GPS to define the geographic neighbors of a given plot to be those within 1 kilometer of the center of the plot, and also collect data on who farmers talk to (informational neighbors). Controlling for the deviation of a farmer’s input from his geographic neighbors, they can then identify learning through the impact of informational neighbors’ choices.<sup>9</sup> Furthermore, they do find evidence of positive spatial correlation in unobserved shocks to the productivity of fertilizer, highlighting the importance of controlling for geographic effects when examining learning.

Another example of using GPS to study learning from neighbors is provided by McKenzie, Gibson and Stillman (2007) who study how negative employment experiences for emigrants affect the expectations of would-be emigrants. These would-be emigrants were all unsuccessful in a random ballot in Tonga that offers an opportunity for ballot winners who obtain employment to move to New Zealand. When interviewed subsequently about their employment (and income) expectations had they moved to New Zealand, the would-be emigrants greatly understated employment rates and incomes compared with the actual outcomes for the emigrants. One factor explaining this understatement is that many ballot winners who moved found that their initial job opening in New Zealand was no longer available, and news of this negative outcome appears to flow back to the would-be emigrants in Tonga. Specifically, if all ballot winning emigrants within a six kilometer circle (based on the GPS measurements) did

> 9 They also allow for the error term to be spatially correlated across plots as a general function of their physical distance, using the spatial GMM estimator of Conley (1999).

- 8 -
Policy Research Working Paper 10408

# **Abstract**

Existing research points to a possible link between slow-onset symptoms of climate change and migration. It is also known that rates of urbanization are fastest in some of the world’s poorest countries, which are incidentally also at greater risk of climate-induced migration. These separate findings suggest that slow-onset climate phenomena such as droughts have likely become a key driver of urbanization across much of the developing world. While intuitive, this link has not been convincingly established by extant research. This study examines the climate-urbanization nexus by constructing a novel measure of urban growth that uses remotely sensed information from the World Settlement Footprint dataset. Relying on panel data that cover the entire globe between 1985 and 2014, the paper shows that drought leads to faster urban growth. The results indicate that a hypothetical drought lasting 12 months is associated with a 27 percent increase in the average annual increment of built-up area. The paper leverages novel data from several Sahelian cities to illustrate that much of this growth takes the form of non-infill development that extends outward from previously built-up localities.

This paper is a product of the Urban, Disaster Risk Management, Resilience and Land Global Practice and the Water 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 vchlouba@nd.edu, mmukim@worldbank.org, ezaveri@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
Several key messages emerge from the recent literature on economic growth and development, surveyed by Spolaore and Wacziarg (2013): that technology and productivity tend to be highly persistent even at very long horizons, long-term persistence holds at the level of populations rather than locations, and long-term genealogical links across populations play an important role in explaining the transmission of technological and institutional knowledge and the diffusion of economic development. The recent literature, taken together, also implies that there are limits to what policy makers can do to significantly alter the growth paths that ultimately determine the wealth of nations. By themselves, these findings do not explain the development and persistence of two (or more) distinct (high and low) growth paths for developing countries or how they should be modeled. It is also not clear whether there could be significant mobility across these distinct groups over time, engineered through policy changes and improvements in the quality and quantity of human capital endowments through migration and reforms of the educational system.

The two development tracks hypothesis has positive and negative aspects. On the positive side, many developing countries are acting as engines of growth for their regions. The negative aspect is captured by Paul Collier (2008) in his formulation of “the Bottom Billion.” Although his emphasis is on failed states, the countries he discusses form a large subgroup of our lowperforming country group. Governance issues, which were not yet a focus in the 1980s by international financial organizations, are an important dimension of development. The challenge posed to the development agenda by this group of low-performing countries is unique, calling for measures beyond “development business as usual” (see, for example, World Bank 2011b).

_c. Technological change, total factor productivity growth, and the embodiment hypothesis_ In the early decades of the postwar period, the industrial countries experienced healthy growth of productivity, thanks to the commercialization of military technological developments in the United States and the catch-up phenomenon in Western Europe and Japan. However, in the interest of stability, the industrial countries opted for a restrictive regulatory environment. As a result, a number of rigidities developed in these economies, which had a dampening effect that began to be felt in the early 1970s. These effects, together with the oil price shocks and the policy response to them, led to a serious slowdown in productivity growth and rising inflation and unemployment in the OECD countries in the 1970s and early 1980s.

25
# 1. **Introduction**

The theory of cost of living indices is now well established. Text books such as that by Deaton and Muellbauer (1980) give an account of the concepts and principles involved in the theoretically ideal price index, and how these link to the standard usage of the Laspeyres and Paasche indices. In practice, application of this theory requires information on prices and on expenditures for the households whose standard of living is to be evaluated, and this is where the problems begin. The information base in many developing countries, especially in Africa, is such that we do not have a sufficiently broad based coverage of prices and expenditures for households in the country. The Consumer Price Index (CPI) for many countries is based on prices and expenditure patterns of the capital city only, or of urban areas. Rural areas are not covere4 in any systematic way. While inadequate at the best of times, these indices are particularly inappropriate at a time of structural adjustment policies, whose whole raison d'etre is to alter price relativities, most of which have a significant effect on rural households.

This paper reports on an exercise in economic statistics -- an attempt to estimate a regional price index for C8te d'Ivoire using the strengths of two indepen tent data sources. The two sources are the CMte d'Ivoire Living Standards Survey (henceforth CILSS) and the Intemational Comparisons Project (henceforth ICP). The CILSS has revolutionized the analysis of household behavior in CMte d'Ivoire because of the detailed information it provides on household incomes, expenditures, employment, and other socio-economic characteristics, but, as we shall see, the price data it provides leaves much to be desired. The ICP provides a more detailed account of regional variation in prices than any other source for CMte d'Ivoire, but does not have any data on expenditure patterns or other socioeconomic variables.

The story of this paper is the story of how we set about combining these two sources to produce a regionai price index which we would argue to be superior to earlier estimates for Cote d'Ivoire. The account will be of interest to those who are struggling to combine 1
those from Ravallion (2002) and Ravallion, Chen, and Sangraula (2007), who focus on the distribution effects. Combining equations (2) and (3) yields: Since _sU_ + _sN_ + _sA_ = 1 and hence _dsU_ + _dsN_ + _dsA_ = 0 , equation (4) can be rewritten as To estimate equation (5), data is needed on the average income in each of the three sectors which is only available for a limited set of countries and time periods. Consequently, equation (5) is simplified to: where _y_ denotes the average income of the whole economy, represented by GDP per capita.

To help interpret the impact of urban agglomeration on poverty, the terms in the expression in the brackets of coefficient _βU_ in equation (5) are rearranged: ( _PU_ − _PA_ ) + ( _sU_ ∂ _PU_ / ∂ _sU_ − _sA_ ∂ _PA_ / ∂ _sA_ ) . The first term, ( _PU_ − _PA_ ) , represents a “ceteris-paribus” effect of transformation from agriculture to metropolitan activities equal to the intersectoral difference in current poverty levels. It could be seen to reflect the Kuznets process, affecting overall inequality, if the sectoral inequalities differ. The second term, ( _sU_ ∂ _PU_ / ∂ _sU_ − _sA_ ∂ _PA_ / ∂ _sA_ ) , corresponds to the change in the poverty level due to the effects of sectoral concentration on within sectoral poverty, i.e. the intra-sectoral income distributions. In other words, the coefficient on the change rate of the share of urban population, _βU_ , 8
|Export Development Project|
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|Export Development Project|
|Export Development Project|
|Export Development Project|
|Micro and Small Enterprise Trainingand TechnologyProject<br>Ivan Rossignol|
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|Micro and Small Enterprise Trainingand TechnologyProject<br>Ivan Rossignol|
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|Micro and Small Enterprise Trainingand TechnologyProject<br>Ivan Rossignol|
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|National Agricultural Research Project(NARP) (02)<br>Moctar Toure|
|National Agricultural Research Project(NARP) (02)<br>Moctar Toure|
|National Agricultural Research Project(NARP) (02)<br>Moctar Toure|
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|National Agricultural Research Project(NARP) (02)<br>Moctar Toure|
|Mombasa and Coastal Water SupplyEngineeringand Rehabilitation Alain R. Locussol|
|Mombasa and Coastal Water SupplyEngineeringand Rehabilitation Alain R. Locussol|
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|Mombasa and Coastal Water SupplyEngineeringand Rehabilitation Alain R. Locussol|
|Mombasa and Coastal Water SupplyEngineeringand Rehabilitation Alain R. Locussol|
|Mombasa and Coastal Water SupplyEngineeringand Rehabilitation Alain R. Locussol|
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|Mombasa and Coastal Water SupplyEngineeringand Rehabilitation Alain R. Locussol|
|Universities Investment Project<br>Bruce N. Jones|
|Universities Investment Project<br>Bruce N. Jones|
|Universities Investment Project<br>Bruce N. Jones|
|Universities Investment Project<br>Bruce N. Jones|
|Universities Investment Project<br>Bruce N. Jones|
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|Universities Investment Project<br>Bruce N. Jones|
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|Institutional Development and Civil Service Reform Project<br>John Titsworth|
|Institutional Development and Civil Service Reform Project<br>John Titsworth|
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|EmergencyDrought RecoveryProject(EDRP)<br>Christine E. Cornelius|