rafmacalaba/data-use-mentions-extended huggingface dataset:
used to prepare a system of national accounts. The expenditure data and socioeconomic information are used to assess the impact of policy on general consumption patterns. The coverage of the data set is not as wide as one would wish, for detailed welfare and poverty analysis. For example, the surveys do not gather data to help analyze the production patterns of the poor, for the rural areas, the crop mix, the quality of their land, the value of their assets such as livestock, or even the degree to which they rely on wage labor compared to better off farmers. There is no information on the asset base typical of the poor and the sort of assets the poor lose first, when coping with survival crises. The lack of these indicators makes it difficult to build up a comprehensive picture of the capabilities and resources of the poor - in terms of physical assets and human resource endowment.

The 1985/86 sample is designed to be nationally representative. A two-stage stratified sample design is used. In the first stage, 1224 enumeration areas (EAs) were selected with probability proportional to the number of census households in the area. Stratification is done on rural/urban and state of residence basis. A total of 70 EAs comprising 40 from urban and 30 from rural were selected. From each EA 20 households were selected. In the second stage, sampled areas were randomly allocated to each month with 4 urban and 3 rural EAs.
# **C. SIMULATIONS**

A series of simulations were conducted in order to assess the sensitivity of the calculated real effective exchange rate to changes in price indices of some trading partners and changes in trade weights including more informal trade. The objective **CHART 10: REER MOVEMENT IN UEMOA (INCLUDING** was to get precise empirical **INFORMAL TRADE): 1999-2004** measures of the elasticity of 110 the REER in both zones to 105 both price and trade shares. Since the wholesale price 100 indicator (WPI) is considered 95 a more comprehensive measure of inflation in an 90 economy than the CPI, the 85 WPI’s were included for the trading partners for which there is a consistent and accurate long-run series. The REER REERINF results of the experiment suggested that a change in price index has no major effect on the REER. Because of the global deflation due to overcapacity, the use of alternative price indices does not have any significant effect on the REER.

In a second set of simulations, the trade weights assigned to the trading partners were adjusted to account for informal trade between many of Francophone countries and Nigeria and Ghana. Unfortunately, such trade escapes both the customs regime and official cross-border statistics. Accordingly, for the REER in the UEMOA zone, the trade weights of Ghana were increased from 4.5 to 5.0 percent and the weights of Nigeria increased from 7.3 to 11 percent, and all other weights of trading partners were adjusted accordingly so the combined total of country weights still equaled 1 percent. In the CEMAC zone, the weights for Nigeria were increased from 7.3 percent to 9 percent to account for the large volume of unrecorded Cameroon-Nigeria crossborder trade of livestock, machinery, and oil. The effects of these revisions was to

**CHART 11: REER MOVEMENT IN CEMAC (USING INFORMAL TRADE): 1999-2004**

<!-- Start of picture text --> 110<br>105<br>100<br>95
Bangladesh (Steele et al., 2017), and Belize (Hersh et al., 2021), among others.<sup>3</sup> While often subsumed under the term “machine learning,” this modern approach to poverty mapping actually consists of several practices that differ from more traditional approaches. Most obviously, the modern approach substitutes non-parametric statistical methods for the parametric approaches traditionally used for poverty mapping. In addition, the modern approach relies heavily on remotely-sensed covariates rather than covariates derived from census data. Finally, poverty maps produced with machine-learning methods generally share a common validation procedure. The key feature of this procedure is that model performance is assessed by calculating the _R_<sup>2</sup> from a regression of the observed poverty measures on the estimated poverty measures for the same geographical units.<sup>4</sup> The poverty measures used in this procedure are most often direct, sample-based estimates rather than the true poverty measures for the regions of interest. While direct estimates are unbiased, they can be imprecise estimates of true poverty measures, meaning that it is unclear whether the validation procedure is informative of actual model performance (Corral et al., 2021b). In this paper, we provide a more rigorous assessment of the performance of the modern approach to poverty mapping. Our approach consists of constructing a pseudo-census (hereafter “census”) that we use to conduct design-based simulation experiments. The census provides the true values we wish to estimate. Our experiments entail repeatedly drawing survey samples from the census where each sample produces direct poverty estimates for the sampled geographic areas. For each direct estimate, the presence of the census means that we observe the true poverty measure corresponding to the direct estimate and we use these true poverty measures to gain insight not only into the credibility of model validation based on direct estimates, but also the performance of machinelearning methods when validated against the true poverty measures. Given that population censuses rarely collect detailed data on income or expenditures, we
.a.|n.a.|
|unreserved in industry|(0.247)|(0.119)|(0.309)|||
|<br>|||D. Change in male segmentation|||
|Increase in share of products|-0.133|-0.214**|-0.370***|-0.150*|0.025|
|unreserved in industry|(0.082)|(0.104)|(0.081)|(0.077)|(0.094)|
||||E. Change in female segmentation|||
|Increase in share of products|0.252**|0.023|0.245*|0.267**|0.035|
|unreserved in industry|(0.105)|(0.131)|(0.139)|(0.117)|(0.079)|
||||F. Change in combined segmentation|||
|Increase in share of products|-0.025|-0.180*|-0.181|-0.000|-0.093|
|unreserved in industry|(0.118)|(0.101)|(0.135)|(0.095)|(0.121)|

Notes: Estimations include 50 industry-level observations, are weighted by log industry employment, winsorize changes at 5%/95% level, include initial values of dependent variables, report robust standard errors, and are transformed to unit standard deviations.
Policy Research Working Paper 10893

# **Abstract**

This paper characterizes economically optimal investments in Africa’s road network in partial and general equilibrium —based on a detailed topography of the network, road con-struction costs, frictions in cross-border trading, and economic geography. Drawing from data on 144 million trans-continental routes, it first assesses local and global network efficiency and market access. It then derives a large network connecting 447 cities and 52 ports along the fastest routes, devises an algorithm to propose new links, analyzes the quality of existing links, and estimates link-level construction/upgrading costs. Subsequently, it computes market-access-maximizing investments in partial equilibrium and conducts cost-benefit analysis for individual links and several investment packages. Using a spatial economic model and global optimization over the space of networks, it finally elicits welfare-maximizing investments in spatial equilibrium. Findings imply that cross-border frictions and trade elasticities signifi-cantly shape optimal road investments. Reducing frictions yields the greatest benefits, followed by road upgrades and new construction. Sequencing matters, as reduced frictions generally increase investment returns. Returns to upgrading key links are large, even under frictions.

This paper is a product of the Office of the Chief Economist, Africa Region and the Kiel Institute. 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 author may be contacted at skrantz@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
development and social services, among others. The law outlines investor rights and obligations, and reinforces the notion that investments and intellectual property are protected from expropriation and theft. It defines economic zones, industrial zones, high-tech zones and export processing zones, and outlines investment procedures for companies wishing to invest in one of these areas.

19 Data come from UNCTAD Bilateral FDI Statistics. Data from Korea were last updated in 2010. As of this year, Korea passed Japan as the largest investor in Vietnam.

25
significant results from the endline survey. Each row is its own dependent variable. Monetary values are in USD PPP and are winsorized at the top 1% of values in order to limit the influence of outliers. The regressions also have month-by-year fixed effects, community-level controls (Irbid/Mafraq governorate and population quartile), and household-level controls (vulnerability-assessment quartile, shelter program, baseline number of children, baseline number of children plus adults, respondent gender, and respondent age). In the parentheses are robust standard errors clustered at the locality level. Regressions are weighted by the number of people interviewed in each household. Statistical significance represented by * (10%), ** (5%), and *** (1%). Q-values are calculated per Anderson (2008) and correspond to various families in the pre-analysis plan. The lower and upper bounds refer to the ones proposed in Lee (2009) to correct for attrition. The lower bounds marked by NA are not possible to report because trimming the lower tail would result in no variation in the variable, making the regression impossible to run as it becomes a column of zeros. The outcome "Focus Respondent Childcare Chores" was pre-specified in two outcome families; the table reports the lower of the two q-values; the higher q-value is 0.02 A-4
substantial time-series variation. This variation will allow us to identify the effect of changes in transport costs on changes in geographic concentration.

To summarize, the descriptive evidence points to a significant decrease in the geographic concentration of manufacturing industries in Canada over the last 20 years, no matter whether that geographic concentration is measured in terms of plant counts, employment, or sales. The pace of decline, however, likely differs across industries in systematic ways. Understanding which factors drive that decrease to what extent and for which industries – with a special focus on transport costs – is the key objective of the remainder of this paper.

# **.2 3 Transport costs**

The second key ingredient of our analysis is an industry-specific measure of transport costs. Contrary to most existing studies, we use _direct measures_ constructed from detailed micro-data files on shipments within Canada.

To estimate ad valorem rates, we first use a model to predict trucking firm (carrier) revenues for a 500 kilometers trip by commodity for the average tonnage using shipment (waybill) data from Statistics Canada’s Trucking Commodity Origin-Destination Survey (see Brown, 2015, for details). We estimate the ‘prices’ charged by trucking firms as a function of distance shipped, tonnage, and a set of commodity and firm fixed effects. To begin, we assume firms set prices such that both fixed and variable (linehaul) costs are just covered. Firms are assumed to set prices based on a fixed component and kilometers shipped: _Rm_ , _kc_ = _α_ + _βdk_ , where _Rm_ , _kc_ is the revenue earned by carrier _m_ for shipment _k_ composed of commodity _c_ , _α_ is the fixed price component, _β_ is rate per kilometer, and _dk_ is the distance shipped. Of course, firms may also price on a per tonne-km basis and this needs to be taken into account. Assuming firms set prices based on an unknown average tonnage _t_<sup>_∗_</sup> shipped implies that the rate per tonne-km is _Rm_ , _kc_ =
undervaluation that also leads to property tax undervaluation. With an average tax rate on capital value of 2 percent, the aggregate amount of lost property taxes can be calculated. These losses are shown in Table 15<sup>34</sup> . These estimates are meant only as broadly suggestive of the aggregate revenue losses that might be present. They again indicate that undervaluation of transaction value has a major impact that cascades beyond its effect on stamp duty collections.

|**Table 15. Forego**|**ne Property Tax Collections, 200**|**0-2001 **(in Rs.Mill|ions)|
|---|---|---|---|
|**Percent**|**Percent of Base Subject to Pro**|**perty Tax**||
|**Undervaluation**|**25%**<br>**50%**|**75%**|**100%**|
|10%|373.3<br>746.5|1119.8|1493.1|
|25%|1119.8<br>2239.6|3359.4|4479.2|
|50%|3359.4<br>6718.8|10,078.2|13,437.6|

As with the capital gains tax, State government attempts to improve valuation for stamp duties generates benefits for a different level of government, in this case, urban governments that impose the property tax.

<u>State Sales and Central Excise Taxes.</u> A less direct link may be between stamp duties and State sales and central excise taxes. Suppose that the existence of high stamp duties lead companies operating in the real estate sector, especially builders, to undervalue properties. Such undervaluation saves the company tax payments, thereby increasing company revenues. However, these increased revenues are
.637|509.667|
|Working in agriculture sector|0.261<br>(0.224)|1440|0.071|0.222|0.423|

Notes: The variables are constructed based on the Egyptian Labor Force Survey waves between 2006-2019. Individuals in the sample are those who graduated between 2006-2019 and are aged 15 years of age and older. Data are collapsed on the cohort of graduation, governorate of residence and educational attainment.

Table 3: Summary statistics: economic condition indicators at the national level

||Mean/Sd|N|
|---|---|---|
|Youth employment rate, males<br>aged between 15-24|34.764<br>(4.322)|29|
|Oil price (US $ per barrel)|48.362<br>(31.787)|29|
|GDP growth|4.381<br>(1.598)|29|
|Wheat price (Us $ per bushel)|4.529<br>(1.549)|29|

Notes: The table provides the mean for each of the 4 variables on the country level (Egypt) over 29 years (1991-2019). Data source: World Bank data.

9
and registers, sample surveys allow more detailed questions to be asked about the characteristics and situations of households. If survey instruments identify displaced populations based on individuals’ and households’ self-reported migration history (including patterns and causes) they can enable the disaggregation of detailed data by displacement status (UNSD 2014).

There are opportunities to mainstream forced displacement into international survey instruments, but this has only been done in a handful of cases. Several standardized international sample surveys have been designed for special purposes including the Living Standards Measurement Study (LSMS),<sup>75</sup> Labor Force 71 The UN census recommendations for the 2010 World Population and Housing Census Programme stipulate that refugees and IDPs living in camps should be counted and their numbers disaggregated in population statistics, however there is no requirement to separately distinguish displaced people living outside of camps (UNHCR 2016). The United Nations Economic Commission for Europe’s (UNECE) guidelines include a question on reason for migration, population with a refugee-like background and IDPs as non-core topics/questions (UNHCR 2016).

72 While most countries include questions on country of birth and citizenship, only about 40 percent include a question on year of migration, less than a quarter include a question on reason for international migration, and about a fifth include a question on the reason for internal migration (UNHCR 2016).

73 E.g. Kyrgyz Republic 1999 (refugee status), West Bank and Gaza 2007 (refugee status), Zambia 2000 and 2010 (purpose of stay), Germany 1970 (federal refugee identity card), Greece 2001 (reason for settling in Greece), Sudan and South Sudan 2008 (type of household including IDP and refugee), Liberia 1990 (ever displaced by war since 1990), Uganda 2014 (refugees).

74 UNHCR is collaborating with the Statistics Norway on systematically embedding forcibly displaced peoples in national statistics exercises and collaborates with national authorities and with UNFPA in various countries on the design of census exercises that include refugees, IDPs,
’s household surveys

We leverage data from the Chad High-Frequency Phone Survey (HFPS)<sup>8</sup> conducted in May-June 2020 by INSEED (with support from the World Bank) to assess the actual impact on female-headed households in Chad. The main objective is to identify the type of households directly or indirectly affected by the pandemic and identify its main impact channels. The survey is a 30-minute Computer Assisted Telephone Interview (CATI) conducted at the household level and contains limited information at the individual level. As such, we are unable to assess the gendered impact on women in general through the survey. Since we know the gender of the household head, we can assess the impact on female-headed households.

The Chad HFPS 2020 sample was drawn from ECOSIT 4 (conducted in 2018-2019), which is the same survey that we used to engender the Chad SAM. The final survey sample consists of 1,748 households

> 8 Accessible at <u>https://microdata.worldbank.org/index.php/catalog/3792.</u>

5
# **_Notes_**

1. Our review focuses on models dealing primarily with the income dimension of poverty. The Development Economics Development Prospects Group at the World Bank has established a framework known as MAMS or Maquette for MDG Simulation. This framework allows an economy-wide analysis of shocks and policies on income poverty and a set of Millennium Development Goals (MDGs) related to health, education, and infrastructure (see Löfgren and Diaz-Bonilla 2005).

2. Most economic models of behavior are based on the principle of optimization, according to which each agent attempts to implement the best possible action given her/his objective and the prevailing constraints. The maximum value function is the maximum attainable value of the objective function expressed in terms of the parameters characterizing the environment of the problem (see Dixit 1990).

3. POVCAL is programmed in Fortran 5.0 and uses the parametric specification of the underlying Lorenz curve. Currently, it supports the following specifications: (1) the general quadratic model (Villasenor and Arnold 1984) and (2) the Beta model (Kakwani 1980).

4. The ASCII data file must be in tabular form whereby each row of data corresponds to a record and columns represent the variable for the subgroups. Variables must be organized by order of subgroups.

5. In other terms, the household is facing a linear budget constraint. The institutional underpinning of this assumption consists of efficient markets with negligible transaction costs (Deaton and Muellbauer 1980).

6. According to Lindblom (2001), social coordination aims at minimizing conflict and promoting cooperation viewed as an exchange of help. Lindblom also notes a fundamental distinction between the notions of market and market system. There are markets whenever people frequently pay others to do something. A market system is a collection of markets designed to organize and coordinate social interaction.

7. This is a record of the foreign exchange inflows and outflows associated with trade, with factor and interest payments, and with institutional transfers, including migrant remittances.

8. It is
ensure that the identifying variation comes from countries that transition from IDA to IBRD (since the fixed effects will be collinear for countries that remain in the same status throughout). Given the fact that IDA graduation policy based on the GNI per capita cut-off came into existence in 1987, for our main results, we restrict the analysis to data from 1988-2015. In our robustness checks, we report the results from the full sample of years – 1961-2015.

# **5. Regression results**

Table 4A reports the marginal effects of estimating Probit regressions of the likelihood of any project (Columns 1-4) and the likelihood of any HD project (Columns 5-8). For each outcome variable, the first column reports the results from estimating equation 1 without controls and without country fixed effects. The second column then adds the control variables. The third column then reports results including country fixed effects but no controls. The fourth column then adds the control variables. All regressions include fiscal year fixed effects. Results for other variables are reported in the same format. We primarily focus our discussion on the fourth column, which includes year and country fixed effects, as well as the control variables, but present all results for completeness.

> 13 In other versions of the results, we also included controls for other bilateral and multilateral net flows (DAC data from OECD) as well as countries’ own spending levels in the HD sectors (data from IMF). We chose not to include these covariates because the data are incomplete (resulting in a large loss of number of observations) and because these data did not have much explanatory power. Results are available upon request.

15
datasets of Bangladesh for the years 2007, 2011, and 2014. We compute the point estimates and standard errors incorporating the sampling weights as well as respecting the survey design.<sup>14</sup> Excluding the non-usual residents, we were able to consider the information on 50,215 individuals from 10,398 households in the 2007 survey, 79,483 individuals from 17,139 households in the 2011 survey, and 77,680 individuals from 17,299 households in the 2014 survey.

Table 2: The five ordered categories of access to sanitation facilities

|Category|Description|
|---|---|
|Open defecation|Human faeces disposed of in felds, forests, bushes, open bodies of water, beaches or <br>open spaces or disposed of with solid waste|
|Unimproved|Pit latrines without a slab or platform, hanging latrines and bucket latrines|
|Limited|Sanitation facilities of an otherwise acceptable type shared between two or more house|
|Basic unsafe|A basic sanitation facility which is not shared with other households, but excreta ar<br>disposed safely, such as fushed but not disposed to piped sewer system, septic tank <br>latrine|
|Improved|Sanitation facility which is not shared with other households and where excreta are <br>disposed in situ or treated of-site and includes fush/pour fush to piped sewer system,<br>tank or pit latrine, ventilated improved pit latrine, composting toilet or pit latrine w<br>slab|

One target of the United Nations’ sixth Sustainable Development Goal (whose aim is to ‘ensure availability and sustainable management of water and sanitation for all’) is ‘by 2030, [to] achieve access to adequate and equitable sanitation and hygiene for all and end open defecation.’ In order to hit the target, the Joint Monitoring Programme (JMP) of the World Health Organisation and the UNICEF proposes using ‘a _service ladder approach_ to benchmark and track progress across