rafmacalaba/datause-encoder-data huggingface dataset:
# **Gender Gaps in Property Ownership in Sub-Saharan Africa**<sup>*</sup>

Isis Gaddis<sup>+</sup> Rahul Lahoti Wenjie Li World Bank Azim Premji University World Bank

**JEL:** J16, D13, D31, P48

**Keywords:** Gender gaps, property ownership, intra-household inequality, Sub-Saharan Africa

> * This study has been prepared as a background paper for the report Accelerating Poverty Reduction in Africa. We thank Kathleen Beegle, Bénédicte de la Brière, Markus Goldstein, Caren Grown and Dominique van de Walle for valuable comments. Of course, all errors are our own.

> + Corresponding author. Email: igaddis@worldbank.org.
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- 918

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10

# Learning During Project Design

Information abQut clients. The Bank's PHN projects have been designed on the basis of substantial quancitative knowledge about clients, but very little qualitative knowledge, especially in the health subsector. The situation is summarized in the following table, showing percentages of projects with given types of information.

||Pr|oject Type||
|---|---|---|---|
|Information<br>Type|Population|Nutrition|Health|
|Basic demographic|88%|81%|83%|
|KAP|58%|38%|19%|
|Determinants of behavior|15%|10%|6%|

In the above table, basic demographic information includes data on family size, age, fertility, etc.; on nutrition status; and on epidemiological status. The great majority of all project design teams had this kind of information. KAP information refers to the knowledge, attitudes, and practice of clients, i.e., their PHN-related beliefs and behaviors, on which there was information for many population projects, but much less often for nutrition and health projects. Project designers were by far the least well informed in all subsectors about the determinants of behavior--why clients believe and behave as they do, as opposed to what they practice. The data show no trend over the past six years toward improvement in the amount of qualitative information available, except in the case of the health sector. The fact that population has the most qualitative information and health the least, probably reflects the worldwide fertility survey effort during the 1970s and early 1980s in population and the fact that the Bank has been active longest in the population sector, whereas data are far weaker and the Bank's activities only six years old in tne health sector.

The unavailability of qualitative information about clients during project design is of serious concern in sectors where extension is the heart of the task; how is IEC--which is included in almost all the projects--to be effectively planned in the absence of such information? Other sectors in the Bank have been experimenting in recent years with anthropological techniques (mainly structured interviewing and participant observation) for rapid collection of information about project clients. This began in the urban and rural development sectors as a joint effort with the Project Policy Department's Public Sector Management Unit. The original idea was to collect qualitative information from clients during implementation about their reactions to projects, so that mid-course corrections could be made to make designs mode responsive to local needs. More recently, these techniques have been applied further upstream during project preparation, so
|FableS - F|||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|TheWVurld atWrk_|||||||||||||
|_Estimated assharesofworldlaborfo_|_rce_||||||||||||
||=__|_____________I_<br>LaborForce|Unemployed|Workers in|Workers in|Workersin|Wage|Non-wage|Wage|Non-wage|Wage|Non-wage|
|||(ages 15-64)||agriculture|industry|services|agriculture|agriculture|industry|industry|services|services|
|_______________________||(%/o)|(%)|(%)|(%)|(%)|(%/o)|(%/o)|(/o)|(/)|(/)|(/)|
|<br>World||<br>100.0|<br> <br>4.9|<br>43.4|<br> <br>19.4|<br> <br>32.3|<br> <br>3.5|<br>40.0|<br>11.4|<br> <br>8.0|<br>20.9|11.4|
|lillti Inconme<br>econondes||15.4|1.3|0.7|<br>4.2|<br>9.2|<br>0.3|0.4|3.8|0.5|7.9|1.3|
|Middle inconm**e**cononIes||26.6|1.8|7.6|<br>6.6|<br>10.5|<br>2.0|5.7|5.1|<br>1.5|<br>7.2|3.3|
|**[.4mw**income economles||58.0|1.7|35.1|<br>8.5|<br>12.6|<br>1.3|33.9|2.5|6.0|_5.8_|6.8|
|East Asia and Paciflc||38.9|1.1|21.9|<br>6.8|<br>9.1|<br>1.0|20.9|2.5|4.3|4.7|4.4|
|Europe and Central Asia||9.7|0.8|2.1|<br>3.2|<br>3.6|<br>0.4|1.7|2.6|0.6|2.9|0.7|
|Latin America and Caribbean||6.7|0.4|1.5|<br>1.6|<br>3.3|<br>0.6|0.9|1.2|<br>0.4|2.3|1.0|
|Middle East and North Africa||3.2|0.4|0.9|<br>0.7|<br>1.2|<br>0.2|0.7|0.5|0.2|0.9|0.3|
|O.E.C.D.||15.0|1.2|0.7|<br>4.1|<br>9.0|<br>0.3|0.4|3.7|0.4|7.7|1.3|
|Southi Asia||17.8|0.5|10.8|<br>2.5|<br>4.0|<br>0.4|10.4|0.8|1.8|<br>1.7|<br>2.3|
|Sg,h-SalersuraMAic_|_|<br>8.6|0.5|5.2|<br>0.7|2.1|0.5|4.8|0.3|0.5|0.8|1.3|

42
**Table 9:  Imports by Product and by Regional Market** (in x 2001 rubles)

|Good|Central F|ar East|North|Siberia|South|Urals|Moscow|St.<br>Petersb.|Tumen|North-<br>west|
|---|---|---|---|---|---|---|---|---|---|---|
|AGR|4.6|1.4|0.8|4.3|5.0|7.6|7.9|2.0|1.3|0.8|
|AIR|0.3|0.1|0.1|0.4|0.3|0.6|0.9|0.1|0.2|0.1|
|CHM|12.5|4.0|2.8|18.8|9.7|25.5|47.9|8.9|4.9|3.6|
|CNM|2.7|0.5|0.4|3.3|2.8|4.6|11.6|2.2|0.8|0.7|
|COL|0.6|0.2|0.2|1.3|0.4|1.2|0.6|0.2|0.2|0.2|
|CON|8.0|4.0|3.4|10.2|10.0|20.4|15.3|4.7|16.4|1.7|
|CRU|1.5|0.6|0.2|2.2|1.4|5.1|0.6|0.9|1.1|0.0|
|ELE|0.3|0.1|0.1|0.4|0.3|0.8|0.5|0.1|0.2|0.1|
|FIN|1.6|0.6|0.5|2.2|1.6|3.7|4.2|0.8|1.7|0.3|
|FME|9.5|1.2|0.8|3.1|7.8|11.7|13.4|4.3|2.9|2.8|
|FOO|19.1|7.2|3.0|14.4|17.9|25.3|79.1|36.2|4.2|7.7|
|GAS|0.1|0.0|0.0|0.1|0.1|0.3|0.2|0.0|0.2|0.0|
|HEA|0.9|0.4|0.3|1.3|1.0|2.3|2.8|0.5|0.9|0.2|
|HOU|3.5|1.4|1.1|4.6|3.9|8.0|10.7|1.8|2.2|0.7|
|MWO|35.8|14.3|8.2|18.0|31.1|60.3|198.5|49.6|20.1|10.9|
|NFM|3.9|2.1|1.5|13.2|4.7|10.9|9.2|3.5|2.2|1.2|
|OIL|4.1|1.1|1.0|4.5|4.0|9.0|6.4|1.6|3.3|0.8|
|OTH|1.7|0.6|0.5|2.2|1.7|4.1|3.7|0.8|2.0|0.4|
|OTI|1.2|0.3|0.2|1.1|1.1|2.1|1.5|0.4|0.6|0.2|
|PST|0.1|0.0|0.0|0.1|0.1|0.2|0.2|0.0|0.1|0.0|
|RLW|0.1|0.0|0.0|0.2|0.1|0.3|0.3|0.1|0.2|0.0|
|SCI|0.0|0.0|0.0|0.0|0.0|0.1|0.1|0.0|0.0|0.0|
|TMS|0.8|0.3|0.2|1.0|0.8|1.8|2.0|0.4|0.7|0.2|
|TPP|2.7|0.6|1.1|0.7|3.6|2.0|22.1|7.3|0.1|2.8|
|TRD|1.0|0.4|0.3|1.5|1.1|2.6|2.4|0.5|1.5|0.2|
|TRK|0.4|0.1|0.1|0.5|0.4|0.9|0.9|0.2|0.5|0.1|
|TRO|0.1|0.0|0.0|0.2|0.2|0.3|0.3|0.1|0.2|0.0|
|CLI|25|10|8|32|29|56|84|13|13|5|
|Total|142|52|35|142|140|268|528|140|81|41|

See Tables 1 and 2 for sector and region definitions.

Source: Roskomstat unpublished surveys and authors' calculations.

45
market dominance.

# **4 Robustness**

In this section, we subject our findings to a series of robustness tests. At the outset, our results are crucially dependent on the adjustment for intangible capital in the measurement of ROIC and markups. In unreported robustness tests we investigate whether our results are affected by if the adjustment is partial. We vary the intangible capital adjustment from 25% to 75% of the amount recommended by Peters and Taylor [2017] and repeat the specifications in Table 4. All our results are materially similar suggesting that our results are robust to smaller adjustments to intangible capital.

## **4.1 Alternate Classification of Superstars**

We next narrow our definition of star firms to see whether the role of market power and size differs for the most profitable firms. Appendix Figure A3 plots the mean ROIC for the Top 100 and Top 150 firms each year and confirms that there is no run-up in ROIC over time for even the top 100 or 150 firms. In unreported robustness tests, we vary our definition of star firms to the Top 100 firms and Top 150 firms in terms of ROIC and repeat our estimations from Table 4. Although we do not have as much power in these tests, the results confirm that in high skilled industries, large firms are more likely to be in the top 100 or 150 firms earning super normal returns.

While the above definitions are based on returns to equity, as an alternate definition, we define stars in terms of Tobin’s Q. Again following Peters and Taylor [2017], we define _Q_<sup>_TOT_</sup> as the ratio of Firm value to _TOTCAP_ which is the sum of physical ( _PPENT_ ) and intangible capital ( _ICAP_ ): where _V_ is the market value of the firm defined as the market value of equity (=total number of common shares outstanding (Compustat item _CSHO_ ) times closing stock price at the end of the fiscal year (Compustat item _PRCC_ ) plus the book value of debt (sum of Compustat items _DLTT_ 26
different options. In North America and in Europe, four options<sup>11</sup> are available for a company handling the pallet issue (Lacefield 2004:1). First, the seller can ship goods with a limited-use pallet (a softwood pallet). Shippers use frequently this technique because pallet costs are the lowest. However, this turns out to be costly per number of trip (see Table 2 for details). Secondly, the seller can use the pallet rental market. Even though the rental market accounts for only 10% of the pallet market, the leader on the market<sup>12</sup> achieves 2 million of pallet movement per day, is present in 38 countries and serves 100,000 customers worldwide. Thirdly, the seller can own pallets but rely on a third-party provider to manage them. Lastly, the seller that owns pallets can join a pallet exchange system.

**Table 2: Pallet Costs in Various Materials**

||**Hardw**<br>**ood**|<sup>**Softwood **</sup>|<sup>**Plastic**</sup>|
|---|---|---|---|
|**Cost (new)**|$9.00|$6.00|$60.00|
|**Cost (rebuilt)**|$6.00|N/A|N/A|
|**Estimated Life**|25 trips|2 trips|100 trips|
|**Cost per Trip**|$0.36|$3.00|$0.60|

Source: Minnesota Office of Environmental Assistance.

Note: Prices are for the US in USD and indicative.

The exchange system is probably the most interesting option for many companies. For this reason, hundreds of Canadian companies joined together and established a successful pallet exchange system (see Box 1).

> 11 A fifth option consists of a mix of different options.

> 12 CHEP (Commonwealth Handling Equipment Pool) from Brambles Group, was developed during WWII in Australia for efficient handling of defense supplies. When the US Army left Australia, they left behind pallets. In 1949, the Australian Government decided to privatize CHEP. The company was sold to Brambles in 1958. Within a few years, CHEP operated the largest pallet pool in the Southern Hemisphere.

11
# **Appendix 2: Definition of outcome measures used in the analysis**

_Outcomes as defined for the Philippines data_ The analysis concentrates on children's participation in education and work. For education, we consider current school attendance (in primary or secondary school), regular school attendance, and days of school attendance in the two weeks prior to the interview. We define regular school attendance as attending school for at least 85 percent of the days that school was in session in the two weeks prior to the interview (self-reported).

For work, we focus on participation in economic activities, days worked in the 12 months prior to the interview (with or without pay), and annual earnings. Work without pay refers to any work without pay on a farm, work in the private or public sector, work for own account, and work in a business belonging to the child or the household. Work without pay does not include household chores. We separately examine participation in work for pay outside the household, work without pay both inside and outside the household, work for pay inside the household, as well as participation in the following occupations: (i) farmers, forestry workers, and fishermen, (ii) laborers and unskilled workers, and (iii) all other occupations. We check the robustness of the estimated impact on work using the same set of outcome variables, but reported for the seven days preceding the interview (instead of the 12 month recall period). We focus primarily on work in the 12 months prior to the interview, because this outcome variable is less likely to be affected by seasonality concerns.

To calculate annual earnings, we first estimate children’s individual hourly wage rate by dividing the last pay they received by the hours worked over the period covered by the last pay. We multiply this hourly wage rate by the estimated number of days worked in the last year and “usual” hours worked per day in the job. Finally, to examine summary shifts in child behavior as a result of the program, we analyze four mutually exclusive combinations of school attendance and work in the last 12 months: in school only, in work only, in work and in school, and neither in work nor in school.

40
# **Thailand**

Table 6a: Returns to levels of education - Employed for wages

||1990|2004|Change (%)<br>2004-1990|
|---|---|---|---|
|_Basic controls only_||||
|- Primary|0.301|0.394|_30.9_|
|- Junior Sec./primary|0.374|0.316|_-15.5_|
|- Senior Sec./ primary|0.636|0.603|_-5.2_|
|- Tertiary/primary|1.093|1.289|_17.9_|
|_Basic controls + Industry dummies_||||
|- Primary|0.221|0.253|_14.5_|
|- Junior Sec./primary|0.306|0.214|_-30.1_|
|- Senior Sec./ primary|0.571|0.430|_-24.6_|
|- Tertiary/primary|1.089|1.072|_-1.5_|
||1990|2004|Change (%)<br>2004-1990|
|_Basic controls only_||||
|- High school general/ primary|0.471|0.482|2.3|
|- High school vocational/ primary|0.730|0.719|-1.5|
|- Tertiary/primary|1.097|1.289|17.5|
|_Basic controls + Industry dummies_||||
|- High school general/ primary|0.389|0.327|-15.9|
|- High school vocational/ primary|0.666|0.533|-20.0|
|- Tertiary/primary|1.094|1.078|-1.5|

Table 6b: Returns to skill over time – Employed for wages

||1990|2004|Change (%)<br>2004-1990|
|---|---|---|---|
|_Basic controls only_||||
|- Upper Sec. and above/ Low sec. and below|0.857|0.989|_15.4_|
|-At least some tertiary/Secondary and below|1.074|1.259|_17.2_|
|_Basic controls + Industry dummies_||||
|- Upper Sec. and above/ Low sec. and below|0.846|0.696|_-17.7_|
|- At least some tertiary/Secondary and below|1.056|0.955|_-9.6_|

15
# **FIGURES**

Figure 2. SuTPs Living Outside Camps vs Recent Migrants in the LFS <!-- Start of picture text --> 1400000 90000<br>GOV<br>80000<br>1200000<br>UNHCR<br>70000<br>1000000<br>UNHCR (registered)<br>60000<br>800000<br>50000 LFS-Near Syrian<br>Border<br>40000<br>600000<br>LFS-National<br>30000<br>400000<br>LFS-Other Regions<br>20000<br>200000<br>10000<br>0 0<br>12/31/2011 12/31/2012 12/31/2013<br><!-- End of picture text --> _Sources_ : AFAD 2013, MOI, UNHCR, Turkey LFS 19
-3-

**Figure 1: Taxation of Corporate Profits: Relation of CIT and PIT**

## Relation CIT and PIT

<!-- Start of picture text --> No integration: Integration of Full integration:<br>classical system distributed profits conduit system<br>Corporate level Shareholder level<br>Dividend deduction Split-rate Imputation Schedular<br>treatment<br><!-- End of picture text --> Source: Cnossen, 1993.

# _Dividends_

In practice, in tax systems around the world, the integration of the corporate and personal income taxes is limited to dividends. There are two possibilities. Either integration is undertaken at the company level. Complete integration is attained when dividends are fully deductible from taxable profits of the corporation. This is the way corporate interest payments are treated. Partial integration results from distributed profits taxed at a lower rate than retained earnings (,,split-rate system"). But integration can be attained at the shareholder's level as well. Under a system of imputation, gross dividends are included in the personal income tax base. Corporate income taxes are regarded as prepayments and can be deducted from the final income tax liability. Under a schedular system, a flat-rate tax applies to dividends (the tax rate being often identical to the lowest marginal rate of the personal income tax). This guarantees partial relief from double taxation, at least for shareholders with a marginal income tax rate above the flat-rate.<sup>4</sup> 4 There are many other possible ways to achieve full or partial integration. The latter could be achieved, for exatnple, with all profits taxed at the same corporate income tax rate, but only parts of dividends being included in the base of the personal income tax. Another way for partial or full integration is the "double split-rate method" (effective in Austria until 1989) where the corporate as well as the personal income tax on dividends are applied at reduced rates.
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 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