rafmacalaba/datause-encoder-data huggingface dataset:
39 percent – graduated primary school, and over a quarter of those who completed primary school but not secondary school live on less than $3.10 per day. Fourth, the moderate poor, despite having similar profiles in terms of age and household composition, are considerably better educated and are less likely to work in the agriculture sector than the extreme poor. Finally, when conditioning on other observed characteristics, having two or fewer children, completing secondary education, and living in an urban area are strongly and positively associated with economic welfare within countries.

Taken together, these findings emphasize the central importance of policies and programs that benefit households in rural areas and those with large numbers of children in reducing extreme poverty. This includes not only direct support, but also efforts to reduce the future prevalence of these types of households, such as speeding the demographic transition by increasing female education levels. The results also suggest that continued improvement in educational attainment and quality, as well as more rapid urbanization and increased non-agricultural employment, can further hasten movement from extreme to moderate poverty.

The rest of this paper is organized as follows. Section 2 describes the data and the methodology used to harmonize and calculate poverty estimates across different national surveys. Section 3 reports and analyzes the demographic profiles of the poor in comparison with those of the nonpoor. Section 4 considers the robustness of key results to alternative line-up methods, spatial deflation, and varying samples due to missing data. Section 5 concludes.

# **2. Data and methodology**

# **2.1. Data**

The World Bank’s procedure for estimating global poverty rates is an extraordinarily dataintensive exercise. Global poverty estimates are derived from a collection of nationally representative survey data on household welfare – either income or consumption per capita – from 142 economies in the developing world. This collection of survey data is then combined with complementary data on population, inflation, real economic growth, and Purchasing Power Parity (PPP) exchange rates.

Estimating poverty rates for different types of households requires additional data on individual characteristics, comparable across countries and regions, from the same household surveys used to calculate poverty. Poverty profiles typically utilize a set of variables that are relatively straightforward to obtain such as age, gender, education, and sector of work. Nonetheless, compiling these variables from diverse household surveys, which differ in the quality of their data and the nature of the questionnaires, and harmonizing variable names and codes across surveys, is a remarkable achievement. These heavy data requirements are the main reason why only a few empirical studies have examined patterns across such a large number of countries.

This analysis is based on the September 2016 vintage of the Global Micro Database (GMD), a collection of globally harmonized household survey data recently developed by the Data for Goals 4
context of fragility and shocks. First, the approach requires a baseline survey, which is sometimes – for example in Mogadishu – not existent. Second, the approach relies on a structural model estimated from the baseline survey.<sup>4</sup> In the case of shocks, the structural assumptions, which cannot be tested, are often violated. Thus, poverty updates based on the violated assumption tend to under‐estimate the impact of the shock on poverty. Therefore, cross‐survey imputation methodologies are not applicable in the context of shocks and fragility.

A new methodology is proposed combining an innovative questionnaire design with standard imputation techniques. This substantially reduces the administering time of a consumption survey to about 60 minutes while at the same time credible poverty estimates are obtained. Thus, the gain in administering time is bought by the need to impute missing consumption values. Due to the design of the questionnaire, the method circumvents systematic biases as identified for alternative methodologies.

After explaining the methodology in more detail in the next section, the performance of the methodology is assessed _ex post_ using collected household budget data in Hargeisa, Somalia. Next, the methodology is applied to newly collected data in Mogadishu, Somalia, where full consumption data collection was impossible due to security constraints. The consistency of the consumption estimates is evaluated by performing validity checks. A conclusion discusses the limitations of the methodology, the benefits especially in combination of using CAPI technology and the need for further research.

# **Methodology**

## **Overview**

The rapid consumption survey methodology consists of five main steps (Figure 1). First, core items are selected based on their importance for consumption. Second, the remaining items are partitioned into optional modules. Third, optional modules are assigned to groups of households. After data collection, fourth, optional consumption modules are imputed for all households. Fifth, the resulting consumption aggregate is used to estimate poverty indicators.

> 4 Christiaensen et al, 2010; Christiaensen et al, 2011.

3
We use the latest three rounds of the household survey data from Vietnam—the Vietnam Household Living Standards Surveys (VHLSSs)—in 2010, 2012, and 2014. Being similar to the Living Standards Measurement Study (LSMS) surveys supported by the World Bank, these surveys are implemented biennially by Vietnam’s General Statistical Office (GSO) and collect rich data on household demographics, education, occupation, assets, and consumption. These surveys are regularly employed by the Government of Vietnam and international organizations to provide estimates on household welfare and poverty measures.<sup>13</sup> Since the VHLSSs collect panel data, these surveys offer an ideal setting for us to evaluate the different imputation methods. The key idea is to construct each welfare measure as if we did not have consumption data, and then evaluate the former against the latter.

In particular, we can construct a wealth index and compare how it performs against the actual consumption data in measuring poverty (Category A), impute consumption from one previous round to the next round and compare the imputed consumption against the actual consumption (Category B), and compare the synthetic panels against the actual panel data (Category C). For this category, a particularly useful feature of the VHLSSs is that it has a rotating panel design, whereby approximately half of the households are followed and half are refreshed in each new survey round. We will make use of this feature to provide poverty estimates using the synthetic panels constructed from the cross-sectional component of the VHLSSs, and then validate these estimates against the “true” rates that are based on the panel component of the VHLSSs.

# **III. Completely Missing Consumption Data**

> 13 An additional advantage of these data is the availability of community targeting, i.e., a household poverty status as classified by the local government. We will make use of this feature and present it together with the analysis based on consumption data.

12
Policy Research Working Paper 8994

# **Abstract**

The purpose of this paper is to introduce applications of RUSMOD—a microsimulation model for fiscal incidence analysis in the Russian Federation. RUSMOD combines household survey micro-data and fiscal policy rules to simulate the Russian tax-benefit system: the size and distribution of taxes collected and benefits paid, and the impact of the system on different population groups. Microsimulation models, such as RUSMOD, are habitually used in developed countries, and can be versatile budgetary policy tools. Using this model, the current tax-benefit system in Russia is examined. The impact of the system is measured across the income distribution, age groups, family types, localities, as well as across time. One of the applications of RUSMOD this paper aims to assess is the role of the tax-benefit system in explaining the incidence of informal employment in Russia. The paper investigates whether the existing system creates disincentives for formalization in terms of reducing disposable incomes and increasing poverty and inequality, and whether a hypothetical tax reform would be able to reduce the opportunity costs of formalization for informal workers, improve distributional outcomes, and increase fiscal revenues.

This paper is a product of the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at mmatytsin@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
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# **_5. Do the Determinants of Firm Exit Vary by Firm Types?_**

Table 6 examines whether the factors affecting exit vary by type of firm. In the first column, three dummies are created for firm size (<10 as small, 10-49 as medium and >50 as large). There are some non-linear results with respect to diversity and competition. For diversity, the effect is still associated with higher rates of exit – but less so for larger firms, exporters, firms with foreign partners and corporations. For competition, the overall effect of increasing the probability of exit is mitigated for large firms and for exporters. Part of this could reflect larger firms being older and more established, often with better connections. Or it could reflect the importance of attaining economies of scale that many small firms fail to do. But the most striking results are related to the impact of productivity.

The effects of productivity gaps on increasing exit are found to be _higher_ for _larger_ firms. This is consistent with Harding et al.’s finding in Sub-Saharan Africa that small firm survival was driven by different dynamics than that of larger firms and that productivity was only significant for larger firms. The effect is mirrored for exporters and foreign owned firms, older firms and corporations. All but government owned firms where the effect is not significant. If smaller, younger, domestically oriented firms are less subject to competitive pressures, this implies a less efficient allocation of resources, and that firm dynamics are less likely to contribute to increases in aggregate productivity.<sup>26</sup> The results for age are striking. Young firms (<5 years) have significantly different responses on all but one interaction. They benefit in particular from higher

> 26 We test this hypothesis in “The Productivity Effects of Firm Turnover in Moroccan Manufacturing” 2009.

22
2 percent

# 2.2.3 unemployment insurance

3. **Tax on Property**

- 3.1 Tax on Real Estate Transactions

- 3.2 Gift and Inheritance Tax

# 3.3 Motor Vehicle Tax

- 3.4 Boat Tax

- 3.5 Sign Tax

- 3.6 Tax on Weekend Cottages

- Tax paid by legal entities and individuals at the time of a real estate transaction Paid on real estate and personal property over a certain value

Tax paid by individuals on vehicles less than five years old and engine displacement of 1.6 liters

- Paid by individuals on boats exceeding 5 meters in length Paid by legal entities and individuals on a sign displayed

- Paid by individuals on the construction value of the structure

# 15 percent

0 percent to 12 percent paid by second degree, third 10 percent more, others 20 percent more 1500 dinars per year 900 dinars per year DM 50 to DM 250 annually from .10 to I percent on the value of construction

# 4. **Domestic Taxes on Goods and Services**

- 4.1 Sales Tax on Products

- 4.2 Sales Tax on Services

- 4.3 Sales Tax on Surcharge

Tax paid on final consumption, but on alcoholic and nonalcoholic beverages, beer, tobacco products, coffee, petroleum and petroleum products, paid on first sale Paid by legal entities and individuals on services subject to the tax A general surcharge on goods, levied on the sales-tax inclusive price 5 percent to 20 percent 10 percent 10 percent

- 4.4 Excise Taxes

4.4.1 Tax on Petroleum Products Paid by the producer or importer per liter DM .08 to DM .40 depending on the product 4.4.2 Tobacco Tax Paid by the producer or importer DM 0.22 to DM 2.30 per pack 4.4.3 Beer Tax Paid by the producer or importer DM 20 to DM 30 per hectol iter 4.4.4 Alcoholic Beverage Tax Paid by the producer or importer DM 8 to DM 10 per liter of pure alcohol content 4.4.5 Nonalcoholic beverage Tax Paid by the producer or importer from DM I to DM 20 per hectoliter 4.4.6 Tax on Imported Paid by the legal entity or individual on a DM 1900 to DM 8000 Automobiles new imported automobile exceeding power of 75 KW 4.4.7 Tax on Imported Coffee Paid per kilogram by the legal entity or from DM I to DM 4 per kg individual who brings coffee into the republic

# 5. **Taxes on International Trade**

- 5.1 Import Duties

from 0 to 21 percent Source: International Monetary Fund.

14
**Table 2: Non-migrant women in migrant households (%)**

|||**1997**||**2000**||**2004**||**2006**|
|---|---|---|---|---|---|---|---|---|
|**age 16-20**|||||||||
|living in migrant household|11.1|(31.6)|14.4|(35.2)|26.9|<br>(44.5)|36.8|<br>(48.4)|
|parent(s) migrated|5.3|(22.5)|7.2|(26.0)|11.7|<br>(32.2)|16.8|<br>(37.5)|
|sibling(s) migrated|5.4|(22.6)|6.1|(24.1)|9.8|(29.9)|21.2|<br>(41.0)|
|obs.||117||153||130||155|
|**age 21-35**|||||||||
|living in migrant household|6.3|(24.3)|12.4|<br>(33.0)|15.3|<br>(36.1)|21.0|<br>(40.8)|
|husband migrated|3.4|(18.1)|7.5|(26.5)|10.1|<br>(30.2)|8.7|(28.3)|
|child(ren) migrated|0.2|(4.3)|0.0|0.0|0.6|(7.9)|0.0|0.0|
|obs.||649||466||248||186|
|**age 36-50**|||||||||
|living in migrant household|18.3|<br>(38.7)|26.9|<br>(44.4)|40.5|<br>(49.1)|43.6|<br>(49.6)|
|husband migrated|3.1|(17.4)|5.4|(22.6)|10.9|<br>(31.2)|11.8|<br>(32.2)|
|child(ren) migrated|15.4|<br>(36.1)|24.3|<br>(42.9)|35.0|<br>(47.7)|39.4|<br>(48.9)|
|obs.||821||840||716||635|
|**age 51-60**|||||||||
|living in migrant household|18.8|<br>(39.1)|26.6|<br>(44.2)|35.3|<br>(47.9)|44.1|<br>(49.7)|
|husband migrated|1.2|(11.0)|1.0|(10.2)|3.2|(17.6)|6.6|(24.8)|
|child(ren) migrated|17.1|<br>(37.7)|23.9|<br>(42.7)|32.6|<br>(46.9)|39.4|<br>(48.9)|
|obs.||336||399||447||497|
|**age >61**|||||||||
|living in migrant household|7.3|(26.1)|12.1|<br>(32.7)|24.8|<br>(43.3)|30.4|<br>(46.1)|
|husband migrated|0.9|(9.6)|0.4|(6.5)|0.4|(6.6)|2.5|(15.6)|
|child(ren) migrated|4.1|(20.0)|9.2|(29.0)|21.3|<br>(41.0)|24.7|<br>(43.2)|
|obs.||219||240||250||270|

Notes: Standard deviations in parentheses. The total rural sample is used. Non-migrant women are defined as women who were present at the time of each specific survey round 32
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# How Roads Influence Development

Transportation plays a multifaceted role in the pursuit of development objectives. Restriction of accessibility limits efficient factor mobility, and defers the transfer of human and material resources to places where they can be employed most productively. Conversely, transportation development helps to attain an efficient distribution of population, industry and income.

Rural areas with low standards of living are characteristically those with inadequate methods of moving people and goods, probably because of deficient access between villages.and markets, schools, medical, economic, administrative and social services which affect the day to day lives of rural people (3). Transportation is an essential ingredient of almost everything man does to supply himself with the necessities of life. Road transport is particularly important for developing countries, where it provides about 80 to 90 percent of the total inland and/or border crossing transport of people and goods. An effective road network can hasten progress in agricultural and rural development, industry and trade, the viability of urban areas, and the expansion of jobs, education and personal opportunity (4). The World Bank's Long-Term Perspective Study _(5)_ emphasizes that although better market incentives (especially related to prices and inputs) to farmers remain important factors in agriculture, the effects of these would be blunted if the physical barriers and economic costs of transporting goods to and from markets remain high.

# Sources of Data

Data used in this analysis were gathered from different sources. GNP and population data come from the statistical annexes of "Sub-Saharan Africa - From Crisis to Sustainable Growth" (5) and the World Development Report 1990 (6). The data on road length, classification and condition were compiled from different World Bank reports (7, 8), "World Road Statistics 1985-1989" (9), Highway Statistics (10), Statistical Abstracts of the United States (11), Annual Bulletin of Transport Statistics for Europe (12), and World Transport Data (13).

The main variables included in this study are defined as follows. Gross National Product (GNP) is the measurement of a nation's total market value of the final goods and services that are produced in the economy during a given time period, normally one year. GNP per capita is a country's gross national product divided by its population, henceforth denoted by PGNP. Spatial road density is a country's road length per land area, and road density is per capita length of the road network.

Road conditions are defined as in the World Bank policy paper on road deterioration (5): (a) Good: paved roads substantially free of defects and requiring only routine maintenance, or unpaved roads needing only routine grading and spot repairs; (b) Fair: paved roads having significant defects and requiring resurfacing or strengthening, or unpaved roads needing reshaping or resurfacing (regraveling) and spot repair of drainage; and (c) Poor: paved roads with extensive defects and 3
measures are the following.

**Input coefficient.** Given that _Zij,t_<sup>_rs_ismeasuredinU.S.dollars.Itisusefulto</sup> define a currency-free input coefficient _a_<sup>_rs_</sup> _ij,t_<sup>=</sup><sup>_Z_</sup> _ij,t_<sup>_rs/Y_</sup> _j,t_<sup>_s_.Moreoverwecanaggregate</sup> _a_<sup>_rs_</sup> _ij,t_<sup>acrosssupplyingcountriestoobtain</sup>

The latter measures the normalized inputs used by sector- _s_ of country _j_ in its production sourced from sector- _r_ output (regardless of its geographic origin) in year _t_ . In what follows we associate a high input coefficient of the parent firm to backward integration.

**Output coefficient.** By the same token, we can define _b_<sup>_rs_</sup> _ij,t_<sup>=</sup><sup>_Z_</sup> _ij,t_<sup>_rs/Y_</sup> _i,t_<sup>_r_asa</sup> currency-free output coefficient. In turn, this can also be aggregated across using countries _j_ to obtain

The latter measures the normalized output sold by country _i_ ’s sector- _r_ geared toward sector- _r_ (regardless of the country) at year _t_ . In what follows we associate a high output coefficient of the parent firm to forward integration.

**Upstreamness.** While the previous two measures provide information about the connectedness of two sectors for a given (making or using) country, they are not immediately informative about the overall relative positioning of any sector and country or any sector-to-sector link and country in the global value chain. To determine the general upstreamness of a country-sector pair, we can iteratively make use of _a_<sup>_rs_</sup> _ij,t_<sup>toobtain:</sup>

This equation shows that the output of a country-sector can be expressed as that supplied directly for final consumption, plus that supplied in the production for final consumption in all country-sectors, plus that supplied to a supplier for final consumption in all country-sectors, etc. From equation 4 we can define a measure of upstreamness as: 8