rafmacalaba/data-use-mentions-extended huggingface dataset:
potential market for the PPP operators. These data were collected from the World Bank economic indicators. Definition of all the data and their sources are provided in Annex 1.

Unfortunately, detailed information on the contract types is only available for 70 of the 123 developing countries for which we have collected the data related to IRAs. Table 2 provides with the distribution of these 70 subsample countries across regions. Note that all six countries in East Asia have private participation in their water and sanitation sectors, while two countries in South Asia lack an IRA in their respective sectors.

Table 2: Distribution of sample countries across regions

|(1) East Asia|(2) Europe and<br>Central Asia|(3) Latin America<br>and the Caribbean|(4) Middle East and<br>North Africa|(5) South Asia|(6) sub‐Saharan<br>Africa|
|---|---|---|---|---|---|
|6|16|16|8|2|22|

Table 3 summarizes the basic statistics on each of these variables for the developing countries subsample we ended up working with in our assessment of the drivers of the supply of PPP in developing countries. The sample size is the same for all variables except decentralization. It shows that the sample is quite heterogeneous in some of the key control variables, such as water and sanitation and access rates, since the relatively high mean is matched by a high standard deviation. The same observation applies to the variables used to approximate the quality of governance.

Table 3: Summary statistics for the developing country sample

||Observations|Mean|Std. dev.|Min|Max|
|---|---|---|---|---|---|
|����<br>|70|0.46|0.50|0
page 25 one with only the VH Ws, and measuring the impact of each intervention on health status relative to a control group where neither intervention is introduced.[17] Alternatively and less satisfactorily, the parameters a and b could be estimated at relatively little cost by multiple regression on nonexperimental data from the region of interest. Either of these estimation techniques has the additional advantage over "guess-timation" of correcting for the problems of disease interdependence and competing risk, and thus allowing relaxation of the unpalatable assumption that health effects are additive.

The cost function parameters could be "gvess-timated" in a specific country by working with experienced health ministry managers and depending on their judgement as to the costs of various combinations of activities.[l8] Here too it would be feadLble and preferable to estimate these parameters statistcally using a sample of mobile teams and fixed centers that are performing some or all of the vaccination and VHW supervision functions. With enough observations, a moie flexible functional form could be chosen in lieu of the constant elasticity form used here. With increased flexibility, changes in average unit cost could be attributed to changes in coverage and intensity as well as to changes in output mix as modeled here. [19] It is useful to contraet the present study with two other cost-effectiveness studies of primary health care in developing countries, both of which were led by economists from the University of Michigan. A team based at the School of Public Health constructed the linear-programming model referenced in Tables 2 and 4 above, which depends on 3,696 different parameters in place of the two parameters in the objective function used here (Grosse et al, 1979, Appendices A, B and C). Although the SPH model deals with separ4te packages of interventions as discrete administrative entities just as the present paper treats Strategy F and Strategy X as distinct - the SPH model is completely linear and thus would require modification to address the problem of strategy choice with joint costs.

An independent team based at Michigan's Center for Research on Economic Development
these fixed and recent boundaries is useful because it allows us to analyze changes in the same area aiming to answer the question of how city form has changed through time.

The Global Human Settlements population grid (GHS-POP) uses data from the Gridded Population of the World and reallocates these estimates using the GHS BUILT data set. An alternative would have been to use data from LandScan, which uses a proprietary algorithm to provide population estimates information on elevation, slope, and land cover, as well as locations of road and rail networks, hydrologic features and drainage systems, utility networks, airports, and populated urban places (Henderson et al 2020). While LandScan reports estimates of ambient population averaged throughout the day, GHS POP reports nighttime (residential) population estimates.

The entire data set consists of 13,135 cities with at least 50,000 inhabitants in 2015. The data set focuses on measures of city built-up and city population for four points in time: 1975, 1990, 2000, and 2015. It also includes other economic, geographic and environmental variables such as city GDP, elevation, precipitation and temperature, CO2 emissions, PM 2.5 emissions and concentration levels, for similar points in time. We construct a sample of cities that have recorded positive built-up and GDP since 1990. We drop the 1975 layer as it potentially underestimates built-up areas due to the coarser resolution of the Landsat2 Multispectral Scanner (60m compared to 30m). Using the 1975 data would introduce an upward bias in city growth estimates. To further reduce measurement errors and avoid outliers, we drop cities with population less than 10,000 inhabitants in 1990 and 2000, and those with a population/builtup coefficient larger than 500,000 in 1990. This gives us a final sample of 9,468 cities, which includes cities of all sizes (Table 1). More than half the cities in the sample have less than 200,000 inhabitants.

**Table 1: Distribution of Cities by City Size and Region**
||**S**<br>|**hares of house**<br>|**hold inco**<br>|**me spent on:**<br>|<br>|
|---|---|---|---|---|---|
||Food|Education|Health|Durables|Adult|
|**A. No controls**||||||
|Migrant mother|0.026|-0.082|0.089|-0.007|0.165**|
||(0.335)|(0.112)|(0.150)|(0.019)|(0.083)|
|Size of household|-0.419***|-0.074***|-0.114|-0.005|-0.027|
||(0.102)|(0.023)|(0.070)|(0.005)|(0.022)|
|Observations|337|337|337|337|337|
|Uncentered_R_<sup>2</sup>|0.443|0.278|0.0163|0.00285|0.162|
|_F_statistic|8.604|8.604|8.604|8.604|8.604|
|Hansen J statistic|0.847|1.025|1.018|2.127|0.812|
|p-value|0.357|0.311|0.313|0.145|0.367|
|**B. Controls included**||||||
|Migrant mother|-0.293|-0.082|0.082|-0.017|0.149
thus not be perfectly consistent with other data sources for any specific variable (e.g. 2001 exports of a specific commodity between a pair of countries, measured at the tariff-line from COMTRADE). However, they provide the global consistency necessary for analytical modeling. Also, given that official African trade data are of varying quality and may not account for informal trade flows (RATIN, 2009), the GTAP Database has the advantage of being a globally consistent and balanced database. Additionally, the analysis in this paper focuses on the sensitivity of this ‘representative’ Tanzanian economy<sup>9</sup> to a very specific set of stressors, where the comparability of the effects of the different realizations of the stressors (i.e. the climate shocks) will be maintained as long as the same benchmark representative dataset is used in every realization.

We retain the empirically robust assumptions of constant returns to scale and perfect competition, and introduce factor market segmentation, following the methodology of Keeney and Hertel (2005). This segmentation is particularly important in countries where poverty occurs in rural areas. Farm and non-farm mobility of factors are restricted by specifying a constant elasticity of transformation function which allows “transformation” of farm employed labor and capital into nonfarm uses and vice-versa. This allows for persistent wage differences between the farm and non-farm sectors, and is the foundation of the inter-sectoral distributional analysis. In order to parameterize these factor mobility functions we draw on the OECD’s (2001) survey of agricultural factor markets – the best available database of the necessary elasticities with the widest country coverage. However, given the uncertainty about the appropriate values of these parameters for Tanzania, we conduct a systematic sensitivity analysis with respect to these parameters. We assume a constant aggregate level of land, labor, and capital employment reflecting the belief that the aggregate employment of factors is unaffected by the climate shocks that are affecting grain production.

The model is adjusted to distinguish between land classes with different biophysical characteristics, following the approach of
of experience, it was formally decided to introduce a substantial modification of the Colombian decentralization reform in 2001 (Law N. 715, December 2001) along two main lines: (a) the simplification of resource allocation, through the creation of a General Co-Sharing System ( _“Sistema General de Participaciones”_ ) which includes all funds directed to education and allocates them according to students enrolled; and (b) the unification of the responsibilities at the level of only one political actor (department or municipality), accompanied by the automatic “accreditation” of all municipalities with more than 100,000 habitants and a possible “accreditation” of all municipalities assuming they fulfill certain minimum institutional conditions. Doing so the country has decided to give up the shared responsibility model altogether taking the path, slowly but surely, of a “municipalization” model or at least of a “geographically” differentiated model where the department would have full responsibility on the delivery only when the municipality is too small or unprepared.

# **<u>Bolivia</u>**

**The shared responsibility model led municipalities to over-invest and the role of departments is still unclear. However, following the decentralization reform, investment in education became more progressively distributed and more responsive to local needs, due an effective use of “voice”.**

## **Evidence:**

A study made by Ruiz and Giussani (1997) on Bolivia, also largely focused on intermediate indicators, finds the same “potential” accountability problem in Bolivia (potential because the Bolivian reform was much less advanced than the Colombian one in 1997) and fears that, by being only responsible for school infrastructure, municipalities might over-invest, not taking into account recurrent needs. This is another possible outcome of the same initial division of responsibilities which might not be all negative if investment is low and voice is effectively 27
The results have several policy implications. Severe salinity needs to be addressed if the recent gains in infant and neonatal mortality are to be sustained. Policies need to be developed to prevent intrusion of saline water into drinking groundwater, particularly in the coastal areas of Bangladesh. Adoption of technology such as desalination could be considered. However, rainwater does not contain salubrious minerals. Hence consumption of groundwater over rainwater should be encouraged. Over the long run, the causes of increasing salinity such as climate change need to be addressed. Finally, our analysis has several limitations. We used the FAO salinity definitions for irrigation, as there are no corresponding definitions of salinity in WHO regulations. There is a need to develop such regulations for drinking water. We also do not have the information on the exact drinking water salinity for the household survey under BDHS, as this parameter was not collected as part of the survey. Further, due to lack of data as a proxy variable for drinking water salinity, we used groundwater EC values from an interpolated map at the national scale which included groundwater EC data from coastal districts that were installed and sampled by the Bangladesh Water Development Board. As is evident from the literature, the EC of groundwater may have seasonal variations and concentrations of other chemicals which result in seasonal and temporal variations in the host groundwater. The interpolated EC from point data is smooth in spatial distribution because of the geospatial interpolation method (IDW) applied for mapping. The interpolated EC values may be more or less than the actual EC values of the tubewell water used by the participants in the BDHS clusters. Households in rural Bangladesh usually have separate tubewells for drinking water supply, suggesting that well water chemical concentrations are likely different across the households recorded in BDHS clusters. Finally, this study relies on self-reported outcomes and covariates that can introduce recall bias in the analyses. Hence, we were unable to control for important factors determining early neonatal death such as low birth weight, premature births, birth asphyxia, meningitis, and sepsis. Further
# **Supplementary References**

- **Adhikari, Ajay, Chek Derashid, and Hao Zhang** , “Public policy, political connections, and effective tax rates: Longitudinal evidence from Malaysia,” _Journal of Accounting and Public policy_ , 2006, _25_ (5), 574–595.

- **Bach, Laurent, Antoine Bozio, and Cl ́ement Malgouyres** , “Lh ́et ́erog ́en ́eit ́e des taux dimposition implicites des profits en France: constats et facteurs explicatifs.” PhD dissertation, Institut des politiques publiques (IPP) 2019.

- **Bachas, Pierre, Matthew H Fisher-Post, Anders Jensen, and Gabriel Zucman** , “Globalization and Factor Income Taxation,” March 2022, (29819).

- **Carreras, Marco, Chandu Dachapalli, and Giulia Mascagni** , “Effective corporate tax burden and firm size in South Africa: A firm-level analysis,” Technical Report, United Nations University 2017.

- **Clark, W. Steven** , “Using Micro Data to Assess Average Tax Rates,” in P.B. Sorenson, ed., _Measuring the Tax Burden on Capital and Labor_ , MIT Press, 2004, pp. 319–358.

- **Gaertner, Fabio B, Brent Glover, and Oliver Levine** , “A Re-examination of Firm Size and Taxes,” _Available at SSRN 3928145_ , 2021.

- **Gauthier, Bernard and Mark Gersovitz** , “Revenue erosion through exemption and evasion in Cameroon, 1993,” _Journal of Public economics_ , 1997, _64_ (3), 407–424.

- **and Ritva Reinikka** , “Shifting tax burdens through exemptions and evasion: An empirical

- investigation of Uganda,” _Journal of African economies_ , 2006, _15_ (3), 373–398.

- **Guha, Atulan** , “Company size and effective corporate tax rate: study on Indian private manufacturing
CAMEROON’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE As of 2008, Cameroon and the **Figure 24. Expansion of mobile and land-line markets in Cameroon** Democratic Republic of Congo were the top two leading markets for mobile 3,000,000 telephony in Central Africa, with around 2,500,000 4.5 million subscribers in each country, 2,000,000 followed by Chad with 1.1 million 1,500,000 1,000,000 subscribers and Gabon with 1 million 500,000 (figure 24).Since 2006 fixed-line penetration has grown rapidly with the - 2000 2001 2002 2003 2004 2005 2006 2007 2008 expansion of limited-mobility portable phones offered by the incumbent operator, Orange MTN Fixed line CAMTEL.<sup>12</sup> Such phones are a fixed-line Source: Balancing Act 2008. solution that offers mobility of up to 40 Note: In 2008, there were 125,000 fixed lined and 150,000 fixed/mobile lines. kilometers and both mobile and fixed handsets. In 2008, two years after the launch, users of this technology increased from 28,000 to 150,000, surpassing the number of traditional fixed lines in the country (125,000). The number of landline subscriptions per 100 people grew from 0.6 in 2000 to 2.2 in 2009.

**Figure 24. Expansion of mobile and land-line markets in Cameroon**

# **Challenges**

Cameroon has lagged in reforming its telecommunication sector. Even though the sector regulator, ART (Agence de Régulation des Télécommunications), was established by the Telecommunications Act in 1998, no further legislation has been enacted and the regulatory landscape remains unclear. The market is still not fully liberalized. South Africa’s MTN and Orange of France dominate the mobile market, which is one of the highest in Cameroon’s peer group, as measured by the Herfindahl-Hirschmann Index (figure 25).<sup>13</sup> There are numerous Internet service
Policy Research Working Paper 7825

# **Abstract**

The impact of the growth of the local supply of public schools in the post-Colonial period on intergenerational mobility in education is a first-order question in the Arab World. This question is examined in Jordan using a unique dataset that links individual data on own schooling and parents’ schooling for adults, from a household survey, with the supply of schools in the subdistrict of birth at the time the individual was of age to enroll, from a school census. The identification strategy exploits the variation in the supply of basic and secondary public schools across cohorts and subdistricts of birth in Jordan, controlling for year and subdistrict-of-birth fixed effects and interactions of governorate and year-of-birth fixed effects. The findings show that the local availability of basic public schools does, in fact, increase intergenerational mobility in education. For instance, a one standard deviation increase in the supply of basic public schools per 1,000 people reduces the fatherson and mother-son associations of schooling by 18–20 - percent and the father-daughter and mother-daughter asso ciations by 33–44 percent. However, an increase in the local supply of secondary public schools does not seem to have an effect on the intergenerational mobility in education.

This paper is a product of the Operations and Strategy Team, Development Economics Vice Presidency. 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 authors may be contacted at assaad@umn.edu.

_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
of recent data for each data category Total recent data indicated to have been collected across all economies 78/140 Source: Ekhator Mobayode and Hoogeveen (2021) Note: Evidence that a survey was collected can be explicit like in a “survey section” of the website or “implicit” like in a report, summary table and/or any mention or reference to the data on the website. * indicates instances where collection of recent microdata was not indicated on NSOs website, but the research team discovered it on an external website. These include Iraq: Rapid welfare monitoring survey SWIFT 2017/2018 downloadable from <u>https://microdata.worldbank.org/,</u> Egypt (2014) downloadable from http://www.dhsprogram.com/ and Iraq MICS 2018, Oman MICS 2014, Tunisia: MICS 2018, West Bank and Gaza (Palestine) MICS 2019/20 downloadable from <u>https://mics.unicef.org/surveys.</u>

# **(ii) Frequency of Macro Economic Data in MENA**

While the SCI, SPI, and ODIN indicators capture the quality of the data ecosystem to a large extent, they do not necessarily encompass the extent and availability of high-frequency data. Table 3 presents the availability of high-frequency data on GDP, industrial production, and unemployment for 19 economies in the MENA region. The date of the most recent data available is also indicated. This information is compiled from the websites of various statistical offices or data portal initiatives, central banks and ministries of planning, economy, or finance across the MENA region. The findings are benchmarked against Mexico, which serves as a good comparator for the MENA region given its upper-middle-income economy status and a well-functioning data ecosystem. Of the 19 economies in the MENA region, 15 report quarterly data on GDP. Some economies lack information for the year 2020 entirely. Economies in conflict such as Libya (2014) and Yemen (2017) have outdated data
Policy Research Working Paper 8928

# Stability and Evolution of Preferences for Improved Cookstoves

A Difference-in-Difference Analysis of a Choice Experiment from Ethiopia _Sahan T. M. Dissanayake George Voigt Abbie Cooper Abebe Damte Beyene Randall Bluffstone Zenebe Gebreegziabher Daniel LaFave Peter Martinsson Alemu Mekonnen Michael Toman_ Development Economics Development Research Group June 2019
percent increases in the minimum wage reduced employment for workers 18 and 19 years old by 2 and 4 percent, respectively, whereas they actually increased employment for workers 20 to 25 years old. This study also found a significant reduction in hours worked or cutbacks from full to half‐time work. Faced with an imminent increase in labor costs, businesses replaced less skilled workers (18 and 19 year‐olds) with workers who were more skilled (20 to 25 year‐olds). In Chile, Montenegro and Pagés (2004) found that the minimum wage had a negative effect on the employment of young low‐skilled workers over the period 1960‐1998.

Recent studies on Costa Rica and Honduras by Gindling and Terrell (2005, 2009) found that the minimum wage had similar adverse effects for less skilled workers. Based on information from household surveys over the period 1988‐2000, Gindling and Terrell (2005) found that a 10 percent increase in the minimum wage generated a 1.09 percent contraction in employment and 0.6 percent reduction in the number of hours worked. The study also examined the effects at different points in the range of skills, finding that the minimum wage disproportionally affected workers in the middle and lower range (from the second to the fifth decile). Overall, the evidence for Costa Rica suggests that businesses adapt to increases in the minimum wage by replacing less skilled workers with those who are more skilled.

14
140|156|32.4|10.7|9.9|<br>9.5|8.6|
|**World**|11,341|35,707|51,270|69,340|97,540|152|180|100|100|100|<br>100|100|

Notes: EU-10 includes new members of the EU as of May 1, 2004 (Cyprus, Czech Republic, Estonia, Hungary, Malta, Latvia, Lithuania, Poland, Slovakia and Slovenia); SEE-5 includes Albania, Bosnia and Herzegovina, Croatia, Macedonia and Serbia and Montenegro: NAFTA-3 includes Canada, Mexico and the U.S.;MENA refers to Middle-East and North Africa and EFTA-3 include Iceland, Norway and Switzerland. Source: Derived from UN COMTRADE database as reported by Turkey.

A dramatic fall in the importance of Middle East and North Africa in Turkey’s trade turnover accompanied reorientation of trade towards the EU-15 in 1985-1990. While Middle East and North African countries accounted for 36 percent of total trade turnover in 1985, this share fell to 17 percent in 1990. Subsequently, trade turnover with this region has kept pace with Turkey’s total trade, with its share remaining at 11-12 percent.<sup>3</sup> Had the share of Middle East and North Africa remained at its 1985 level, the share of the EU-15 would have remained unchanged 3 MENA countries took 74 percent of Turkish exports to ROW in 1985 and only 31 percent in 1995-2003. Imports from MENA accounted for 52 percent in 1985. By 1995 they fell to 21 percent and 18 percent of ROW imports in 2003.