rafmacalaba/data-use-mentions-extended huggingface dataset:
the developing world.

# **2. Materials and Methods**

## **2.1 The Case for Low-Cost Air Quality Monitoring**

The first step towards managing and reducing air pollution is monitoring. One needs to quantify its scale, magnitude and spatial distribution to really understand where the challenges lie. However, air quality data inadequacy is often linked to the lack of institutional capacity mainly attributed to the associated resource burdens of air quality monitoring e.g. distinctive traditional certified reference instruments are expensive to set up and maintain (Castell et al., 2017 and Hugh et. al 2019). This often leads to sparse distribution of monitoring networks in developing countries; moreover, the unique practical realities of urban centers in resource-strained countries make traditional reference monitoring unaffordable. Consequently, various international environmental agencies and regulatory bodies including US-EPA, European Environment Agency and the United Nations Environment Programme (UNEP) recognize the dire need for supplementary monitoring techniques to provide indicative air quality data.

> 7 These studies include Nyangababo (1987), Nyangababo & Salmeen (1987), Matagi (2002), ICF International (2009), Schwander et al (2014), Kirenga et al (2015), KCCA (unpublished) (2019).

> 8 For example, Nyangababo (1987), and Nyangababo and Salmeen (1987) use lichens and moss for assessment of heavy metal traces in ambient air from industries and traffic; whereas the study is a good reference baseline for ambient heavy metal concentrations in Kampala, it cannot be a representative indication of the current air quality situation given the drastic changes in the physical environment within the last 30 years. Matagi (2002) provides a qualitative profile of emission sources but lacked quantitative measurement data. Schwander et al. (2014) whose findings were based on two 24-hour measurements in a single location can only be used to characterise levels within the monitoring location and restricted to 24-hour averaging periods. Kirenga et al., (2015) was the first study to focus on
Policy Research Working Paper 10168

# **Abstract**

The COVID-19 pandemic has disrupted survey and data systems globally and especially in low- and middle-income countries. Lockdowns necessitated remote data collection as demand for data on the impacts of the pandemic surged. Phone surveys started being implemented at a national scale in many places that previously had limited experience with them. As in-person data collection resumes, the experience gained provides the grounds to reflect on how phone surveys may be incorporated into survey and data systems in low- and middle-income countries. This includes agricultural and rural surveys supported by international survey programs such as the World Bank’s Living Standards Measurement Study—Integrated Surveys on Agriculture, the Food and Agriculture Organization’s AGRISurvey, or the 50x2030 Initiative. Reviewing evidence and experiences from before and during the pandemic, the paper analyzes and provides guidance on the scope of and considerations for using phone surveys for agricultural data collection. It addresses the domains of sampling and representativeness, post-survey adjustments, questionnaire design, respondent selection and behavior, interviewer effects, as well as cost considerations, all with an emphasis on the particularities of agricultural and rural surveys. Ultimately, the integration of phone interviews with in-person data collection offers a promising opportunity to leverage the benefits of phone surveys while addressing their limitations, including the depth of content constraints and potential coverage biases, which are especially challenging for agricultural and rural populations in low- and middle-income countries.

This paper is a product of the Development Policy Team, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at pwollburg@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly
evidence suggests that moves to unfreeze the economy will fail unless there is confidence that, in fact, the risk has passed.

Several recent papers suggest that NPIs have had an impact in the United States. Engle at al (2020) use daily average changes in distance traveled in every U.S. county as a proxy for reduction in exposure to COVID-19 and find that an official stay-at-home restriction order corresponds to reducing mobility by 7.87%. Brzezinski et al (2020), also using cell phone data, find that a lockdown increases the percentage of people who stay at home by 8% across U.S. counties. Painter and Qiu (2020) show that the introduction of shelter-in-place policies is associated with a 5.1 percentage point increase in the probability of staying home (see also Andersen (2020)).

However, voluntary de-mobilizing behavior that intensifies with prevalence of the disease is also an important driver and affects the effectiveness of official measures. Auld (2006), for example, finds that during the AIDS epidemic, an average respondent decreased risky behavior by about 5% in response to a 10% increase in AIDS prevalence. Further, the 1918 Spanish Flu epidemic suggests that the predisposition of the population to demobilize drove both the incidence of official restrictions and their effectiveness. On the one hand, as Crosby (2003) details, that restrictions were binding is revealed by the fact that in San Francisco “The places of amusement opened first, to huge crowds starved for entertainment (p. 99)” and in Philadelphia “The long thirst was over, and arrests on drunken and disorderly charges bounded back up to and beyond normal levels” (p. 85). However, it is also true that while the San Francisco Department of Health could request that people to smother coughs and sneezes, only when enough fatalities were registered were “San Franciscans...scared enough to accept drastic measures to control the epidemic” (p.95)—and ex post, “
the poverty criteria but happen to reside in the eligible zones.

In the case of Panama, there was already considerable experience with the use of zonal subsidies. As noted above, IDAAN had traditionally used zonal criteria in determining eligibility for existing water subsidies.

More generally, the Ministry of Planning has developed a 'poverty map' intended to guide the allocation of social investments. The map is based on a combination of socio-economic data collected as part of the 1990 census and more detailed data from the 1997 Living Standards Measurement Survey. The data were used to construct a poverty index that
/mark>

- <mark>Friedl, Mark A., Damien Sulla‐Menashe, Bin Tan, Annemarie Schneider, Navin Ramankutty, Adam Sibley, and Xiaoman Huang. 2010. "MODIS Collection 5 Global Land Cover: Algorithm Refinements and Characterization of New Datasets."</mark> _<mark>Remote Sensing of Environment</mark>_ <mark>114 (1): 168–82.</mark>

- <mark>Fujita, M., P. R. Krugman, and A. J. Venables. 2001.</mark> _<mark>The Spatial Economy: Cities, Regions, and International Trade</mark>_ <mark>. Cambridge, MA: MIT Press.</mark>

- <mark>GADM (Global Administrative Areas). 2012. "GADM Database of Global Administrative Areas, Version 2.0." http://www.gadm.org/.</mark>

- <mark>Henderson, J. V. 1974. “The Sizes and Types of Cities.”</mark> _<mark>American Economic Review</mark>_ <mark>64 (4): 640–56.</mark>

- <mark>Henderson, J. Vernon, Adam Storeygard, and David N. Weil. 2012. "Measuring Economic Growth from Outer Space."</mark> _<mark>American Economic Review</mark>_ <mark>102 (2): 994–1028.</mark>

- <mark>Henderson, M., E. T. Yeh, P. Gong, C. Elvidge, and K. Baugh. 2003. “Validation of Urban Boundaries Derived from Global Night‐Time Satellite Imagery.”</mark> _<mark>International Journal of Remote Sensing</mark>_ <mark>24 (3): 595–609.</mark
Donaldson and Storeygard (2016). Early applications to the measurement of economic activity and welfare include Henderson et al. (2012), which also established the value of these data as predictors of economic outcomes (see also Pinkovskiy and Sala-I-Martin, 2016). These early studies confirm that national income can be accurately predicted using NTL data.<sup>2</sup> Economists have since expanded the set of predictors extracted from remote sensing data and increased the spatial resolution at which economic welfare including poverty is estimated. Jean et al. (2016) is among the early studies to use a comprehensive set of predictors derived from remote sensing data to estimate poverty at the small area level. The studies vary in the resolution of imagery used, with higher resolution images allowing for a richer set of predictors that can be extracted from them (see e.g. Marx et al., 2019). The increased accessibility of remote sensing data combined with the continued demand for highly disaggregated poverty data has prompted a surge in the production of poverty maps in recent years. In the years following Jean et al. (2016), dozens of remote sensing-based poverty and/or household welfare maps have been produced, see e.g. Blumenstock et al. (2015), Blumenstock (2016), Bosco et al. (2017), Burke et al. (2021), Engstrom et al. (2021), Imran et al. (2014), Pokhriyal and Jacques (2017), Steele et al. (2017), Watmough et al. (2016), Watmough et al. (2019), Zhao et al. (2019), and Lee and Braithwaite (2022).

This highlights the importance of assessing the accuracy of remote-sensing-based poverty maps. One logical entry-point in this regard is an assessment of how closely a poverty map obtained using remote sensing data tracks a poverty map derived from population census data. Such assessments are currently missing from the literature. Existing studies typically validate
Policy Research Working Paper 6038

# **Abstract**

This research estimates the impact of macroeconomic fluctuations on import protection policies over 1988:Q1– 2010:Q4 for five industrialized economies—the United States, European Union, Australia, Canada and South Korea. There is evidence of a strong countercyclical trade policy response in the pre-Great Recession period of 1988:Q1–2008:Q3 during which increases in domestic unemployment rates, real appreciations in bilateral exchange rates, and declines in the GDP growth rates of bilateral trading partners led to substantial increases in new temporary trade barriers. It then applies this pre-Great Recession empirical model to realized macroeconomic data from 2008:Q4–2010:Q4 and find it predicts a surge of new import protection during the Great Recession—e.g., for the US and EU, the model predicts new trade barriers would cover an additional 15 percentage points of nonoil imports, well above the baseline level of 2-3 percent of import coverage immediately preceding the crisis. Finally, the research examines why the realized trade policy response differed from model predictions. While exchange rate movements played an important role in limiting new import protection during the Great Recession, there is also evidence of one particularly important change in trade policy responsiveness; i.e., in this period, governments refrained from imposing new temporary trade barriers against foreign trading partners experiencing their own weak or negative economic growth.

This paper is a product of the Trade and Integration Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at cbown@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the
# 1 Updating pre-conflict poverty estimates

Official poverty estimates for Syria are available for 1997, 2003 and 2007 and produced with UNDP technical assistance. Poverty measurement builds on nationally representative household budget survey data from the Household Income and Expenditure Surveys (HIES) conducted by the Central Bureau for Statistics (CBS – the official statistical agency in Syria).<sup>6</sup> CBS conducted another round of the HIES in 200910, but no poverty estimates were released. Since the conflict erupted in 2011, no new rounds of HIES were conducted.

The methodology adopted by UNDP to compute official poverty estimates builds on the construction of “household-specific poverty lines”, reflecting caloric requirements which vary by household composition and regional variation in the cost per calory and non-food allowance.<sup>7</sup> Two different poverty thresholds are considered, one reflecting basic needs (referred to as the “extreme” or “lower” poverty line – henceforth LPL) and a more generous one reflecting a “reasonable” level of basic needs (referred to as the “moderate” or “upper” poverty line – henceforth HPL). The LPL – once expressed in USD using the 2011 PPP conversion factor – is close to the international poverty line for lower-middle-income countries ($3.65 at 2011 PPP), while the UPL is close to the one for upper-middle-income countries. Table 1 below summarizes the available poverty estimates.

_Table 1: Official poverty rate and national poverty lines_

||**1997**|**2003**|**2007**|
|---|---|---|---|
|||**_Poverty rate (%)_**||
|**Lower Poverty line (LPL)**|14.26|11.39|12.3|
|**Upper Poverty line (UPL)**|33.22|30.13|33.6|
||**
# **(4) Data**

# **(4.1) Data for China: CFPS (2016, 2010)**

The data for estimating the intergenerational mobility of education in China come from the China Family Panel Study (CFPS)- 2016 wave. In order to match children-parents pairs irrespective of residency status at the time of the survey, we take advantage of the Family Member Module, which has an innovative T-Tables design feature. As discussed in Xie and Hu (2014), the T-Tables in CFPS consist of three tables - T1, T2, and T3. Table T1 (on family members living together) and Table T2 (on immediate relatives not living together) record the basic socio-demographic characteristics of every family member and her immediate relatives (parents, children and spouses) who are not living with them. Table T2 (on relations) identifies the relations of all the family members and the corresponding relations between T1 and T3 members. Therefore, three tables jointly present a complete family network, and most importantly, allow us to avoid truncation of the sample.

More specifically, in order to identify all the children-fathers pairs, we take the following steps. For each individual currently living in the family, own education and father’s education are directly available in the Family Member module irrespective of whether the father is coresident at the time of the survey. This subsumes two cases involving three generations. (i) If such an individual is the household head (or head’s spouse) who lives in the family, their parents are matched, irrespective of whether they are co-resident or not. (ii) If such an individual is household head’s (or head’s spouse’s) child, who lives in the family, the child’s parents are of course matched, i.e. they are household head and head’s spouse. However, step 1 misses the following case: if household head’s (or head’s spouse
, No. 118/1996 to 340/2003_ ) The scheme of Slovak Republic was established in March 1996 and was revised in 2001 in accordance with the EU directive 94/19/EC. The system is jointly administered and funded. The central bank made an initial contribution and may make loans to the fund. Anonymous deposits and deposits of owners, directors and senior managers are not covered. The premium rates for building societies are half of those of commercial banks. Membership is mandatory and premium rates range between 0.1% and 0.3%. Until 2002, the coverage limit for deposits was thirty times the average monthly salary of previous year as published by the National Statistical Office, whereas the rate with savings banks was sixty-fold. In 2002, the bank deposit limit was raised to forty times the average monthly salary. Finally, in 2003 the coverage limit was set at 90% of inaccessible deposits not to exceed EUR 20,000.

_Sources_ : Own survey of deposit insurers, Garcia (1999).

**Slovenia.** ( _Deposit Guarantee System, Banking Act_ ) The deposit guarantee scheme of Slovenia was introduced in 2001. Between 1991 and the end of 2000 there was an explicit unlimited coverage by the government. The system is government legislated and administered. Funding is provided on an ex-post basis such that banks are not obliged to pay premiums but to invest a minimum 2.5% of the guaranteed deposits in the Bank of Slovenia bills. The membership to the scheme is mandatory for all deposit taking institutions. The coverage limit was SIT 4.2 million in 2001 which went up to SIT 5.1 million (about $ 27 thousand) in 2003. The coverage is calculated per depositor per institution and it is extended to foreign currency deposits as well.

_Sources_ : Own survey of deposit insurers, IADI Survey: Slovenia (2003).

**Spain.** ( _Deposit Guarantee Fund, Royal Decree Law 4 & 18_ ) Spain has separate deposit guarantee funds for
destruction of formal firms due to the rise in the minimum wage was accompanied by an increase in both inactivity and unemployment – rather than a rise in informal employment.

We also use the HLFS data to investigate whether the minimum-wage increase impacted the wages of workers who are informally employed using the HLFS data. In contrast to the EIS registry data, the HLFS data also collect information on the wages of informal workers. While informal workers are by nature not covered by official labor regulations including the minimum wage, prior literature suggests that minimum-wage increases for formal workers can have positive spillovers on workers who are informally employed.<sup>30</sup> The existence of such spillovers has important welfare implications because it implies that workers’ wages benefit from the minimum wage even for those whose employment is outside the formal sector. Columns (6) to (8) of Table 10 report the difference-in-difference estimates of the minimum-wage increase on wages using the HLFS data. We find a large, positive, and statistically significant effect of the minimum wage on the wages of formal workers (Column 7). We do find a positive effect of the minimum wage on the wages of informal workers, but the magnitude of the estimate is about three times smaller, and it is not statistically significant (Column 8). Thus, the spillover effects of the minimum wage on the informal sector appear to be limited; this finding contrasts with findings of some studies in other contexts, particularly for some Latin American countries.<sup>31</sup> However, this result is compatible with the lack of impact of the minimum-wage increase on informal employment that we have also found. This lack of a pronounced spillover effect could be explained by the high degree of segmentation between formal and informal employment in Turkey, compared to greater integration between the two types of employment in other developing economies.

# **6. Conclusion**

This paper examines the impacts of a large increase in the minimum wage on firms and workers in Turkey. We
**Figure 10. Workers with earnings between the current and the proposed minimum wage, % workers in selected categories (occupation and industry), 2018**

# **a. By occupation**

<!-- Start of picture text --> Armed forces 0.7<br>Elementary occupations 14.4<br>Machine operators 8.0<br>Craft workers 7.6<br>Skilled agricultural 0.0<br>Service and market sales workers 15.6<br>Clerks 3.1<br>Technicians 3.1<br>Professionals 1.2<br>Senior officials 1.2<br>0 2 4 6 8 10 12 14 16 18<br>Percent<br>b. By industry<br>Other Services, Unspecified 5.7<br>Public Administration 1.7<br>Financial and Business Services 10.3<br>Transport and Comnunications 3.1<br>Commerce 16.5<br>Construction 5.7<br>Public utilities 4.2<br>Manufacturing 10.2<br>Mining 4.5<br>Agriculture 14.9<br>0 2 4 6 8 10 12 14 16 18<br>Percent<br><!-- End of picture text --> _Source: Own estimates using 2018 Kosovo LFS._ Who are they? Decomposition of the affected group

**Certain groups of workers are more likely to be affected by the rising minimum wage, including youth, unskilled workers, workers with non-professional occupations, and informal-sector workers.** A large share of workers who earned between the current and the proposed minimum wage were young workers aged 20-34 (50.5 percent). Only 3.5 percent were aged 15-19, given teenagers’ low employment rate; 37.4 percent were aged 35-54 and 8.7 percent were aged 55-64.  There were more men than women in this
> _Source:_ Estimates based on multiple labor force and household surveys (see annex table A1).

_Note:_ This figure shows JQMs by country for the wage employed only. The JQM is comprised of four dimensions, and the highest possible JQM attainable for an individual is equal to 4.

Interesting patterns emerge in comparing job quality across economic sectors using cross-country data (figure 4). Sectors with the highest job quality include public administration, finance and business services, and (often public) utilities. Jobs in these sectors generally score higher in each dimension, but particularly in the benefits and stability dimensions. The average scores in these dimensions of the three top sectors mentioned above are 0.25 and 0.33 points higher than the average scores in these dimensions in the remaining seven other sectors. In addition, their average scores in the income and working conditions dimensions are higher by between 0.09 and 0.15 points. These sectors are also the ones with the highest share of wage employed. In contrast, at the other end of the spectrum, the low-skill sectors of agriculture and construction have the lowest levels of job quality. Agriculture workers have significantly lower scores than the rest of the economic sectors in all dimensions except working conditions, while construction workers fare particularly poorly in the benefits and stability dimensions.

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