rafmacalaba/data-use-mentions-extended huggingface dataset:
and economic censuses (collected every decade), establishment (formal & informal sector), consumption/income, health and labor force surveys (collected every 3 years) and price surveys (collected monthly) along with their public dissemination at the lowest level of disaggregation allowed for while maintaining data anonymization.<sup>7</sup> These investments are not limited to supporting data collection, but also include capacity building to assure data quality, harmonization of data collection methods and tools across surveys and countries, data anonymization and dissemination and technical assistance (TA) aimed at improving data integration and interoperability.

The microdata component is complemented with investments in statistical modernization aimed at increasing the capacity to combine survey and non-survey (administrative; transaction, big) data for the production of statistics. In a world that is digitizing rapidly, NSOs need to start to exploit the opportunities for statistics production by utilizing available data from administrative processes (such as population and business registries), transactions (e.g. scanning data from supermarkets; financial transactions; VAT and tax records) or other big data sources (satellite imagery; social media etc.) and combining these with available microdata sets. Utilizing more secondary data sources requires improvements in data governance. Data governance includes establishing the infrastructure and technology, setting up and maintaining the processes and policies, and identifying the individuals and organizations that have both the authority and responsibility for handling and safeguarding specific types of data. At the national level, data governance also covers data interoperability, standards and quality control. A MENA Statistics Compact could support statistical modernization by offering capacity building and technical assistance aimed at reshaping data production processes and improving data governance systems.

.

> 7 This is just a minimalistic number of microdata sets that would have to be collected regularly. A strong case can also be made to include income surveys (particularly in countries with a large formal sector), perception surveys and wealth surveys.

21
Figure 9: Past intergenerational mobility and subjective ranking <!-- Start of picture text --> Probability of positioning in given steps of the economic ladder<br>Steps 1 and 2 Steps 5 and 6 Steps 9 and 10<br>1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10<br>Decile of Consumption Decile of Consumption Decile of Consumption<br>I have done worse than my paren ts I have done the same as my parents<br>I have done better than my parents<br>.6 .6 .6<br>.5 .5 .5<br>.4 .4 .4<br>.3 .3 .3<br>.2 .2 .2<br>Probability of positioning Probability of positioning Probability of positioning<br>.1 .1 .1<br>0 0 0<br><!-- End of picture text --> Note: this graph plots the probability of respondents positioning themselves in different steps of the economic ladder by objective decile of consumption and perceptions on whether the respondent has done better than his/her parents . The left panel plots the probability of choosing steps 1 and 2; the center panel plots the probability of choosing steps 5 and 6 and the right panel plots the probability of choosing steps 9 and 10.

propose to look at whether subjective ranking shows the same pattern as other well-being measures in their relationship with relative income using LiTS data.

In section 3.1 we have already discussed the role of ranking within reference groups as a source of information for individuals’ perception about their position in the income distribution. In the analysis that we carry out in the following lines we are using a different working assumption – that individuals’ response about their position in the income ladder should be understood as a measure of well-being. It is, thus,
# **Microbiology Analysis**

||Contro|l mean<br>**District**<br>**School**|Treatm<br>**level interv**<br>**component**|ent mean<br>**ention**<br>**sample**||
|---|---|---|---|---|---|
||N|Avg.|N|Avg.|Difference|
|**Prevalence of E.Coli in water**|85|0.329|74|0.514|-0.184*|
|**Log10 E. coli, MPN/100ml, Child**|86|0.584|74|0.463|0.121|
|**Log10 E. coli, MPN/100ml, Mother**|86|0.702|74|0.829|-0.127|
|**Log10 E. coli, MPN/100ml, Object**|82|0.595|72|0.621|-0.026|
|**Stool sample, Ascaris detected**|86|0.023|74|0.000|0.023|
|**Stool sample, Blastocystis detected**|86|0.105|74|0.108|-0.003|
|**Stool sample, Giardia detected**|86|0.105|74|0.041|0.064|
|**Parasite count in stools**|86|0.233|74|0.149|0.084|
|**Prevalence ofparasites in stools**<br>*p < 10%, ** < 5%, *** < 1%|86|0.186|74|
methodology proposed by Lustig (2018) was followed. This method calculates per-capita income before and after each of the tax interventions, as shown in Figure III-1. More precisely, the following income definitions are used for each household:

- _Market income:_ This income includes salaries before tax and before contributions to the social security system, as well as income received from yields on capital assets (interest, dividends, leases) and transfers from private institutions or other households.

- _Disposable income:_ direct transfers are added to market income to build this income, which in the case of Colombia stems from transfers received from government programs such as Familias en Acción, from which direct taxes, that is, income tax is subtracted. Similarly, contributions made to the social security system are subtracted.

- _Consumable income:_ Consumable income is defined as disposable income plus indirect subsidies, minus indirect taxes. Indirect taxes included in this analysis are VAT, consumption tax, and excise taxes applied to tobacco and alcoholic beverages, as well as contributions in the payment of utilities for strata 5 and 6. Indirect subsidies correspond to those received by strata 1, 2, and 3 in the utility rates. The fuel subsidy has not been included in the analysis.<sup>30</sup>

- _Final income:_ Lastly, the in-kind benefits for health and education services are added to consumable income to obtain final income.

> 28 For more information on the administrative data used in each section see the annexes in Nuñez et, al. (2019).

> 29 In this version of the document, the 2010 input-output matrix was used since that for 2015 had not yet been published by DANE.

> 30 To be able to model fuel subsidies it is necessary to have the fuel reference pricing series. The work team is searching for said information to be able to model these subsidies.

14
ZIMBABWE‘S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 18. Zimbabwe’s Internet market and southern African peers**

<!-- Start of picture text --> a. Internet service trends, Zimbabwe, 2000–08 b. Internet service trends, southern Africa<br>20 350<br>5.0 12<br>18<br>4.5 300<br>10 16<br>4.0<br>14 250<br>3.5<br>8<br>12<br>200<br>3.0<br>10<br>2.5 6 150<br>8<br>2.0<br>6 100<br>4<br>1.5<br>4<br>50<br>1.0<br>2 2<br>0.5<br>0 0<br>0.0 0<br>2000 2001 2002 2003 2004 2005 2006 2007 2008<br>Internet users (per 100 people)<br>Internet users (per 100 people)<br>International Internet bandwidth (bits per second per<br>International Internet bandwidth (bits per second per person)<br>person)<br>Internet users<br>Internet users<br>International internet bandwidth<br>International internet bandwidth<br>Madagascar Malawi Angola Zambia Mozambique Lesotho Zimbabwe Botswana South Africa Namibia Mauritius<br><!-- End of picture text -->

## _Source:_ AICD.

Mobile is the best hope for extending connectivity to most Zimbabweans. Telecel reports its network has population coverage of 68 percent (Orascom Telecom 2009), while Econet states ―all urban areas and most rural areas have been covered (Econet Wireless Zimbabwe 2010)‖ The country is thus close to reaching its coverage gap, which has been calculated to be 13 percent (figures 16, 17)
# **1. Introduction**

The rate of return to schooling has been of special interest from the standpoint of public policy, as the large number of past theoretical and empirical studies shows. Recent empirical studies that rely on theoretical advances that improved the understanding of crucial endogeneity and heterogeneity issues in the returns to education literature have almost exclusively used data from developed countries. However, from a policy perspective, evidence on developing countries would be valuable in assessing the effectiveness of education policies, especially when the focus of the policy maker is a section of the population, such as those who are liquidity constrained. As the interest in economics moves away from the effect of policies on the average individual and toward their effect on subsections of the population, research on returns to schooling in developing countries needs to focus on identifying the effects of education initiatives (such as compulsory schooling reforms, free education at certain levels, and accessibility to schools) on the disadvantaged, rather than the average individual.

A variety of empirical studies, mainly for developed countries, have used supplyside institutional features of the education system (such as compulsory schooling laws and accessibility of schools, among others) as instruments in instrumental variables (IV) estimations of the returns to schooling. The main finding is that such estimates of returns to schooling are typically larger than ordinary least squares (OLS) estimates. Card (2001) interprets this finding as suggesting that the marginal returns among the loweducation subgroups (which are typically affected by supply-side interventions) tend to be relatively high, reflecting their high marginal costs of schooling rather than low ability.

1
l<br>Industrial|ess deposit<br>Developing|
|---|---|---|---|---|---|---|---|---|
|1975-77|-1.0|-0.4|-0.9|-3.1|-2.8|-4.2|2.8|4.1|
|1980-82|3.3|1.6|2.4|-1.9|0.1|-2.3|4.1|4.6|
|1985-87|5.8|3.7|5.3|0.9|3.2|1.5|4.4|5.0|
|1990-92|6.7|6.8|6.2|2.5|3.3|2.5|5.1|6.7|
|1995-96|3.1|4.4|3.5|_5.0_|1.8|3.5|4.0|6.6|

Note: The mean of the median across countries is shown. For the spread mean shown is for 1995-97.

S These figures and data are based on countries for which annual data is available for at least 12 years within 198093. It is not a balanced pool: the number of countries varies somewhat from year to year, but more according to the series, from a mean of 35 for money market and 36 for treasury bill rates to 59 for intermediation spreads and 62 for deposit rates. This sample selection strategy represents a compromise between the desirability of including as many countries as possible with the risks of too unbalanced a pool. One potentially important but hidden source of sample-selection bias could arise to the extent that reporting of data to _IFS_ is correlated with a liberalized interest rate regime.

4
**Findings:** The proportion of scientific articles in the databases is the same as the proportion of documents published in peer-reviewed scientific journals (“AIDS”, “Sexually Transmitted Diseases” and “The Pan American Journal of Public Health” are examples of such publications).  Among the three Andean countries, Peru had the highest proportion of papers published in these journals, probably due to the fact that it has one of the longest records in HIV research in Latin America.  Peru’s academic sector has been actively involved in the process since the beginning of the epidemic.  Ecuador, on the other hand, is the country with the highest proportion of non-published papers or papers without publication status data (40%). In Bolivia, the proportion of non-published papers is 25%. Non-published papers have an impact on the use and quality of the information; nonpublished papers complicate access to information and the socialization of the findings. They generally make the papers produced weaker, because they have not undergone the detailed and enriching process of peer review.

# **Type of epidemiological design**

In **Peru** , 49 documents used various kinds of cross-sectional designs (86%). Among these, 28 documents used cross-sectional serological surveys supplemented by surveys with socio-demographic and behavior variables (49%); 18 used knowledge, attitudes and practice (KAP) surveys (32%); and three used STI bacteriological studies supplemented by socio-demographic surveys (5%). Of the remaining eight documents (14%), there were four cohort studies (7%), two case-control studies (3%), one case series study (2%), and one clinical trial (2%). In **Ecuador** , 36 documents used various kinds of cross-sectional designs (90%). Among these, 20 documents used KAP surveys (50%); 10 used serological surveys with or without supplementing KAP surveys (25%); and six included crosssectional survey studies about other
Policy Research Working Paper 6301

# **Abstract**

This paper explores a rich dataset of monthly firm-level data on the population of exporters of Lao PDR from 2005 to 2010. The survival analysis uses a discretetime logistic model based on firm-product-destination triplets while accounting for unobserved heterogeneity. It looks in detail at the role played by two important and related determinants of survival: experience and networks. These are particularly relevant for developing countries, where relevant export experience in firms is likely to be limited and networks leveraging it all the more important. The analysis reveals the positive impact of having prior experience with the export product and destination, experience with importing, as well as using a developed neighboring country as launch platform. Networks are found to be most relevant when they are most specific—the largest impact comes from province level aggregations of firms selling the same product in the same market during a particular month. A competing risks model was also investigated to discern the impact of these determinants on the likelihood of experiencing an upgrade to a superior product versus termination when a trade contract ends.

This paper is a product of the Poverty Reduction and Economic Management Sector Department, East Asia and the Pacific Region. 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 rrecord@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
|17,000|42,000|
|Hybrid orOPVmaize seed (10kg)|17,000|17,000|34,000|

<mark>Source: VVC member baseline survey</mark> <mark>* As a point of reference in December 2010 $1 USD = 1480 TZS</mark>

# _<u>Section A2. Institutional Arrangements</u>_

The National Agricultural Input Voucher Scheme (NAIVS) was overseen and implemented by the Ministry of Agriculture, Food Security and Cooperatives of Tanzania (MAFC) in collaboration with other public agencies and private sector participants.

# **NAVIS Oversight**

The NAIVS National Forum was the apex organization and endorsed the allocation of vouchers across targeted districts based on the adopted guidelines and selection criteria guidelines for NAIVS implementation. The forum endorsed the NAIVS annual work plan and budgets

> 1 DAP is the most commonly used basal fertilizer in Tanzania. MRP, manufactured in Northern Tanzania, is technically less efficient, because a certain amount of nitrogen is needed for plants to absorb phosphorus. MRP+N was a new product at the time of the study in 2011, technically equivalent to DAP, but produced in very limited quantities.

> 2 The seed provided in the package was sufficient to plant half of a hectare (100 percent) of maize and one-quarter of a hectare (50 percent) of direct-seeded rice. The lesser quantity of rice was proposed because rice is a self-pollinating crop, and good quality seed can easily be multiplied by farmers themselves, thus encouraging the spread of new rice varieties.

2
over-reported in the survey. Spending on categorical benefits for housing and utilities is close to external estimates, while spending on monthly cash payment is underestimated by 65 percent. Programs that appear to match the external data well include maternity capital, child allowances up to 16 (18) years, housing subsidies and state social assistance (Table A-7). Spending on birth grants, childcare allowances up to 1.5 years and childcare compensations are under-simulated by 40-50 percent. Spending on allowances for the third child is underestimated by more than 90 percent. The tax revenues are under-simulated, and almost by the same amount for all taxes (30-35 percent), suggesting that the reason could be related to underrepresentation of employed people (and, hence, earnings) in the survey. Another reason could be that that we are using budgetary projections of social expenditures and tax revenues for 2017, rather than actual numbers.

**Table A-7. Programs included but not simulated in RUSMOD, annual amounts (in million rubles), 2017**

||RUSMOD|External|Ratio|
|---|---|---|---|
|**Benefits**||||
|Public pensions|9,283,404|7,397,510|1.25|
|Unemployment benefit|28,628|39,040|0.73|
|Scholarships|63,212|82,400|0.77|
|Monthly cash payment|172,558|495,258|0.35|
|Categorical benefits for housing/utilities|240,666|243,662|0.99|
|**Taxes and Social Insurance contributions**||||
|Property and vehicle taxes|218,114|||
|Social allowance onpersonal income tax|29,982|||

Source: Rosstat and RUSMOD for 2017 policy year and 2016 RLMS-HSE data

**Table A-8. Programs simulated in RUSMOD, annual amounts (in million
**(survey estimates,<br>updated Dec 2022)|UNICEF Global<br>Database|N=202<br>countries<br>(date depends<br>on survey<br>year)|https://data.unicef.org/wp-<br>content/uploads/2022/05/UNICEF_WHO_WB_Global_Expanded_Datab<br>ases_Overweight_Dec_2022.xlsx|
|**Child anaemia**: Percentage of children<br>aged 6−59 months with a Hb<br>concentration less than 110 g/L, adjusted<br>for altitude.|WHO Global Health<br>Observatory|n=124<br>countries,<br>2019|https://www.who.int/data/gho/data/indicators/indicator-<br>details/GHO/prevalence-of-anaemia-in-children-under-5-years-(-)|
|**Children and adolescents 5-19**||||
|**Overweight**|https://www.foodsystem<br>sdashboard.org/|2016|Proportion of 5-19 year olds with BMI-for-age > 1 SD above median of<br>WHO growth reference for this age group; Based on NCD-RisC<br>estimates|
|**Obesity**|As above||As above, but with BMI-for-age>2 SD above growth reference|
|**Underweight**|As above||As above, but with BMI-for-age<1 SD below growth reference|

43
19

**Table** 5: **Coefficient Estimates and Implications for Economic Growth Rates.**

||Change in the Value||Change in Annual|
|---|---|---|---|
||of Growth Determinants||Growth Rates|
|Investment as a Share of GDP:|+ 2.5 percentage points|IMPLIES|+ 0.25 percent growth.|
|Human Capital:|+ I year of adult education|IMPLIES|+ 0.1O percent growth.|
|Policy Framnework:|+ 0.5 in the Index Z|IMPLIES|+ 0.20 percent growth.|
|Labor Force Growth Rate:|+ 0.02 percentage points|IMPLIES|- 0.10 percent growth.|

# **V. Convergence and Growth: What Lies Ahead for CEE Countries?**

We have estimated a theoretical model using cross-section data. As previously mentioned, we have allowed for the existence of differences across countries which go beyond different initial levels of output per capita. Thus, the performance of a country also depends on its own _environment factors_ or, as we have also called them, its own _growth determinants._ In sum, convergence and growth will depend on the level of human capital, the features of a country's policy framework, the rate of growth of the labor force (or "fertility" rates, see Box 2 for a discussion on this data), and the investment level on physical capital. In particular, to thoroughly examine how these factors affect convergence and growth for a given country, it is important to analyze how they compare with those of other countries. For example, in our case, the "EU average country" is a reasonable baseline. We assume the "EU average country" is one which maintains the current EU Average growth determinant values.
>20<br>0.0 0 0 0<br>materials Energy (RHS) Food and Beverages Others (RHS) materials Energy (RHS) Food and Beverages Others<br>Construction Construction<br><!-- End of picture text --> Source: World Bank; World Integrated Trade Solution (WITS); World Trade Organization A.B. EMDEs = emerging markets and developing economies; LICs = low-income countries. A. 2017 data. Listed price control policies are retrieved from the latest (2003-19) country Trade Policy Review publication. Sample includes 12 low-income countries, 63 other EMDEs and 6 advanced economies. B. 2017 data. Listed price control policies are retrieved from the latest (2003-19) country Trade Policy Review publication. Sample includes 12 low-income countries, 63 other EMDEs and 6 advanced economies. Share of 4-digit Harmonized System (HS) category subject to controlled prices in high-level groupings of 2-digit HS categories. Construction materials aggregate includes HS68 and HS73, Energy aggregate includes HS27, Food and Beverage aggregate includes HS01 to HS22. Other aggregate includes all other imports.

EMDEs often impose price controls on exportable commodities. This may involve a monopoly marketing agency, which purchases from domestic producers at a fixed price, and resells to foreign purchasers at the world price. This arrangement implicitly taxes producers when the resale price exceeds the purchase price (Ghosh and Whalley 2004) or subsidizes producers when the resale price falls below the purchase price. About 25 percent of EMDEs that rely heavily (with more than 10 percent of goods exports) on a single export commodity group impose price controls on it 5 Unregulated prices depend on the world price, transport costs, local monopoly power or other hurdles to the movement of goods, and harvest conditions (Aksoy and Ng 2010).

6