rafmacalaba/data-use-mentions-extended huggingface dataset:
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# II. CHARACTRISTICS **OP THE** FOR-PRO'IT PRIVATE **SECTOR** IN PAMILY **PLANNING0**

This section addresses the issues surrounding the supply of contraceptive products and services. Demand and utilization vill be discussed in the next section. Private sector involvement in family planning spans the gamut including research and development into nev or improved contraceptive products and technologies; production and packaging of contraceptive products; the distribution of products to government and commercial outlets; and, the direct provision of services through, retailers, private clinics and hospitals. The information and analysis presented in this section come from extant data sources and from discussions vith executives of multinational pharmaceutical firms that produce or market their products in the developing world. All evidence presented here is derived from the indicated source or obtained from confrential interviews.

# Production

Producers can be categorized into two general groups. The first group is pharmaceutical manufactures that produce a line of ethical/medical prescription products in addition to contraceptives; the second group consists of firms specializing in non-medical contraceptive methods like the condom. This demarcation is not exact because some of the former firms produce ethical and non-ethical contraceptive products. Table II.1 contains a list of the major multinational producers that export to the developing vorrd, along with the location of their corporate headquarters, countries where production facilities are located, and the range of products produced.

In general, producers are American and European multinational corporations that have local subsidiaries or direct licensing agreements with local 6. Nongovernmental organization characteristics are provided in Levis (1985). **al.tp:asily Plarning Paper** (lbolel
land reform has accomplished its goal remains anecdotal. Finally, South Africa chose to follow a decentralized model of "negotiated" land reform whereby poor people receive a grant to enable them to acquire land. As this model is currently being debated or implemented in a number of other countries including Brazil, Colombia, Guatemala, Honduras, the Philippines, and Zimbabwe, evidence on emerging experience is of broader interest.<sup>2</sup> We use data from a recent survey of land reform beneficiaries to examine to what extent the South African land reform program has been successful in combining equity and efficiency. To do so, we examine evidence on the targeting and productive impact of the land reform program, the degree to which the program lived up to expectations, and the scope to improve performance through modifications in program design. Section two presents a general description of rural poverty and the agricultural structure

> 2 For Brazil see Buainain et al. 1999, Navarro 1998 and the documents at http://www.dataterra.org.br and http://www.nead.gov.br; for Colombia Machado and Suarez 1999, for Guatemala see the Peace Accords (Government of Guatemala 1996), for Honduras see Korczowski, 1999; for the Philippines see Hayami et al. 1990 and Deininger et al. 1999, for Zimbabwe see Moyo 1998 and Government of Zimbabwe 1998. The underlying concept of providing a grant for poor people to acquire land has also been applied in India (Agarwal, 1998).

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important when forcing the model to include dummy variables at the regional level, the level for which the sample survey is considered to be representative. Forcing the inclusion of regional dummies in the model selection process generally increases the predictive accuracy of the model, by controlling for fixed characteristics of the region. In addition, including regional dummies enables model selection algorithms such as LASSO and stepwise to prioritize variables that best explain within-regional variation for inclusion in the model. Finally, since these algorithms use the sample to determine how many variables to select, using more disaggregated predictors ensures that a richer and more accurate predictive model is selected. The household model may also slightly benefit from predicting a continuous welfare variable, rather than discarding information—about how close a household is to the poverty line—by first converting it to an estimated headcount poverty rate, although it is not clear that this difference is important empirically.

Table 3 shows empirical comparisons of accuracy, as measured by R<sup>2</sup> , from selected evaluations. R<sup>2</sup> is shown because it is commonly reported and it tends to track closely with Spearman rank correlation, which is in turn useful for evaluating targeting performance. In most cases, unfortunately, the evidence reported below is based on a single real-life survey. Only in Mexico, to our knowledge, is there simulation evidence comparing area and household-level models using geospatial auxiliary data. In each case, models are selected using LASSO and regional dummies are included.

_Table 3: R_<sup>_2_</sup> _by method and in or out of sample, relative to validation data_

|Country|Indicator|Sample|Validation<br>data|In-sample||Out-of-samp|le|
|---|---|---|---|---|---|---|---|
|Model||||Household-<br>level|Area-<br>level
work by Cadot, Carr`ere, and Strauss-Kahn (2011) and Jaud, Kukenova, and Strieborny (2018), we measure this distance by the Euclidean distance between the vector of the exporting country’s relative factor endowments and the vector of the exported product’s relative factor intensities: The Heckscher-Ohlin theory maintains that exported products should intensively use those factors of production that are abundant and therefore cheap in the exporting country. Consequently, the relative factor intensity of the exported products should be as close as possible to the relative factor endowment of the country that exports them. In the above equation, _κc_ , _hc_ and _lc_ are the exporting country’s endowments in physical capital, human capital and land, all relative to the fourth production factor - labour (e.g., _κc_ ˆ represents the capital/labor ratio in a country _c_ ). _κk_ , _h_<sup>ˆ</sup> _k_ and<sup>ˆ</sup> _lk_ are the corresponding relˆ ative factor intensities of the exported product (e.g., _κk_ represents capital/labor intensity for a product _k_ ). Since the production factors are measured in units that are not directly comparable, we standardize the differences between the product’s factor intensities and the country’s factor endowments to have zero mean and unit variance (“std” in Equation 1 stands for “standardized”).<sup>12</sup> Our approach should be seen as complementary to the Ricardian theory that stresses technology as an alternative source of comparative advantage in international trade. Our focus on factor endowments is motivated both by data availability and existing evidence that Ricardian forces of technological differences do not bias tests of the Heckscher-Ohlin model. Online Appendix A2 discusses these issues in more detail.

The data on relative factor endowments and factor intensities is taken from Cadot, Tumurchudur, and Shirotori (2009). While finding proxies for factor endowments at the country level is relatively straightforward (physical capital constructed according to the perpetual
Education Development Index (IDEB). IDEB was created in 2007 by INEP and compiles in a single index the average scores obtained by students on standardized evaluations and the school flow.<sup>41</sup> The index is calculated using data from the school census and _Prova Brasil,_<sup>42</sup> and it is available at the school, municipal, and state level. This variable is used at the school level, for the years 2005, 2007, 2009, and 2011. Finally, we look at dropout rates at school level, provided by INEP; these data are available from 2007 to 2011. In our analysis, we focus on dropout rates in the first and second cycles of primary schools (first to fifth grade and sixth to ninth grades, respectively).

Our empirical analysis also looks at the heterogeneity of impacts across a number of program characteristics, such as _Mais Educação_ coverage at the school level (percentage of students benefiting from the extended day), spending per student, and main thematic areas of activities available in the program. We compute spending per student using administrative data from MEC on the total financial transfer to the school divided by the number of students in the school participating in the program. Schools’ thematic areas are also provided by MEC/FNDE. The program’s coverage is calculated by dividing the total number of students enrolled in _Mais Educação_ in a given year, gathered from MEC, by the total number of enrollments in school census in the previous year.<sup>43</sup> We also assess whether the impacts of the program differ substantially depending on where the school is located, analyzing characteristics such as city population (IBGE), city GDP per capita ( _Instituto de Pesquisa Econômica Aplicada_ [IPEA]), and city spending on education and culture (National Treasury Secretariat). All these variables are available at the city level in IPEADATA. All nominal monetary variables are deflated by the IPCA<sup>44<
2 mg/L. Following a similar methodology described in section 2.4, each VLSS commune is matched to its upstream pollution counterpart.

# _Data for Sub‐Saharan Africa_

We use data from 90 Demographic and Health Surveys (DHS) spanning a period of 23 years to account for all child and household variables presented in the analysis. Figure 2 shows the 31 countries included in the analysis from Sub‐Saharan Africa, as well as Morocco and the Arab Republic of Egypt. The dots represent the approximate locations of the communities where households in the survey live. We focus on anthropometric measures of children up to 5 years of age. We convert children’s heights into Z‐scores using the WHO growth standards (WHO 2006). Doing so allows us to assess child height relative to well‐nourished children of the same age and sex. For our main outcome variable, we use height‐for‐age Z‐score (HAZ) and low HAZ (i.e. HAZ below ‐2), which reflects stunting. Water quality data come from a machine learning algorithm presented in Damania et al. (2019). Each birth record is then matched to nitrate pollution flowing from urban centers that are farther upstream. These urban centers were identified using data of urban agglomerations from Africapolis (OECD/SWAC (2018)).

# 3. Empirical Methods

To estimate the long‐run health impacts of childhood exposure to nitrogen pollution, the research design exploits quasi‐random variation in exposure to nitrogen pollution experienced by different birth cohorts in different districts. Specifically, the analysis compares height outcomes between exposed and non‐exposed cohorts, controlling for average differences in these outcomes across birth years and across districts. The estimating equation for individual‐ level outcome Y of person i during time t and born in district d and state s is presented below.

Y��� �α�βN<sup>�</sup> ������,��� _3_ �
accounting of foreign-born highly educated is based on the US wage of immigrants from each country of origin (in year 2000). We calculate the weekly wage of college educated immigrants from country j as a linear combination of the wages of US-born college educated and non college educated workers. We then use those weights to distribute a highly educated foreign-born between more and less educated US equivalents. With this method, for instance, a highly educated Vietnamese is considered as equivalent to 0.73 units of US college graduate and 0.27 units of US less educated. If foreign-born college educated migrants from a certain country are paid more than US born they are considered pure college equivalent.

Our fourth and ...nal correction is based on Mattoo et.al (2008) who calculate the probability that an immigrant from country j with tertiary education (completed at home prior to migration) obtains a skilled job in the US labor market. Using the data from 1990 and 2000 censuses and controlling for individual factors (such as age, experience and years since migration), they calculate these probabilities for hypothetical individuals identical in every respect except their origin country. They ...nd that migrants from South Asia perform very well while those from Eastern Europe and Latin America are placed in relatively unskilled jobs.

The results from all of the four corrections are reported in Figure 7. Some corrections imply smaller gains for host countries (especially for the US, Canada, Australia and Singapore) The largest change, relative to the baseline, is for the US where we now observe a loss of around 0.2% in average wages due to ten year of immigration, which is still a small percentage. The corrections do not change the overall picture and the a positive impact of immigration in most European countries is maintained. The limit of all corrections is that, for data limitations, they are all based on schooling quality and labor market assessment of immigrants in Canada or the US and not in European countries.

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# **3. Alternative measures of welfare – the asset index method**

Given the well known problems in measuring income and expenditure, increasing use is now being made of alternative wealth rankings based on the household ownership of assets, such as car, refrigerator or television, as well as characteristics of the household dwelling such as type of flooring materials, type of toilet and access to basic services including clean water and electricity. In order to create an index from the information on asset ownership it is necessary to aggregate the individual responses. A number of different techniques have been used. The simplest approach is to assign equal weights to the ownership of each asset or presence of each household dwelling. However, such a simple additive approach assumes the welfare value of each element is equivalent e.g. having a radio has the same welfare impact as having access to a flush toilet. As an alternative to simply calculating an index based on the sum of the assets, it is possible to use statistical techniques to determine the weights in the index. The two most common approaches for doing this are principal components analysis and factor analysis (Bollen, Glanville and Stecklov 2001; Gwatkin et al. 2000).

Using the 1999 census, we used information on household ownership of assets and characteristics of the household dwelling to create an index of welfare for Azerbaijan. The list of items included in the index is shown in Table 1 in Technical Note 2. It is important to note that with this technique the socio-economic status of the households is defined in terms of asset or wealth, rather than in terms of income and consumption (see Technical Note 2, for further details of the implementation of this method).

Figure 7 shows the spatial distribution of the proportion of people living in the poorest 40 percent of household ranked using the asset index method.

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However, for in-sample areas the household level model is significantly more accurate, such that the household-level model is more accurate overall (results not shown). In Tanzania, the area-level model also generates slightly more accurate predictions than the household-level model, although the difference is negligible. Interestingly, in the one simulation comparison in Mexico, the two models perform essentially equally well in-sample but the household model is moderately more accurate outof-sample. Mexico also is different than the other cases in using a relatively small number of proprietary geospatial variables, which may also partly explain why estimates are far more accurate out-of-sample in Mexico than Burkina Faso. In addition, the simulation results reported for Mexico are based on arealevel aggregates instead of sub-area level aggregates, due to the lack of sub-area identifiers in the census data. This may explain why the household model and area-level model perform equally well in sampled areas in this context.

The comparisons reported in Table 3 are far from conclusive and should be interpreted with caution, since all except for one case are based on a single sample. In addition, the area-level models estimated here use direct survey estimates of variance as inputs into the model, obtained using the HorvitzThompson approximation of variance. As noted above, using smoothed variance estimates and accounting for spatial correlation should increase the accuracy of area-level models. Finally, the evaluation metrics are often themselves EBP estimates based on household census data, since official welfare measures are never observed in the census. Nonetheless, despite the limited evidence so far, the household level model appears to generate more accurate predictions than the area-level model in the majority of cases, sometimes by substantial margins. Additional evidence would be useful to get a better sense of the conditions under which household models or area-level models generate more accurate estimates.

As noted above, a key benefit of incorporating sub-area level auxiliary data in a household model framework is increased efficiency. In Burkina Faso, the mean estimated mean-squared error for sampled areas was half as small when estimating a household model with sub-area predictors (Edochie et al
add that basic knowledge of management can be taught by business consultants to entrepreneurs of MSEs. In recent years, an increasing number of randomized controlled experiments have been carried out to test the effectiveness of management training or consulting provided to entrepreneurs (e.g., Karlan and Valdivia, 2011; Drexler, Fischer, and Schoar, 2010; Bruhn, Karlan, and Schoar, 2010; Field, Jayachandran, and Pande, 2010; Bjorvatn and Tungodden, 2010; Mano et al., 2012). These experiments have been conducted in various part of the developing world, but they share surprisingly similar results. We also conducted management training experiments in Clusters 1 and 2, together with the results of experiments in two other survival clusters in Vietnam and Tanzania (see Table 2) and obtained similar results. The outline and main results of our experiments may be summarized as follows.

In each cluster, management training was provided to entrepreneurs in a classroom setting by local business consultants in local languages and by international consultants accompanied by an interpreter. The program consisted of three modules: marketing and entrepreneurship, production management including an introduction to a few basic concepts of Kaizen, and record keeping. Before the program, we conducted a baseline survey of firms in the cluster and assigned sample firms randomly to the treatment group or the control group. It was up to the entrepreneurs in the treatment group to decide whether to attend the training program, which lasted three weeks.<sup>8</sup>

> 8 In the cluster of garment firms in Dar Es Salaam, Tanzania, the training program had four modules and lasted four weeks.  Three modules were the same as in other study sites, and the fourth was about color
38 In Mali, the monopoly distribution company reports the total number of low‐voltage (but not residential) customers in the survey year. The number of households reporting connection to the grid exceeds the number of low‐voltage customers by 30 percent. If all voltage customers are assumed to be residential— which they are not—and an average of two households are assumed to be connected to a shared meter, the percentage of households with their own unshared meter is calculated to be 54 percent. The percentage increases to 65 percent if the assumption is changed to three households for every shared meter on average.

Lastly, in Ethiopia, the monopoly distribution company reports the total number of residential customers in the year before the survey year. The number is 1.65 million customers in 2012, against 1.63 million households that reported having their own private meters out of a total of 4.2 million households reporting connection to the grid in 2013. These numbers strongly suggest that many households that replied that they had private meters were actually sharing their meters with their neighbors, with the distinction understood by survey respondents to be between having meters installed on their premises (private meters) versus connected to neighbors’ meters (shared meters). This in turn would mean that the percentage of households sharing meters was much greater in Ethiopia than the survey finding of 60 percent.

# Outages

Seven surveys—Ethiopia, Madagascar, Malawi, Mali, Niger, Nigeria, and Senegal—asked questions about power outages. Because questions asked varied from country to country, it is not possible to compare them on a common basis. The results are summarized in Table 14 for households connected to the grid who replied to the questions on the frequency, duration, or both of blackouts. Power outages were common in all the countries, and serious in Malawi (six out of every seven households reporting daily blackouts over the last 12 months), Niger, Nigeria, and Senegal.

Table 14: Summary of power blackout information

|Country
We start by examining the prevalence of different types of employment in urban SSA and their dynamics. In particular, we quantify the share of jobs in each type of employment in the region and at the country level. We then briefly explore how the share of jobs correlates with GDP per capita, as well as how the change in the share of jobs correlates with changes in GDP per capita. We conclude that urban informality may decline in the long-run but it is a permanent part of the urban landscape in the short-run.

The informal sector comprises 56.3 percent (lower bound) to 64.6 percent (upper bound) of the urban informal labor market in Africa. Figure 3 presents the average distribution of urban workers by employment type for the 26 African countries in our GMD sample. We present the average using the weights available in the household surveys and an average giving an equal weight to each country. The two methodologies give broadly similar results.

The most prevalent employment type in urban Africa is low-educated self-employed workers, who we define as being in the informal sector, followed by informal wage employees. These two employment types comprise nearly half of urban employment and most of urban informal employment. An estimated 29.4 percent of urban workers in the 26country sample can be classified as LE-SE. Another 18.6 percent can be classified as informal wage employees. These shares are smaller than the national averages estimated in ILO (2018), reflecting the highly informal nature of rural jobs included in the national estimates.<sup>38</sup> The share of urban LE-SE is three times larger than the share of urban higheducated self-employed. Only 18.5 percent of the African urban workforce are formal sector employees in the private sector; the same share as who are informal sector employees.

# **Figure 3. Distribution of urban employment by employment types, 26 African countries in the SSA region**

<!-- Start of picture text --> Avg. (Pop. weighting)