rafmacalaba/datause-encoder-data huggingface dataset:
# **Appendix 1: Principal Sources of Data Used:23**

Global Development Finance 1999 CDROM Total Debt Stocks (EDT). Total Debt(EDT)/Exports of goods and services (%) Total Debt(EDT)/GNP (%) Interest arrears, Official creditors Interest arrears, Private creditors Principal arrears, Official creditors Principal arrears, Private creditors Short-term Debt/Total debt (EDT) **(%)** IBRD Debt Outstanding and Disbursed (DOD).

PPG, Official Creditors - DOD and TDS.

PPG, Private Creditors - DOD and TDS.

Multilateral Debt/Total Debt (EDT) **(%)** Debt Service, total long-term Reserves/Imports of goods and services (months) - together with data from _World Development Indicators 1999 CDROM_ Exports of Good and Services Exports of goods and services (% of GDP) Exports of goods and services (annual % growth) Imports of goods and services (% of GDP) Gross national savings, including NCTR (% of GNP) Current Account Balance (% of GDP) GNP per capita (constant 1995 US$) GNP per capita growth (annual %) Gross National Product (US$) GDP growth (annual %) Terns of Trade adjustment (constant LCU) Inflation, consumer prices (annual %) - together with data from IMF _International Financial Statistics._ Expenditure, total (% of GDP) Current revenue, excluding grants (% of GDP) General government consumption (% of GDP) World Development Indicators CDROM 1999 Exchange Rate - Local currency units per US$ (annual average). Money and quasi money (M2) as % of GDP For the OECD & G7 Countries: GNP per capita growth (annual %) Current account balance (% of GDP) GNP at market prices (constant 1995 USS) IMF International Financial Statistics Three month LIBOR: Offer London Industrial Inflation Export Unit Values: All Developing Countries, Non-Oil Developing Countries.

World Debt Tables/Global Development Finance Reports 1984-99 Interest, Principal, and Total Debt Due data came from the following series: Contractual Obligations on Outstanding long-term debt: - Principal and Interest to: a) Bilateral Creditors 23 Data from principal sources was supplemented with additional information from the World Bank LDB, Unified Survey and from EIU reports.

31
SUDAN’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE specific corridors, whereas administrative costs are based on costs involved in transporting imports into a country. Port and border delays were quantified into costs based on the assumption that delays cost $5 per day per tonne of imports.

Port-related delays **Figure 7. Expense of moving freight within Sudan and across southern Africa** are the primary reason why it takes 18 days 300 longer to move freight 250 from Port Sudan to 200 Kosti than from 150 Durban to Lusaka, a 100 route that is 800 km 50 longer and spans three 0 countries. The time Port Sudan- Kosti Durban-Lusaka required to move Port Road transport Border Administrative freight within Sudan does not include _Source:_ AICD calculations based on Nathan (2010); UNLJC and FAO (2005), and Yoshino (2010). border-related delays _Source:_ AICD calculations based on Nathan (2010); UNLJC and FAO (2005), and Yoshino (2010).

or customs clearance processes that imports or exports to another country would require. The main cause of the delays within Sudan are the extremely long wait times by vessels for a berth and long dwell times in Port Sudan. Durban, one of the most efficient ports in Africa, records far fewer delays.

In addition to the long delays, it costs more to move freight from Port Sudan to Kosti than it does to import freight to Lusaka from Durban. The extremely high costs posed by Port Sudan are reflected in the total cost required to move freight. Moving freight within Sudan is also much more difficult than across southern Africa (table 2). It is striking that even though imported freight to Zambia is subject to various customs fees and border delays, it is still cheaper overall.

> <sup>12</sup>
||**Table**|**9:** **Non-US/EU**|**/EFTA IPAs**|**(Bilateral Tr**|**ade, 42 IPAs)**||||
|---|---|---|---|---|---|---|---|---|
||(1)<br>Low-IP|(2)<br>AI|(3)<br>BIO|(4)<br>CHEM|(5)<br>ICT|(6)<br>MED|(7)<br>PT|(8)<br>Other|
|**Exports**<br>log(GDPi)|0.361***<br>(0.075)||||||||
|Sector × log(GDPi)||0.433***|0.023|0.200**|0.234***|0.375***|0.332***|0.247***|
|||(0.078)|(0.093)|(0.080)|(0.086)|(0.096)|(0.114)|(0.072)|
|Sector × LI × IPAi|–0.465**|0.655***|–1.410**|–0.418|3.091***|0.328|0.685**|0.794***|
||(0.190)|(0.241)|(0.553)|(0.424)|(0.297)|(0.329)|(0.330)|(0.287)|
|Sector × LMI × IPAi|0.040|–0.649|–0.721|0.167|0.111|–0.750*|–0.755*|–0.512|
||(0.154)|(0.431)|(0.516)|(0.299)|(0.490)|(0.454)|(0.431)|(0.318)|
|Sector × UMI × IPAi|0.306|–0.898|–1.518***|–0.179|–0.549*|–0.071|–1.115***|–0.536*|
||(0.206)|(0.598)|(0.543)|(0.308)|(0.333)|(0.533)|(0.389)|(0.288)|
|Sector × HI × IPAi|0.069|–0.110|–0.591|–0.197|0.727**|–0.374|0.001|–0.110|
||(0.283)|(0.346)|(0.720)|(0.293)|(0.339)|(0.259)|(0.299)|(0.122)|
|**Imports**|||||||||
|log(GDPj)|0.569***<br>(0.042)||||||||
|Sector × log(GDPj)||0.127***|0.026|–0.024|–0.053|0.115***|0.050*|0.014|
|||(0.030)|(0.049)|(0.038)|(0.072)|(0.024)|(0.029)|(0.029)|
|Sector × LI × IPAj|0.026|0.104|–0.472|–0.069|1.056***|0.149|0.182|0.196|
||(0.173)|(0.115)|(0.409)|(0.238)|(0.245)|(0.187)|(0.130)|(0.167)|
|Sector × LMI × IPAj|–0.084|–0.012|0.160|0.323***|0.046|–0.046|0.050|–0.060|
||(0.077)|(0.130)|(0.200)|(0.082)|(0.164)|(0.127)|(0.100)|(0.082)|
|Sector × UMI × IPAj|–0.053|–0.492**|–0.433|0.141|–0.142|–0.479|–0.272|–0.146**|
||(0.135)|(0.198)|(0.398)|(0.215)|(0.228)|(0.369)|(0.189)|(0.068)|
|Sector × HI × IPAj|<br>0.224|<br>0.166|<br>–0.155|<br>–0.038|<br>–0.144|<br>0.019|<br>0.160|<br>–0.123|
||(0.145)|(0.182)|(0.319)|(0.171)|(0.286)|(0.173)|(0.207)|(0.128)|
|Observations||||||||1,031,880|
|Number of country pairs||||||||27,077|
|Pseudo_R_<sup>2</sup>||||||||0.953|

_Notes_ : Robust standard errors two-way clustered by exporter and importer in parentheses. Includes exporter- and importer-specific time trends, exporter- and importer group-sector year fixed effects, and bilateral pair fixed effects. TRIPS compliance by country included not reported. Observations at 5-year intervals: 1995, 2000, ... 2015. *** _p_ < 0.01, ** _p_ < 0.05, * _p_ < 0.1.

29
I use the difference in difference method to analyze the impact of village migration rates on outcomes in the census data. The estimating equation is: where _yivt_ represents the outcome of individual _i_ of village _v_ at time _t_ . _Mvt_ is the share of individuals from the village _v_ that are in Gulf-Malaysia at time _t_ . _μt_ represents the time fixed effects to capture national trends in migration and also in the outcome variables. _γv_ is the village fixed effects that captures all time-invariant characteristics of the village. _εivt_ represents idiosyncratic error term. I allow the errors to be correlated across individuals of the same village across time.

The identification assumption of Equation (2) is that the trends in labor market characteristics in villages with different migration rates are the same. Village specific inherent factors that could affect the levels of migration as well as the levels of outcomes are subsumed by the specification and are not problematic for identification. Neither is the national trend in outcomes as well as the migration patterns. The variation that identifies Equation (2) arises from the difference in migration rates in the same village across time after taking into account the national (or regional) trend in migration rates and outcomes.<sup>9</sup> Figure 3 shows the distribution of village level migration rates in 2001 and 2011, separately for India and non-India migration. The wide variation in the rates, both within each year and between each year help identify the parameters.

Since the impact of migration is likely to be different for individuals based on whether they have a current migrant in the household or not, I estimate Equation (2) by interacting all the explanatory variables with a dummy indicating whether the household has a migrant in Gulf-Malaysia. This is numerically same as estimating Equation (2) separately for those living in a household with, and without a migrant. I present the fully interacted specification for compactness.

# **4 Results and discussion**

In this section, I present simple theoretical frameworks on the impact of migration on poverty, consumption, expenditures, schooling, and labor market outcomes. Then I discuss the results of estimating Equations (1) and (2) in light of the frameworks.

## **4.1 Impact on poverty and expenditures**

### **4.1.1 Framework**

If migrants have proper information, then migration typically results in welfare gain for the migrants. This could mean higher earnings and/or better amenities upon migration. In the context of migration from Nepal to Gulf-Malaysia, the amenities while abroad are probably not a pull

> 9The key results do not change if I allow regions to have their own time trends in outcomes. The results are omitted for compactness, but are available upon request.

9
Policy Research Working Paper 9886

# **Abstract**

The traditional consumer price index is often produced at an aggregate level, using data from few, highly urbanized, areas. As such, it poorly describes price trends in rural or poverty-stricken areas, where large populations may reside in fragile situations. Traditional price data collection also follows a deliberate sampling and measurement process that is not well suited for monitoring during crisis situations, when price stability may deteriorate rapidly. To gain realtime insights beyond what can be formally measured by traditional methods, this paper develops a machine-learning approach for imputation of ongoing subnational price surveys. The aim is to monitor inflation at the market level, relying only on incomplete and intermittent survey data. The capabilities are highlighted using World Food Programme surveys in 25 fragile and conflict-affected countries where real-time monthly food price data are not publicly available from official sources. The results are made available as a data set that covers more than 1200 markets and 43 food types. The local statistics provide a new granular view on important inflation events, including the World Food Price Crisis of 2007–08 and the surge in global inflation following the 2020 pandemic. The paper finds that imputations often achieve accuracy similar to direct measurement of prices. The estimates may provide new opportunities to investigate local price dynamics in markets where prices are sensitive to localized shocks and traditional data are not available.

This paper is a product of the Development Data Group, Development Economics and the Fragility, Conflict and Violence Global Theme. 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 bandree@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
and other educational disruptions to losses in average lifetime educational attainment. In general, as child age, the opportunity cost of staying in school increases. This may make it harder for households to justify sending older children back to school after a forced interruption, especially if households are under financial stress. To simulate dropouts, we take the expected shock on income from Global Economic Prospects (GDP per capita growth projections) and estimate the expected effect of this income shock on dropouts using agegroup specific dropout-income elasticities.<sup>21</sup> In 2021, the world economy experienced a strong recovery from the first phase of the pandemic (Global Economic Prospects 2021 and 2022). The GDP per capita is expected to have grown by 4.4 percentage points in 2021, which signals a strong recovery compared to the October 2020 estimate of -0.8 (Macro Poverty Outlook, percentage points). However, growth has been uneven and concentrated in higher-income countries. Thus, while the estimated increase in dropout is lower than in 2021, that improvement is mostly driven by growth in advanced economies.

Income shocks from COVID-19 could increase the out-of-school population by 0.26% for primary school children and 0.23% for secondary school children, or an additional 4.0 million children to drop out from school around the world. Of these children, 3.7 million live in low-income countries, and 1.8 million are between 12 to 17 years of age. Figure 5 shows how dropout relates to changes in income shocks across the different economic projections, during a period in which growth rates have improved under the most recent projections but still underperform pre-pandemic growth.

**Figure 5. Estimates of student dropout by 2020 and 2021 growth projections release**

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# Outcome 3. Percent below Minimum Proficiency on PISA

Globally, the percentage of 15-year-olds below minimum proficiency on PISA could rise by 12.3 percentage points in the intermediate scenario (see Figure 6 below). These results imply a rise in the share of students who are not able to “identify the main idea in a text of moderate length, find information based on explicit though sometimes complex criteria, and reflect on the purpose and form of texts when explicitly directed to do so” (PISA’s definition of a minimum level of proficiency).

> 21 Figure B2.

14
**Table 4 Weighting of factors for provincial-local expenditure functions for Canada**

|Expenditure Category|Need/Cost Factors<br>Relative Weights|
|---|---|
|Transportation &|Snowfall (Annual - in centimeters) SNOW<br>0.1020|
|Communications|Highway Construction Price Index (HCPI)<br>0.6580|
||Paved roads and streets per square kilometer of area (RSPR)<br>0.0005<br>|
||Non-cultivatable area as a proportion of total area (NCAR)<br>Total<br> <br>0.2357<br>1.0000|
||Index = (0.10*ISNOW +0.66*IHCPI + 0.0005*IRSPR +<br>0.24*INCAR)*ISRP|
|Post-Secondary Education|Full time enrollment in grade 13+(000)(PSS)<br>0.048|
|(PSE)|Percentage of Population having a minority language as mother<br>tongue (ML)<br>0.19|
||Provincial Unemployment Rate (UR)<br>0.018|
||Education Price Index (EPI)<br>0.717|
||Help Wanted Index (HWI)<br>0.010|
||Foreign Post-Secondary Students (FPS)<br>0.017|
||Total<br>1.000|
||Index = (0.18*IPSS + .70*IML + .08*IUR + .04*IFPS)*IHWI*IEPI|
|Elementary and Secondary<br>|Population under 18 (PO17)<br>0.014|
|Education (ESE)|Population Density (PD)<br>0.017|
||Education Price Index (EPI)<br>0.969|
||Total<br>1.000|
||Index = (.02*IPD + ,98*IEPI)*IP017|
|Health (HE)|Alcoholism (Hospital separations for Alcohol related cases)<br>(ALCO)<br>0.123|
||Urban Population (PU)<br>0.877|
||Total<br>1.000|
||Index = (0.123*IALCO + 0.877*IPU)|
|Social Services (SS)|Single Parent Families (SPF)<br>1.00|
|Police Protection|Criminal Code Offenses (CCO)<br>0.39|
||Proportion of Population in Metropolitan (PMAR) Areas<br>0.61|
||Total<br>1.00|
||Index = (.39*ICCO + .61*IPMAR)|
|General Services (GS)|Private sector wages (Industrial composite) (AMW)<br>0.769|
||Percentage of population having a minority language as mother<br>tongue (ML)<br>0.001|
||Population Density (PD)<br>0.023|
||Population (POPF)<br>0.039|
||Snowfall (Annual - in centimeters) (SNOW)<br>0.168|
||Total<br>1.000|
||Index = (. 001*ML + 0.175*ISNOW + 0.80*IAMW + .024*IPD)*IPOPF|

Note: Calculations based on regression coefficients. The use of a variable prefixed by I means that a relative index of the variable is used.

_Source:_ Shah (1994a) 27
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sectoral share of Agriculture in total employment is severely under reported and using such data would lead to spurious estimates of sectoral gaps in productivity.

2. For many country-years, the source used to construct the series refers to a particular period or month. Consider a case in which such a survey was conducted in a period in which many workers changed their activity from the sector in which they usually work to a sector in which they shifted temporarily to satisfy the transitory high demand. For instance, many workers leave their usual jobs to satisfy the increased demand for labor in agriculture during harvest season. Such a scenario will lead to under estimation of sectoral share of employment in the sector from which the workers moved temporarily.

The two concerns above necessitates the cleaning of the data before it could be used for analysis. The specific details of how the data on sectoral share are adjusted from the raw data are provided in the data appendix. Data correction basically consists of two main steps: first, for each country selecting a data source that is free of any major concerns (for example, limits with regards to geographical coverage, period of the year, age groups, sector/occupations etc.) and second, including annual data constructed from other sources provided the average annual change in sectoral shares of employment remain reasonable. The final dataset consists of more than 1,800 data points spread across 150 countries. Yet, the number of data points available for a country varies significantly: from 35 in Switzerland to a single observation in around 40 countries.

The next step is to harmonize the sectors across WDI and GTAP. The WDI reports value added and employment data for the three broad sectors of Agriculture (A), Industry (I) and Services etc. (S) which can be aggregated from the constituents sectors in the International Standard Industry Classification (ISIC, revision 3). On the other hand, the GTAP reports data using a much finer classification of 57 sectors and uses a combination ISIC and Central Product Classification (CPC) sectors to aggregate its sectors. To harmonize the sectors, I map the 57 GTAP sectors to the three broad sectors used in the WDI database using the United Nations Statistics Division correspondence tables. The mapping is near perfect with the following three exceptions:

1. GTAP sector 1 ( _pdr_ ) consists of 2 sectors, of which one (husked rice) maps to Industry in the WDI classification while the other (rice, not husked) maps to Agriculture

2. GTAP sector 10 ( _oap_ ) consists of 7 sectors of which one (hides, skins and furskins, raw) maps to Industry in the WDI classification while the rest map to Agriculture 3. GTAP sector 12 ( _wol_ ) consists of 4 sectors of which one (pulled wool, greasy, including fleece-washed pulled wool) comes under Industry in the WDI classification while the rest map to Agriculture 10
# Appendix Table 15: Gender attitudes

||A man should<br>have a final<br>say in<br>decisions<br>(planter)<br>|A man should<br>have a final<br>say in<br>decisions<br>(spouse)|<br>Taking care of<br>kids is the<br>responsibility<br>of women<br>(planter)|<br>Taking care of<br>kids is the<br>responsibility<br>of women<br>(spouse)|<br> <br>Domestic<br>work is really<br>a job (planter)|<br> <br>Domestic<br>work is really<br>a job (spouse)|<br> <br>Woman who<br>earns money<br>independently<br>is unruly<br>(planter)|<br> <br>Woman who<br>earns money<br>independently<br>is unruly<br>(spouse)|
|---|---|---|---|---|---|---|---|---|
|Seedling Subsidy|0.07|0.04|-0.01|0.07|0.06|0.09*|-0.01|0.04|
||(0.05)|(0.06)|(0.05)|(0.05)|(0.04)|(0.05)|(0.05)|(0.05)|
|Couples' Training|-0.08|0.07|-0.00|-0.06|0.03|0.02|0.11*|0.04|
||(0.06)|(0.07)|(0.06)|(0.07)|(0.05)|(0.06)|(0.06)|(0.06)|
|Seedling Subsidy +<br>Couples' Training (beta)|-0.01|0.11*|-0.01|0.01|0.09*|0.11**|0.10*|0.08*|
|H0: Seedling Subsidy +|||||||||
|Couples' Training = 0 (p-|0.90|0.08|0.86|0.81|0.06|0.03|0.07|0.09|
|val)|||||||||
|Observations|1426|1038|1426|1038|1426|1038|1426|1038|
|Mean for control group|2.99|2.87|2.82|2.92|2.96|3.02|2.37|2.03|
|Mean for individual<br>training|3.05|2.91|2.81|2.99|3.04|3.11|2.39|2.08|

Robust and clustered standard errors in parentheses.

* p<0.1 ** p<0.05 *** p<0.01

Note: Baseline control variables in all regressions: Age of household head, Household size, Number of adults, Number of children, Dependency ratio, Married planter, Polygamous relationship, Matrilineal ethnicity, Planter has IGA, Asset index, Credit constrained, Share of plots owned, Average distance to plots (minutes), Number of plots, Average plot size.

4