rafmacalaba/data-use-mentions-extended huggingface dataset:
on any expenditure items, relying on a linear time trend to nowcast poverty becomes riskier. This issue is exacerbated by the fact that the prediction from 2011/12 to 2017/18 spans seven years, which is much longer than the three-year gap when projecting from 2011/12 to 2014/15. We therefore assume that the coefficients remain unchanged between 2011/12 and 2017/18. We recognize that this likely understates the extent to which poverty has changed, because it holds the estimated coefficients from 2011 constant, including the intercept.

T Similar to Newhouse and Vyas (2019), the xch vector in equation 1 consists of an intercept as well as household and district level demographic variables, labor market indicators as well as districtlevel rainfall shocks. We include several additional variables, not present in the data used by Newhouse and Vyas (2019) to compensate for the absence of the service expenditure variables in the SCS Health 2017/18. These additional variables include characteristics of the household head such as gender, marital status, and, as explained in the Appendix, the type of cooking fuel.

T In order to choose the explanatory variables to be included in the xch vector, we consider two shrinkage or regularization methods: the least absolute shrinkage selection operator (LASSO) regression method and the Stepwise regression algorithm. Both methods reduce the number of predictors to be included in the final specification of the model, with the aim of reducing the variance of the projections at the cost of a negligible increase in the bias of the coefficients. The LASSO algorithm (Tibshirani 1996), solves the residual error minimization problem of the linear T model in a manner that only a subset xch of all the xch potential variables are chosen in the final model used for projections. On the other hand, there are several ways to carry out stepwise regressions. The forward selection starts with no variables and tests each additional variable using 7
0.950|0.824|0.947|
|to marginal|(0.057)|(0.096)|(0.109)|(0.045)|(0.045)|(0.033)|

Note: Data are drawn from the DHS. Means are population weighted. The correlation coefficient is that between the joint probability and the relevant undernutrition rate from Table 7. Elasticities estimated by double-log regression. Robust standard errors in parentheses.

47
14

**Figure 6  Percentage of people living in households that cited LPG or electricity as the primary source of energy for cooking and annual expenditures per capita in urban areas**

<!-- Start of picture text --> 100 $6,000<br>90<br>$5,000<br>80<br>70<br>$4,000<br>60<br>50 $3,000<br>40<br>$2,000<br>30<br>20<br>$1,000<br>10<br>0 $0<br>LPG Electricity Expenditures per capita in 2022 dollars<br>Source : World Bank staff calculations using household survey data.<br>: The South African survey did not ask enough questions to enable estimation of total household expenditures.<br>Figure 7 Percentage of people living in households that cited LPG or electricity as the primary<br>source of energy for cooking and annual expenditures per capita in rural areas<br>70 $2,800<br>60 $2,400<br>50 $2,000<br>40 $1,600<br>30 $1,200<br>20 $800<br>10 $400<br>0 $0<br>LPG Electricity Expenditures per capita in 2022 dollars<br>% of population<br>Annual expenditure per capita<br>% of population<br>Annual expenditure per capita<br><!-- End of picture text --> _Source_ : World Bank staff calculations using household survey data.

_Note_ : The South African survey did not ask enough questions to enable estimation of total household expenditures.

**Figure 7  Percentage of people living in households that cited LPG or electricity as the primary source of energy for cooking and annual expenditures per capita in rural areas**

_Source_ : World Bank staff calculations using
, Censo Industrial: Brasw1, Serie Nacional, Vol. 2, Part I, Rio.

- IBGE (1981b), Censo Industrial: Brasil, Producao Fisica, Serie Nacional, Vol. 2, Part 2, Rio.

IBGE (1983), Censo Demografico: Mao de Obra, Vol. 1, Tomo 5, No. 1, Rio IBGE (1984), Censo Industrial: Dados Gerais: Brasil, Vol. 3, Tomo 2, Rio. ILO (1976) International Recommendations on Labour Statistics, Geneva. INEGI (1985), Estadisticas Historicas de Mexico, Mexico.

Japan Productivity Center (1984), Measuring Productivity, UNIPUB, New York.

- Joint Economic Committee (1981), East European Economic Assessment, US Congress, Washington DC, 1981.

- Joint Economic Committee (1982), USSR: Measures of Economic Growth and Development, 1950-1980, US Congress, Washington DC.
# Is foreign aid fungible? Evidence from the education and health

# sectors

## Nicolas Van de Sijpe<sup>_∗_</sup>

**Keywords:** foreign aid, fungibility, public education expenditure, public health expenditure. **JEL classification codes:** E62, F35, H50, O23.

### **Sector Board:** Economic Policy (EPOL).

> _∗_ Departmental Lecturer in Development Economics, Department of International Development, and Research Associate, Centre for the Study of African Economies (CSAE), University of Oxford. Address: Queen Elizabeth House, 3 Mansfield Road, Oxford, OX1 3TB, United Kingdom. E-mail: nicolas.vandesijpe@qeh.ox.ac.uk. I am grateful to Sudhir Anand, Channing Arndt, Steve Bond, Maarten Bosker, Paolo de Renzio, Markus Eberhardt, Bernard Gauthier, Benedikt Goderis, Clare Leaver, Anita Ratcliffe, M ̊ans S ̈oderbom, Francis Teal, Dirk Van de Gaer, David Vines, John Wilson, the participants at various conferences and seminars, and especially Chris Adam, Paul Collier, Shanta Devarajan, Jon Temple, Frank Windmeijer, Adrian Wood and three anonymous referees for useful comments and discussions. I am also grateful to Ali Abbas, Nicolas Depetris Chauvin, Ibrahim Levent and Gerd Schwartz for assistance in obtaining data. I would like to thank the people at the OECD Development Assistance Committee (DAC), especially C ́ecile Sangar ́e, for patiently answering my questions about their development assistance data and Brian Hammond for comments on the aid data construction. I gratefully acknowledge funding from the Department of Economics, the Oxford Centre for the Analysis of Resource Rich Economies (OxCarre), the Oxford Institute for Global Economic Development (OxIGED) and St. Catherine’s College, University of Oxford. This paper presents the research results of the Belgian Programme on Interuniversity Poles of Attraction (contract no. P6/07) initiated by the Belgian State,
per capita derived from survey data. The choice between them is often made based on the social-economic conditions of the country chosen for the analysis and microdata collected. Income is usually used to measure welfare in rich and developed countries. For example, poverty in most of European Union countries is measured by income, but in most of the developing world consumption is deemed a better measure for poverty as income suffers from measurement error and tends to be underreported (Azzarri et al. 2010). Consumption includes both purchased goods, services and home production and is believed to be a better candidate to measure lifetime welfare in less developed countries. Consumption can be better measured than income in countries where agriculture and self-employment are important employment options. Consumption is also less subject to seasonal volatility in rural areas compared to income and is more likely to be representative of general welfare (Coudouel, Hentschel and Wodon 2012; Haughton and Khandker 2014). Using income for poverty measurement may have its benefits as well. For example, contributions to change in income poverty can be established by income sources and this information will be very useful and easy to use for policy recommendations. Indeed, a recent paper by Meyers and Sullivan (2009) argues

> 5 One caveat is that information on changes in sample frame in the selected period was not available in open sources. In order to test the robustness of obtained trend the team re-estimated poverty without survey weights. Results are qualitatively the same and available upon request.

3
(Table E.26). In Table E.20, we also look at whether treated sections are more exposed to the epidemic due to their proximity to index cases in Guinea or Sierra Leone or their road density. We find that, if anything, treated sections are slightly further from index cases.

We look for evidence of spillovers between treated and control sections, particularly indications that patients traveled from control to treated sections. Such reallocation within our sample would amplify our treatment effects on Ebola reporting. Assuming that patients minimize travel costs, spillovers should be largest in treated sections that border (populous) control sections. In Table E.21, we interact our treatment indicator, first, with the number of bordering control sections and, second, with the population (based on 2004 census data) in bordering control sections. If patients from control sections report in adjacent treatment areas, the coefficients on these interactions will be positive; yet, our estimates are negative and insignificant.<sup>37</sup> Following a similar logic, spillovers could occur via road networks. Using data from Open Street Map, we count how many control sections a treatment section is connected to via the road network (see Figure E.8). Table E.23 interacts this variable with our treatment indicator and, again, finds no indication

> 37 We also calculate each clinic’s proximity to the next nearest control clinic in the full sample. (We do not have exact coordinates of clinics, and thus geolocate clinics using the centroids of the census enumeration areas that contain the clinics.) In Table E.22 we find that treated sections report more cases when their treated clinic is far from the next control clinic — the opposite of what we would expect if spillovers are amplifying our effects.

30
distribution of physical tasks like cooking, cleaning, and making repairs. Despite openness to more equal household contributions, the intensity of respondent preferences for male employment _outside_ the home limit the potential impact of this theoretical openness to greater household burden-sharing. The results suggest that women’s unpaid labor burden is primarily a side effect of preferences for a male breadwinner family model, rather than strong preferences over who does specific household tasks.

# **7 Conclusion**

This paper began by asking to what extent attitudes and perceived norms (or normative expectations) around household roles hinder the emergence of more gender-equal distributions of labor in Morocco. As across the MENA region, women are disproportionately out of the paid labor force and in charge of most unpaid household and care tasks. Drawing on results from an original online survey of predominantly urban, relatively educated Moroccans, with embedded experiments, I found that individuals’ attitudes toward household duties themselves appear a relatively unimportant determinant of individual behavior in this context. Instead, both (a) perceptions of men’s attitudes toward household roles and (b) attitudes toward men’s roles outside the home—specifically, strong preferences among both men and women for a male breadwinner—appear the most important factors contributing to women shouldering a disproportionate unpaid labor burden and, often, a heavy double burden of paid and unpaid labor.

Respondents acknowledge that women shoulder a higher share of the household burden of labor than men do. Yet men also take a rosy view of their contributions: married male respondents are more likely to estimate that their own contributions to unpaid labor are relatively equal to their spouses, while married women report being primarily responsible for tasks more than sharing them equally. Among single respondents, men also provide a lower estimate of the share of household labor undertaken primarily by their mothers (potentially because they are more likely to live independently), but neither male nor female single respondents cite fathers as contributing much at all. Despite these 30
Equation 4 estimates the amount of FDI inflow into manufacturing industry i in year t measured at the 2-digit VSIC level. (I) is the set of other industry level characteristics that may affect FDI inflows such as industry wise productivity and industrial tax; μ is industry fixed effects and π is the error term. Similar to equations (1), (2) and (3), we also used fixed effects for dirty and clean industries based on the Mani and Wheeler (1998) classification. Industry wise productivity is measured by net value-added per worker. We also include we also amount of taxes paid by each industry as a control variable. The variable of interest is P which is the industry-wise pollution intensity. If FDI does not show an increase in dirty industries, then =0. δ 2

# **IV. SAMPLE AND DATA**

Annex 1 provides a list of variables used in the industry-level analysis, unit of analysis and their data sources. Data on net turnover, industrial wages, fixed capital, foreign investment and environmental abatement cost come from the General Statistical Office (GSO) of Vietnam. Collection of these data in Vietnam is a recent exercise. As result, detailed data is not available for a longer time frame. The GSO collects these data on a yearly basis since the year 2000 through enterprise surveys and to the extent that this was the first time such an exercise has been conducted in Vietnam, the data seem fairly reliable. Although, ideally we would have liked to have data for much longer timeframe, a most comprehensive industrial survey for Vietnam is available only for the years 20002002. Since this comes on the heels of the bilateral and multilateral trade agreements, it should provide reasonable approximation of the post-openness trends in industrial production.

This data is organized by industry according to the Vietnamese Standard Industrial Classification (VSIC)<sup>11</sup> . Data on exports from Vietnam comes from the Vietnam Trade Database. This data was also organized according to the VSIC.

>
, 2012 calculate income gains from trade using trade flows. Both studies use inversion equations similar to Hotz and Miller, 1993, that are related to the inversion equation in our approach. Different from them, our method is implemented using partially observed outflow data, instead of stayer data, which may be unobserved or unreliable in conflict environments.

This paper proceeds as follows. Section 2 lays out the discrete-choice framework and the theory of the welfare impact analysis. Section 3 introduces the Eastern Ukraine case study. Section 4 presents the estimates for migration elasticities, welfare implications, and the equivalent income shock from the conflict. Section 5 discusses the key findings and caveats. The last section concludes.

> 2Onder et al., 2019 discuss the conditions for analyzing the income equivalent of the mortality driven decreases in statistical life spans, with an application to the Syrian conflict.

> 3For an excellent review of the economic causes and consequences of wars, see Blattman and Miguel, 2010, and for a comprehensive review of recent advances in understanding the long term implications of exposure to war on human behavior, with an emphasis on cooperative social behavior, see Bauer et al., 2016

> 4For a detailed overview of this approach, including the conditions shaping its feasibility, see Abadie, 2021.

4
Figure 5. Geographical Distribution – Night Light Density Before and After the Rohingya Influx <!-- Start of picture text --> Before 2017 (Average 1992 - 2016) After 2017 (Average 2017-2021)<br>Night Light Density Night Light Density<br>0.000 0.000<br>0.001 - 1.400 0.001 - 1.400<br>1.401 - 1.750 1.401 - 1.750<br>1.751 - 2.700 1.751 - 2.700<br>2.701 - 17.000 2.701 - 17.000<br>Refugee Camps Refugee Camps<br>Refugee Camps Refugee Camps<br><!-- End of picture text --> _Notes:_ The maps illustrate the Cox’s Bazar district in Bangladesh segmented into one-square-kilometer grids. The Cox’s Bazar area has 2,392 grid cells. The left panel displays the distribution of night light density between 1992 and 2016, prior to the influx of Rohingya individuals into Bangladesh. The right panel portrays the distribution of night light density between 2017 and 2021, following the start of the Rohingya influx. The maps were generated using the harmonized night light data series spanning from 1992 to 2021, as developed by Li et al. (2020).
Level of FE is thana times year. For the estimation, data from 2000, 2005 and 2010 rounds of HIES are combined and then households from only those thanas that are common to all three rounds are kept. Figures in parentheses are t- statistics. In the activity list excluded category is the small miscellaneous activities, and among the sources of startup capitals excluded category is informal lenders. Regressions additionally include household variables (head’s age, sex and education, land and non-landholding), community prices of consumer goods, daily wage, etc., and agroclimate characteristics (land elevation, average number of sunny months, and excess rain amount per month). Sources: HIES surveys, 2000, 2005 and 2010.

**Table 12: Marginal return on enterprise investment**

|**Share of different sources (%)**|**Model 1**|**Model 2**|
|---|---|---|
|Manufacturing and processing activities|-|0.049**|
|Transport activities|-|0.027**|
|Trading activities|-|0.006**|
|Service activities|-|0.245**|
|Misc. Small activities|-|0.035**|

44
At a glance, Malawi’s cluster distribution for exclusive rights is quite balanced. Country level statistics, however, mask differences between matrilineal and patrilineal communities. When parsing the Malawi data into matrilineal and patrilineal marriages (Figure 8b), one observes stark gender differences in the compositions of clusters. Women landowners in matrilineal marriages are more likely to be in exclusive clusters (31 percent, compared to 16 percent for men) and vice versa for patrilineal marriages (12 percent, compared to 35 percent for men). For the “not hold” cluster, the majority in both customs are female. There are more joint landowners in patrilineal marriages as well.

# **4.2 Rigidity of Rights Bundles within a Cluster**

Within a cluster, we find substantial variation in bundles of rights (Table 7). In Malawi, for example, the cluster in the first column reflects about 60 percent of observations (landowner-parcel level) that do not have the right, in the following order, to bequest, collateral, rent, and sell (“NNNN”). The second most commonly recurring bundle type is ENNN (bequeath is exclusively held and no other rights held), reflecting about 7 percent of observations.

Cluster bundles vary in terms of their rigidity, or how much rights vary within a cluster. Ethiopia has the least variation in rights bundles. The majority in the “exclusive” cluster (approx. 84 percent) hold exclusive rights for all four rights, while 88 percent in the “joint” cluster hold all joint rights. When one randomly chooses a parcel-landowner combination from Ethiopia who has a right to bequeath, for example, there is a high probability that this landowner will also have the other two transfer rights. Within Ethiopia, for the cluster where rights are mostly not held, 65 percent of landowners hold no rights at all (NNNN) — a substantial number but nevertheless reflecting less rigidity than Tanzania.

For Tanzania and Malawi, the “joint” cluster has more variation of
residential areas (25 percent), reflecting income gaps between those areas (Table 1).<sup>8</sup> In addition, even getting to a bus stop itself takes more time in informal residential areas. A larger proportion of housing is located more than 500 meters away from the nearest bus stop in informal settlements (34 percent) than in formal residential areas (only 14 percent).

To understand the trade-off faced by households in their residential choices, a detailed spatial diagnostic is necessary. Job accessibility for a housing depends on the distance from job clusters and the availability of transport to reach there. If housing rents are determined in the market by not only the dwelling and neighborhood characteristics but also the level of job accessibility, households face a trade-off over them in their residential choices. The existing stock of housing with good job accessibility may be scarce in a not-well-connected city like Nairobi, and thus it can be expensive to live in such housing. Some may have to sacrifice living conditions to live in proximity to job opportunities, given the importance of job accessibility in their labor performance. Low-income households could particularly face a challenge to access job opportunities if low-rent residences in informal settlements do not really offer reasonable job accessibility. Therefore, it is critically important to investigate the spatial distribution of job accessibility in the city and its relationship to spatial distribution of poverty (or households in different income groups) and housing rents.

# **3. Methodology**

## **3-1. Data**

_Transport_ To calculate job accessibility in Nairobi, it is necessary to account for various transportation modes: walking, driving and using the semi-formal minibus (matatus) network. Data for the ‘minibus network’ travel times are available online in the General Transit Feed Specification (GTFS) format (see Figure 2 for the minibus network).<sup>9</sup> These data were compiled by the Digital Matatus consortium comprising University of Nairobi, Columbia University, Massachusetts Institute of Technology, and
(2020) made a point that consumers buy units, not kilos of manufactured goods, and thus the per-unit measure is more relevant from the perspective of trade models. The per-unit freight rates are available only in a handful of datasets: e.g., the US Imports by Census and

> 20They estimated the price elasticity of freight rate to be 0.6. The elasticity consistent with the ad-valorem functional form would have to be equal to one.

> 21Theoretically, remote in this case refers to the higher transportation costs to the destination markets. Due to the lack of transportation costs for many countries, the measure of remoteness is often based on the average distance or tradeweighted average distance. Lugovskyy and Skiba (2015) constructed the MQC (Multilateral Quality Compensation) index that combines the trade-weighted importer’s preferences for quality and the composition of transportation costs at the exporter-HS6 level and discusses their distribution (see their Fig 1).

24
|0.41||||
|=1 if incomplete high school|0.08||||
|Units: 0–1|||||
|Both|||||
|Correlation between parents wages|0.54||||
|Units: 2003 US dollars per hour|||||
|Sample Size|||||
|Number of Households||600||150|

Note: Table reports definitions and descriptive statistics of the estimation sample’s main variables. Sample 45 extracted from the second wave of ECLS-B for children born in 2001. Sample size is rounded to the nearest 50 to comply with ECLS-B confidentiality rules.