rafmacalaba/data-use-mentions-extended huggingface dataset:
Policy Research Working Paper 8616

# **Abstract**

Data collected for refugee registration and to target humanitarian assistance include information about household composition and demographics that can be used to identify gender-based vulnerabilities. This paper combines the microdata collected by United Nations High Commissioner for Refugees to register refugees with data from its Home Visit surveys to analyze income poverty rates among refugees with a gender lens. It finds distinguishing between different types of male and female principal applicant (PA) households is important in the setting of Syrian refugees in Jordan. Poverty rates for couples with children do not differ by gender of the PA but for other household types poverty rates are higher for those with female PAs. Households formed because of the unpredictable dynamics of forced displacement, such as sibling households, unaccompanied children, and single caregivers, are extremely vulnerable, especially if the principal applicant is a woman or a girl.

This paper is a product of the Gender 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/research. The authors may be contacted at lhanmer@worldbank.org and erubiano@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
- Apeti, A. E. (2023a). Household welfare in the digital age: Assessing the effect of mobile money on household consumption volatility in developing countries. _World Development_ , 161:106110.

- Apeti, A. E. (2023b). Sovereign debt default and inequality. _Industrial and Corporate Change_ .

- Apeti, A. E., Bambe, B.-W.-W., and Combes, J.-L. (2023). _On the Macroeconomic Effects of Fiscal Reforms: Fiscal Rules and Public Expenditure Efficiency_ . hal-03961062f.

- Apeti, A. E. and Edoh, E. D. (2023). Tax revenue and mobile money in developing countries. _Journal of Development Economics_ , 161:103014.

- Asiedu, E., Azomahou, T. T., Gaekwad, N. B., and Ouedraogo, M. (2021). The determinants of electricity constraints by firms in developing countries. _Energy Economics_ , 104:105605.

- Asiedu, E. and Freeman, J. (2009). The effect of corruption on investment growth: Evidence from firms in latin america, sub-saharan africa, and transition countries. _Review of Development Economics_ , 13(2):200–214.

- Bambe, B.-W.-W., Combes, J. L., Kaba, K., and Minea, A. (2022). Inflation targeting and developing countries’ performance: Evidence from firm-level data. Technical report, Orleans Economics Laboratory/Laboratoire d’Economie d’Orleans (LEO ....

- Barth III, M. J. and Ramey, V. A. (2001). The cost channel of monetary transmission. _NBER macroeconomics annual_ , 16:199–240.

- Bernanke, B. S. (1983). Irreversibility, uncertainty, and cyclical investment. _The quarterly journal of economics_ , 98(1):85–106.

- Bodenstein, M.,
Policy Research Working Paper 5556

# **Abstract**

Immigrants in Rome or Paris are more visible to the public eye than the Italian or French engineers in Silicon Valley, especially when it comes to the debate on the effects of immigration on the employment and wages of natives in high-income countries. This paper argues that such public fears, especially in European countries are misplaced; instead, more concern should be directed towards emigration. Using a new dataset on migration flows by education levels for the period 1990–2000, the results show the following: First, immigration had zero to small positive long-run effect on the average wages of natives, ranging from zero in Italy to +1.7 percent in Australia. Second, emigration had a mild to significant negative long-run effect ranging from zero for the US to −0.8 percent in the UK. Third, over the period 1990–2000, immigration generally improved the income distribution of European countries while emigration worsened it by increasing the wage gap between the high and low skilled natives. These patterns hold true using a range of parameters for the simulations, accounting for the estimates of undocumented immigrants, and correcting for the quality of schooling and/or labormarket downgrading of skills. All results go counter to the popular beliefs about migration, but they are due to the higher skill intensity of both emigration and immigration relative to non-migrants.

This paper is a product of the Trade and Integration Team, Development Research Group. 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://econ.worldbank.org. The author may be contacted at cozden@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
Only 45.7 percent of students from our entry cohort graduate—15.1 percent graduates on time (in five years) and 30.6 percent graduates late (in 6-8 years).<sup>6</sup> Dropout rates are far from uniform over time. As Figure 2 shows, over a quarter of college students (half of all dropouts) leave in the first year, and the first two years account for about 70 percent of all dropouts. Similar to enrollment rates, dropout rates vary widely across student types (Table 2). Conditional on income, higher ability students have lower dropout rates; on average, the dropout rate gap between the highest and lowest ability quintiles is equal to 25 pp. In contrast, dropout rates do not vary much by income—suggesting that free college might affect enrollment more than dropout decisions.

Final college outcomes vary substantially by ability. High-ability students are more likely to graduate, whether on time or late (Figure 3), whereas low-ability students are more likely to drop out, particularly in year 1. After year 1, dropout rates vary little across abilities.

**Figure 2: Dropout Timing.**

<!-- Start of picture text --> 60<br>% relative to entry cohort<br>% relative to all dropouts<br>50<br>40<br>30<br>20<br>10<br>0<br>1 2 3 4 5 6 7 8<br>College year<br>Percent<br><!-- End of picture text --> _Source_ : Calculations based on SPADIES, for students from the 2006 entry cohort (first semester). _Notes_ : The blue line shows percent of students who drop out in a given college year. The red line shows, among all dropouts, percent of those who drop out in a given college year.

**Figure 3: College Outcomes by Ability.**

<
# **1. Introduction**<sup>1</sup>

Household sample surveys are invaluable tools for understanding economic dynamics, social structures, and the distribution of resources within societies. Among the various indicators collected in these surveys, wealth stands out as a fundamental indicator of economic well-being and household stability.

The inclusion of wealth data in household surveys is crucial for several reasons. Firstly, wealth provides insights into the economic status and living standards of households, providing a more comprehensive view beyond income alone. While income reflects the flow of resources over time, wealth captures accumulated assets and savings, providing a measure of long-term financial security and capacity for investment. Additionally, wealth data allow policy makers, researchers, and analysts to assess disparities in asset ownership, intergenerational wealth transfer, and access to economic opportunities, thereby informing targeted interventions and policy initiatives aimed at reducing inequality and promoting economic inclusion.

Net wealth encompasses tangible assets such as land, real estate, valuables, and financial instruments like savings accounts and stocks, while debts constitute the negative component. Traditionally, the real items considered in household surveys are those for which households hold full property rights. However, this approach may not capture the full spectrum of resource ownership held by households.

Solely focusing on assets with full property rights may overlook significant aspects of wealth accumulation and resource utilization. In many contexts, individuals or households may possess partial rights or entitlements to certain assets, such as in the case of usufruct contracts, where individuals hold the right to use and enjoy property owned by others. Neglecting the patrimonial value of such assets can lead to a distorted understanding of wealth distribution and economic well-being, particularly in communities where informal arrangements or non-traditional property rights prevail.

This paper aims to address the gap in wealth assessment methodologies by advocating for the inclusion of assets with partial rights in household surveys. Specifically, we will explore the importance of taking account of use rights, such as those conferred by usufruct contracts, when estimating household wealth. By incorporating
# **2. Growth drivers in the business-as-usual baseline**

In this section we explore the assumptions that are required to calibrate the LTGM-PC to simulate Malaysia’s business-as-usual baseline over 2020-50. To create a business-as-usual baseline for the Malaysian economy in the long run, we need to calibrate the future paths of the growth drivers like investment, the growth of both TFP and human capital, as well as other key parameters. These assumptions are summarized in Table 2.1. We calibrate the LTGM-PC as the foundation of the analysis and add the LTGM-TFP extension and the LTGM-HC extension to simulate TFP growth and human capital growth, respectively. Additionally, short-to-medium term forecasts produced by the IMF in its Article IV report help us calibrate the future paths of public and private investment. We use population growth projections (by age cohort) from the UN for demographic trends until 2050. The remaining projections are determined by assuming that long-term trends in Malaysia remain constant and by performing some steady state calculations. The labor share of income is calibrated to 50%, which is close to its value from Penn World Tables version 9.0 (PWT 9).

**Table 2.1. Summary of Assumptions for the Malaysia Business-as-Usual Baseline**

|**Variable**|**Baseline**|**Source/Comments**|
|---|---|---|
|Labor Share|50.0%|Similar to the 2014 PWT 9 figure of 53%|
|Depreciation Rate(aggregate)|5.8%|PWT 9 figure for 2014|
|Capital-to-Output Ratio|2.25|At steadystate value|
|Human Capital Growth|0.6% 0.1%|Calculated usingthe LTGM Human Capital Extension|
|TFP Growth|0.9% 0.6%|Similar to the last 15-year average and the last 30-year<br>median usingPWT 9|
|Investment-to-GDP Ratio|24%|Based on projections of public and private-investment
Notes: Countries are classified as unfriendly if less than 50 percent of Americans believe country is a friend or ally. Imports are identified with six-digit Harmonized System (HS) codes: face masks (630790), penicillin (300410), infant formula (190110), crude oil (270900), electric car batteries (850760), and semiconductor chips (854231).

First, in the category of health products, face masks, of which 73 percent come from China in 2022, is an outlier in terms of reliance on non-friendly countries. ‘Non-friendly’ countries provide less than 20 percent of penicillin imports, and less than 5 percent of infant formula imports. Evenett (2020) makes a similar point in his study of prepandemic international sourcing patterns of medical goods and medicines for France, Germany, the United Kingdom, and the United States.

Second, as noted earlier, in the case of face masks, imports from China provided important relief and increased resilience during the pandemic. Note the spike of imports from non-friendly nations during the second quarter of 2020 in Figure 7. Hence, decoupling from unfriendly nations may increase resilience to specific geopolitical shocks, but decrease resilience to other shocks unrelated to politics. The general point is that it is impossible to think about resilience without specific reference to the type of shock to which resilience is sought.

Third, Panel B of Figure 7 shows how import dependence on non-friendly nations has evolved in the past decade for some strategic goods. The sharp increase in the case of semiconductor chips reflects the rising popularity of the “foundry” model in the past two decades, with many foundries located in Malaysia, Vietnam and China (see Table A1 in the Appendix) – countries that are all perceived as non-friendly by a majority of Americans in the YouGov survey. Similarly, in the case of electric batteries, the growing dependence on China is evident in the figure. However, import shares for some goods appear responsive to policy. The share of crude oil sourced from ‘non-friendly’
the elasticity of the poverty headcount index with respect to the change in (mean) expenditure estimated by Christiansen, Demery, and Paternostro (2003, Table 4, p. 326) for Ethiopia. The elasticity of -1.8 (which is within the two-standard error interval of the point estimate provided by Ravallion (2001)) reflects the case where consumption growth is “pro-poor”, in the sense that it entails a more than proportional effect of increases in consumption on poverty—or, equivalently, the case where consumption growth is accompanied by lower income inequality.

32
to $653 (an increase by 30.6 percent) in SSA.<sup>8</sup> Crop production, fishing and industrial (or export) agriculture, which on average jointly accounted for two-thirds of the primary sector all experienced decline in growth during 2006-2013, and thus in contribution to overall growth (Figure 1). The decline is most pronounced in industrial agriculture where growth was negative in four of eight years, reaching as low as -45 percent in 2011, in effect slowing down growth from 6.3 percent in 1995-2005 to 0.9 percent in 2006-2013, and contracting by -3.4 percent in 2014. Thus, its contribution to GDP growth reversed from +1 percent to -2 percent during the respective periods. This weakness is attributable to high production costs, including energy cost, and lack of competitiveness in both input and output markets.

> 8 World Bank Development Indicators 2014.

7
Boosted Regression Forests, and Cubist regression. Unlike Krennmair et al., the machine learning models do not include a conditional random effect. Despite that, the results indicate that all three machine learning methods generate substantially more accurate estimates than the linear EBP model, particularly out of sample. Of the machine learning methods, boosted regression forests and extreme gradient boosting perform equally well except in Sri Lanka, where extreme gradient boosting produces slightly more accurate estimates. The random effect block residual bootstrap approach proposed by Krennmair et al (2022) also works well when applied to gradient boosting, providing coverage rates ranging from 94 to 97 percent.

Finally, Lee and Braithewaite (2022) propose an innovative iterative process that combines tree-based machine learning and deep learning to generate wealth estimates. This paper first trains a model using extreme gradient boosting to predict the probability of being in different wealth classes using interpretable geospatial features, and then uses the predicted values from this procedure to train a convolutional neural network on satellite imagery. The predicted probabilities of being in different wealth classes from the convolutional neural network are subsequently fed back into the boosting model. This process is repeated iteratively until there is convergence. This improves upon the methodology in Jean et al. (2016) by avoiding the use of night-time lights to train models. The authors report that the average out-of-sample R<sup>2</sup> from withheld countries is 0.90.

In general, several studies suggest that small area estimates generated by combining survey and geospatial data are more accurate than those based solely on survey data, sometimes by significant margins. This is notable because small area estimates based on geospatial data are subject to model bias; for example, a model that uses night-time lights as a predictor may underestimate poverty in a poor area that happens to contain a highway, if the high level of night-time lights associated with highways makes the area look less poor from the sky than it actually is. However, at least when predicting poverty rates at higher levels such as subdistricts
|
|Functional form|GLM|OLS|GLM|POISSON|GLM|

Note: NI=Not Included

# **5. Results**

Due to the large number of variables in the dataset, and the high level of association among many of them (leading to high auto-correlation), we administered principal component (PC) analyses that helped us group these correlated variables and reduce the number of variables with inter-correlation on the right hand side of the estimated equation. We constructed a working dataset that includes the PC variables and some other variables that were incorporated in our regression analyses. The PC variables are described in Annex 3, but the results of the PC analysis are presented below. Following the PC results, we present the descriptive statistics of the variables in the working dataset. Finally, the Results section is concluded with the various regression outcomes and their interpretation.

> 7 Some of the variables presented here are explained in the section “Principal Component Results”.

24
the survey.

# **Accuracy of data**

Data recorded in disaster inventories typically relies on reports from one or few sources. Especially where events are small and therefore only reported in a single data source, this exposes disaster inventories to possible inaccuracies in the source from which information is drawn.<sup>37</sup> Even though reports of the number of people affected and the economic impact of any disaster are almost always estimates, disaster inventories rarely provide information that quantifies the uncertainty associated with any individual or collection of estimates.

Estimates from microdata, on the other hand, are based on a large number of data points collected from the population of interest. This also allows to explicitly quantify (part of) the uncertainty inherent in estimates of disaster impact. While the large samples underlying estimates from microdata attenuate the effect of inaccuracies in individual data points, microdata, as any data source, is still subject to possible systematic measurement error.

# **Comparability of source data**

Disaster inventories, and EM-DAT in particular, stand out for their broad coverage of events across geographies and time. To achieve this, disaster inventories need to rely on a wealth of different sources that vary according to their reliability, reporting standards, and definitions. Such idiosyncrasies are seldom 43
Policy Research Working Paper 9875

# **Abstract**

Although research has established the importance of state capacity in economic development, less is known about how to build that capacity and the role of external partners in the process. This paper estimates the impact of a typical development project designed to build state capacity in a low-income country. Specifically, it evaluates a multilateral development bank project in Tanzania, which incentivized investments in local state capacity by offering grants conditional on institutional performance scores. The paper uses a difference-in-differences methodology to estimate the project impact, comparing outcomes between 18 project and 22 non-project local governments over 2016–18. Outcomes were measured through two rounds of primary surveys of nearly 500 local government officials and nearly 3,000 households. Over the course of the project, measured state capacity improved in project areas, but due to comparable gains in non-project areas, the project’s value-added to change in state capacity is estimated to be zero across all the dozens of relevant variables in the surveys. The data suggest that state capacity is evolving in Tanzania through endogenous changes in trust and legitimacy in the country rather than from financial incentives offered by external partners.

This paper is a product of the Development Impact Evaluation 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 vdimaro@worldbank.org, devans@cgdev.org, skhemani@worldbank.org, and tscot@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,
Table 7: Short-term impacts of _Mais Educação_ on educational outcomes

|Dependent Variable:|Dropout rates<br>Test score<br>mathematics|Test score<br>Portuguese|
|---|---|---|
||(1)<br>(2)|(3)|
|Panel A: Impac|ts on fifth-grade variables||
|Dummy_Mais Educação_* year 2009|1.057<br>−3.953**|−2.482|
||(1.283)<br>(1.664)|(1.544)|
|_R_ <sup>2</sup>|0.796<br>0.807|0.809|
|Observations|1,992<br>1,992|1,992|
|Dummy year 2009|Yes<br>Yes|Yes|
|School fixed effects|Yes<br>Yes|Yes|
|Panel B: Impact|s on ninth-grade variables||
|Dummy_Mais Educação_* year 2009|−0.137<br>−5.305***|−2.351|
||(1.100)<br>(1.889)|(1.872)|
|_R_ <sup>2</sup>|0.81<br>0.818|0.797|
|Observations|1,730<br>1,730|1,730|
|Dummy year 2009|Yes<br>Yes|Yes|
|School fixed effects|Yes<br>Yes|Yes|

_Sources:_ Authors’ calculations using MEC/FNDE administrative data, _Prova Brasil,_ School Census, and INEP.

_Note:_ Treatment group are schools that joined _Mais Educação_ in 2008 and remained in the program until 2011. This table reports coefficients from the ordinary least squares estimation of equation (5) in the paper, replacing
Table 4-1 Percentage distribution of tapped water and fuels for cooking in towns in three provinces in China: 2000

|_____________<br>province|__________<br>tapped|_________<br>water<br>|_________<br>fuel re|______<br>sources|______<br>for hou|__________________<br>sehold cooking<br>|
|---|---|---|---|---|---|---|
||<br>yes|<br>no|<br>gas|<br>elect|<br>coal|<br>wood/grass<br>others|
|_____________|__________|_________|_________<br>(%)|______|______|_________________|
|Zhejiang|84.17|15.83|<br>80.70|0.37|2.68|14.59<br>1.66|
|<br>Henan|56.53|43.47|11.56|0.21|76.51|11.25<br>0.46|
|Sichuan|72.15|27.85|36.06|10.02|38.04|14.42<br>1.46|
|National|69.32|30.68|40.97|2.66|34.82|20.46<br>1.10|

_______________________________________________________________________ Sources: calculated from Tabulation on the 2000 Population Census of the P. R. China, Table 8-3b, page 1859-1860.

Similarly, there were wide gaps in bath facilities among three provinces, although the difference in kitchen availability in households appeared small. In Zhejiang, about half of households (48%) in towns did not have bath facilities, whereas in Henan, threefourths of households did not have bath facilities (Table 4-2).

Table