rafmacalaba/data-use-mentions-extended huggingface dataset:
an idiosyncratic error term; we use the sandwich variance estimator (sometimes referred to as the Huber- or Huber-White robust estimator), which allows for correlation of the residuals within clusters (i.e., PSUs) and is therefore robust to violations of the assumptions of identically and independently distributed residuals.<sup>25</sup>

# _3.2.1  Dependent variables_

We examine the following measures of wellbeing: real monthly per capita food consumption; per capita daily calorie intake; per capita daily protein intake; household dietary diversity; and expenditure shares by broad food groupings. Finally, we examine the demand for wheat products (per capita intake) as a result of the price increases. Our priors are that the large increases in wheat prices will be associated with reductions in wellbeing in terms of total food consumption, total calorie intake, and diversity. In terms of wheat and protein consumption, it is not clear whether the substitution effect or income effect will dominate. When the price of one good increases, the relative price of the other goods declines, and the substitution effect implies that consumption of the relatively cheaper other goods will increase. The countervailing income effect is driven by the fact that as the price of one good increases, real income declines, which results in decreased consumption of normal goods. (For example, as wheat prices in Afghanistan increased, the price of lamb relative to wheat declined; this would suggest more consumption of meat. However, the increase in wheat prices also led to a decline in real income, suggesting less consumption of meat. Therefore the overall effect is an empirical question.) We also expect a change in the composition of food expenditures, with a general movement towards cheaper, lower quality foods.

Total food consumption is an informative measure of wellbeing in itself, but is also important because it is a core component of poverty indicators. To calculate the value of total monthly consumption on food (in Afghani), we map district price data to quantity data
Table 2 also presents separate statistics for single individual households (households with one adult and no children) as our main analysis will focus solely on these households (we discuss why below).

Table 2: Summary Statistics as of June, 2014

|Variables|Mean|Median|
|---|---|---|
|Per Capita Income (single individual households)|330.51<br>(324.30)|200.00|
|Per Capita Income (all households)|151.17<br>(184.97)|76.66|
|Number of Members Per Household|3.04<br>(1.46)|3.00|
|Observations (single individual households)|4,232,528||
|Observations (all households)|28,932,001||

_Note:_ This table shows summary statistics for households in the Cadastro Unico<sup> ́</sup> database as of June, 2014. Per-capita income denotes household, monthly, per-capita income and is measured in Brazilian reais. The PPP conversion from US dollars to Brazilian reais was 1.813 in 2014 (OECD, `https://data.oecd.org/conversion/purchasing-power-parities-ppp.htm` ).

# _3.2 The Transfer Schedule and the June 2014 Reform_

At the beginning of our data (December 2011), the extreme-poverty threshold was equal to R$70 percapita, per-month and the poverty threshold was equal to R$140 per-capita, per-month.<sup>27</sup> The basic benefit was equal to R$70 per-month, while the variable benefits were based on the number and ages of the children in the household (see Appendix B.4 for more information on the variable benefits). In June 2014, the government increased both the benefits and thresholds by 10%. Thus, the extreme-poverty threshold was raised from R$70 to R$77 per-capita,
García, S. (2012). Impacts of conditional cash transfer programs on educational outcomes in developing countries: a meta-analysis.</mark> _<mark>RAND Labor and Population Working Paper Series, WR-921-1</mark>_ <mark>.</mark> <mark>Vaz, A., Pratley, P., & Alkire, S. (2016). Measuring women’s autonomy in Chad using the Relative Autonomy Index.</mark> _<mark>Feminist Economics, 22</mark>_ <mark>(1), 264-294</mark> 23
- <u>Cazes, S., Verick, S., and F. Al-Hussami (2012). “Diverging trends in unemployment in the United</u> States and Europe: Evidence from Okun’s law and the global financial crisis.” _Employment Working Papers_ , ILO.

- <u>Chetty, R. (2006). “A General Formula for the Optimal Level of Social Insurance,”</u> _Journal of Public Economics_ 90: 1879–1901.

- <u>Demirgüç-Kunt, A., Lokshin, M. and I. Torre (2021). “Opening-up Trajectories and Economic</u> Recovery: Lessons after the First Wave of the COVID-19 Pandemic.” _CESifo Economic Studies_ 67(3), 332-369.

- <u>Ding, L. and J. Molina (2020). “Forced Automation’ by COVID-19? Early Trends from Current</u> Population Survey Data.” Federal Reserve Bank of Philadelphia. - - -

- <u>https://philadelphiafed.org/ /media/community development/publications/discussion</u> -

- <u>papers/discussion paper_automation.pdf</u>

- <u>Drahokoupil, J. and T. Müller (2021). “Job retention schemes in Europe: A lifeline during the</u> Covid-19 pandemic.” ETUI Working Paper 2021.07. Available at https://www.etui.org/publications/job-retention-schemes-europe

- <u>Elgin, C., M. A. Kose, F. Ohnsorge, and S. Yu (2021). “Understanding Informality.” CERP</u> _Discussion Paper 16497_ , Centre for Economic Policy Research, London.

- <u>Eyméoud, J., Petrosky-Nadeau, N.,  Santaeulàlia-Llopis, R. and E. Wasmer (2021). “Contrasting</u> US and European Job Markets during COVID-19.” _FRBSF Economic Letter_
of them being rural villages. In contrast, the 2008 DHS had a much larger sample size consisting of 36,800 household in 886 clusters (69% in rural areas).<sup>15</sup> We restrict the sample to those born outside of urban areas because our causal factor of

> 11 There have been some concerns about the quality of household surveys in Nigeria. However, we expect that a standardized survey like the DHS, which has been used in many countries (including two previous rounds in Nigeria), would have reasonable quality controls.

> 12 The DHS has been widely used to perform indirect estimation of population dynamics, such as fertility and mortality, in those places where there is no reliable census data.

> 13 According to the protocols used by the DHS, for children younger than 60 months of age, height is measures to the nearest 0.1 centimeter. While the standing height is measured for children 24 month or older, a measuring board is used to measure child’s length when children are younger than 24 month of age. Weight is measured to the nearest 0.1 kilogram.

14 An international reference is useful since growth in height and weight of well fed, healthy children under 5 years of age from different ethnic backgrounds and different continents is reasonably similar (WHO Multicentre Growth Reference Study Group, 2006).

15 The 2003 DHS used the 1991census frame while the 2008 DHS used the 2006 census frame. There is no available information regarding the definition of a rural area in the 2006 census frame. Some observers have suggested that 13
# 1. INTRODUCTION

One in ten people worldwide lives in extreme poverty. Despite significant achievements in economic growth, the benefits have been distributed unevenly across countries, with poverty becoming more rooted in countries affected by conflict, violence, and weak institutions. The share of the global poor living in fragile and conflict-affected countries increased from 14% in 2008 to 23% in 2015 and is expected to increase to 50% by 2030 (World Bank, 2018). In Afghanistan, the share of people living below the national poverty line increased from 38% in 2011 to 55% in 2016 (Afghanistan Living Conditions Survey, ALCS, 2016). Identifying policies that reduce the growing gap in fragile and conflict-affected areas is therefore critical for reducing poverty.<sup>1</sup> The poor face multiple constraints that reinforce their socioeconomic status. Low levels of human capital endowments and limited access to productive inputs constrain their selfemployment and wage labor opportunities. They are frequently exposed to uninsured risks, both man-made and natural, that are particularly acute in fragile and conflict settings (Dercon, 2008). Persistent poverty and conflict also place a cognitive load on individuals that impairs decision-making and may expose households to further economic duress (Mani et al., 2013; Haushofer & Fehr, 2014; Mullainathan and Shafir, 2013). The ultra-poor in Afghanistan face many of these constraints simultaneously. In the target group for this study,<sup>2</sup> five in six households have an illiterate household head. Four in five households live below the Afghan National Poverty Line of USD 30 (nominal) per person per month (USD 112 PPP).<sup>3</sup> Just 1.5% of households save anything, while two-thirds of households are in debt. Indicators for women paint an even starker picture: less than 4% of primary women in the household can read and write, two in three of these women are depressed, and just over half of eligible girls attend school.
we find that income and income structure still are strongly correlated with a country's level of urbanization. The results are not substantially different than without fixed effects, although NOEDUC and NOEDUC2, CEREAL or black market premia are never significant. Secondary education and rural-urban wage differentials (RUW) are significant, as well as SIVARD is usually significant, although it is now negative. Thus, while civil strife is correlated with higher urbanization across countries, it tends to reduce a given country's urbanization level. The negative crop shock variable is now significant and negative, suggesting 'that countries that have experienced drops in yields in the previous five years, will tend to have a lower than otherwise expected level of urbanization. This could be due to the fact that rural people become too poor to move after a large reduction in crop yield, but in general the results on the crop shock variables are weak and highly sensitive to sample changes.

> 13 Since civil and political rights data are only available from 1975 onwards, we omitted them from regression 1 1.

19
job for less than a year on the sample of individuals 20 and older. The probability of being on the job for more than five years is estimated on the sample of individuals 25 and older, and the probability of job tenure exceeding 10 years is estimated on the sample of individuals 30 and older.<sup>15</sup> To account for the effect of individual characteristics on job tenure, we include in **X** a dummy for the worker’s gender, the worker’s age and age squared, education-level dummies, a dummy if a worker is married, the share of children, and household size. Regarding job characteristics, we include a dummy for the nature of the job contract (temporary or permanent) and whether the job is full- or part-time. A temporary job could capture the differential ability of firms to release workers (Gregg and Wadsworth 2002). Workers employed part-time or self-employed are more likely to break their tenure than full-time workers (Garcia-Cabo and Mudero 2019). We control for occupation by a set of dummies based on the two-digit International Standard Classification of

> 14 <u>Bratberg, Salvanes, and Vaage (2010)</u> used a similar methodology to analyze changes in job stability in Norway. 15 Data on tenure derived from the recall questions on the year when a worker started her current job might contain systematic errors correlated with the duration of tenure (Duncan and Hill 1985, <u>Bollinger et al. 2019). These errors</u> might bias estimates of tenure duration. We are unaware of the analysis of such biases for the EU LFS, but typically these errors could lead to a slight overestimation of the longer-term tenure, which, qualitatively, should not affect the main conclusions of our analysis.

16
**Compensation schemes.** Schools offer either a performance-pay contract or a fixed wage contract. Under the former, the teacher receives a base wage of _W_ plus a bonus _B_ if her performance  ̄ exceeds a threshold _y_ . Under the latter, the teacher simply receives a base wage of _G_ . The outside sector offers a performance-pay contract with a low base wage (normalized to zero) and a bonus _β_ if performance exceeds a threshold _z_  ̄.

**The impact of management practices.** We assume that people management has two effects. The first relates to the _structure_ of compensation: good people management practices enable managers to observe, and contract on, the performance of their employees—i.e. to offer a performancepay contract. The second relates to teacher motivation: good people management practices enable managers to cultivate the intrinsic motivation of their staff—i.e. to increase ∆. We assume that operations management also has two effects. The first relates to the _level_ of compensation: good operations management practices free up resources and enable managers to offer a higher level of base pay. The second relates to household effort: good operations management practices help to create a stimulating environment for students and parents —i.e. to increase _γ_ .

We classify schools into three management types: _high_ (strong people and strong operations management), _intermediate_ (weak people but strong operations management), and _low_ (weak people and weak operations management). Performance metrics are indexed accordingly by _i_ = _H, I, L_ . We assume that high management schools are found exclusively in the private sector, while the public sector consists of a mix of intermediate and low management schools. This implies that performance-pay contracts are only offered by private schools (and the outside sector). Figure 6 provides evidence that this key assumption is well-supported in our PISA data. Here, we plot empirical cumulative distribution functions (CDFs) of the PISA-based people management score by sector and find that the private sector CDF (
# **1. Introduction**

In most countries of the world, population data count and distribution data in the form of census enumerations are used for population segmentation, planning, and a myriad of other functions that support the government. In Low and Middle-Income Country (LMIC) settings, spatially referenced data on populations are crucial for targeting interventions, monitoring the impact of population growth, health and environmental applications, poverty mapping and transport and city planning (Hay et al. 2005; Balk et al. 2006; Tatem et al. 2007). Enumeration areas (EAs) are the operational geographic units for the collection of census data (United Nations Statistics Division 2010). The set of all EAs of a country constitutes a partition of that country, with EAs not overlapping with each other. In principle, EAs are designed such that each unit contains a similar population size and conforms to certain constraints imposed by the logistics of counting large numbers of people with limited resources. Given data on population density at a sufficiently high spatial resolution, the process of designing EAs can be automated, which could substantially accelerate census mapping.

In some LMIC contexts, and particularly in conflicted affected areas, EAs are based on old population data or do not exist, a fact that has far-reaching consequences. In particular, the lack of properly-defined EAs means that there is no nationally representative sampling frame. National sampling frames are used to draw representative samples from the population to understand the geographic distribution of population characteristics (Turner 2003). Without sample weights provided by a national sampling frame, survey data sampled from a country’s population are likely to be biased, typically undersampling vulnerable populations (Thomson et al. 2012; Ellard-Gray et al. 2015). Therefore, the creation of EAs to form a sampling frame creates significant benefits to generate nationally representative data to allow evidence-based analysis informing governments and NGOs. Especially in fragile contexts where data are even more important due to the need of timely and evidence-based planning in volatile situations, up-to-date
These observations align with results derived from land use data. Specifically, after the Rohingya arrived, areas closer to the camps experienced a decrease in dense-open forests and an increase in land covered by grass and crops, compared to the period before 2017. The consistency of the raw data trends supports the validity of the parallel trends assumption.<sup>2</sup> Furthermore, the findings remain robust when we control for differential linear pre-trends between the treatment and control groups. These effects are also visually evident in map illustrations depicting changes in the areas around the camps.

We also analyzed the impacts of proximity of host-community individuals to the refugee camps on several groups of outcomes including adult and household welfare (wealth, income, expenditures, food consumption, and assistance); labor markets; public services (health and education); safety perceptions; and host attitudes toward the Rohingya. These estimates include multiple hypotheses-testing corrections considering the large number of outcomes evaluated.

The findings suggest that the welfare effects of camp proximity on adults and households in host communities are limited and mostly related to humanitarian aid. Particularly, when the distance between the individuals and the camps decreases by 30 km, the likelihood of receiving humanitarian food aid increases by 8.2 pp, the likelihood of receiving food in-kind increases by 5.5 pp, and the likelihood of receiving assistance in the form of household items increases by 10.6 pp. The results do not allow us to distinguish statistically significant effects on wealth, income, or expenditures.

The findings related to labor market outcomes suggest that when the weighted distance from an individual to the refugee camps drops by approximately 30 km, the likelihood of having a written contract increases by 4.6 pp. These are large effects, representing deviations from the mean of 33 percent. Unsurprisingly, the results also suggest a higher likelihood of employment by nongovernmental organizations (NGOs, likely represented by humanitarian organizations) and reductions in employment by all other employers.

> 2The parallel trends assumption posits that the treatment
014)|(0.014)|(0.013)|(0.012)|
||||||-0.033*|-0.039**|-0.054**|-0.049***|-0.055***|
|# outstanding commercial bank loans in 2008 (log)|||||(0.017)|(0.018)<br>|(0.020)|(0.013)|(0.013)|
|Deposit insurance||||||0.065<br>(0.058)||||
|EAP|||||||0.038<br>(0.104)<br>|||
|ECA|||||||0.019<br>(0.102)|||
|High income|||||||0.022<br>(0.128)|||
|LAC|||||||0.084<br>(0.108)|||
|MENA|||||||0.155<br>(0.095)|0.121**<br>(0.046)|0.107**<br>(0.045)|
|South Asia|||||||-0.216*<br>(0.108)|-0.245***<br>(0.085)|-0.253***<br>(0.085)|
|Offshore financial center|||||||||0.104<br>(0.068)|
|Constant|-0.065|-0.174|-0.155|-0.151
- 72 -

consolidated Central Government wages and salaries are for 1993 and are taken from IMF Government Finance Statistics Yearbook, 1995. Data on wages in manufacturing (monthly basis) is taken from the International Labor Office's Yearbook of Labor Statistics 1995 and refers to 1992.

# Finland

Paid employment in non-agricultural activities is drawn from the ILO's Yearbook of Labor Statistics 1995 and refers to 1994.

Data on Central Government, Non central government, Education and Health employment are taken from Public Management: OECD Country profiles, OECD 1992 and relate to 1991. Responsibility for education is shared between the central government and the municipalities, with the states contributing to the finance of approximately 70% of running costs. Higher education is financed by the central government. Vocational training is also funded by the central government. Health care is largely the responsibility of local government. Data on military employment are taken from the International Institute for Strategic Studies: The Military Balance Survey of 1995-96. Data include conscripts (23,900), but do not include personnel of paramilitary units, i.e., the Frontier Guard (3,500), under the authority of the Ministry of the Interior.

Data on wages in manufacturing (monthly basis) is taken from the International Labor Office's Yearbook of Labor Statistics 1995 and refers to 1993.

# France

Paid employment in non-agricultural activities is drawn from the ILO's Yearbook of Labor Statistics 1995 and refers to 1994.

Data on Central Government, Non Central govemment, Education and Health employment are taken from Public Management: OECD Country profiles, OECD 1992 and relate to 1989. Responsibility for education is shared between the central government, the departments and the municipalities. However, the central government pays for the salaries of all teachers employed in public schools. Health care is largely the responsibility of the central government. Data on military employment are taken from the Intemational Institute for Strategic Studies: The Military Balance Survey of 1995-96. Data include conscripts (189,200), but exclude
0.04|0.2|0|1|139182|
|Any Business Credit|0|0.07|0|1|139182|
|Enterprise Registered|0.21|0.4|0|1|139182|
|Female Owner|0.18|0.38|0|1|139182|
|SC/ST Owner|0.21|0.41|0|1|139182|
|Age|10.63|8.57|0|120|139176|
|Keeps Accounts|0.07|0.25|0|1|139182|
|Problem - power|0.05|0.22|0|1|139182|
|Problem - raw materials|0.03|0.18|0|1|139182|
|Problem - demand|0.12|0.32|0|1|139182|
|Problem - fnancial recovery|0.11|0.31|0|1|139182|
|Problem - credit|0.09|0.28|0|1|139182|
|Problem - labour|0.01|0.12|0|1|139182|

_Notes_ : This table presents summary statistics for rural unincorporated non-farm enterprises based on the NSS survey undertaken in 2010-11. _Revenues_ and _Value Addition_ refer to monthly enterprise revenues and value addition in current values. Own account enterprises are enterprises which do not hire any workers.

41
indices, that are explained within the model and their values are endogenously changed (determined).

# **4. Data Source and Model Calibration**

In the CGE models, a set of mathematic equations translates the structure of an economy and describes the behaviour of all agents and the equilibrium conditions of all markets. A calibration procedure fixes the parameters for the model’s equations (called benchmark equilibrium) and, then the model can be solved for an alternative scenario associated with any changed policy regime. A comparison between the counterpart or comparative scenario and the benchmark scenario makes it possible to assess effects on allocation and income distribution. CGE model is designed to analyze “What if ” question. For example, what is the impact of foreign aid ($1000 million US dollars) on receipt country’s income, output, welfare and other key economic indicators? The second advantage of CGE model is that it catches the sectoral and regional linkage effects. While partial equilibrium model are unable to provide the overview of the counterpart scenarios.

The AIDCGEM model is calibrated for the year 2001 using the GTAP data base, version 6 and foreign aid data is mainly extracted from OECD STAT<sup>*</sup> .

GTAP data base, around which the model has been built, is a cross-section data of international trade flows and national input-output tables. All the information in the data base is reported in values converted to US dollars. The behavioral parameters utilized in the GTAP model are described in Dimaran _et al._ (2006). They define the magnitude of behavioral responses to changes in relative prices. In particular, there are four sets of behavioural parameters in GTAP data base: (i) elasticities of substitution, in both consumption and production; (ii) transformation

> ***** Foreign aid data set is extracted from OECD STAT

> ( Available at http://www.oecd.org/document/21/0,3746,en_2649_34665_43230357_1_1_1_1,00.html)

*