rafmacalaba/data-use-mentions-extended huggingface dataset:
Vegetation Index (NDVI) explains about 4 percent of the variation in per capita consumption across Sri Lanka (Engstrom et al., 2021). Daytime satellite imagery has also been shown to predict 15 to 17 percent of changes in village-level asset indices extracted from Living Standards Measurement Surveys in Sub-Saharan Africa. However, that rises to 35 percent when predicting an index of changes in asset in ownership at the village level, and 50% when that index of changes is aggregated to the district level (Yeh et al, 2020). Khachiyan et al (2021) find that models trained on satellite imagery can explain between 32 and 46 percent of decadal changes in income and population in the United States.

Data from search engines have also been used to assess people’s real-time well-being without relying on survey questionnaires. _Google Trends_ and its local equivalents (such as Baidu Trends in China and Naver Trends in the Republic of Korea), based on search frequency, provide an index for search intensity (or relative popularity) by topic or term over time in a certain geographical area. A number of recent studies have used data from _Google Trends_ to explore the changes in psychological well-being during the COVID-19 pandemic _(_ Brodeur et al., 2021; Foa et al., 2020; Ma et al., 2021 _)_ . However, these studies focus on measuring the impact of lockdowns on search behavior and do not compare search data with large-scale phone survey data. In addition, data from search engines are also not nationally representative since they are only available for areas with access to the internet and collected from users of Google for online search, while data from satellites are affected by cloud cover, raising questions about their ability to track economic changes more broadly.

In this paper, we exploit a set of real-time indicators from different sources to predict changes in welfare in developing countries during the COVID-19 crisis. Six main findings emerge:

1. The selected real-time indicators predict a significant share of the cross-regional variation in the
(2014) present strong evidence that AGOA, EBA, ACP (African Caribbean Pacific)-EU and GSP programs of EU, US, Canada and other advanced economies have a positive effect on developing countries’ exports to the corresponding developed markets. Similarly, Rose (2004) finds a strong positive impact when the GSP was extended from advanced to developing countries though there was no impact due to participation in GATT (WTO). Yet, there are other studies that yield seemingly contradictory results on the overall impact of PTAs.

In a study of 184 countries for the period 1953-2006, Herz and Wagner (2011) find that, on average, participation in a PTA led to a 4% reduction in exports. Herz and Wagner (2011) show that GSP tends to foster developing countries’ exports in the short-run, but hampers them in the long-run, hence suggesting that GSP does not seem to serve as an instrument to enhance economic transformation of developing economies.

In Sub-Saharan Africa, evidence on the impact of such non-reciprocal trade agreements is very scarce. Similar to the evidence of impact of PTAs, in general, results of impact are also mixed. Using a simple partial equilibrium framework, examination of the potential impacts of AGOA by Mattoo et al. (2003) suggest that there are increased prospects for African countries to raise exports due to AGOA. Examining the scope and value of AGOA in 2002, Brenton and Ikezuki (2004) suggest that eligible countries would see very small gains in exports in products eligible under AGOA, since most already have access under the GSP. Benefits, however, are expected to be sizable due to the apparel provision. Using disaggregated product data up to the year 2006,<sup>13</sup> Frazer and Van Biesebroeck (2010) show that there is a strong positive impact on imports to the US

> 13For most countries AGOA was in effect in late 2000 or 2001. Hence this effect only captures a very short-run effect.

associated with AGOA. These 11
>Vladimir ~~~~~~Gorod*t<br><!-- End of picture text -->

**Note: 1- lowest quintile, 5 highest quintile Source- Vladirnir and Gorodets Longitudinal Household Survey, December** 1996.

A comparison of the categories of households planning to improve their living situation and those who would like to take a long-term loan (Fig. 2.10) shows that:

- t in Vladimir and Gorodets, there are similar trends and proportions as regards loans

- only half of the households intending to obtain housing through miarket methods are willing to take out a loan. The rest seem to rely on other sources;

- of those **on** housing waiting lists, some (about one-third) have nothing against paying to solve this **problem (provided** they are able to receive financing). The **propor-tion** of such households in Vladimir is **2.4%/, and** in Gorodets, 1%;

- it is especially important that _5.9%_ of the households in Vladimir and _5.2%_ in Gorodets, who do not see **how** they could improve their living conditions, would like to **iuse** a long-term loan to get access to the housing market.
# **III. Data: The LSMS Methodological Validation Program (MVP)**

In order to address the gaps in the area measurement literature and extend the applicability of studies to the plot conditions common to developing countries, the Living Standard Measurement Study – Integrated Surveys on Agriculture (LSMS-ISA) of the World Bank has prioritized land area measurement in its research agenda. The Global Strategy to Improve Agricultural and Rural Statistics has also identified improving the measurement of crop productivity, and by necessity farm area measurement, as a top priority (World Bank et al., 2010). Because agricultural statistics are often marred by controversy over methods and overall quality, stringent validation of the available measurement methodologies is essential. With financial support from UK Aid, the Living Standards Measurement Study (LSMS) has partnered with national statistical offices in the design and implementation of methodological validation studies. The methodological studies completed through the LSMS have a particular focus on the feasibility of implementation in large-scale household surveys, thus the recommendations on best practices are made in consideration for both the highest data quality and practicality of implementation under the constraints common in this type of survey. To date, methodological validation studies on land area measurement have been conducted in Tanzania (Zanzibar), Ethiopia and Nigeria. This paper uses the data collected within these studies, which are briefly described in what follows.

Data for Zanzibar, Tanzania come from the Measuring Cassava Productivity (MCP) study. The MCP focused on testing several methods for measuring cassava production, including crop-cutting, harvest diary, various means of assisted harvest diary, and various recall periods. To complement the measurement of cassava production, cassava plots were also measured using three methods of area measurement. Fieldwork extended from June 2013 to May 2014, with area measurement completed from August 2013 to January 2014. The study was conducted in two districts, one on Unguja and one on Pemba Island, Zanzibar. The sample consisted of 1,247 households, with 1,932 cassava plots measured for area. Partners in the
260 households.

The second survey studied the Rural Roads and Markets Improvement and Maintenance Project – II (RRMIMP-II), which included improvement of 574 kilometers of FRBs to bitumen-surfaced standard, construction of 1,900 meters of culverts, 1,750 meters of bridges and 2,200 meters of small drainage structures on rural roads, and improvement of 136 Growth Center Markets and 41 Ghats (river jetties for boats and vessels). The first phase of the RRMIMP survey collected benchmark information on 872 households from 18 villages during May-September 1997, and the second phase covered the same households between August, 2000-February, 2001. Both projects were funded by the World Bank as part of its effort to promote rural infrastructural development, and, consequently, rural growth and poverty reduction.

In both project and control road areas, one roadside village was selected for each road project. The selected roadside villages indicate the immediate influence areas of road intervention. In addition, one remote village was selected for each of the sample roads to assess the decay effects of road development. The remote villages were similar to the roadside villages and were chosen such that they 7
villages)**|200|200|200|200|200|200|

**Notes:** All data comes from the Village Leaders Survey, collected in October 2015. The between group variation reported in Column 2 is computed through a one-way ANOVA analysis of the dependent variable across districts. For each measure of disruptions, Columns 3 through to 6 report estimated coefficients on assignment to treatment from regressions that use incrementally larger sets of villagelevel covariates. All regressions include district fixed effects and calculate robust standard errors. Column 3 reports the coefficient on ELA from a regression of the Ebola measure of interest on treatment assignment. The coefficient in Column 4 is obtained controlling for: number of dwellings, whether the village is a political stronghold (i.e. it is the residence of a chief), number of NGOs active pre-Ebola, average PPI score, share of Christians, distance from Freetown and distance from Kailahun (where the first Ebola case was recorded). In Column 5 distance from the nearest facility of interest (PHU in Panel A, Secondary School in Panel B, both in Panel C) and interaction between ELA assignment and these distances are added as regressors. None of the coefficients on the interaction between ELA and distance from each facility is statistically significant (not shown). Column 6 includes all regressors and interactions employed in Column 5, plus features selected by a penalized regression (Elastic Net) of the Policy index (dummy) on all village characteristics and their interactions. The Primary Health Unit functioning score is assessed on a monthly basis between July 2014 and September 2015, and later aggregated into an index ranging between 0 and 100. Secondary schools were considered as having re-opened on time if they were open in April 2015. The Disruption Index is constructed following Anderson [2008]. It aggregates variables in Panel A and B, and assigns higher values to communities more exposed to disruptions.
<!-- Start of picture text --> 1000 18.00<br>900800 | 14.0016.00<br>3 12.00<br>&8 600 | 2g 10.00<br>S500 Fi<br>8 400 a<br>a 300 6.00 r] = a<br>200 | 4.00 Py =<br>-0 [ | .0.00 i | i l i| =<br>1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 re<br>Decil Decil<br>m Benef. In-patient attendance Benef. Out-patient attendance = Benef. Communitary health m Benet. In-patient attendance Benef. Out-patient attendance = Benef. Communitary health<br><!-- End of picture text -->

<!-- Start of picture text --> 900 30.00<br>800<br>;00 25.00<br>500¥8 600 22 20.00<br><48.g&& 400300 a§ég 10.0015.00 |<br>5.00<br>0 | | | | | 0.00 a a = |lf FSF<br>1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10<br>Decil Decil<br>m Pre-school attendance Primary school attendance _mTM Secondary school attendance m Pre-school attendance Primary school attendance _TM Secondary school attendance<br>w Tertiary attendance m Special education attendance w Tectiary attendance m Special education attendance<br><!-- End of picture text -->

<!-- Start of picture text --> 1600 50.00<br>1400 45.00<br>40.00<br>1200<br>Pa 35.00<br>3 1000 5<br>aQ8&SoC g%
interest  are  the  worker’s  annual  real  wages  in  Brazilian _reais_<sup>13</sup> ,  tenure  at  the  firm  in  months, gender,  and  age.

We  restrict  observations  as  follows.  First,  we  draw  a  one  percent  random  sample  from  the complete  list  of  workers  ever  to  appear  in  the  national  records.  We  match  the  sampled workers  back  to  the  population  data  to  find  all  firms  in  which  these  workers  were  ever employed  over  time  to  create  a  complete  employment  history  of  this  one  percent  random sample  of  the  population  of  the  Brazilian  formal-­‐sector  labor  force.<sup>14</sup> Next,  we  keep  only workers  with  valid  worker  identification  numbers  to  ensure  that  we  can  track  individuals over  time.  As  is  standard  in  the  literature,  we  include  only  prime-­‐age  workers  between  the ages  of  15  and  64  years,  workers  with  a  positive  average  monthly  wage,  and  workers  in private-­‐sector  jobs.  Finally,  for  workers  with  multiple  jobs  in  a  given  year,  only  the  most recent  job  is  included  in  the  sample.  If  a  worker  has  multiple  current  jobs,  only  the  highest paying  job  is  included.

# **2.3.   Complementary   data**

**Export   Status** Brazilian   firms’   tax   identification   numbers   are   common   across   many databases,   allowing   us   to   match   the   RAIS   data   to   complementary   firm-­‐level   data   sources. Information   on   firm-­‐level   export   transactions   is   available   from   the   Brazilian   Customs   Office ( _Secretaria   de   Comércio   Exterior_ ,   SECEX).   SECEX   records   all   legally-­‐registered   firms   in   Brazil with   at   least   one   export   transaction   in   a   given   year.   We   match   the   SECEX   firm-­‐level   exporter status   data   to   our   RAIS   worker   data   by   the   firm’s   tax   identification   code   to   identify   workers at   exporting   and   non-­‐exporting   firms.   We   define   an   indicator   variable   equal   to   one   if   a worker   holds   a   job   at   a   firm   with   a   positive   dollar   value   of   free-­‐on-­‐
|•<br>Using these teachers’ numbers, estimate the number of teacher positions needed in future to|maintain the target PTR|
|---|---|
|•<br>Calculate how many more or fewer teachers are needed per calculated required teacher posi<br>of 2020|tions versus the current number of teachers as|
|Step5. Estimate teacher replacement needs due to teacher attrition in EAP MICs<br>||
|•<br>Use the indicator “teacher attrition rate from primary education, both sexes (%)” to estimat<br>annually, from World Bank EdStats or data obtained from countries<sup>7</sup>|e how many teachers will need to be replaced|
|Step6. Estimate total teacher recruitment needs in EAP MICs<br>•<br>Estimate total teacher recruitment needs by considering the increase/decrease in teacher pos<br>in the year 2030 (a target year of SDG4)|itions and teacher replacement due to attrition|
|Step7. Conduct robustness checks under different scenarios<br>•<br>Conduct robustness checks under two scenarios:<br>`o`<br>i)**High 2020 Teacher Stock Scenario**“more teachers who are already in teachin<br>`o`<br>ii)**Low 2020 Teacher Stock Scenario**“fewer teachers who are already in teachin<br>in 2030”|g workforce in 2020 remain teaching in 2030”<br>g workforce in 2020 remain in teaching force|
|**B. Robustness check**||
|**Indicators**<br>**Base Scenario**<br>**High 2020 Teacher Stock**<br>**Scenario – lower attrition**|**Low 2020 Teacher Stock**<br>**Scenario – higher attrition and**<br>**higher PTR
advanced economies compensates for the cyclical effect by reacting in the opposite direction, reducing GDP by 0.65 percentage point. This leaves the net increase in GDP of 0.35 percentage point for advanced economies. These estimates suggest that, when faced with a commodity price shock of the same magnitude, the overall change in GDP can be more than three times bigger in EMDEs than in advanced economies solely because of the fiscal policy reaction.</mark> The above approach is applied to the commodity price boom of 2003–08 to illustrate the role of fiscal policy. During this period, commodity export prices increased about 76 percent for the EMDEs and 66 percent for the advanced economies in the sample. The analysis estimates the direct effect of the commodity price shock on output (the “rain” component) by applying the 2003-2008 cumulative price shock to the estimated parameters. The results indicate that the total effect on output to be 5.4 percent for EMDEs and 4.6 percent for advanced economies—that is, a difference of 0.8 percentage point (Figure 5.B). The procyclical response of fiscal policy in EMDEs (“pours” component) in another 1.1 percentage points to growth, bringing EMDE growth to 6.5 percent over this period.

In contrast, fiscal policy in advanced economies reacted in a countercyclical fashion, subtracting about 3 percentage points from growth, bringing advanced-economy growth to 1.6 percent. In other words, of the 4.9 percentage points difference between total EMDE and advanced-economy growth in the sample, 4.1 percentage points (or 84 percent) can be explained by the responses of fiscal policy—procyclical in EMDEs and countercyclical in advanced economies.

> 17 The model is based on the Blanchard and Perotti (2002) identification method, employing quarterly data for GDP and government expenditure from the IMF’s International Financial Statistics database for the period 1990-2019. In computing the “pours” component, the value of the multiplier after four quarters is
>performs better|
|Ito & Sallee<br>(2018)|Japan|2001-2013|Characteristics of new<br>vehicles sold|Simulation|Attribute-based regulation<br>is an imperfect substitute<br>for flat policies with<br>compliance trading|
|Whitefoot &<br>Skerlos (2012)|United States|2006|Data on technology features<br>(e.g., cylinder deactivation)|Simulation|There is larger incentive to<br>produce light trucks than<br>to produce passenger cars,|

53
|Goyal and Howlett,<br>2019|No|By how much and how do internal and external evaluations<br>from ADB projects differ?|The results show that internal evaluations focused more on micro- and meso-level<br>characteristics, while external evaluations laid more emphasis on meso- and macro-level<br>constructs, such as dimensions of policy and the institutional environment in the<br>recipient country, or its level and rate of economic growth.|
|---|---|---|---|
|Guillaumont and<br>Laajaj, 2006|Yes (Table<br>A1.2 and<br>Text)|Does aid effectiveness increase when receiving countries face<br>exogenous shocks and does it decrease when the total amoun<br>of aid increases?|t<br>During economic vulnerability, as the total amount of aid increases, project effectiveness<br>decreases, likely due to absorptive capacity limitations.|
|Hanson and Sigman,<br>2019|Yes (Table<br>A1.2 and<br>Text)|In what types of institutional environments are international<br>development projects most likely to succeed?|Using a new measure of state capacity with continuous coverage from 1960-2015 from<br>Hanson and Sigman (2013), the authors find that projects are most likely to succeed<br>where state capacity is relatively high, regardless of regime type or Freedom House<br>scores. In turn, successful World Bank projects can have a positive impact on state<br>capacity.|
|Hernandez, 2017|No|To investigate whether World Bank conditionality in DPLs is<br>affected by the presence of ‘‘new” donors (such as China) by<br>using panel data for 54 African countries over the 1980–2013<br>period.|Empirical results indicate that the World Bank delivers loans with significantly fewer<
13 The highly differentiated attitude towards privatization is manifested in serious disproportions in the demographic composition of the population living in privatized and non-privatized housing (see Fig. 1.1 below). In the privatized sector, 33.7% of the dwelling space is held by pensioners, and 44.2%, by households with children. In municipal or enterprise-owned housing stock (that is, in the non-privatized sector), the share of pensioner households is comparatively low (10.5%), while the proportion of households with children is very high (68.5%). However, in the housing market, the ratio of these two categories is essentially different. Sociological surveys (Guzanova, 1994) show that families with children are more active on the market given a greater urgency of their housing problem, a better financial status, and readiness to pay for better-quality housing.

**Fig. 1.1.** Types of households living in privately owned (i) and non-privatized (ii) housing Moscow, 1994.

<!-- Start of picture text --> 33.7% 68.5%<br>M Pensioners<br>/-^10-105%<br>* Adults without<br>children<br>22.1 . l 44.2% l l l t 21.0% D Familieswith<br>children<br><!-- End of picture text --> Source: The Moscow Longitudinal Household Survey, December 1994.

_c._ **_Households' value orientations_** and their willingness to acquire their own housing influence intentions vis-a-vis privatization, and these, in turn, essentially depend on the level of education attained by members of the household. The Moscow survey demonstrates that the higher the level of education, the greater the inclination to privatize one's dwelling. The privatization rate among respondents with a graduate academic degree was 53%; among those with a university education, 45%; and
studies in the hydrological literature have begun to show that groundwater can be a major and potentially long‐term contributor to contamination of surface water (e.g. Yu et al. 2018; Delsman et al., 2015; De Louw et al., 2010; Holman et al., 2008). The interactions between river water and</mark>

> 4 https://www.esa‐landcover‐cci.org/.

> 5 <mark>We</mark> aggregate land cover categories 10 through 30 to a single cropland class. This is also consistent with the <mark>widely‐ used global vegetation classification scheme of the</mark> International Geosphere Biosphere Programme (IGBP). 6 The surface irrigation data set is available here:

> http://www.fao.org/nr/water/aquastat/irrigationmap/index10.stm.

5
(Kramer, 2020).

# **2.1 On the Persistence of Polygyny in SSA: A Brief Review of the Literature**

Given that the sex ratio in SSA has been near unity for decades (Jacoby, 1995), the reason why so many countries in this region still oppose the abolition of polygyny has been a matter of considerable debate. On the one hand, the literature has advanced our understanding of the forces driving its disappearance in the Western world (Gould et al., 2008), while also contributing evidence of its causal effect on multidimensional poverty in the developing world (Tertilt, 2005; Smith-Greenaway and Trinitapoli, 2014; Wagner and Rieger, 2015; Arthi and Fenske, 2018). On the other hand, there is no consensus in this literature on why this marriage institution persists in Sub-Saharan Africa. For example, Jacoby (1995) argues that polygyny persists in SSA because women’s comparative advantage in food production increases their value to men as husbands and household heads (Jacoby, 1995). However, Fenske (2015) provides evidence that this cultural practice is least common in those parts of Sub-Saharan Africa where women have historically been most important in agriculture. This lack of consensus suggests that the socioeconomic forces contributing to the persistence of polygyny in SSA are still not well understood.

Of course, we know from Anderson (2007) that polygyny in SSA correlates with the bride price custom whereby marriage is only sanctioned after the payment of transfer in cash or in-kind from the groom or his family to the bride’s family. However, this association is non-linear, as shown in Figure 1 drawn using data from Giuliano and Nunn (2018). Bride price in Figure 1 is measured as the fraction of a population’s ancestors who practiced bride price traditionally. Polygyny is measured as the proportion of the population living in polygynous unions.

7