rafmacalaba/data-use-mentions-extended huggingface dataset:
reduction. Brambilla's (2005) policy simulations suggest that after equalizing tariffs between the two countries, Argentine consumers gained 393 dollars per vehicle sold, whereas their Brazilian peers lost 204 per vehicle. Tovar (2012) finds sizeable welfare gains of $3,000 per vehicle from trade liberalization but concludes that 95 percent of these welfare gains for consumers came from expanded product variety, not from price changes. Unlike our study, none of these papers accounts for the environmental costs and benefits of a trade policy.

# 2. Data and Policy Context

This section describes the data sources, provides key characteristics of Colombia’s new vehicle market, and outlines the major policy changes affecting hybrid and electric vehicles during the data period.

## 2.1 Data

The primary data set used to estimate the model parameters and conduct counterfactual simulations consists of quarterly observations of new vehicles in Colombia between 2015 and 2020. For each vehicle, we observe new registrations, prices, and attributes. This subsection describes the construction of the data set.

Data on vehicle registrations and attributes from IHSMarkit are the primary data source. A vehicle is defined by a unique model name, trim, engine size, and fuel type. Each quarter, there are typically about 30,000 vehicles with positive registrations. The definition of a vehicle is highly specific and distinguishes between vehicles sold under the same model’s name, but which have different fuel types (such as gasoline or hybrid), engine size (such as 4-cylinder or 6- cylinder), or trim level (such as the “standard” or “sport” trim). For example, a gasoline-powered Mazda 3 Standard trim with a 1.6-liter engine is a unique vehicle. This level of detail is comparable to some recent papers on Europe (Grigolon, Reynaert, and Verboven, 2018) or the United States (e.g., Leard, Linn, and Zhou 2021), and it is unusual for papers on low- or middleincome countries. As
In summary, understanding the evolution of the unorganized sector in developing and emerging economies is a very important task for regional scientists, development economists, and policy makers. Our inquiry provides some initial steps towards understanding its evolution for India. One strong conclusion emerges from the Indian experience—single, grand theories about India’s unorganized sector and its transitions are likely to be inadequate. The persistence of the unorganized sector across so many different states and industries is too great to afford a single, unifying explanation. Taking somewhat smaller steps, our work highlights that growth of the organized sector does reduce the unorganized sector share. There are also some key establishment size distribution properties for India in how the transition occurs.

The plan of this paper is as follows. Section 2 discusses our data and the levels of unorganized activity in the Indian economy. Section 3 estimates production functions for Indian manufacturing establishments. In both of these sections, we make comparisons to U.S. establishments where appropriate. Section 4 presents the decomposition of state-industry changes, and Section 5 analyses industrial traits in the organized sector and changes in unorganized sector activity. The final section concludes and discusses implications from this work.

# **Indian Data for the Organized and Unorganized Sectors**

We employ cross-sectional establishment-level surveys of manufacturing and service enterprises carried out by the Government of India. Our manufacturing data are taken from surveys conducted in fiscal years 1989, 1994, 2000, and 2005. The service sector has only more recently been surveyed in fiscal years 2001 and 2006. In all six cases, the survey was undertaken over two fiscal years (e.g., the 1994 survey was conducted during 1994-1995), but we will only refer to the initial year for simplicity. This section describes some key features of these data for our study.<sup>2</sup> It is important to first define and characterize the distinction between the organized and unorganized sectors in the Indian economy. These distinctions in the Indian context relate to establishment size.
of other countries in the region (China, Malaysia, Philippines, Vietnam). However, the current delivery system is unable to address the growing burden of non-communicable diseases (NCDs). As NCDs expand, Mongolia will need to address both their underlying causes as well as increase early detection and chronic disease management.

Access to education is very high in Mongolia. The country has high enrollment rates in education. At 79 and 59 percent for women and men, respectively, tertiary education gross enrollment rates in Mongolia are the highest among comparison countries, and among the highest in the World. Similarly, gross enrollment rates for secondary education are 82 and 80, respectively, also very high in international perspective. Primary education gross enrollment rates are the same for boys and girls (86 percent).<sup>12</sup>

# **III. Methods, data and assumptions**

The methodology adopted in this study is based on the methods outlined in “ _Commitment to Equity Handbook. A guide to estimating the impact of fiscal policy on Inequality and Poverty_ ”, by N. Lustig (2016) and developed in a previous work (Lustig & Higgins, 2013). This methodology has

> 11 Data collected by (Schlirf, Johansen, Georgieva, & Wu, 2018) who estimate that actual production costs are approximately 51 percent higher than reported costs. This number refers to both electricity and central heating. This study separates this adjustment into 39% for electricity and 79% for central heating given that electricity companies report less deficit in their accounts than central heating companies. 12 Data from World Development Indicators.

6 MONGOLIA Distributional impact of taxes and transfers / May 2018
country. Moreover, the gap between rich and poor could easily vary between countries so the Brazilian poor could well be relatively poorer than the Brazilian rich compared to the Egyptian poor relative to the Egyptian rich.

# _C) Attainment Profiles_

'We use the data for children aged 15 to 19 to create an "attainment profile" which shows graphically the proportion of individuals that completed each grade or higher (Figure 1). For example this means that the level at grade 1 shows the proportion that ever attended school and completed first grade. One minus this proportion is the proportion that never completed even one year of schooling.<sup>9</sup> The slope of the enrollment profile is a simulation of drop-outs.'<sup>0</sup> The difference between the proportion that completed grade 5 or higher and those that completed grade 6 or higher is an estimate of the proportion of all children that dropped out between 5th and 6th grade. This is not the usual drop-out _rate,_ as the denominator is all children as opposed to the proportion of those reaching 5th. In the attainment profile figures the drop-out _rate_ is the vertical drop between grades as a proportion of the absolute height.

Figure 1 shows the attainment profiles for each of the 35 countries (some with profiles for more than one survey) with the profile of the poor, middle and rich identified. Since much of the paper is an exploration of the interesting results and patterns that emerge from these graphs we'll walk through the interpretation of the graphs by describing the first country, Benin in detail.

> 9 We are therefore not distinguishing between attending school but never completing even one grade and never having attended school at all.

> **0** This is a simulation because we are not observing an individual's progression through the school system but a cross section of attaimnents of this cohort.

10
# **Figures**

**Figure 1: Income and Consumption by Levels of Gross National Income (GNI) per Capita (Atlas method)**

<!-- Start of picture text --> Income and Consumption Income and Consumption<br>all countries GNI per capita below 12535 USD<br>Figure 2: Differences in countries that collected income and consumption at the same time.<br>1980 1990 2000 2010 2018 1980 1990 2000 2010 2018<br>year year<br>Income Consumption Income Consumption<br>Source: Authors’ own elaboration using PovcalNet data.<br>100000 Upp_middle<br>80000<br>60000<br>Value<br>GNI per capita<br>40000<br>Low_middle<br>20000<br>Low<br>0<br><!-- End of picture text --> _Source:_ World Bank.

23
# **ANNEX. SURVEY-TO SURVEY IMPUTATION METHODOLOGY**

## **1. Methodology**

The estimates of poverty and welfare status of the households rely on survey data with a complex consumption module containing large set of detailed questions on prices and quantities consumed. Given its complexity the collection and analysis of that data involves significant investment of time, money and analytical efforts. On the other hand, there is a need for timely poverty estimates for evidence-based policies in the face of the high cost of fielding comprehensive surveys to track income and/or expenditure has led to the development of a variety of approaches for estimating poverty in the absence of consumption expenditure or income data.

In this section, we explore the survey-to-survey approach, which used the common observed assets and household characteristics in order to impute a proxy for welfare. The survey-to-survey imputation requires that at least one previous comparable survey contains household-level income or consumption information. The method draws upon the imputation literature (see Brick and Kalton, 1996 for a discussion of various techniques), which utilizes non-missing data in a larger data set to predict the values for missing variables, and from the poverty targeting literature (see Grosh and Baker, 1995) which seeks proxies for poverty status from household characteristics. The survey-to-survey imputation method extends that to using common variables in two data sets, only one of which contains consumption to predict consumption values in the second data set. A common application of this method in poverty analysis is “poverty mapping,” which uses consumption and poverty estimates from a household survey imputed into census data to achieve very fine levels of geographic disaggregation. (see Rao, 2003, for a discussion of the general technique of small area estimation, and Elbers et al., 2002, specifically related to poverty mapping.) For examples of surveyto-survey imputation for poverty analysis, see Stifel and Christiaensen (2007), Tarozzi (2007), Grosse et al (2009), and Douidich et al (2013).

A more formal presentation of the S2S
||Self-employment income compensation||
|---|---|---|
|NL|Monetary compensation for employers|Temporary social assistance for self-employed|
|PL|Wage compensation<br>Self-employment income compensation||
|PT|Wage compensation||
||Self-employment income compensation||
|RO|Wage compensation<br>Self-employment income compensation|Compensation for parental leave|
|SE|Wage compensation||
|SI|Wage compensation<br>COVID-19 universal income for self-<br>employed||
||COVID-19 lump sum allowance for<br>vulnerable groups||
|SK|Wage compensation||
||Self-employment income compensation||
||Additionalpandemic nursingbenefit||

Source: Own compilation based on EUROMOD Country Reports<sup>3</sup>

> 3 - - <u>https://euromod web.jrc.ec.europa.eu/resources/country reports</u>

31
Table 2: Summary Statistics

|**Variable**|**Mean**|**Std. Dev.**|**Min.**|**Max.**|**N**|
|---|---|---|---|---|---|
|Employment Growth|0.01|0.51|-1.96|1.95|15465|
|Capital Growth|-0.03|0.87|-2|2|15395|
|Employees|11.26|84.09|1|4800|30930|
|Capital|299741.18|6762496.28|0|750000000|30921|
|_Fgn_ 2006|71.07|29.04|10|100|338|
|_Fgn_ 2011|68.93|30.97|13|100|142|
|Start Year|1996.69|11.03|1920|2011|30930|

with the weight being the the proportion of sector _s_ ’s output supplied to another sector _k_ . This is taken from the 2006 input-output matrix for 81 industry sectors which is matched to the 221 sectors following the 4-digit ISIC3.1 classification.<sup>21</sup> Note that inputs supplied within the sector are not included since this is already captured by _φ_<sup>_H_</sup> _s,t_<sup>.Likewise,</sup><sup>_φF_</sup> _s,t_<sup>is</sup> the share of foreign employment in upstream (or supplying) sectors feeding into _s_ , with the different sectors’ _φ_<sup>_H_</sup> _k,t_<sup>values weighted by the share of inputs</sup><sup>_s_sources from</sup><sup>_k_(again excluding</sup> within-sector purchases)
This means that _X_ reflects the incidence/likelihood of episodes, where the RER is below, is equilibrium level beyond a certain threshold _k_ . The response, as we see, is binary which is a choice among two possible outcomes is. We model this response as a linear regression problem and the probability of achieving an undervalued RER beyond some threshold _k_ such as 5, 10, 20 and 25 percent. We regress the binary outcome on potential explanatory variables such as intervention, exchange rate arrangements, openness, monetary and fiscal variables. The expected value of achieving undervaluation in the model (given a set of explanatory variables z) is: Our _Probit_ analysis therefore evaluates the impact of active macroeconomic policies on the probabilities of RER undervaluation with using our event-analysis database.

# **3.2. The** **_Tobit_ Model**

The _Tobit_ model is a type of _censored regression_ model where the latent variable cannot always be observed while the explanatory variables are always observed. The _Tobit_ model has the following general specification: The latent variable, _E_ <sup>_x_</sup> _i_   is  '<sup>_z_</sup> _i_ . The estimation of this model is similar to one of truncated regression. The log-likelihood for the censored regression model is: 14
small firms, but as we have seen this does not appear to be the whole explanation for the relationship between firm size and wages in Africa. Kingdon et al. (2004) distinguish between labor market institutions and intra-firm organizational mechanisms as potential explanations for this result and labor market segmentation more generally. We summarize their arguments next.

As for the importance of labor market institutions, Kingdon et al. (2004) cite numerous studies providing evidence that unions play a central role in many African labor markets. The authors report estimates of the union premium ranging from 3 to 28 percent in Ghana, Kenya, Nigeria, South Africa and Tanzania, controlling for observed worker skills. The authors acknowledge, however, that systematic identification of the union effect is complicated by the factor that unionization is highly correlated with other leading explanations for labor market distortions including other regulations, monopoly power of producers, and firm size more generally. A related possible explanation is that there is rent-sharing as a result of a bargaining process between the firm and the workers. The idea is that when business is good the workers manage to bid up their wages by threatening to walk out if the firm refuses to accommodate their demands. Evidence of such rent-sharing effects on wages has been found for Ghana (Teal, 1996) and Zimbabwe (Velenchik, 1997).

It is also possible large firms pay relatively high wages in order to provide workers with incentives to exert effort as in efficiency wage models. Fafchamps and Söderbom (2004b) derive a theoretical model in which the size-wage relationship is generated by the cost of monitoring workers rising with firm size. Using worker-firm matched data across 10 African countries, and contrasting SSA with Morocco, their estimates indicate that a doubling in the number of production workers will increase the labor cost per unit of effort by 9 percent for Morocco and between 11 and 14 percent for SSA. This is the penalty large firms have to incur in order to motivate workers. This incentive problem appears to be more severe in SSA than in Morocco
**Landsat Data** The Landsat program is managed by the United States Geological Survey (USGS) and consists of several satellites that capture global imagery at frequent intervals. Unlike satellites that measure nightlights, the Landsat satellites records daytime reflection of sunlight at different spectral bands. These bands include wavelengths of visible and invisible light, and thus rich information on objects on the ground. Depending on their structure, objects on earth reflect different portions of the electromagnetic spectrum, and therefore form a “spectral signature”. Landsat data is organized into individual georeferenced and time-coded images and covers all of Earth except for the polar regions.

The Landsat program dates back to the early 1970s and has launched several satellites since then. The currently active satellites (Landsat 7 and Landsat 8) provide a spatial resolution of 30x30 meters, and their output can therefore be classified as “medium-resolution” imagery. While Landsat imagery lacks the detail of commercial mapping services such as Google Maps, the spatial resolution is fine enough to visually recognize certain features like cities and other land use. Besides visible light that can be recognized by the human eye, the satellites also capture non-visible light such as infrared and thermal heat. While widely used in environmental sciences, the vast Landsat dataset has only recently been introduced into the social sciences, mostly in urban studies. Several papers have examined the predictive power of Landsat imagery for population counts and urban boundaries, e.g. Goldblatt et al. (2016). There is very little usage of Landsat in Economics despite the fact that Landsat imagery is available easily at no cost.<sup>4</sup> This paper uses Landsat 7 which launched in 1999 and is still operating to produce data.<sup>5</sup> Landsat 7 records eight different spectral bands and has a temporal resolution (the time until the satellite revisits a certain position on earth) of 16 days. Table 1 provides an overview of the recorded bands and their resolution. The analysis uses annual simple composite images
Table B.3a: Changes in inequality <u>(Gini index) for the countries using phone surveys</u>

|Country|Region|2019|2020<br>(COVID-19)|Change, Gini points|
|---|---|---|---|---|
|Burkina Faso|Sub-Saharan Africa|48.10|48.14|0.04|
|Bulgaria|Europe & Central Asia|40.19|41.59|1.40|
|Central African Republic|Sub-Saharan Africa|58.48|55.83|-2.66|
|Congo, Dem. Rep.|Sub-Saharan Africa|42.42|42.21|-0.21|
|Gabon|Sub-Saharan Africa|37.89|38.10|0.21|
|Ghana|Sub-Saharan Africa|43.58|43.09|-0.49|
|Guinea|Sub-Saharan Africa|29.55|30.88|1.33|
|Gambia, The|Sub-Saharan Africa|35.88|35.67|-0.22|
|Guatemala|Latin America & Caribbean|48.13|48.74|0.61|
|Honduras|Latin America & Caribbean|48.11|48.33|0.22|
|Croatia|Europe & Central Asia|28.88|30.55|1.68|
|Kazakhstan|Europe & Central Asia|27.90|27.92|0.02|
|Kenya|Sub-Saharan Africa|40.32|41.45|1.14|
|Lao PDR|East Asia & Pacific|39.46|39.04|-0.42|
|Mali|Sub-Saharan Africa|36.21|37.87|1.66|
|Myanmar|East Asia & Pacific|31.58|30.92|-0.66
Policy Research Working Paper 8688

# **Abstract**

Tanzania is home to the third highest population of stunted children in Sub-Saharan Africa, with about 2.7 million children under the age of five failing to reach their full potential of growth attainment compared with the reference population as per the World Health Organization standards. Several studies have shown that stunted growth during childhood entraps the future of children in a vicious circle of recurrent diseases, reduced human development, and lower earnings, thus increasing their likelihood of being poor when they grow up. To reduce stunting, the Government of Tanzania and development partners are introducing a convergence of multisectoral interventions adapted to local needs. However, the existing stunting data are representative only at higher administrative levels, thus making it difficult to implement these efforts. The paper uses the 2016 geo-referenced Demographic and Health Survey in conjunction with relevant spatially gridded covariate data, such as nighttime lights, water and sanitation access, vegetation index, travel time, and so on. Geospatial techniques, such as model-based statistics and Bayesian inference implemented using the INLA algorithm, along with appropriate model validation exercises are employed to develop high-resolution maps of stunting in Tanzania at 1×1-kilometer spatial resolution. The maps show that areas of consistently high stunting rates tend to be more common in rural parts of the country, especially throughout the western and southwestern border areas. There is high prevalence of low stunting in the urban areas around Dar es Salaam, Arusha, and Dodoma, as well as in the south of Lake Victoria.

This paper is a product of 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/research. The authors may be contacted at goseph@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about
,<br>105 Apricots, cherries Onions and shallots Aluminum waste and scrap Copper waste Aluminum, not 105 Dried fruits Onions and shallots cherries and peaches<br>peaches and scrap alloyed Aluminum<br>0 0 waste and scrap<br>-0.5 -5 0Aluminum, not 0.5 1 1.5 2 2.5 3 -1 -0.5 -5 0 0.5 1 1.5 2<br>alloyed<br>-10 East Asia import growth rate, 2005-10 -10<br>MNA import growth rates, 2005-10<br>Export growth rate in Tajikistan, 2005-10<br>Tajikistan export growth rate, 2005-10<br>Tajikistan export growth rate, 2005-10<br>Tajikistan export growth rate, 2005-10<br><!-- End of picture text --> Source: COMTRADE data and author’s calculations.

Three types of products can be derived from Figure 5: products experiencing a limited supply-demand growth which Tajikistan is exporting a lot (big bubbles in the bottom left corner, e.g. aluminum alloys or aluminum not alloyed); products experiencing a moderate supply-demand growth which Tajikistan is exporting moderately, e.g. dried fruits, onions or apricots; and products with a strong supply-demand growth which Tajikistan is exporting too little (small bubbles in the top right , e.g. aluminum wire).

The first type of products does not need a particular action from the government to sustain its expansion: traditional supply chains can keep exports high as long as commodities importers are 7
**Table 1:** Overview of Indonesian targeting survey variables

|**Type**|**Theme**|**Variables included**|
|---|---|---|
|Core|Demographics|Household size & HH size<sup>2</sup>, number of<br>people by age groups by gender,<br>ur-<br>ban/rural, family structure, family smart<br>card|
|Optional|Housing|Materials used in the foor, wall, roof,<br>source of drinking water and cooking wa-<br>ter, type of lighting & cooking fuel, toi-<br>let facilities, septic tank, foor space per<br>capita, ownership status.|
||Assets|Household<br>ownership<br>of:<br>motorcycle,<br>car, computer, fridge, boat, motorboat,<br>phone, water heated, air conditioning|
||Education|Household members’ total levels of educa-<br>tional attainment and enrolment|
||Employment|Employment status, employment sectors|

# **Baseline PMT**

The Indonesian PMT implemented in the country’s social registry provides the baseline method for the early stopping algorithm’s performance. Its construction uses the data described above, and follows the canonical PMT approach in Algorithm 1 except for two significant changes. The first is in line with McBride and Nichols [2018], who pointed out that PMT is an outof-sample prediction task and that a cross-validation approach to variable selection leads to better performance than in-sample measures such as information criteria. Accordingly, we substitute selection via an information criterion with 5-fold cross-validation and a mean squared error (MSE) selection criterion. The stopping criterion is triggered when the cross-validated error no longer declines. MSE is the preferred metric as it aligns with the objective function of predictive model OLS<sup