rafmacalaba/data-use-mentions-extended huggingface dataset:
Household Survey (IHS) 2010<br>Maldives Demographic and Health Survey (DHS) 2009<br>Mah Demographx and Health Survey (DHS) 2018<br>Namibia National Household Income and Expenditure Survey (NHIES) 2015<br>Nigeria General Household Survey Panel (GHSP) 2010, 2012, 2018<br>Demographic and Health Survey (DHS) 2018<br>Rwanda Labor Force Survey (LFS) 2018<br>Senegal Census 2013<br>Demographx and Health Survey (DHS) 2018<br>South A frica Demographic and Health Survey (DHS) 2016<br>General Household Survey (GHS) Yearly from 2009-2018<br>Tanzania Household Budget Survey (HBS) 2011<br>National Panel Survey (NPS) 2010, 2014<br>Uganda National Panel Survey (NPS) 2009, 2010<br>National Household Survey 2009<br>Functional Difficulties Survey 2017<br>Demographx and Health Survey (DHS) 2016<br>Child Labor Baseline Survey 2009<br>Zimbabwe Intercensal Danographic Survey 2017 4<br><!-- End of picture text -->

|East Asia & Pacific<br>|||
|---|---|---|
|Cambodia|DemographxandHealthSurvey(DHS)|2014|
|Fiji|<br>PopulationCensus|2017|
|Phillipines|<br>ModelFunctioningSurvey|2016|
|Samoa|<br>LabourForceandSchool-to-WorkTransitionSurvey|2017|
|TimorLeste|<br>DemographxandHealth Survey (DHS)|2016|
|Tonga|Population Census|2016|
||LaborForce Survey (LFS)|2018|
|Tuvalu|Population Census|2017|
|Europe&CentralAsia|||
|Moldova<br>|PopulationCensus<br>|2014<br>|
|Serbia<br>|School-to- Work TransitionSurvey(SWTS)<br>|2015<br>|
|Tajikistan|Survey ofWater, Sanitation, andHygiene(WASH)|2016|
|Latin America
food insecurity increased to 59 percent in the aftermath of the onset of the pandemic, largely driven by the sharp increase in food insecurity among non-PSNP households. The average household reported a food gap of 1.3 months in the August 2019 survey, but this grew to 1.6 months in June 2020. Non-PSNP households reported a 0.5 month increase in food gap while the corresponding increase for PSNP households was 0.1 month. Compared to August 2019, the food gap increased in three of the four surveyed regions. The largest increases were in SNNP (from 1.8 to 2.6 months) and Tigray (from 0.5 to 0.9 month). While the mean food gap was highest in Oromia, it changed little between the two rounds. PSNP beneficiaries report smaller increases in mean food gap (0.2 versus 0.5 month).

We asked respondents about their perception of their food security status in the last three months preceding the June 2020 survey compared to similar months the previous year. The food security question is framed in terms of households’ ability to satisfy their food needs. As shown in Figure 3, almost half of the respondents (49 percent) report that their ability to satisfy their food needs worsened, and the rest report that it remained about the same (48 percent) or improved (3 percent). The worsening of the problem of satisfying food needs appears to have been greater in areas that are more severely affected by the pandemic. In addition to the household survey, we collected zone-level (sub-regional) data on spread of COVID-19 until June 2020. March 13 marked the first case of COVID-19 in Ethiopia and the pandemic begun to take off in June where cumulative new cases reached 5,846 by June 30 (EPHI, 2020). We construct an indicator variable of zone level status of COVID-19 spread using the number of cases reported as of end of June 2020. The indicator variable takes value 1 for zones in the top tercile
these coefficients to Vietnam, whose resource endowments are shown in table 13, yields the following results:

- (2) Predicted Ln (Xm/Xp) = -5.11 + 1.47.1n (6.28)- 0.6.1n (0.0075) =0.515

- (3) Predicted Xm/Xp = exp.(0.515) = 1.673

- (4) Predicted Xm/X = 1.673/(1 + 1.673) = 0.63

It appears that Vietnam's predicted earnings from manufacturing exports (Xm/Xp in equation 3) amount to 167% of earnings from primary exports instead of its actual 58%. Thus, its predicted share of manufactures in total exports (Xm/X in equation 4) is 63% instead of just 37.6%. In other words, with a more efficient use of its available resources, Vietnam could be expected to increase its share of manufactures in total exports by more than 25 percentage points.

Of course, these estimates must be taken with a grain of salt. First, in the analysis of Wood and Mayer, workers' skills are measured by the average number of years of schooling of the adult (over-15) population, while natural resources are measured by total land area. Both of these measures are imperfect. In particular, average years of schooling may overestimate the skills of Vietnam's workers since it does not take into account the quality of schooling nor skill acquisition through training or on the job experience -areas in which Vietnam are usually considered to fare poorly compared to other countries. In addition, total land area does not account for variation among countries in the quality of their land nor in their oil reserves. Since Vietnam is an oil exporter, total land area may understate Vietnam's 14
the financial crisis at over 100% of GDP; it was over four times the ratio in Ireland. Portugal with 68% was a distant second and Spain was closer to Ireland (Figure 4). These debt ratios are much higher than either the EZ or EU averages. In the 2009 recession, debt ratios rose even faster.

**Figure 5.a: Current account balance Figure 5.b: Real effective exchange rate as a share of GDP (%)                                          (index 2000=100)**

<!-- Start of picture text --> 5.00 140<br>0.00<br>130<br>‐5.00<br>120<br>‐10.00<br>110<br>‐15.00<br>‐20.00 100<br>2000 2002 2004 2006 2008 2010 2012 2014 2000 2002 2004 2006 2008 2010 2012 2014<br>Greece Ireland Portugal Spain Greece Ireland Portugal Spain<br><!-- End of picture text --> Source: Source: European Commission, Annual macro‐economic database Source: IMF, International Financial Statistics.

_<u>Worsening external accounts and rising external debt for all.</u>_ The boom in aggregate demand led to strong import growth particularly in the two years immediately preceding the crisis; net exports were at a huge deficit in GIS (over 12% of GDP in Greece) and Ireland’s huge surplus halved, leading to substantial current account deterioration (Figure 4a).<sup>10</sup> By 2007/8, _the level_ of the overall current account deficit was particularly high in Greece (15% of GDP), followed by Portugal (9.7/12.1%) and Spain (over 9%), but _the deteriorations_ in Ireland and Spain were dramatic, the current account deficit worsening by over 100%. In Ireland, a 2003 surplus changed to a
- 23 -

At first glance, our sample data also seem to support the fact that competitive pressure exists in the road procurement market of Nepal. Although the level of competition is still low, the bids relative to engineering cost estimates tend to decline, as the competition increases (Figure 21). Of course, however, this is merely on a simple correlation basis; the competition effect has to be tested formally by estimating Equation (1).

**Figure 21. Competition and normalized bids**

<!-- Start of picture text --> 2.5<br>ln( bid / cost ) = 1.741 - 0.222 ln( num )<br>R2 = 0.1927<br>2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>0 5 10 15<br>Number of bids<br>Bid / Cost estimate<br><!-- End of picture text --> To capture the impact of security issues, _securitybefore_ is included, which is defined by the number of security-related incidents in a project host district during the three months prior to the auction. Note that this is constructed based on the information of incidents before each auction. This is the best information that potential bidders can know and incorporate in their bid calculation. Based on the past history of security incidents, they are assumed to estimate the likelihood of similar future incidents.

_X_ controls for other contract-specific technical characteristics, such as engineering cost estimates, length of roads, width of the road, thickness of the surface, and used materials (see Table 4 for details).<sup>10</sup> Note that the engineering cost estimate, which is calculated by DDCs prior to public tendering, is included in _X_ to control the observable and unobservable size

> 10 A small but positive value is used to avoid logarithms of zero in independent variables
schools, particularly in small municipalities. Additionally, for the RDD analysis, we constructed a more balanced (trimmed) sample using the distribution of the p-score and dropping units with a propensity score below 0.1 or above 0.9 to make estimates more precise and less sensitive to changes in specification (Crump et al. 2009).

To test the identifying assumption of the RDD approach formally, we carried out a number of continuity checks on baseline characteristics at the individual and household level –all of them available from the census of the poor and used for the _Sisben_ proxymeans test – that could be associated with the outcomes of interest. Figure 3 and Table 9 show graphical and regression analysis on the relationship between the _Sisben_ poverty index score and these variables. Because of the large size of the sample used in the analysis, these differences are very precisely estimated. In general, there are not remarkable statistically significant differences in pre-program characteristics between the two groups on each side of the cutoff that could be argued to drive the results. In cases where the differences are statistically significant, the magnitude of the discontinuity is either relatively small in economic sense to drive the results (on average 0.046 more children in households right above the threshold of eligibility) or the direction of the difference (smaller household size or larger home ownership) is unlikely to introduce a bias that could affect the interpretation of our results.

Another potential concern in the RDD is the possibility that individuals could manipulate the assignment variable (the _Sisben_ poverty index score) and generate nonrandom sorting around the threshold. For example, the RDD approach may be invalid if more motivated and education-driven people seek to influence the value of their scores by taking actions such as hiding assets, ̳borrowing‘ children from other families to increase the size of the household, or bribing local authorities and program administrators. This may undermine the comparability of people on each side of the cutoff. While it is impossible to fully rule out this type of behavior in the context of
# Temporary Migration for Long-term Investment<sup>∗</sup>

Laurent Bossavie<sup>1</sup> , Joseph-Simon G ̈orlach<sup>2</sup> , C ̧aglar Ozden<sup> ̈1</sup> , and He Wang<sup>1</sup>

> 1World Bank

> 2Bocconi University, CReAM, IGIER, IZA and LEAP

**JEL codes:** J61, D15, F22, O13

**Key words:** Temporary migration, entrepreneurship, credit constraints, developing countries, migration costs

> ∗We would like to thank Nelly Elmallakh and participants at the 13th Migration and Development conference, the Economics of Migration online seminar series, Migrations and Organizations Conference at the University of Pennsylvania, Social Protection and Labor Workshop at the World Bank and colleagues at Bocconi University for helpful comments and suggestions. We are also very thankful to Rubaba Anwar for providing excellent field coordination support during the implementation of the survey in Bangladesh, and to the Bangladesh Bureau of Manpower, Employment and Training for granting clearance to carry out the Bangladesh Return Migrant Survey (BRMS). Funding for this research was provided by the Rapid Social Response Multi-Donor Trust Fund, the DEC Research Support Budget (RSB), Knowledge for Change Program (KCP) and the Multi-Donor Trust Fund for International Trade within the World Bank.
Rs 60,000. All loans have joint liability at the CO level although new loans are issued even if some CO members are overdue.<sup>6</sup> Besides credit, NRSP offers training in various vocational skills and provides up to 80 percent financing for infrastructure projects in the village.

# **2.2 The Experiment and Marketing Intervention**

The study was conducted in five branches in the districts of Bahawalpur, Hyderabad, and Attock, spanning different agro-climatic regions of Pakistan.<sup>7</sup> Figure 1 shows the location of the study districts.

NRSP staff conducted a complete listing of the occupation of CO members in the study branches to identify those who were engaged in a non-farm activity. After the listing, a baseline survey was conducted in November 2006 in a sample of 747 COs, selected so that their membership was between 5 and 26 members. The original sampling framework included all CO members that according to the listing exercise had a non-farm business and five other members selected at random from each CO. In practice, enumerators ended up interviewing everyone that attended a special CO meeting that was called to conduct the baseline survey. The resulting sample consisted of a total of 4,162 members interviewed, and 2,284 members (54.9%) that were in good standing. The timeline of the experiment is presented in Figure 2.

Using data from the listing exercise, COs were randomly allocated into two groups, one of which was assigned to receive business training. Training sessions were

> 6 Borrowers are required to find two guarantors, who can be members of the same CO. NRSP staff uses guarantors as a means of exerting peer pressure, rather than enforcing repayment from them.

> 7 These branches are as follows: Matiari and Tando Muhammad Khan in Hyderabad, Attock in Attock and Bahawalpur (rural and urban) in Bahawalpur.

7
then calculated using the official population number of 4.5 million (last official estimate and the population estimate on which the sample weights are based). Dar es Salaam GDP adds up to 13 trillion shillings (US$5.6 billion), and total losses due to the April 2018 flood account for between 2 and 4% of it.

_Table 6 - Total losses experienced by households on a city level in US$ and % of city GDP_

|**April 2018 flood:**|**Losses lower bound***|**Losses upper bound****|
|---|---|---|
|Direct losses (house and asset)|US$101,480,000|US$215,860,000|
|Indirect losses (health & labor)|US$5,547,000|US$11,825,000|
|**Total losses**|US$107,027,000|US$227,685,000|
|**% of city GDP**|1.9%|4.0%|

_*assuming households that were not reached in follow up survey were not affected (weights based on flood risk strata)_ _**assuming households that were not reached in follow up survey were affected in the same way as the ones that were reached (weights based on flood risk strata)_ The cost is most likely an underestimation of the true cost of flood exposure for households in Dar es Salaam. It does not take into account the non-monetary well-being impacts, such as health impacts or the cost of having water and sanitation service disrupted. It also does not fully take into account the impacts on income and career development that not showing up for work or having the family business damaged or disrupted can have. Finally, it does not account for the behavioral impacts that flood exposure can have on, for example, the propensity to invest
## approaches, we utilize both the DOU and DB classifications.

To begin, we apply the DOU and DB classifications to the WorldPop and GHSPOP gridded population layers, which are then overlaid with geo-located HBS data. When GPS information is available in a country’s HBS, the overlay process is straightforward. However, for countries lacking GPS information, we overlay the data at the lowest possible geographic unit level. The population shares of each DOU/DB category are calculated for each geographic unit, by generating aggregated statistics, such as poverty rates at the national and sub-national levels with an application of sampling weights. To obtain a binary indicator for households in each type of DOU and DB area in a country’s HBS, we employ a “threshold” approach. This involves determining the number of people residing in grid cells of a particular type for each geographic area. If the majority of the population in an area lives in rural locations, the area is classified as rural. Conversely, areas are classified as urban under the DOU approach if the majority of the population resides in urban centers or clusters. Similarly, for DB indicators, areas are classified as core areas, suburbs, or towns based on the share of the population in each respective category.

It is worth noting that Henderson et al. (2019) also utilize one of the urban definitions employed in our study (the DOU approach) and gridded population data (GHSPOP). While both studies adopt a microdata approach, Henderson et al. (2019) primarily rely on the Demographic and Health Survey (DHS), whereas our study utilizes official household budget surveys. While the DHS provides valuable insights into a range of demographic and health outcomes, often more detailed than what is included in HBS data, the HBS data also encompasses a consumption (or income) variable to measure poverty, which is absent in the DHS data.

# **4.2 Integrating new urban classifications into global poverty measurement**

To improve the accuracy and
new cement floors and applying a fresh coat of paint to the windows and exterior walls of the house.

Small geographical clusters of 3 to 6 eligible study households were assigned to either receive the home improvements (treatment group) or to the control group. To assess potential spillovers, some households in treated clusters were not targeted for the program (similarly to households in control clusters), thus creating exogenous variation in the distance between targeted (i.e. offered treatment) and untargeted households. Untargeted households were not told about the program, although they could have felt frustrated if they learned about it, especially if they lived in treatment clusters close to neighbors that had been offered the program.

After an initial visit where the program was announced to targeted households, the program was launched in November 2019 but had to be paused in March 2020 due to COVID-19. By March 2020, about half of the targeted houses had been upgraded and importantly, the rest of the targeted households were never told about the plans to delay the implementation. Construction eventually resumed in July 2020 and concluded in April 2021. COVID-19 lockdown measures thus created exogenous variation in the time that elapsed between the announcement of the program and its eventual implementation. In addition, we use two rounds of follow-up data, collected after the program implementation, to measure within-household variation in exposure time to the program.

We use and overall of life as our main measures housing quality quality family of SWB. These evaluative measures have been used extensively in the literature, including in Cattaneo, Galiani, Gertler, Martinez, and Titiunik (2009) and Galiani et al. (2018).

We adapt the model of adaptation developed by Kimball et al. (2015) to account for delay disappointment and find that disappointment losses, while statistically significant, only account for less than 4% of subjective measures of well-being. Consistent with Galiani et al. (2018), we also find that permanent gains are indistinguishable from zero, suggesting that in the long-run, because disappointment
of Population 25 years and older|Barro and Lee (1993, 2000); Cohen and Soto (2001)|
|Rates of Return to Education|Psacharopoulos (1994); Bils and Klenow (2000)|
|Educational Quality<br>USPTO Patent Counts|Hanushek and Kimko (2000)<br>USPTO website|
|USPTO Utility Patent Counts|Hall, Jaffe and Trajtenberg (2001)|
|USPTO Utility Patent Citations|Hall, Jaffe and Trajtenberg (2001)|
|Scientific and Technical Journal Articles|W DI|
|Royalty and license fees, payments (BoP, current US$)|W DI|
|Royalty and license fees, receipts (BoP, current US$)|W DI|
|Gross Foreign Direct Investment (% of GDP)|W DI|
|Manufactures imports (% of merchandise imports)|W DI|
|Merchandise imports (current US$)|W DI|
|Manufactures exports (% of merchandise imports)|W DI|
|Merchandise exports (current US$)|W DI|
|Personal computers (per 1,000 people)|W DI|
|Internet users (per 1,000 people)|W DI|
|Internet hosts (per 10,000 people)|W DI|
|Telephone mainlines (per 1,000 people)|W DI|
|Mobile phones (per 1,000 people)|W DI|
|Television sets (per 1,000 people)|W DI|
|Radios (per 1,000 people)|W DI|
|Daily newspapers (per 1,000 people)|W DI|
|Economic Openness Index|Sachs and W arner (1995)|
|Institutional Quality Index|Knack and Keefer (1995)|
V ́elez, 2013), and (iii) Korea in 2010 from 31 to 37 (Kim and Kim, 2013).

For all the pros of income tax records, the availability of the data is unfortunately rather limited, particularly in developing and emerging economies. The World Top Incomes Database (Alvaredo et al., 2015) for example includes no countries from the Middle East and North Africa region. Furthermore, data derived from tax records are less useful in places where tax evasion is more pervasive, as is the case in many developing countries. It should also be noted that combining household survey data and tax records is not without complications because the two data sources use different income definitions (disposable versus taxable) and have different units of analysis (households versus tax units, which could be individuals).

In the absence of data from tax records, this study explores the feasibility of using data on house prices to estimate the top tail of the income distribution.

1Diaz-Bazan (2014) generalizes the method of Atkinson (2007) and Alvaredo (2011) by allowing for a more general choice of the cut-off level for joining up the distributions. Morelli et al. (2015) review the literature attempting to combine household surveys and tax data in rich countries.

2
26,717|92,767|
|Rwanda|23,601|28,398|55,398|73,349|

Note: Authors’ aggregation based on UNHCR statistical population online dataset, accessed in September 2014.* denotes that the data on Sudan would be inflated (e.g. 35,529 in 2013) if people in refugee‐like situations were included in the figures (see notes, Figure 1).

Naturally, the ranking of the countries hosting most refugees is a bit distorted since refugees are more likely to flee to neighboring countries. Large (and for most, landlocked) countries, like e.g. the Republic Democratic of Congo, Ethiopia, Kenya and Sudan are likely to share borders with more conflict‐prone countries and to host many refugees, while being themselves affected by social unrest and be a source of a fair amount of refugees. As can be seen from Table 3, a different pattern may be obtained while weighting such figures by either the size or the population of the hosting countries. That is particularly the case when weighting by the size for countries like DRC, or Tanzania. On the contrary, smaller countries like Burundi, Rwanda, or Djibouti ranked at the top in terms of number of refugees hosted per inhabitant.

10
at atmospheric levels above the surface is higher than at the surface by district for each fiscal year. Inversions are measured as the share of days with an inversion. Inversion strength is the average difference in temperature when there is an inversion. All inversion data and weather controls are population weighted averages across districts. The unit of observation is the district year. In all columns we control for the first difference of the number of days in one of fifteen precipitation bins, the number of days the mean temperature is in one of twenty-five bins, a second order polynomial of relative humidity and surface pressure, four wind speed bins, ten wind direction bins, and interactions of relative humidity and the square of relative humidity with all of our temperature bins. All columns include fiscal year fixed effects. In all columns we cluster standard errors at the district level. We winsorize GDP growth at 1%. (* p _<_ .10 ** p _<_ .05 *** p _<_ .01).

22