rafmacalaba/data-use-mentions-extended huggingface dataset:
, Gubert, and Robilliard 2010), Mexico (Lacuesta 2010; Reinhold and Thom 2013), Romania (Ambrosini, Mayr, Peri, and Radu 2015) and Egypt (Marchetta 2012; Wahba 2015; El-Mallakh and Wahba 2017; El-Mallakh and Wahba 2021).

In the context of migration from Bangladesh, however, human capital accumulation is unlikely to be the main motivation. The vast majority of migrants from Bangladesh are employed in low-skilled jobs in the destination countries. These occupations generally rely on physical strength, providing limited opportunity for learning and human capital accumulation. In addition, the occupational patterns of individuals before, during, and after migration do not seem to be compatible with the human-capital accumulation hypothesis (Table A3). For example, while two-thirds of temporary migrants in the sample were employed in the construction sector at their destination abroad, but only 10 percent after returning home (Panel A). In the HIES data, this ratio is only slightly lower (7 percent) for those who did not migrate. In addition, 24 percent of the returning migrants were employed in construction 29
implement a face-to-face survey of newly arrived deportees at the Air Force airport in Guatemala City. This airport is the main arrival point for deportees sent to Guatemala and typically receives 3-5 planes of deportees 4 or 5 days per week. The first stage of the survey was implemented upon arrival and was conducted from October 2019 and March 2020. After that, COVID-19 made in-person data collection impossible. To initially recruit respondents for a survey, our survey 10
1990s saw a boom in these developments with major private investment flows into electricity and telecoms – at least for the larger middle income countries in Latin America and Asia.

According to the World Bank Private Participation in Infrastructure (PPI) Database, private investment in electricity (including privatization revenues) in DTEs rose from $1 billion in 1992 to $18 billion in 1995 and $44 billion in 1997. However, since 1998 – and particularly since 2000 – private investment flows, particularly _foreign_ private investment flows, into DTE infrastructure industries have fallen sharply. For electricity, they have fallen back very sharply so that, since 1999, only in 2000 were flows to DTE countries as measured in the World Bank PPI Database (at $25 billion) above the 1995 level. Between 2000 and 2001, they almost halved; and, between 2001 and 2002, they almost halved again. The 2002 level of private investment flows in electricity in DTEs, as measured in the World Bank PPI Database, was $5.5 billion – lower than in any year since 1991.

As a result of the major and potentially sustained private investment downturn in electricity and other infrastructure industries, there has, in recent years, been growing scepticism about whether the new model, based on private investment and competition in non-natural monopoly segments, is appropriate in developing countries – even middle-income countries let alone low-income countries such as those in SubSaharan Africa. This scepticism seems to be shared by many capital market participants.<sup>3</sup> Regulation and regulatory governance have played a major role in this evolution of opinion. The optimism of the late 1980s and early 1990s that DTE electricity and other similar infrastructure industries could be commercialized and financed from private investment flows was heavily associated with a strongly optimistic view that viable and effective regulatory arrangements could readily be established in many if not all DTEs<sup>4</sup> . This optimistic view was reflected in the classic 1994 paper by Levy and Spiller.

However, the establishment of the
households in treated villages.<sup>23</sup> Second, we use information from the operational followup survey in order to construct the share of program recipients in treated villages that received the E1 form as of May 1999.

In order to maintain full comparability with the estimates of program spillover reported in the rest of the paper, we estimate at the village-level the linear model reported in equation (1) using those program implementation measures as dependent variables on the sample of treated villages that we consider in the main empirical analysis.<sup>24</sup> The dependent variables are time-invariant, and, hence, we match this information with only the first round (1998) of the data of the program roll-out. As documented in columns 1–3 of table 9, these three measures of efficiency of program implementation are unrelated to the frequency of the treatment in the areas surrounding evaluation villages.

We next use the yearly secondary school census in order to construct school supply aggregate measures for the 358 evaluation neighborhoods in our sample (with both treated and control villages as centroid), and estimate at the neighborhoodlevel the linear model reported in equation (1) over the two years of the program roll-out (1998–1999). In all specifications, we control for the baseline (1997) value of the dependent variable. The OLS estimates are reported in panel B of table 9, which are very small in magnitude and not statistically different from zero.

23. While food stipends were distributed to all villages assigned to the treatment group at the same time in March 1998, only 56 percent of the those localities received the first scholarship transfer in March 1998; 36 percent received them two months later, and the remaining eight percent six months or more after incorporation into the program. These administrative delays appear concentrated in some regions, notably in the states of Queretaro and San Luis Potosi. As a further check, we have re-estimated equation (1) without those two states. Results (available upon request
# **Economies included in the Global Findex survey and database**

|**Economy**|**Region**<sup>**a**</sup>|**Income group**|**Data collection**<br>**period**|**Interviews**|**Design**<br>**effect**<sup>**b**</sup>|**Margin**<br>**of error**<sup>**c**</sup>|**Mode of**<br>**interviewing**|**Languages**|**Over-**<br>**sample**<sup>**d**</sup>|**Exclusions and other sampling details**|
|---|---|---|---|---|---|---|---|---|---|---|
|Afghanistan|SAR|Low|Apr 24–May 2|1,000|1.60|3.9|Face to face|Dari, Pashto||Gender-matched sampling was used during<br>the fnal stage of selection.|
|Albania|ECA|Upper middle|Jul 4–Jul 18|1,006|1.58|3.9|Face to face|Albanian|||
|Algeria<sup>e</sup>|MENA|Upper middle|Mar 9–Mar 30|1,000|1.28|3.5|Face to face|Arabic||The sample excludes the deep South and<br>governorates that represent security risks<br>within Algiers Province. The excluded area<br>represents approximately 25% of the total<br
the income per capita figures at the consumption poverty line. Thus, we generate an income poverty line that results in the same proportion of poor households as under consumption per capita. For maize harvest, we take the median level of maize harvest across both 2010 and 2013 (100 kg per capita per year) as the “poverty” line.

Table 1 below gives the poverty transitions for rural households between 2010 and 2013. The percent of rural households that escaped poverty over the period was greater than households who fell into poverty for overall consumption, food consumption and income per capita. The maize harvest per capita transition matrix shows that a relatively similar proportion of households transitioned from above to below median maize harvests as those who transitioned from below to above median values, as expected.

**Table 1. Poverty transitions between 2010 and 2013 for selected welfare indicators**

|**Welfare Indicator**|**Stay**<br>**non‐poor**|**Become**<br>**non‐poor**|**Become**<br>**poor**|**Stay**<br>**poor**|**Total**|
|---|---|---|---|---|---|
|Consumption|47.3|19.7|13.2|19.8|100|
|Food Consumption|43.3|23.4|14.4|18.9|100|
|Income|48.7|21.5|13.0|16.8|100|
|Maize Harvest|33.5|17.0|16.4|33.1|100|

_Source_ : Authors estimations

> 4 Because the analysis performed in the paper is at the household, we report descriptive statistics on household‐level variables, including poverty and vulnerability rates,
# 1. Introduction

<mark>The increase in conflict and natural disasters around the world has affected nearly 132 million globally, with conflict continuing to be the main driver of growing humanitarian needs (UNOCHA 2019). The pattern of fragility, conflict, and violence (FCV) engulfing many parts of the world today is associated with long‐term psychological consequences (Murthy and Lakshminarayana 2006). Parts of the Khyber Pakhtunkhwa (KPK) province and the Federally Administered Tribal Areas (FATA) of Pakistan have suffered from such armed conflict for the last 3 decades. The 2009‐ 10 insurgency and subsequent security operations displaced an estimated 2 million people from both regions. Severe damage to infrastructure and livelihoods has negatively impacted the social and economic fabric of the entire region, which now ranks among the poorest in the country.</mark> Epidemiological studies from these areas in Pakistan have shown high rates of common mental disorders (such as depression and anxiety) among the general population. A meta‐analysis of a subset of relatively rigorous post‐conflict surveys showed the prevalence of depressive symptoms to be around 17.3 percent (Steel and others 2009).<sup>2</sup> Global estimates of productivity losses due to depression are estimated to be around US$1 trillion dollars per year globally (World Health Organization (WHO) 2017). In this context, the question of the impact of common mental disorders, such as depression and anxiety, on economic productivity in FCV regions is a critical one (British Psychological Society 2011).<sup>3</sup> Since economic activity in FCV regions is heavily dominated by small and medium enterprises (SMEs) whose owners are prone to high levels of stress (Ang, 1991), it is pivotal to understand better the extent of prevalence of mental health challenges among SME entrepreneurs as well as its impact on their health and economic outcomes.

Mental health, well‐being, and lasting economic outcomes are intimately related. The WHO defines mental
**Table 3. Effect of preschool availability on maternal employment with various preschool data**

|||||**Wo**<br>|**rk participa**<br>|**tion**<br>||||
|---|---|---|---|---|---|---|---|---|---|
|Preschool data:|PO|DES years o|nly|I|nfer in-betwe|en|L|inear project|ion|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|**Panel A: Public**||||||||||
|Preschool *||0.048**|0.080***||0.057***|0.074***||0.062***|0.082***|
|Eligible||(0.021)|(0.022)||(0.017)|(0.016)||(0.020)|(0.019)|
|Preschool|-0.014|-0.011|-0.017|0.002|-0.016|-0.015|-0.007|-0.021|-0.019|
|density<br>Eligible child|(0.026)|(0.015)<br>-0.051***|(0.015)<br>-0.034***|(0.023)|(0.013)<br>-0.051***|
Evaluating Survey Questions.” _Proceedings of the American Statistical Association_ (Survey Research Methods Section).

Grosh M. and P. Glewwe(eds). 2000. _Designing Household Survey Questionnaires for Developing Countries: Lessons from 15 Years of the Living Standards Development Study_ . Oxford University Press (for the World Bank).

Hausman, J.A. 2001. “Mismeasured Variables in Econometric Analysis: Problems from the Right and Problems from the Left.” _Journal of Economic Perspectives_ 15 (4): 57‐67.

Hausman, J.A., J. Abrevaya, and F.M. Scott‐Morton. 1998. “Misclassification of the Dependent Variable in a Discrete Response Setting.” _Journal of Econometrics_ 87: 239‐269.

Heckman J.J., L. J. Lochner, and P. E. Todd. 2003. “Fifty years of Mincer earnings regressions.” IZA Discussion Paper 775 Heckman J.J., V.J. Hotz,, 1989, Chooisng Among Alternative Nonexperimental Methods for Estimating the Impact of Social Programs: The Case of Manpower Training, Journal of the American Statistical Association, Vol 84, Issue 408 (Dec 1989) Hill, D.H. 1987. “Response Errors in Labor Surveys: Comparisons of Self and Proxy Reports in the Survey of Income and Program Participation (SIPP).” In _Proceedings of the Bureau of Census, Third Annual Research Conference_ .

23
n ratio)**|
|---|---|---|---|---|---|---|---|---|---|
||VAT/<br>CP<sup>1</sup>|SSC/<br>WSSS<sup>2</sup>|Corporate/<br>GDP|VAT|SSC<sup>3</sup>|Corporate|VAT|SSC|Corporate|
||[1]|[2]|[3]|[4]|[5]|[6]|[1]/[4]|[2]/[5]|[3]/[6]|
|Greece|9.74|15.8|2.48|19|28.06|25|0.51|0.56|0.10|
|Ireland|13.94|7.41|2.82|21|10.75|12.5|0.66|0.69|0.23|
|Portugal|12.55|15.14|3.63|21|23.75|25|0.60|0.64|0.15|
|Spain|9.04|18.07|2.80|16|30.15|30|0.57|0.60|0.09|

1: Ratio of the revenue from value added taxes to private consumption.

2: Ratio of revenue from employers’ social security contributions to compensation of employees.

3: Employer’s social security contributions. Source: OECD Economic Surveys: Greece. August, 2011.

Where high tax rates in GPS are resulting in a relatively low share of revenues in taxes, improving collection, reducing exemptions, or increasing the base would help. Table 7 shows a
obtain a sense of how different the consumption patterns are relative to the patterns in other countries in the region, one may compare Bangladesh with India, a neighboring country with similar tobacco products, particularly bidis. Information from India Consumption Expenditure Survey, 2011– 12 reveals that the socioeconomic gradient for consuming tobacco is steeper in India than in Bangladesh (figure 3). Among the poorest deciles, the share of households consuming tobacco is similar, but, among the richest decile, a higher share of households consume tobacco in Bangladesh (55 percent) than in India (36 percent). The share of total consumption assigned to tobacco products is also substantially lower in India than in Bangladesh. However, similar to Bangladesh, there is a small gradient in the share of the budget assigned to tobacco products across deciles. These differences become smaller between Bangladesh and the state of West Bengal, highlighting the greater similarity of cultural and consumption patterns in the latter region of India.

6
**Table 1: Variable definitions and Sources**

|Variables|Definition and source|
|---|---|
|Cases per 1000<br>residents|The number of confirmed COVID-19 cases per 1,000 residents. Calculated based on original coronavirus data<br>from Johns Hopkins Coronavirus Resource Center. Updated to November 19, 2020. In regressions, we<br>use_Log (1+Cases per 1000 residents)_as the dependent variable.|
|Pandemic mortality<br>rate|Total pandemic deaths divided by total confirmed COVID-19 cases. Calculated based on original coronavirus<br>data from Johns Hopkins Coronavirus Resource Center. Updated to November 19, 2020.|
|Delay I|Delay in controlling the international travel, equals to the number of days, divided by 100, from the day that<br>the economy discloses the first COVID-19 case to the day that the government first announces the policy<br>to restrict the international travel. Lower number indicates prompt government response. Calculated based<br>on the dataset constructed by Hale et al. (2020).|
|Delay D|Delay in implementing domestic close-down policy, equals to the number of days, divided by 100, from the<br>day that the economy discloses the first COVID-19 case to the day that the government first announces<br>their domestic closedown policy, which include school closing, workplace closing, public events<br>cancelling, restrictions on gatherings, public transport closing, stay at home requirement and restrictions<br>on internal movement. Lower number indicates prompt government response. Calculated based on the<br>dataset constructed by Hale et al. (2020).|
|Avg Temperature|Average temperature since the month COVID-19 arrived to November, divided by 100.|
|Middle (Top)<br>Distance2China|The distance of the country or special region to China is in the middle (top) tercile in the distance to China.|
population data, the AI also requires data on estimated travel times. The travel time data originally used by Uchida and Nelson (2009) for the AI were based on “... _estimates of the time required to travel 1 km over different road and off-road surfaces...”_ and were derived from a cost surface that was constructed from a variety of Geographic Information System (GIS) data layers. These layers included data on road and rail networks, navigable rivers and water bodies, travel delays for crossing international borders, roughness of terrain and foot-based travel for off-road and paths.<sup>15</sup> The AI estimates used in this paper are taken from Roberts _et al._ (2017) and based on an updated version of this same cost surface layer from Berg _et al._ (2017). This updated layer is derived from more recent (i.e. _circa_ 2010 versus _circa_ 2000) data on roads, railroads, and land cover.<sup>16</sup> Finally, as already described in Section 2, the NTL data that we use in this paper are VIIRS NTL data taken from NOAA’s 2015 annual composite product.

# _3.2. Mapping to districts_

> 12 Importantly, the sampling strategy of the SAKERNAS August rounds is stratified at the district level for these years. We average the commuting flows over three years, rather than using a single year, to smooth out any temporary measurement error in the survey data.

> 13 Alternative population grids that we could have used are WorldPop (http://www.worldpop.org.uk/) and GHS-Pop

> (http://ghsl.jrc.ec.europa.eu/ghs_pop.php). Roberts _et al._ (2017) compare the level of agreement between maps of urban areas generated using the AI and cluster algorithms with different gridded population products. In general, the level of agreement is fairly high.

> 14 See http:
’s tax assessments have a high error rate. The Internal Revenue Department reported nearly 184,000 registered taxpayers in 2001, of which only 39,000 were active, and, of these, only 4,712 and 5,236 (13.4%) filed corporate income tax and VAT returns, respectively.<sup>47</sup> Of those filing VAT returns, only 50.4% filed returns with payments in May 2001. The low active to registered ratio, as well as the high shares of non-filing and non-paying taxpayers, was partly a reflection of the inadequacy of the taxpayer registry and partly a reflection of low compliance rates.

> 47 There are questions about the reliability of URA data on taxpayer registration.

33
# **_Welfare Measures_**

This study employs household total consumption per capita, food consumption per capita, income per capita, and maize harvest per capita as the main outcome measures for detecting vulnerability. Consumption and income capture two different measurements of households’ economic security. Maize harvest is a particularly critical welfare measure in Malawi because of the importance of maize as a staple crop in Malawi (Smale, 1995; Ecker & Qaim, Analyzing Nutritional Impacts of Policies: An Empirical Study 7
here is that we are failing to detect a true, statistically significant, and economically meaningful effect because we lack sufficient observations with sufficient variation. We cannot conclusively rule out this explanation because we did not conduct _ex ante_ power calculations as part of our preanalysis plan. We failed to do this for two reasons. First, it was not clear what values should be used in a power calculation for means and standard deviations in the control group nor what a reasonable expected effect size would be. Second, and quite honestly, we did not think it necessary given the large size of the data collection effort. In total, the pre- and post-outbreak LSMS-ISA data sets contain more than 84,000 observations. Country-specific regressions contain between 3,000 and 15,000 observations. In writing the pre-analysis plan, we did not expect a lack of power to be something we would eventually need to address. Given the evidence on how misleading _ex post_ power calculations can be, we have not done this sort of calculation (Hoenig and Heisey, 2001; Gelman, 2019). Our failure to conduct _ex ante_ power calculations means we cannot provide definitive evidence against the explanation that our analysis lacks power. However, we believe that the large number of observations used in the analysis makes the lack of power an unlikely story.

A final explanation, and the one we find most compelling, is that in the face of a global pandemic and related government restrictions, livelihood diversification was not an effective coping strategy. This is true both in terms of using livelihood diversification as an _ex ante_ strategy, to prepare for a potential shock, and an _ex post_ strategy, to react to a realized shock. Health concerns and government restrictions to stop the spread of the virus may have stripped resource-rich households of their comparative advantage and equalized the vulnerability of income-diverse and income-specialized households. Or perhaps moving away from subsistence farming left households unable to access sufficient food during times of crisis, leaving income-diverse households worse off. In the end, we do not
farmer keeping others’ actions constant.<sup>1</sup> The feature of keeping others’ actions constant is achieved by allowing a maximum of one farmer to receive training per village through a lottery; it allows to shut down complementarities and substitutabilities in demand that would confound the observed willingness-to-pay for others. The upside of such a design choice is that it ensures I measure the externalities produced by one farmer (at the margin) neatly, although at the expense of measuring how externalities change when more than one farmer is trained in a village. I find farmers anticipate substantial positive spillovers from others: the median value a farmer attaches to another farmer receiving the training is equivalent to the wage received from two days of agricultural labor (or $8.6), about half the median willingness-to-pay for themselves (equal to $17.3).

I use my valuation data to show that targeting choices can produce large and quantifiable social gains if they account for externalities. I identify which targeting criterion for the training produces a higher social benefit in two steps. First, I calculate the per capita externality generated by training one farmer - the average willingness-to-pay of

> 1This setup rests on the assumption, supported by the data, that farmers are self-interested and do not engage in altruistic behavior in this setting.

1