rafmacalaba/datause-encoder-data huggingface dataset:
of schools. Based on the available budget, we chose to collect baseline data and the subsequent attendance data in only the 68 schools with the largest number of registered children. This included a total possible sample of 9,768 students. These individuals were chosen from a list of students and the names of the schools that they provided to the SED.

The baseline was conducted between May and July, 2005 and comprised a simple selfadministered survey that the students filled out in class. Of the 9,768 students selected for surveying we were able to locate 9,239 students at the time of the baseline survey in the schools that they claimed to attend. The distribution of these students is provided in Panel B of Table 1. Reassuringly, they have a similar distribution to original registrants and again, are equally distributed within grade-gender categories.

Because the baseline was conducted after the randomization, we were unable to use information on any variables that might have changed immediately as a result of the treatments. The baseline instead allows us to narrow down the sample to those children whose provided information was correct and that we could feasibly track down at the end of the study. From the baseline, we use the following: basic demographic variables, a list of friends the students have of the same grade in school, and most importantly, contact information for tracking students during the follow-up survey.

As a fourth source of data, the research team collected during the last quarter of 2005 data on students’ attendance through direct observation. For this purpose, the team assembled a group of assistants who randomly visited schools and classes. The assistants directly called the roll of all students and students were marked absent if they were not physically present in the classroom. They visited a total of 1,069 classes in the 68 selected schools for 13 weeks, targeting the same 9,768 students originally chosen for the baseline survey. Because we were able to continue looking for all children selected from the 68 schools, this data set is broader than that used for the detailed survey questionnaires as it includes both those students who were found in the baseline and students who, for whatever reason, were not available to be surveyed.

During February and March of 2006 a follow-up survey was conducted. To ensure that the survey did not preferentially treat students still enrolled in school, we conducted the survey at the household level. For the follow up, the research team located the families of 98.14 percent of the baseline individuals – a total of 8,736 students. The survey is a rich source of information, containing data on the participating students (including academic participation, academic effort;

- 13 -
for a sector pair the shareholder and the affiliate belong in is higher on average.

# **2 Data**

In this paper we use a unique combination of data sets that allow us to explore the effects of PTA on firm ownership.

First, we obtain the data on firm ownership from the Bureau van Dijk’s ORBIS data set. Our main explanatory variable -PTAs- comes from the Deep Trade Agreement Dataset prepared by the World Bank, which also includes a detailed text analysis of every treaty’s content. Finally, we use World Input-Output Tables from the WIOD to obtain different measures of GVC organization.

## **2.1 Firm Ownership Data**

The ORBIS data set extensively compiles firm level data such as annual accounts and ownership structure for the period 2007-2018. For the purpose of this analysis the most relevant information is the ownership structure. In this data set a link is defined as an ownership relation of any kind (regardless of the share of ownership) between a parent firm located in country _j_ and sector _s_ and an affiliate located in country _i_ and sector _r_ .

To clean the data set, we drop the duplicated entries and also those observations with relevant information missing such as country or sector. Furthermore, we keep a panel of incumbents (observed during the full sample) and entrants (firms born during the sample).

Finally, we aggregate these data at the country-sector-to-country-sector level and fill in the 0s. In the process we create a new variable called number of connected firms ( _CFij,t_<sup>_rs_),thatcountsthenumberoffirmsincountry</sup><sup>_i_andsector</sup><sup>_r_thatare</sup> owned by firms from sector _s_ in country _j_ .

Note that given the number of countries (209) and sectors (38) this data set is huge. More concretely, we have 209 _×_ 209 _×_ 38 _×_ 38 = 63 million observations per year, which represent almost 600 million observations in total.

Given the size of the sample and to avoid computational problems, we have to focus separately on the frequency and the propensity of any ownership as two types of extensive foreign investment margins. First, we focus on the (non-zero) ownership counts and use log( _CFij_<sup>_rs_).Moreover, we use a binary variable indicating the existence</sup> or not of any ownership link, 1( _CFij,t_<sup>_rs_)asadependentvariable.</sup> 4
the log of GDP per capita.<sup>30</sup> In sum, the various specifications show very similar results.

> 30 The data on GDP comes from the National Accounts Main Aggregates Database (United Nations, 2006). The type of political system was examined using data from the Polity IV data set, and the variable _Polity_ ranges from –10 to +10, where a higher score indicates a more democratic system. In order to control for a possible curvilinear relationship I also used the variable _Polity-squared_ measured as the square of the _Polity_ variable (Marshall & Jaggers, 2003).

20
14 ITN ownership. Indoor residual spraying (IRS) is another effective anti-malarial intervention, which could potentially be more prevalent in households that acquire ITNs. Unfortunately, only the 2008 DHS (and not the 2003 DHS) includes information on IRS, so it is not possible to include IRS in the Oaxaca-Blinder decomposition. However, even in 2008, the rate of IRS remains low: in the sample, just 9 percent of children overall and 11 percent of children in high malaria risk zones lived in households that had received IRS treatment. Additionally, in 2008, the correlation between ITN ownership and IRS was 0.00 overall and -0.03 in high malaria risk zones. The combination of the low prevalence of indoor spraying and lack of correlation with ITN indicate that changes in indoor spraying are unlikely to be driving the results presented here.

Another confounding factor is that between 2003 and 2008 there was an increase in the use of artemisinin combination therapy (ACT) for malaria treatment. In the 2003 DHS, among children who had a fever within the previous two weeks, 11 percent received sulfadoxine-pyrimethamine (SP), the recommended drug treatment at the time. In 2008, among the same category of children, 3 percent received SP and 8 percent received ACT. If ITN usage is correlated with access to ACT treatment, it is possible that part of the decline in mortality attributed to ITN usage is a consequence of increased availability of ACT.

Table 7 also presents results from a similar decomposition for the change in neonatal mortality, which shows a small, statistically insignificant increase. This decomposition is included for the sake of completeness and also as a modest falsification test of the main finding regarding ITN ownership in high malaria-risk zones. Given that malaria and ITN ownership are known not to have a substantial effect on neonatal mortality, we do not expect changes in ITN ownership to explain any part of the change in neonatal mortality. If ITN ownership did explain changes in neonatal mortality, that would suggest that the ITN ownership results for postneonatal mortality were driven by unobserved factors rather than ITN ownership itself. The decomposition for neonatal mortality, however, does not indicate that ITN ownership or any other observed factors can explain the change in neonatal mortality.

The fact that the postneonatal and infant mortality decompositions do not imply statistically significant roles for other candidate factors—including facility births, antenatal care, and access
skilled workers can freely move across all sectors, whereas unskilled ones face two segmented markets and can just imperfectly migrate from the agricultural to the non agricultural segment.

_<u>Table 11: Factor markets effects</u>_

||<br>Emplo|<br>yment|<br>Wages|Unsk<br>Migration|illed Lab<br>as % of:|Cumulative<br>Migration|Unskilled<br>employment|
|---|---|---|---|---|---|---|---|
||Skilled|Un-<br>skilled|<sup>Skilled</sup><br>Un-<br>skilled|Sending<br>Pop|Receiv-<br>ing Pop|2001-2015|2015|
||Y|early gr|owth rates|Yearl|%|Millions|%|
|**Business as Usu**<br>|**al:**<br>|||||||
|Agri||0.02|1.68|1.66||-4.04|21.51|
|Non-Agri||2.20|0.91||0.53|4.04|78.49|
|Economywide|2.0|1.7|1.26|||||
|**Full Liberaliza**<br>|**tion:**|||||||
|Agri||0.18|2.10|1.51||-3.71|21.99|
|Non-Agri||2.15|1.07||0.49|3.71|78.01|
|Economywide|2.0|1.7|1.32|||||
|**Deep Doha:**||||||||
|Agri||0.06|1.78|1.62||-3.96|21.64|
|Non-Agri||2.19|0.93||0.52|3.96|78.36|
|Economywide|2.0|1.7|1.27|||||
|**Weak Doha:**<br>||||||||
|Agri||0.06|1.77|1.62||-3.96|21.63|
|Non-Agri||2.19|0.93||0.52|3.96|78.37|
|Economywide|2.0|1.7|1.26|||||

Source: Authors’ calculations.

Table 11 highlights how trade shocks affect labor market structural adjustments. Due to its agriculture boom and its increased demand for “agricultural” factors of production, the full trade liberalization induces a significant increase in the wage rate for unskilled workers. When compared with the BaU, the yearly rate of growth of wage of unskilled workers in agriculture is 0.4 percentage points higher, and this higher rate accounts for a cumulative 14 year growth of 34% much higher than the cumulative growth of 26% in the BaU. Given this wage incentive, migration decreases and about 340 thousands workers who were moving out of agriculture in the BaU scenario do not switch activity in the full liberalization case. This has some effect on the aggregate distribution of unskilled workers between agriculture and non-agriculture, as shown in the last column of Table 11.

As far as the Doha scenarios are concerned, negligible effects are recorded for the employment structure and some weak wage increase is observed for unskilled in agriculture.

41
Policy Research Working Paper 9987

# **Abstract**

The accumulation of personal wealth, stemming from ownership and control of assets, plays a critical role in advancing women’s and men’s economic opportunities. Yet, it is an understudied dimension of inequality across the developing world. To study individual-level wealth inequality and gender differences in wealth, this paper leverages unique data from nationally representative, multi-topic household surveys that were conducted in Cambodia, Ethiopia, Malawi, and Tanzania and that interviewed men and women in private regarding their personal ownership and valuation of physical and financial assets. The analysis documents substantial gender inequalities in asset ownership and wealth, overall and for specific asset classes. Individual-level wealth inequality measures are substantially higher vis-à-vis comparators based on per capita household consumption expenditures and per capita household wealth, and intrahousehold wealth inequality has a substantial role in explaining overall wealth inequality. While land is a key contributor to wealth inequality across countries, there is cross-country heterogeneity in the relative contributions of asset classes. Self-reporting on asset ownership and valuation, the internationally-recommended best practice, is also shown to lead to higher inequality estimates compared to the business-as-usual survey practice of interviewing a single, most-knowledgeable household member to identify intrahousehold asset owners and values. The discussion expands on the implications of the findings for future surveys and methodological research.

This paper is a product of the Development Data Group, Development Economics. 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 gkoolwal@worldbank.org or tkilic@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
On the other hand, the national accounts de...ne factor prices as perceived by ...rms, before income taxes. Thus, the data-consistent de...nition of the welfare residual with taxes needs to be based on a new de...nition of log prt. Rewrite equation (14) as: This new de...nition of log prt then needs to be applied to equations such as (12) and (13) in section 2.2.

While it is easy to incorporate taxes into the analysis— as noted above, they are present implicitly in the basic expressions derived in section 2.2— the quantitative impact of modeling taxes explicitly can be large. Suppose that output is produced using an aggregate, constant-returns-toscale production function of capital, labor and technology, as in Solow’s classic (1957) paper. Then, without distortionary taxes, only changes in technology change welfare.

Now suppose the average marginal tax rate on both capital and labor income is 30 percent, and the share of consumption in output is 0.60. Suppose the government manages to raise aggregate capital and labor inputs by 1 percent permanently without a change in technology (perhaps via a small cut in tax rates). Then the ‡ow increase in utility is equivalent to an increase in steadystate consumption of 0.5 percent. If the discount factor is 0.95 on an annual basis, the present value of this policy change is equivalent to a one-year increase in consumption of 10 percent of the steady-state level!

# 2.3.3 Government expenditure

With some minor modi...cation, our framework can be extended to allow for the provision of public goods and services. We illustrate this under the assumption that government activity is ...nanced with lump-sum taxes. Using the results from the previous subsection, it is straightforward to extend the argument to the case of distortionary taxes.

Assume that government spending takes the form of public consumption valued by consumers. We rewrite the instantaneous utility function as where G denotes per-capita public consumption, and we continue to assume that C(:) is homogenous of degree one in its arguments. The relevant welfare residual in equation (4) now becomes: 13
# **3. Albert Hirschman and the World Bank**

In the spring of 1963, Albert Hirschman made a proposal to Burke Knapp, Vice President of the World Bank, for an analysis of specific projects financed by the World Bank in developing countries, which would be conducted through an in-depth worldwide field investigation.<sup>7</sup> His original idea was to look at Bank-financed projects in order to test the hypotheses elaborated in his two previous studies on development issues, _The Strategy of Economic Development_ (1958) and _Journeys Toward Progress_ (1963). He planned to conduct extensive on-the-ground investigations, paying attention “to the upkeep and performance of the project itself, to the economic activities that it has stimulated (or destroyed?), and very much also to the wider economic, social and political ramifications of the project such as its educational effects and its contribution to the formation of new local or national elites.”<sup>8</sup> An additional purpose of Hirschman’s, after two books heavily based on Latin American cases, was to expand his expertise in development issues by becoming acquainted with Asia and Africa.<sup>9</sup> From the early months of 1964, Hirschman’s study was framed within a larger collaboration between the Brookings Institution and the World Bank; more precisely, it became a Brookings Institution initiative endorsed by the Bank.<sup>10</sup> In addition to the involvement of Brookings, the Carnegie Corporation conceded a grant to the Brookings Institution to finance Hirschman’s travel expenses, and the Ford Foundation awarded Hirschman a Faculty Research Fellowship for 1964-1965.<sup>11</sup> At that time, the Brookings Institution was setting up a wide-ranging program of studies on American foreign assistance; the quotation above comes from that program. The research program was ambitious: it aimed at building, in a period of five years, a general evaluation of post-war American foreign assistance, preparing supplementary studies on a variety of aspects

> 7 Albert O. Hirschman to Mr. J. Burke Knapp, March 14, 1963, Albert O. Hirschman Papers, Box 57, Folder 5. Hirschman had already collaborated with the World Bank ten years before. From 1952-54 he had been the World Bank’s economic advisor to the Colombian government. In that capacity, he reported to Burke Knapp, who was the Bank’s Vice-President for the Western Hemisphere.

> 8 Albert O. Hirschman to J. Burke Knapp, March 14, 1963, Albert O. Hirschman Papers, Box 57, Folder 5. 9 Albert O. Hirschman to J. Burke Knapp, March 14, 1963, Albert O. Hirschman Papers, Box 57, Folder 5. 10 Robert E. Asher to Professor Albert O. Hirschman, January 24, 1964, Albert O. Hirschman Papers, Box 57, Folder 5, and the attached document “Proposal for a Study of Selected World Bank Projects,” January 23, 1964, Albert O. Hirschman Papers, Box 57, Folder 5; Robert E. Asher to Mr. Richard H. Demuth, February 5, 1964, World Bank Hirschman Folders Vol. 1.

8
with water and sanitation, holding constant either the marginal probability, the wealth-index effect or the combination of the other non-nutritional variables in Table 4. This suggests that other factors besides the health environment may well be playing a more important role, including intra-household inequalities.

# **6. Augmented regressions**

Introducing other household-level factors may enhance power for predicting individual outcomes. There may also be a problem with the weights used in constructing the wealth index; for example, the index may not adequately adjust for economies of scale in consumption. Finally, adding basic individual-level variables such as age and marital status for women may enhance targeting capability.<sup>31</sup> To test these conjectures we augment wealth with such household- (and individual-) level variables. The augmented regressions can be expected to perform similarly to the widely-used PMT method based on the predicted values of regressions calibrated to survey data (Section 2).

To motivate the augmented regressions, we can start by thinking of a simple regression of nutritional outcomes on the wealth index: ���� � �� � ����� � ���� (4) where ���� is the nutritional indicator for individual � in household � in country � and ��� is the household wealth index. Call this Model 1. Since the expected value of nutritional status tends to improve with wealth (the aforementioned wealth effect) rankings in terms of the predicted values from these regressions are very similar to those we have seen already. Model 2 augments (4) to contain household-level variables ���, giving: The vector ��� includes the separate components of the wealth index (essentially to allow a re-weighting of the index), as well as other household-level variables such as size and composition, and characteristics of the head. Dummies for survey month and region of residence are also entered as controls. Finally, Model 3 adds the observable individual-level variables, ����: For the incidence of underweight women, the individual-level attributes include the woman’s age (BMI tends to increase as women age), education and marital status. For children, age, gender, and

> 31 Recent research has argued that widows and remarried women often fare poorly when compared to married once women (Anderson and Ray 2016; Djuikom and van de Walle 2017).

20
iii

# **UNITED REPUBLIC** OF **TANZANIA URBAN SECTOR REHABILITATION PROJECT**

## CREDIT AND PROJECT SUMMARY

|**Borrower:**|United Republic of Tanzania|
|---|---|
|**Implementing Agencies:**|Prime Minister's Office, Ministry of Water, and<br>National Urban Water Authority (NUWA)|
|**Beneficiaries:**|Nine Local Authorities and Urban Water and Sewerage<br>Departments, and Dar es Salaam City Council|
|**Poverty Category:**|Not applicable|
|Amount:|SDR 72.3 million (US$105.0 million equivalent)|
|Terms:|Standard IDA terms with a 40-year maturity|
|**Commitment Fee:**|_0.5%_on undisbursed credit balance, beginning 60 days<br>after signing, less any waiver|
|**Onlending Terms:**|SDR 4.13 million (US$6.0 million equivalent) would be<br>passed on to NUWA as a grant|
|**Financing Plan:**|See Section II.E|
|**Economic Rate of Return:**|ERR for the urban roads and storm drainage component<br>(27% of total project cost) is estimated at 23.9% and<br>for the urban water supply and sewerage component<br>(35 % of total project cost) at 12.4 %|
|**Staff Appraisal Report:**|Report No. 15125-TA dated April 12, 1996|
|**Map:**|IBRD No. 26878|
|**Project ID:**|TZ-PA-2758|
10 related with the efficiency of use of classroomrs. Larger number of teachers enables larger number of classes per classroom, thus increasing efficiency in the use of classrooms. Furthermore, we wanted to explore any possible link between government's funding for general education and the efficiency scores, as there is a wide presumption that lack of funding for primary education is a serious constraint on primary education<sup>4</sup> . In the absence of data on spending on general education, we use as the proxy variable the percentage of municipal budgets spent on education. Finally, to explore any links between spatial characteristics of municipalities and the efficiency scores, we used two additional variables: total municipal land area in square kilometers and the percent of urbanization.

The sirmplest regression to test is the linear form, and we used it as the starting point for the empirical analysis.

ED=a+px+e _Linea_ **_r5)_** Although efficiency scores are continuously distributed and do not suffer from the heteroskedasticity problem encountered in case of dichotomous linear probability model, the calculated value of efficiency from the regression can lie outside the admissible range (0,1). We, therefore, have also estimted a non-linear logistic regression of the following form: _E= 1_ 1 4 6(sXj3X4e) (6) to restrict the values of the dependent variable in the (0,1) interval.

_41_ See, for example, World Bank (1990) policy paper on primary education which explicitly states that in most developing countries, primary education is generally undefunded (p.<sup>4</sup> 3).