rafmacalaba/data-use-mentions-extended huggingface dataset:
Table 14: P75/P25 ratio and Gini Index for the quality of jobs

||(1)<br>P75/P25|(2)<br>Gini|
|---|---|---|
|2007|3.42|0.38|
|2008|3.33|0.39|
|2009|3.22|0.37|
|2011|3.21|0.36|
|2012|3.25|0.36|
|2013|3.23|0.37|
|2014|3.27|0.38|
|2015|3.16|0.36|
|2016|3.27|0.37|
|2017|3.38|0.38|
|2018|3.28|0.38|
|2019|3.47|0.38|
|2020|3.54|0.38|
|2021|3.40|0.37|

_Notes:_ Based on data from EH 2007-2021.

Another inequality indicator that we consider is the Gini Index, a commonly used measure to analyze income inequality across the population. This indicator has remained relatively stable since 2007, consistently hovering around 0.38. Even during the pandemic, the inequality in job quality, as measured by the Gini Index, remained at levels similar to those reported since 2017. However, in 2021, the Gini index decreased by 0.01 point, indicating a slight improvement in reducing job quality inequality (evidenced as well by the P75/P25 ratio). By analyzing these two inequality indicators, it becomes evident that not all jobs offer the same labor conditions, and they are far from all workers. Despite the expectation that inequality would be greater given the average values reported over the years, the Gini Index did not indicate a high level of inequality. Nonetheless, it is clear that labor conditions still fall short of
for local (often municipal) level infrastructure usage indicators.<sup>2</sup> However, there have been surprisingly few papers using micro-data to analyze systematically how infrastructure demand at the household level varies along the income distribution. As a result, little is known about the way demand for infrastructure varies with income at any point in time, and how it evolves over time as household income grows.

> 2 See Straub (2008, 2015) for reviews.

3
ects of proportion of LDO land in a DSD, especially because Sri Lanka implemented a successful nationwide malaria eradication program starting from 1947 (our survey year is 2002).<sup>10</sup> However, unfortunately, we do not have data on historical malaria prevalence at the appropriate level of disaggregation (i.e., at DSD level); the available data are at the district level (average over the years 1937-41). The district level malaria data are not suitable for identi...cation of the e¤ects of LDO restrictions at the DSD level for at least two reasons. First, the district level data do not provide us with enough variations for identi...cation of the LDO restrictions at the DSD level, as there are 243 DSDs in our data set, but we have data on average malaria prevalence before the start of the malaria eradication program

> 10 Note also that the historical malaria prevalence in the DSD of current residence cannot a¤ect the health of most of the households under LDO restrictions in any signi...cant way as they were resettled from relatively malaria free areas.

6
by two types of monitors, the Met One BAM-1020 and the Envea MP101M, which both use a beta ray attenuation measurement technique.

We are able to access data at the hourly or daily level (depending on the station) from 2012 to 2024. The PM2.5 data availability is sparse (Figure 2), both because some air quality stations do not collect PM2.5 data and because of challenges maintaining the regulatory-grade monitors. Beginning in 2012, there is relatively consistent coverage with at least one monitor active, except for 2021 and 2022, which have almost no data. This is important because the data from a regulatory-grade monitor can be used to calibrate other data sources. Additionally, when more than one monitor is reporting data concurrently we can use readings from multiple monitors to validate the data. At the times when monitors overlap, the values are closely aligned. Importantly, on days when the values seem to be improbably high, readings from different active monitors both have very high values, which is reassuring that these represent days of extremely high pollution rather than an issue with the measurement instruments. The data was pre-processed by the CGQA for extreme values. In addition, we removed PM2.5 values greater than 500 _μm_ /m<sup>3</sup> .<sup>6</sup> To analyze the data, we take the values from whichever monitor is active, and in cases where there are multiple monitors, we use data from all active monitors.

# **Low-Cost Monitors**

In the last decade there has been significant advancement in the technical specifications of low-cost monitors and substantial growth in the number of low-cost monitors on the market, leading to an explosion in their use (Kumar _et al._ , 2015; Demanega _et al._ , 2021). In addition to their use by individuals and businesses, the recognition that air pollution in urban areas is hyperlocal led to initiatives in highincome settings to complement networks of regulatory-grade monitors with low-cost monitors to provide air
# VII. **OUT-OF-POCKET PAYMENTS**

VHI need not necessarily cause out-of-pocket payments to fall. If the demand for health care covered by VHI is especially elastic with respect to price, or if the demand for health care generally is especially sensitive to the reduction of risk and uncertainty, VHI could conceivably result in _higher_ out-of-pocket payments.

Both surveys contain estimates _for the previous twelve months_ of cash and in-kind payments for health care. These data are collected from _all_ respondents, whether or not they had been ill in the previous 4 weeks. In addition, the 1997/98 survey allows a reliable estimate to be made of out-of-pocket spending in the 4 weeks prior to the interview<sup>17</sup> , and permits annual health spending to be broken down into inpatient and other health spending. The numbers are in thousands of Vietnamese dong. The double-difference estimate of annual out-of-pocket spending is negative and significant (Table 9), in contrast to the single-difference estimate for 4- week and annual total out-of-pocket spending derived from the 1997/98 data alone. The last two lines of Table 9 point to VHI increasing—albeit not significantly—inpatient care spending, and having a dampening effect on spending on other types of care.

_Table 9: Estimates of VHI impact on out-of-pocket health spending_

||Treatment<br>effect|z-stat|
|---|---|---|
|**Double difference on longitudinal data**|||
|Change in out-of-pocket spending between 1992/93 and 1997/98|-51.967|-2.52|
|**Single difference on 1997/98 data**|||
|Out-of-pocket spending in last 4 weeks|-2.530|-0.27|
|Out-of-pocket spending in 1997/98|-18.744|-0.96|
|Inpatient expenditure in 1997/98|8.636|1.08|
|<br>Non-inpatient out-of-pocket spending 1997/98
particular, Krautheim (2012) argues in his theoretical paper that the relevant information might be destination specific. For example, technical regulations or specific consumer tastes vary across countries. Koenig (2009) finds evidence of export spillovers from French data only when the destination dimension is taken into account. Export spillovers might also occur at a fine product category level that is more detailed than the HS2 categories, which might be highly heterogeneous. For example, in the French case, Koenig et 5
a.|n.a.|n.a.|n.a.|n.a.|n.a.|
|Mongolia|4.77|4.77|11.48|3.07|6.49|2.55|11.54|3.73|
|Pakistan|1.78|4.62|13.07|4.51|5.53|1.01|12.19|5.26|
|West Bank and Gaza|1.70|3.86|10.93|5.35|5.52|0.57|11.18|6.20|
|Peru|4.05|4.49|9.50|5.66|6.23|2.05|9.18|6.24|
|South Africa|4.70|3.59|11.53|4.48|6.03|1.91|11.43|5.26|
|Timor|1.43|1.80|n.a.|n.a.|2.02|1.05|n.a.|n.a.|
|Turkey|1.01|3.50|10.93|4.83|2.80|0.76|10.79|5.27|
|Uruguay|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|
|USA|4.07|3.33|10.64|4.92|5.54|2.13|10.12|5.64|

Source: Authors’ elaboration based on survey data. Notes: Women and men aged 15 to 64. In Argentina, the starting
This work is organized as follows. Section 2 reviews the most recent empirical literature on the causal relationship between climate change and migration. This section also clarifies important conceptual distinctions, summarizes the key open research questions, and takes a closer look at Sub-Saharan Africa, the area where the LSMS-ISA survey program currently operates. Section 3 presents the empirical and methodological challenges and highlights the related data gaps. Section 4 takes a closer look at the LSMS-ISA data collection and discusses its main limitations and possible use for empirical work. Section 5 suggests potential improvements and add-on modules to be integrated into future LSMS-ISA surveys. Section 6 sets up an agenda for future work and concludes.

# **2. Review and current state of the literature**

## _2.1 A synthesis of recent empirical findings_

<mark>In this subsection, we carry out an updated review of the literature by focusing on the period 2010-2022.</mark><sup>3</sup> <mark>Overall, even the most recent literature still provides mixed and inconclusive evidence about not just the magnitude, but even the sign of the climate-migration relationship. Heterogeneity, depending on several context-specific features including</mark> _<mark>, inter alia</mark>_ <u><mark>,</mark></u> <mark>the type and frequency of climatic shocks, peoples’ resources and adaptation strategies, seems to be an irreducible aspect of this delicate matter, that rules out blanket generalizations on the nexus between climate change and (im)mobility. Such complexity calls for caution in issuing predictions that climate change will force tens of millions of people to move within and/or out of their own country (Rigaud et al., 2018; Clement et al., 2021) and to pay more attention to the potential of climate change in preventing voluntary migration and trapping more vulnerable and poorer populations in immobility.</mark> <mark>By analyzing this flourishing strand of the
<!-- Start of picture text --> Figure 1.1 (b): GDP Annual Growth Rates (%)<br>Source: World Bank Regional Tables<br>200 sssssz£n;nn£;+_+n‘+‘n+n+nngn<br>15 a ~~<br>10 NN<br>5 — ——— NY<br>0 \ 7<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 a0) ip 010p<br>——ECA —— Oil and Gas Exporters ——EU10+ Croatia and Turkey —=LLMIC<br><!-- End of picture text -->
Cross-sectional estimates in cols. 1-3 of table 5 suggest that posting of SLMs led to significant increases in the number of registered sales but not non-monetary transfers of residential (panel A) or agricultural (panel B) land. There is no evidence of a separate effect of land week events in specific sectors, beyond that of the national campaigns. Higher urban population shares or numbers of residential or agricultural parcels are associated with higher levels of registered transfers especially for residential land. Distance to the DLO, a proxy for transport cost, is insignificant for sales but significant and large for non-monetary transfers where estimated coefficients suggest that beyond 10 km from the DLO, the likelihood of registration of nonmonetary land transfers decreases by about half.

To support causal inference, we report results from fixed effects specifications (table 5, cols. 4-6). Presence of a SLM is estimated to raise numbers of registered sales per quarter and cell by 0.88 for residential and 1.23 for agricultural land, an annual increase of 71% or 146%. On the other hand, the estimated coefficient on non-monetary transactions of about—0.3 in both cases is marginally significant.

# **4. Exploring informality and transactions in rural areas**

Administrative data, although suitable to provide information on formal transactions at low cost and high levels of temporal and spatial disaggregation, need to be combined with household survey data to obtain evidence on informality. Using a nationally representative rural household survey to do so suggests that, for a number of reasons, including lack of information, high fees, and difficulties in accessing officials, and although those who transferred their land are willing to spend resources on registration.

# **4.1 Assessing levels of informality in rural Rwanda**

While the registry provides data on registered transactions, assessing levels and determinants of informality requires linking household to registry data at the parcel level. To do so in a way that is representative for rural areas in Rwanda, we use the 2015 round of a three-round
affects the transition of non-essential entrepreneurs from the lockdown period to the next. Thereafter, however, we observe a notable weakening of the recovery. The unemployment rate is almost 3 percentage points higher than its _t_ = 2 value in the benchmark and decreases slowly. Investment, capital stock and output remain below their initial levels for a longer period of time.

As mentioned above, financial and labor market frictions are at the core of the weaker recovery. As older businesses exit and are replaced by newcomers, financial frictions impede the growth of those productive entrants who are not yet wealthy enough to overcome credit constraints. Furthermore, as workers leave their former employers and wait to be hired by newcomers, the labor market is congested by the matching frictions, so the unemployment rate remains high for many quarters after the combination of shocks disappear. The evidence of a strong rise in business formation in the US in Section 2 provides a measure of empirical support for this reallocation channel.

# **6 Concluding Remarks**

The Covid-19 pandemic triggered a burst of economic research seeking to understand its economic implications. Motivated by this episode, and also noting a scarcity of quantitative frameworks that can help us think about large temporary shocks more broadly, we propose one in this paper. Our model incorporates the main elements that can capture the first-order effects that operate on impact and during the recovery of an economy hit by such a shock: imperfect access to credit, costly reallocation of workers, recall unemployment (especially relevant to the temporary nature of the shock), and imperfect consumption insurance.

Disciplining the degree of the market frictions in the economy to the US data, we show that the deep but short-lived shutdown results in a deep but short-lived recession followed by a swift recovery. While the recovery is fast in the aggregate variables, it carries lasting scarring effects on young non-essential firms. Paying for capital costs while generating no revenue for one period, these firms sustain a significant damage to their balance sheets, which shows up as significantly lower growth over
fragility and business in the case of Myanmar is unique and is marked by idiosyncratic characteristics of the country’s historical trajectory, ethnic subnational conflict, and other cultural elements, which make it significantly different from other fragile contexts (Egreteau and Mangan, 2018). Conflict affects business development through several different channels; trade through ethnic areas suffers from the legacy and persistence of conflict; transit fees, levied at checkpoints at the porous borders in Myanmar’s mountainous periphery, disincentivize trade, business growth and long-term investment in these areas (Jolliffe, 2015); furthermore, the informal trade that transits through these areas, undermines legitimate businesses, as well as, government efforts to mobilize taxes and build better trade facilitation institutions (Egreteau and Mangan, 2018).

**In Myanmar, women entrepreneurs face different and more severe challenges compared to their male counterparts.** Constraints faced by women entrepreneurs in low income countries mainly arise from gender discrimination, work-family conflict, difficulty raising capital, lack of infrastructure, unstable business, economic and political (BEP) environments, and lack of training and education (Panda, 2018), in addition to heavy household chores and limited access to finance (Tambunan, 2017). Moreover, a study conducted in Indonesia by The Asia Foundation (2013) found that access to finance is a constraint for women because they use personal savings for the initial capital

> 10 Descriptive report, Myanmar Micro, Small and Medium Enterprise Survey, 2017. 11 Ibid.

7
# **3. Case studies**

When the 12 attributes of the data are present, data can yield vast value for development outcomes. This section relies on case studies illustrating how each attribute can bring about positive change. Most examples involve several features either directly or indirectly, and as outlined above, having a single feature in isolation is seldom enough to bring about value.

## **Completeness**

When data cover the entire population of interest it is possible to make inferences that one otherwise would not be able to. One of the most fundamental ways in which countries can make sure their data cover their entire population of interest is through foundational identification (ID) and population registration systems, and particularly by assuring that all individuals are covered in government databases.<sup>3</sup> In Thailand, at the turn of the century, only 71 percent of the population was covered by a public health insurance scheme that was intended to be universal. Yet the country had a near-universal population register in which citizens were issued a personal identification number when they were born or when their household was registered for the first time. Leveraging this register and the personal identification information from the existing public insurance scheme, the government was able to identify the population not covered and increased health insurance from 71 percent to 95 percent (World Bank 2020a and World Bank 2018a).

In the absence of nation-wide administrative data, representative survey data fulfill a similar role. A recent example in Nigeria illustrates the power of data that are representative of the entire population of interest. The Government of Nigeria commissioned the 2015 National Water Supply and Sanitation Survey to understand access to water and sanitation of all households in Nigeria. As part of the survey, 201,842 households, 89,721 improved water points, 5,100 water schemes, and 51,551 public facilities including schools and health care facilities were enumerated. Coupling these data with advanced mapping techniques to better understand geographic variation in access to water and sanitation services
|31|10|
|North Africa||||||||
|Morocco|2,884|106|12|0.9|1.9|60|3|
|South Asia<br>||||||||
|India<sup>~~b~~</sup>|1,077|70|9|3.7|0.6|7|35|
|EU<br>Accession||||||||
|Hungary|12,83<br>7|60|17|1.6|1.6|20|n.a.|
|Poland|11,29<br>6|53|33|1.9|1.9|43|4|

_Sources:_ World Bank Development Indicators, IMF database, Axco Reports, 2008– 2009 data. _Note:_ Unadjusted Life insurance percentages tend to favor the bank distribution system because of the higher proportion of single-premium business sold through bank branches. n.a. = not available. a. Including Health and Accident.

b. Bancassurance accounts for 19 percent of non-LIC Life sales in India.

# **Latin America**

## **Brazil**

_Regulation:_ No limit is placed on insurer/bank cross-ownership, except for government banks, which may hold no more than 49.9 percent of an insurer. In practice all insurance transaction must be intermediated through a registered broker, and Bancassurers normally nominate in-house officers to be registered as brokers. In addition all insurance premiums must be collected via a banking institution. This unusual requirement has actually had an important impact on the development of Bancassurance in Brazil.

35
econometric techniques,<sup>_10_</sup> which are common to the field, in order to estimate heterogeneity in effects across 1) countries, 2) rural and urban sectors, 3) pre-COVID-19 wealth, 4) gender, and 5) time (see Supplementary Materials - Methods). Tracking how people’s lives are differentially affected by the COVID-19 pandemic can enable governments and policy makers to better understand the circumstances faced by their citizenry and to make data-driven, informed policy decisions. The need to understand the contemporaneous impacts and coping strategies of households is and will continue to be important for low-income countries, as they are likely to lack access to a vaccine for longer than high-income countries.<sup>_16,17_</sup> The longitudinal data collected through the high-frequency phone surveys cultivate this understanding by documenting near-real-time trends.

# **3. Results**

# **Government action is reflected in household knowledge of and behavior around COVID-19:**

With the global spread of the SARS-CoV-2 virus, numerous low-income countries followed worldwide trends by declaring states of emergency, issuing stay-at-home orders, closing schools, and imposing curfews.<sup>_18_</sup> Considering the countries in our data set, Ethiopia closed schools on March 16<sup>_19_</sup> and declared a state of emergency on April 8.<sup>_20_</sup> Nigeria and Uganda issued stay-athome orders on March 29 and 30, respectively.<sup>_21, 22_</sup> Malawi attempted to issue stay-at-home orders 3
# **Appendix Table 3: Definition and Sources of variables used in Regression Analysis**

|**Variable**|**Definition**|**Source**|
|---|---|---|
|SME Loans|Total SME loans for a bank expressed as a percentage of total loans|UAB and World Bank SME Survey|
|Investment Loans|Total SME loans for investment purposes expressed as percentage of total SME loans|UAB and World Bank SME Survey|
|Log Total Loans|Logarithm of total loans for a bank|UAB and World Bank SME Survey<br>and Bankscope Database|
|GCC Dummy|Dummy variable that takes value 1 if a bank is in GCC region and 0 otherwise|World Bank|
|State Ownership Dummy|Dummy that takes value 1 if a bank is owned by government and 0 otherwise|UAB and World Bank SME Survey|
|Log Number of Branches|Logarithm of total number of branches for a bank|UAB and World Bank SME Survey<br>and Bankscope Database|
|Separate Unit for SME Clients|Dummy variable that takes value 1 if a bank has a separate unit for SMEs and 0 otherwise|UAB and World Bank SME Survey|
|Maximum Coverage Registry<br>or Bureau|Highest coverage ratio of either the public registry or the private bureau and includes the<br>number of individuals and firms listed with information on their borrowing history from past<br>fiveyears|World Bank‟s Doing Business|
|Legal Rights Index|The degree to which collateral and bankruptcy laws protect the rights of borrowers and lenders<br>and thus facilitate lending. The index ranges from 0 to 10, with higher scores indicating better<br>protection|World Bank‟s Doing Business|
|Time to Register Property|Number of days that property lawyers, notaries or registry officials indicate is necessary to<br>complete aprocedure.|World Bank‟s Doing Business|
|Time