rafmacalaba/datause-encoder-data huggingface dataset:
# Table of contents

|Introduction ....................................................................................................................2|
|---|
|Child Mortality Determinants and Complementarities with Other MDGs ....................4|
|_Analytical Framework ..........................................................................................4_|
|_Evidence on Child Mortality Determinants in SSA ..............................................8_|
|New Evidence from DHS: Comparing Country Experiences in SSA .........................14|
|_An Explorative Analysis of Trends and Determinants ........................................17_|
|_Evidence from Survival Analysis ........................................................................25_|
|_Simulations ..........................................................................................................34_|
|Conclusion ....................................................................................................................40|
|References ....................................................................................................................42|

ii
relevant is the balance sheet, which shows assets and liability. That is also true for a country."<sup>57</sup> ANS is able to fill this gap and to provide an answer to this relevant question: has total wealth risen or fallen throughout the accounting period? In other words, ANS is a measure of a “truer” rate of savings in an economy, after taking into account investments in human capital, depletion of natural resources and damage caused by pollution.

# ANS = (GNS−Depr) + EE − ED − MD − NFD − CO2D − PMD<sup>58</sup>

After discounting consumption of fixed capital from GNS, current spending on education is added as a proxy and first approximation for human capital investments (EE also includes wages and salaries in the education sector and excludes capital investments in buildings and equipment); then rent from the depletion of natural resources is deducted, to account for mineral, energy and forest depletion; and finally damages from carbon dioxide emissions are deducted.

Going one extra mile, however, it was noted that the concept of ANS does not necessarily fully respond to the question of the sustainability of an economy if we do not introduce also the dimension of demographic patterns. When there is a growing population, for instance (which is the case of most developing countries), even a positive ANS may coexist with a declining wealth per capita. Where ANS is negative, of course, it is clear that both total and per capita wealth are shrinking. This paper, therefore, takes into account also the population dimension, assuming it as an exogenous variable. Defining total wealth as TW, population as P, and population growth rate g, then the change in wealth per capita can be represented through the following formula: TW where we deduct a population dilution term (g∗ P ) (the population growth rate times total wealth per capita) from the change in total wealth over population. In fact, we may interpret Δ _T_ W as ANS. In other words, the equation argues a correlation between wealth and fertility rate, telling that total wealth per capita will rise or fall depending on whether the growth rate of total wealth (ΔTW/TW) is higher or lower than the population growth rate<sup>59</sup> .

# **_Methodology and context for the gold and iron wealth estimates_**

> 57 Nobel Prize-winning economist Joseph Stiglitz on GDP and learning from business, Foreign Affairs, 2005 58 Where: ANS = adjusted net saving; CO2D = CO2 damages; Depr = depreciation, ED = energy depletion; EE = education expenditure; GNS = gross national saving; MD = mineral depletion; NFD = net forest depletion; PMD = Particulate Matter damages.

> 59 Cf. Where Is the Wealth of Nations?: Measuring Capital for the 21st Century, THE IMPORTANCE OF population dynamics changes in wealth per capita, The World Bank, 2006, Chapter 5.

26
# **Table 1.  International Comparison of Listed Firms**

||No. Firm|s in<br>Operating<br>Income/|Net Income/|<br>Total<br>Liabilities/|Interest|Current<br>Liabilities/|Retained<br>Earnings/||
|---|---|---|---|---|---|---|---|---|
||1999|TA|TA|TA|Coverage|TA|TA|Tobin's Q|
|Egypt|535|0.05|0.04|0.51|2.79|0.99|0.03|0.99|
|Turkey|33|0.09|0.04|0.58|3.42|0.86|N/A|1.45|
|Pakistan|77|0.08|0.03|0.67|1.66|0.75|N/A|0.9|
|Malaysia|285|0.03|0.02|0.49|1.00|0.81|0.07|1.05|
|India|286|0.06|0.04|0.59|1.68|0.62|N/A|0.89|
|Indonesia|112|0.08|0.06|0.67|1.91|0.80|0.03|1.23|
|South Africa|406|0.09|0.06|0.47|1.45|0.83|0.14|1.14|
|Hong Kong|250|0.02|0.02|0.41|1.10|0.86|0.14|0.83|
|South Korea|453|0.05|0.02|0.59|1.82|0.64|0.03|0.91|
|Greece|157|0.05|0.05|0.47|1.80|0.92|0.01|4.45|
|France|726|0.06|0.04|0.62|1.76|0.76|0.04|1.36|
|Germany|706|0.02|0.02|0.64|1.81|0.53|0.06|1.35|
|Japan|3138|0.03|0.01|0.6|2.14|0.75|0.17|0.99|
|United Kingdom|1428|0.08|0.05|0.53|1.48|0.79|0.11|1.47|
|United States|462|0.11|0.06|0.6|1.81|0.46|0.28|1.95|
|Average (excl. Egypt)||0.06|0.04|0.57|1.77||0.10|1.43|

Source: Egypt—CMA data (own calculations), other countries—Worldscope. Reported are medians. TA stands for total Assets. Tobin’s Q is defined as market value of equity +book value of liabilities over total assets. Interest coverage is defined as earnings before interest and taxes over interest payments.
# **2 Context**

Tanzania is classified as a lower-middle-income country with an annual per capita income of $1,057.7 (constant 2015 US$). Despite the challenges posed by the COVID-19 pandemic and ongoing geopolitical tensions, Tanzania’s economy grew by 5.2 percent in 2023. The country’s economy relies heavily on agriculture, which employs approximately 66 percent of the population and contributes 26.5 percent to the country’s GDP. Following agriculture, the business and trade services sector is the next most significant contributor, providing employment to 16 percent of the population and boasting a 21 percent GDP contribution. Despite the importance of the business and trade services sector, it is predominantly informal, which limits the scale and development of Tanzania’s formal private sector. The creation of new businesses remains remarkably low, with fewer than 0.2 new firms created per 1,000 adults annually (World Bank, 2023). This new business density rate is the lowest among comparable economies, including South Africa (12.5 new businesses per 1,000 adults), Rwanda (2.2), and Kenya (1.6).

Although tax revenue is the primary source of domestic resources, Tanzania’s tax-to-GDP ratio remains relatively low at 11.4, compared to Africa’s average of 15.6, Kenya’s 15.2, and Rwanda’s 17. Currently, approximately 4 million taxpayers are registered with the Tanzania Revenue Authority (TRA) and are thus tax-eligible. However, an overwhelming majority of these are classified as small firms, employing fewer than 10 individuals, and their contribution to domestic revenue taxes is minimal. These small firms typically pay personal income tax through the presumptive tax regime (presumptive taxpayers) or via standard personal income tax if they exceed the TZS 100 million turnover threshold. Medium-sized enterprises, on the other hand, contribute 18.8 percent, while large firms, despite being relatively few in number, dominate contributions, accounting for 58.7 percent of total business tax revenue. This disproportionate distribution of tax contributions was emphasized by the Minister of Finance in the 2022/2023 budget speech, who noted that, in 2021/2022, 80 percent of the TRA’s revenue came from only 20 percent of business taxpayers, yielding a total of TZS 7
In (11), denotes the vector of predicted incomes from regression (12). Under the maintained assumption of a linear relationship between achievement and circumstances, this vector is equivalent to the smoothed distribution, since all individuals with identical circumstances are assigned their conditional mean incomes.

Because of its unique path-independent decomposability properties, Checchi and Peragine (2010) and Ferreira and Gignoux (forthcoming) both use the mean logarithmic deviation as the inequality index _I()._ However, as shown above, the mean log deviation is not ordinally invariant in the standardization to which test scores are submitted, and it is therefore unsuitable for use in the present context. Following the discussion in Section 3, we use the simple variance as our inequality index _I()._ This choice yields our proposed measure of inequality of educational opportunity, as a special case of (11): This index has a number of attractive features. First, it is extremely simple to calculate: It is simply the _R_<sup>_2_</sup> of an OLS regression of the child’s test score on a vector _C_ of individual circumstances. In our application to the PISA data sets, _C_ includes the following ten variables: gender, father’s and mother’s education, father’s occupation, language spoken at home, migration status, access to books at home, durables owned by the households, cultural items owned, and the location of the school attended.

Second, despite its simplicity, it is a very meaningful summary statistic. It is a parametric approximation to the lower bound on the share of overall inequality in educational achievement that is causally explained by pre-determined circumstances. A formal proof is provided by Ferreira and Gignoux (forthcoming). But the basic intuition is to note that (12) can be seen as the reduced form of a (linearized version of a) model such as: In (14) and (15), _y_ denotes achievement, and _C_ denotes the vector of circumstances, as before. _E_ denotes a vector of efforts: all variables that affect achievement and over which individuals do have some measure of control. _u_ and _v_ denote random shocks. Because 15 year-olds may conceivably affect the choice of school they attend, the class they are assigned to, and thus the teachers they interact with, all school characteristic variables, for example, are included in _E_ . So are any direct measures of the student’s own efforts in preparing for exams, for instance. Of course, efforts _E_ can be influenced by circumstances _C_ , but the reverse cannot happen. Variables can only be treated as circumstances if they are pre-determined and entirely exogenous to the individual.

Now return to (12) as a linearized reduced form of (14)-(15). We know that circumstances _C_ are economically exogenous to _y_ . We also know that all effort ( _E_ ) variables (whether or not one 17
Nene, N., 2015. _Budget speech by Minister of Finance_ , National Assembly, 25 February, Cape Town.

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Rains, C. & Penzien, D.B., 2005. Behavioural research and the double-blind placebo-controlled methodology: Challenges in applying the biomedical standard to behavioural headache research. _Headache_ , 45(5): 479-486.

Santos, C., Adeyemi, O. & Tenorio, A., 2006. Attitudes towards directly administered antiretroviral therapy (DAART) among HIV-positive inpatients in an inner-city public hospital. _AIDS Care_ , 18(7): 808811.

Shin, S., Munoz, M., Zeladita, J., Slavin, S., Caldas, A., Sanchez, E., Callacna, M., Rojas, C., Arevalo, J., Sebastian, J.L. & Bayona, J., 2011. How does directly observed therapy work? The mechanisms and impact of a comprehensive directly observed therapy intervention of highly active antiretroviral therapy in Peru. _Health and Social Care in the Community_ , 19(3): 261-271.

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Zewdie D., Lange J. & Kuritzkes D., 2004. Editorial. _AIDS_ , 18 (Supplement 3): S1-S3.

15
# **Women’s Political Representation and Intimate Partner Violence**<sup>∗</sup>

S Anukriti Bilge Erten The World Bank<sup>†</sup> Northeastern University<sup>‡</sup>

## Priya Mukherjee

University of Wisconsin–Madison<sup>§</sup> _Keywords:_ Intimate Partner Violence; Female Leaders; Elections, India; Contraception; Representation _JEL codes:_ D72, J16, J13

> ∗We thank Kathleen Beegle, Sonia Bhalotra, Martin Fiszbein, Raymond Fisman, Tarun Jain, Nicholas Kuipers, Mindy Marks, Dilip Mookherjee, Alessandra Voena, and seminar participants at Boston University for their comments and suggestions. We also thank Kevin Dunne, Owen GrahamO’Regan, Mizuhiro Suzuki, Tianyi Tao, and Amin Younis for their excellent research assistance. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. Bilge Erten gratefully acknowledges the hospitality of the Institute of Economic Development at Boston University. Support for this research was provided by the Research and Development Initiative at Northeastern University and the Office of the Vice Chancellor for Research and Graduate Education at the University of Wisconsin-Madison, with funding from the Wisconsin Alumni Research Foundation.

> †Development Research Group. `sanukriti@worldbank.org` .

> ‡Department of Economics and International Affairs Program. `b.erten@northeastern.edu.`

> §Department of Agricultural and Applied Economics. `priya.mukherjee@wisc.edu.`
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Second, we create **Small Firm Industry** which is a dummy variable that takes the value 1 if the industry’s share of employment in firms with less than 20 employees is ≥ median value of the Index of small firm domination from Beck, Demirguc-Kunt, Laeven, and Levine (2008) and takes the value 0 if it is < the median index value. Beck et. al. (2008) use the methodology in Rajan and Zingales (1998) described above to argue that each industry has a technological firm size that depends on that industry’s particular production process and that these technological differences should persist across countries. They construct each industry’s share of employment in firms with less than 20 employees in the US using data from the 1997 US Census.<sup>11</sup> Finally, we follow Hasan and Jandoc (2012) in constructing **Labor Intensive Industries** which is a dummy variable that takes the value 1 for labor intensive industries and 0 for capital intensive industries.<sup>12</sup> We use data on informal firms from the Unorganized Manufacturing sector for 1994/95 that is collected by the National Sample Survey (NSS) Office of the Ministry of Statistics. The survey uses a stratified two-stage sampling design where the first-stage units are the villages in the rural areas and urban blocks in the urban areas and the second-stage units are the enterprises. The allocation of sample first stage units between rural and urban areas is in proportion to population as per 1991 census with double weightage to the urban sector. As seen in Appendix B, the informal sector in India is very large and dwarfs the formal sector in terms of the number of firms. Firm Size and Age variables are calculated similar to the formal manufacturing census described above.

> 11 Beck et. al. present alternate indices including using 100 employees as the cut-off and using 1992 census to construct the index. Our results are robust to these alternate classifications.

> 12 Hasan and Jandoc (2012) classify the following industries in India to be capital intensive industries: Machinery, Electrical Machinery, Transport, Metals and Alloys, Rubber/Plastic/Petroleum/Coal and Paper/Paper Products. The labor-intensive industries are: Beverages and Tobacco, Textile Products, Wood/Wood Products, Leather/Leather Products and Non-Metallic Products. The remaining industries are not as clearly distinguishable and include: Food Products, Textiles, Basic Chemicals, Metal Products and Other Manufacturing

11
- 25 -

# _2. Population Density_

Population density is highly significant in all regressions. Significance levels show little variation across all base regressions, as can be seen in Table BI in Appendix B. It is highly significant, generally at the 1 percent level, in all sensitivity analyses except when per capita GDP is measured in purchasing power parity dollars and when poverty incidence variables replace the poverty commitment variable, as can be seen from the other tables in Appendix B. Population density positively influences per capita central budget infrastructure expenditures, but the effect is nonlinear. The estimated sign suggests that economies of scale induce a higher level of infrastructure provision in more densely populated countries or that certain types of infrastructure expenditures become more necessary in the context of high population densities.

The impact of population density is substantial as can be seen from Table 6. In the base regression using FCINFPA and including the total flow of foreign savings per capita, the impact of population density is found to increase per capita expenditures until the population density reaches a value of 161, about one and a half times the mean population density in the sample. At the mean population density, population density independently accounts for (1980) US $ 8.05 of the total per capita central budget infrastructure expenditures and implies an elasticity of 0.24 when per capita infrastructure expenditures are at their mean value. The size of the estimated impact is somewhat greater in the alternative base regressions, and sometimes smaller in some of the sensitivity analyses.

# _3. Urbanization_

Urbanization is a significant determinant of central budget infrastructure expenditures. It is significant at the 1 percent level or better in all estimates, with the exception of the estimates replacing PCOM with indicators of poverty incidence, as can be seen from the tables in Appendix B.

Urbanization reduces per capita central budget infrastructure expenditures, _ceteris paribus,_ as hypothesized. The effect of urbanization is substantial. The coefficient range is reasonably stable across all sensitivity analyses. A one percentage point increase in the urbanization rate reduces per capita central budget expenditures on infrastructure by approximately (1980) US $ 0.50. At the sample means for urbanization level and per capita infrastructure expenditures, this implies an elasticity of -1.1.

# _4. Sectoral Balance_

The greater the imbalance between rural and urban sectors, the higher are per capita central budget expenditures on infrastructure, as hypothesized. The coefficient for the ruralurban migration rate, MIGZ, is positive and highly significant in all base regressions (1 percent level or better). It is positive and highly significant in all sensitivity analyses with
Other’s Eye: An Online Intervention Reducing Ethnic Prejudice.” American Political Science Review 112(1):186–193.

- Stewart, Tracie, Jacqueline Laduke, Charlotte Bracht, Brooke Sweet and Kristine Gamarel. 2003. “Do the “Eyes” Have It? A Program Evaluation of Jane Elliott’s “BlueEyes/Brown-Eyes” Diversity Training Exercise.” <u>Journal</u> of Applied Social Psychology 33(9):1898–1921.

- Stichnoth, Holger and Karine Van der Straeten. 2013. “Ethnic Diversity, Public Spending, and Individual Support for the Welfare State: A Review of the Empirical Literature.” <u>Journal</u> of Economic Surveys 27(2):364–389.

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- UNHCR. 2023. “Figures at Glance.” The UN Refugee Agency . **URL:** _https://www.unhcr.org/about-unhcr/who-we-are/figures-glance_

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31
Table 2: Effects of LEAP on finance networks

||(1)<br>SHG|(2)<br>Savingsgroup|(3)<br>Average effect|
|---|---|---|---|
|_Full sample_||||
|LEAP|0.17**|0.16**|0.74**|
||(0.06)|(0.06)|(0.21)|
|Constant|0.05**|0.05*||
||(0.02)|(0.03)||
|N|526|526||
|_ID Poor 1 and 2_||||
|LEAP|0.18**|0.23**|1.07**|
||(0.07)|(0.07)|(0.28)|
|Constant|0.06**|0.03||
||(0.03)|(0.03)||
|N|351|351||
|_Non-poor_||||
|LEAP|0.15**|0.03|0.46**|
||(0.06)|(0.07)|(0.21)|
|Constant|0.04|0.11**||
||(0.03)|(0.05)||
|N|175|175||

Note: Columns 1-2 report OLS estimates with village-clustered standard errors. Column 3 reports the standardized average effect, as described in Kling et. al. (2004) and Clingingsmith et. al. (2009), across the measures in columns 1-2

* = significant at the five percent level, **=significant at the one-percent level

32