rafmacalaba/datause-encoder-data huggingface dataset:
# APPENDIX 1: THE GHQ-12 AND THE MHI-5

The GHQ-12 consists of 12 questions. The questions are displayed below, ordered by the components they measure according to Graetz (1990).

Component 1 “anxiety and depression”:

1. During the past two weeks, have you lost much sleep over worry?

2. During the past two weeks, have you felt constantly under strain?

3. During the past two weeks, have you felt that you couldn't overcome your difficulties?

4. During the past two weeks, have you been feeling unhappy and depressed?

Component 2, “social dysfunction”:

1. During the past two weeks, have you been able to concentrate on whatever you are doing?

2. During the past two weeks, have you felt that you were playing a useful part in things?

3. During the past two weeks, have you felt capable about making decisions?

4. During the past two weeks, have you been able to enjoy your normal day-to-day activities?

5. During the past two weeks, have you been able to face up to your problems?

6. During the past two weeks, have you been feeling reasonably happy, all things considered?

Component 3, “loss of confidence”:

1. During the past two weeks, have you been thinking of yourself as a worthless person?

2. During the past two weeks, have you been losing confidence in yourself?

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were collected between 2007 and 2012. In each country, we use the latest round of ES available. Table 1 provides the list of countries along with the sample size of informal and formal firms.

To analyze the relationship between labor productivity of formal firms and competition they face from informal firms, we use ES data only for formal firms. The sample size here consists of 45,996 firms located in 125 countries. These surveys were conducted between 2008 and 2016. As alluded to above, we use the latest round of ES available in each country (Table 2).

Sampling weights are only available for formal enterprise surveys, because they are representative, and are used throughout the analysis. The weights are normalized so that they add up to 1 for each country. For informal firm surveys, sampling weights are not provided since they are not representative in the selected countries. Therefore, we assign a weight of 1 to each informal firm as commonly done in self-weighted surveys. The weights are normalized so that they add up to 1 for each country. Robust standard errors are reported for all our regressions.

# 2.2 _Dependent variable_

The dependent variable in all our regressions is firm-level labor productivity measured by (log of) total sales of the firm in a typical month during the last fiscal year divided by the number of workers at the firm.<sup>7</sup> Total sales are deflated and expressed in 2009 USD to ensure comparability across countries.<sup>8</sup> For the sample of 18 countries used to gauge the labor productivity gap between formal and informal firms, the average value of labor productivity (in logs) equals 8.5 and the standard deviation is 1.8. The variable ranges between -0.95 and 20.4. For the sample of 125 countries used

> 7 For the informal surveys, total sales and employment information is available for a typical month in the last year. For the formal surveys, information is available only on total annual sales during the last fiscal year and total number of permanent workers employed by the firm at the end of the last fiscal year. Therefore, we divide the annual sales of formal firms by 12 to obtain average monthly sales.

> 8 Commonly used revenue-based measures of productivity may conflate efficiency and price effects. Disentangling efficiency and price effects, by relying on physical productivity measures, may yield distinct and finer productivity patterns, especially at the firm level (Jones and Nordhaus 2008; Cusolito and Maloney 2018).

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but simply stopped responding to quizzes out of lack of interest or motivation to search for phone signal. Figure A1 shows that the number of treated individuals participating in weekly quizzes evolved similarly for the three thematic variations.

# **2.3 Data**

Baseline data were collected in the fall of 2014 using a short household survey instrument. It captured information on household composition, socio-economic status, cell phone usage, social interactions and economic activities, as well as child-specific information regarding health practices, preschool participation, nutritional intake, early childhood practices and attitudes. The baseline was used to identify for each child a "mother" and a "father" figure, i.e. the child’s biological mother and father, or in their absence, the individuals with similar caregiver roles, as defined by the household.

A follow-up survey was implemented in July and August 2015, approximately nine months after households had started receiving the text messages. The duration of exposure is similar to other early childhood interventions where impacts on ECD outcomes were found in nearby municipalities in Nicaragua (Macours et al., 2012).<sup>12</sup> The follow-up survey targeted children in sample households, as well as the main caregiver. We limit the analysis to children under 7 years of age at follow-up (2,803 children), in order to use the full battery of tests, as detailed below. Questions on early childhood practices and attitudes were asked separately to mother and father figures.

The child-level follow-up instrument includes a number of tests to assess early childhood development. First, the four sub-scales of the Denver Developmental Screening Test (Frankenburg and Dodds (1967)) were used to assess social-personal, language, fine motor, and gross motor skills for all children between 12 and 84 months of age. We use a modified version of the Denver previously used as part of a national early childhood stimulation program in Nicaragua.

Three additional tests were conducted for children between 36 and 84 months old: 1)

> 12 The follow-up survey was fielded a bit before the end of the text message intervention so that data collection could take place before the rainy season, when accessibility can become challenging. That said, about 2 percent of observations was collected later, during a short tracking phase in May 2016 when missing households were re-visited.

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Guebas et al., 2005; Sandilyan and Kathiresan, 2015). In terms of width, mangrove belts are still found in the region despite significant deforestation (Spalding et al., 2010). Wide mangrove belts are likely to be an important feature of protection against storm surge as argued by Zhang et al. (2012).

To estimate whether mangroves can reduce hurricane damage, we divide Central America into one km<sup>2</sup> grid cells and construct a cell-year panel for the 2000 to 2013 period. The panel combines measures of economic activity, potential hurricane damage, and mangrove protection. Specifically, we use remote sensing data on nightlights to measure local economic activity. Nightlights have been shown to be a good proxy of economic activity (see Donaldson and Storeygard (2016) for a review), and their high spatial resolution is ideal because the economic impact of tropical cyclones has been shown to be highly localized (e.g., Bertinelli and Strobl, 2013; Elliott et al., 2015). We measure potential hurricane damages using predicted wind speed from the wind field model of Pita et al. (2015). The model is calibrated for Central America and has been validated with historical data. To translate wind speed into potential destruction, we use the damage function proposed by Emanuel (2011). To measure the degree of mangrove protection, we follow Das and Vincent (2009) and calculate the width of mangrove, as measured in 2000, along the closest path to the coast.

We estimate the causal impact of hurricanes on our proxy of economic activity by regressing nightlights on our damage index. The identifying assumption is that hurricane strikes are exogenous conditional on cell and year fixed effects. We then explore the heterogeneity in the impact of hurricanes by interacting our damage index with the predetermined width of mangrove on the path to the coast.

Our results fall into two categories. The first set of results show that, consistent with previous literature, hurricanes have negative short-run effects on economic activity. The estimated effect size suggests that a category three hurricane in the Saffir-Simpson scale can reduce our proxy of economic activity by 16%. We additionally document a 17% reduction in economic activity for coastal lowlands, which are at risk from both wind and storm surge damage.

The second set of results show that the impact of hurricanes, in storm surge prone areas, decreases as the width of mangrove increases. To ensure that the effect is driven by the mangrove vegetation itself, and not by other characteristics of the location of mangrove habitats, we further restrict our sample to cells that have been historically protected by 3
- **Insurance.** The monetary burden of losses as well as responses taken by poor households brings into focus the need to have an effective insurance mechanism to provide protection against such losses and finance the response measures as and when the need arises. But even if insurance products are designed to protect households from floods, the premiums are unaffordable for the poor households. Therefore, a public‐private partnership approach is required to provide insurance and reinsurance facilities in order to induce insurance companies to provide appropriate and affordable products for flood related damages. In most of the flood areas in Mumbai, people have small‐scale industries that are sometimes badly hit and are uninsured. A combined asset/small business insurance as well as medical insurance (typhoid and other water‐borne diseases are a big concern) will go a long way towards strengthening communities.

- **Compensation.** The compensation mechanism of the government is not sufficient and effective as it is not directed specifically at poor households and those below poverty line. In fact, in per capita terms, compensation offered in the aftermath of July 2005 event seems to be higher for non‐poor rather than poor households. Therefore, a mechanism that can have targeted social protection provided to the poor should be a priority for the government. If non‐poor households can access insurance products against floods and other risks, the government may be able to concentrate their resources on helping the poor and non‐insured. This would ensure more efficient utilization of government funds for relief and rehabilitation.

- **Household rainfall monitoring** . Developing an application for hourly monitoring of rainfall that households (including the poorest) can access can assist households in making their own decisions in terms of evacuation and protecting assets, rather than waiting for wards or the local government to sound the alarm. The Disaster Management Cell of MCGM has developed a mobile application (Disaster Management Mobile Application) to give ward‐wise 15‐minute interval data on rainfall, wind speed, and tidal information. However, this application is not widely used since awareness is very low. Given that early warning access is a paltry 10% in the surveyed sample, awareness building about the mobile phone application will help reach the poorest to make better‐informed decision‐making at the household level.

While some characteristics of this case study are specific to Mumbai, many issues related to flood vulnerability outlined in this paper are applicable to many cities in South Asia, as identified by a recent World Bank study on urbanization in the region (World Bank, 2015).

Between 2000 and 2011, South Asia’s urban population expanded by 130 million people and is poised to grow by 250 million more by 2030. Almost 80 percent of these major South Asian cities are exposed to floods, with exposure increasing not only due to a higher population living in risky areas but also climate change.

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# **6. Result**

## **6.1 Descriptive Results**

We observed a decrease in forest area since 2010 in the study region (Table 2). Forest cover loss has accelerated since 2017 compared to earlier years. Forest cover declined by nearly 20 percent between 2017 (pre-arrival) and 2020 (post-arrival) across the entire district, corresponding to an annual loss of 6.7 percent. Forest cover reduction is associated with an increase in settlement areas across the district (Figure 3, Figure 4). In 2020, settlement areas increased by 21 percent relative to the 2017 estimate (pre-arrival) period.

**Table 2: Descriptive land cover changes across all of Cox’s Bazar, 2010-2020**

||**_2020_**|**_2020-2017_**|**_2017-2014_**|**_2014-2010_**|
|---|---|---|---|---|
|_Class Name_|Share of Total|Net change (ha)|Net change (ha)|Net change (ha)|
||Land Area ha<br>(%)|(% Change)|(% Change)|(% Change)|
|_Forests/_|52629|-12807.99|-135.99|-3663.99|
|_grassland_|(25%)|(-19.57)|(-0.21)|(-5.59)|
|_Water/_|39875|-2272.50|1367.46|3249.99|
|_Fish-ponds_|(19%)|(-5.39)|(3.35)|(8.66)|
|_Developed/_|82631|14511.42|4028.76|-192.06|
|_Settlements_|(39%)|(21.30)|(6.29)|(-0.30)|
|_Barren/_|12202|1757.07|-980.01|3447.81|
|_Bare grounds_|(6%)|(16.82)|(-8.58)|(30.18)|
|_Planted /_|26302|-1201.50|-4280.22|-2841.75|
|_Cultivated_|(12%)|(-4.37)|(-13.47)|(-8.94)|

Note: The 2020 column shows the share of the total area in ha. The brackets are percentage areas for each class. The rest of the columns shows the net change between the two periods in hectares and percentage changes in the bracket.

We find strong evidence of LULC change from forests to settlements within the areas identified as current refugee camps. The official refugee camps occupied a total land area of 2,545 ha. Before the Rohingya influx in February 2017, 54 percent of the existing campsites were forested compared to only 2 percent in 2020 (post-arrival), which corresponds to a forest cover loss of 1,337 ha (Figure 4). The NDVI map visualizes the study area’s vegetation 21
<!-- Start of picture text --> Lorenz Curves - Urban 2007 -<br>~ . fa<br>0 :<br>Percentiles (p)<br><!-- End of picture text --> <!-- Start of picture text --> Lorenz Curves - Rural 2007 -<br>2<br>©<br>2<br>+<br>a 2<br>N<br>oO<br>0 |<br>Percentiles (p)<br><!-- End of picture text -->
# **Figure 2: Employment Results at Midline and Endline**

<!-- Start of picture text --> Employment status<br>Midline Endline<br>Woman Woman<br>Husband Husband<br>-.2 0 .2 .4 .6 -.2 0 .2 .4 .6<br><!-- End of picture text --> Note: Figure 2 depicts the coefficient plots with point estimates and the 95% confidence interval from Table 4.

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# **Table 12: Data sources used in the estimation of the number of MFI accounts**

This table shows the distribution of data sources used to estimate the number of accounts in MFIs around the world. The third column represents the percentage in terms of countries. The last column represents the percentage in terms of accounts.

|**Data Source**|**#**<br>**countries**|**% sample**|**% final**<br>**count**|
|---|---|---|---|
|FA Survey '09|14|16%|44%|
|MicrofinanceExchange (MIX)|75|84%|56%|
|Totals|89|100%|100%|

Note: MIX numbers are values voluntarily reported by MFIs and aggregated to country level.

# **Table 13: Estimates for numbers of accounts by income group and by category of providing instituion.**

This table shows the estimation for the number of accounts by income group and category of providing institution. Countries are divided according to the World Bank income group definition. The last column represents the percentage of accounts in non-bank institutions (MOFIs + SSFIs + MFIs).

||**Commercial**<br>**Banks**<br>|**MOFI**<br>_Panel A:_<br>|**SSFI**<br>_World_<br>|**MFIs**<br>|**Total**<br>|**%**<br>**Non-**<br>**Bank**|
|---|---|---|---|---|---|---|
|Low Estimate|4.75 Bn|0.68 Bn|0.34 Bn|0.01 Bn|5.78 Bn||
|Preferred Estimate|5.01 Bn|0.71 Bn|0.44 Bn|0.02 Bn|6.18 Bn|18.99%|
|High Estimate|5.27Bn|0.82 Bn|n.a.|n.a.|6.55Bn||
||_Panel B: M_<br>|_iddle income a_<br>|_nd developing co_<br>|_untries_<br>|||
|Low Estimate|2.05 Bn|0.16 Bn|0.34 Bn|0.01 Bn|2.56 Bn||
|Preferred Estimate|2.22 Bn|0.19 Bn|0.41 Bn|0.02 Bn|2.84 Bn|21.76%|
|High Estimate|2.39Bn|0.25Bn|n.a.|n.a.|3.08Bn||
||_P_|_anel C: High In_|_come countries_||||
|Low Estimate|2.67 Bn|0.52 Bn|0.00 Bn|0.00 Bn|3.19 Bn||
|Preferred Estimate|2.79 Bn|0.53 Bn|0.03 Bn|0.00 Bn|3.35 Bn|16.67%|
|High Estimate|2.91 Bn|0.57 Bn|n.a.|n.a.|3.51 Bn||

Note: Value per 1000 adults numbers have as base, population from all countries (including those which we have no estimate of bank accounts).

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A brief description of figures 1a and 1b is detailed in what follows —to gain some intuition. The horizontal axes denoted by “Bad-Score” capture the relative exposure of households to negative shocks. Extremely low values correspond to households who experience less amount of exogenous shocks. Equivalently, high values correspond to households who tend to experience extreme degree of negative shocks. The overall estimated distribution of scores range between - 1.12 and 7.83 with a standard deviation of about 1.01. To illustrate further: the distribution of household shocks is explored across different shock quartiles and regions in Niger. Figure 2a count the number households in each quartile of the estimated shocks, whiles figure 2b displays the distribution of average scores of the estimated household shocks across different regions. The former appears to suggest a _missing middle_ phenomenon where relatively a few number of households are exposed to negative shocks in the middle quartiles compared to the first and last shock quartiles.

In examining the average score of estimated shocks, Dosso, Agadez, and TillabEri regions experienced more bad shocks (above zero) over the two periods. This is not the case for Niamey. The regional-level discrepancy in the distribution of shocks speaks to the observed differences in poverty outcomes. Together: the figures suggest a tremendous amount of spatio-temporal variation in the exposure of households to negative shocks. The level of variation in our household level data is even larger than what is shown in these quartile or regional level aggregates. The next section exploits this variation to examine the immediate implications of the estimated shocks on household consumption expenditures and poverty standing.

# **3 SHOCKS: EFFECTS & HOUSEHOLD RESPONSES**

# **Exploring Linkages with Poverty and Consumption**

There is a large literature that tests for consumption smoothing of households using rainfall shocks as exogenous shocks to income (Paxson 1992; Wolpin 1982; Seshan & Zubrickas. 2015; Joseph, Nyarko & Wang 2015). We have a direct measure of households’ poverty status and consumption outcomes. The former is a classification of whether a household is _poor_ or _not_ based on national poverty lines, while the latter captures _per capita_ expenditure of household's consumption. This section examines whether both non-poverty status and household ‐ 8 ‐
# **5 Conclusion**

This short analysis of UNHCR data provides several insights. First, it shows that available refugee data can be used to clarify some important parts of the policy debate. Much of this debate has been informed by advocacy on the one side, and xenophobia on the other. Using evidence can help provide a more nuanced perspective of a complex issue.

Second, it highlights the limitations of available data. In particular in the absence of individualized data, it is not possible to calculate the average duration of exile, but only an over-estimate. It is also not possible to calculate an expectancy which could be useful for policy makers. Should individualized data be available, such calculations would become possible.

Third, it provides an over-estimate of the mean duration of exile at around 11 years and of the median duration at about 4 years. The mean duration of exile has been fairly stable since the late 1990s, at 11 to 15 years.

Fourth, it underlines the plight of those who are in protracted exile. Their number has remained steady since the mid-1990s, at 5 million to 7 million people. The mean duration of their plight, however, has gradually increased and exceeds 20 years.

Fifth, it suggests that all these numbers are very sensitive to two country contexts: Afghanistan, which pushes all averages up (and without which the average duration of protracted situations is reduced by almost half) and the Syrian Arab Republic which still brings them down. Yet, when both situations are excluded, neither the mean nor the median varies substantially.

And sixth, it shows that in many cases a reduction in the average duration of exile is the result of an inflow of new refugees, and hence of an aggravation of the situation.

Unfortunately, comparable data are not available to carry out similar calculations for IDPs. The definition of a “protracted situation” for IDPs is also complex, considering that there is no clear definition of what would constitute a measurable end-point for their ordeal. Case studies may provide more useful insights than aggregate global figures.

These numbers re-emphasize the importance of effective humanitarian interventions on the right scale. They suggest that development actors have a role to play but that they need to focus their interventions on a set of discrete situations. Time-series also show that the perceptions of a crisis spinning out of control are not supported by the available evidence. And let us never forget that this is not about numbers but about people and about an immense amount of human suffering.

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Prior to the diffusion of the Internet, several studies estimated price and income elasticities of the demand for paper and paperboard products. For example, Buongiorno (1978) used a panel of data for the years 1963 through 1973 from 43 countries to estimate income and price elasticities for the demand for various paper categories: newsprint, printing and writing paper, and other paper and paperboard. Baudin and Lunderberg (1987) estimated price and income elasticities based on a panel of data from 56 countries for the period of 1961 through 1981. Zhang and Buongiorno (1998) examined the demand for paper by focusing on the substitution between paper and plastic in the US packaging market. Chas-Mail and Buongiorno (2000) used panel data to estimate demand equations for 14 European Union countries for the period of 1969 to 1995. McCarthy and Lei (2010) estimated a dynamic demand model for pulp and paper products in four major trading regions: Asia, Europe, the North American Free Trade Agreement Area (NAFTA), and South America, based on a panel of data available for the years 1961 through 2000 from the United Nations Food and Agricultural Organization. A common finding across all these studies has been the historically low price and income elasticities of the demand for paper and paperboard. Information on the elasticity of the demand for paper is crucial for policy design. For example, the price and income elasticities of the demand for paper are immediately linked to the effectiveness of taxes seeking to reduce the negative externalities arising from the production of paper.

Another strand of literature studies the determinants of Internet usage. Andres et al. (2010) analyze the process of Internet diffusion across the world using a panel of 214 countries during the period 1990–2004. Their estimated diffusion curves provide evidence of slow “catching up.” The paper also explores the determinants of Internet diffusion and shows that network effects are crucial to explain this process. Furthermore, Chinn and Fairlie (2007) use panel data of 161 countries for the years 1999-2001 to identify the determinants in cross-country disparities in the usage of personal computers and Internet. Although income differences play a major role in explaining the digital divide, Chinn and Fairlie show that there are other important determinants such as regulatory quality and level of infrastructure. Estache et al.

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