rafmacalaba/data-use-mentions-extended huggingface dataset:
|0.000|
|Contacts a local political party official (1=Yes)|1,923<br>,|0.22|0.38|0.000|
|Index of formal authority contact|1,923|-0.10|0.34|0.000|
|Contacts a religious leader (1=Yes)|1,923|0.66|0.49|0.000|
|Contacts a traditional ruler (1=Yes)|1,923|0.45|0.32|0.000|
|Index of informal authoritycontact<br>|1,921<br>|0.12<br>|-0.30<br>|0.000<br>|

~~Note: Data come from follow-up survey. Male refers to head of household while female to their~~ spouse. P-values were calculated from a regressions of each variable on a gender dummy. The regression included village fixed effects and standard errors were clustered at the geographical cluster level.
2006. The survey was requested by the Ministry of Planning and designed by the World Bank. Data collection was coordinated by the University of Burundi. The CWIQ Survey Technology has been applied in different African countries for the purpose of generating standardized indicators of poverty and welfare. Developed by the World Bank

> 9For example, in their evaluation of Progresa in Mexico, Gertler et al (2012) estimated that for each peso transferred, households consumed 74 cents and invested the rest, increasing long-term consumption by only 1.6 cents.

> 10“Questionnaire des Indicateurs de Base du Bien-être” or “Core Welfare Indicator Questionnaire”.

9
. Temperature also varies by remote sensing product, with one product reporting a maximum temperature of 23 degrees Celsius while another reports the maximum temperature that day as 27 degrees Celsius.

In this paper, we quantify the significance and magnitude of the effect of measurement error in remote sensing data from each of the above sources. We test this by modeling the relationship between weather and smallholder agricultural productivity, as measured through nationallyrepresentative, panel household surveys. Besides being a topic of research itself, agricultural production is often used to proxy for a variety of economic outcomes, including economic growth (Deschˆene and Greenstone, 2007), intra-household bargaining power (Corno et al., 2020), and migration (Jayachandran, 2006). We combine nine geospatial weather data sets (six precipitation, three temperature) with the geo-referenced household survey data from six Sub-Saharan African countries that are being supported by the World Bank Living Standards Measurement Study – Integrated Surveys on Agriculture (LSMS-ISA) initiative. The objective is to provide systematic evidence on mismeasurement in remote sensing data due to methods used to obfuscate exact household coordinates, metrics used to quantify the weather, and remote sensing data source.

Our goal is to provide guidance to researchers looking to use remote sensing weather data in economic applications regarding what data sources and weather metrics have strong predictive power over a large set of contexts along with which are only useful in highly specific settings.

2
there are even cases of further losses (Jack et al. 2023; Gambi and de Witte 2023). During the school closures, online tutoring programs were shown to reduce learning loss significantly in several randomized controlled trials (see, for example, Carlana and La Ferrara 2021; Gortazar et al. 2022). A large-scale randomized trial testing low-technology interventions – SMS messages and phone calls – with parents to support their child in Botswana improved learning by 0.12 standard deviation (Angrist et al. 2022). Even low-cost, low- technology interventions can recover learning losses in some cases (Angrist et al. 2023), but more intense instruction will be needed in others (Crawfurd et al. 2023). High dosage tutoring was shown to be effective even before the pandemic; online tutoring variants are just as effective and much less costly (Guryan and Ludwig 2023).

# **Comparisons**

There are only two other international comparisons using global achievement data, both using PIRLS (Jakubowski et al. 2023; Kennedy and Strietholt 2023). For PIRLS, losses are about a year’s worth of learning. But using PISA, we find seven months’ worth of losses. This could be because more time has passed since the pandemic: PIRLS compared results over time to 2021, while PISA looked at results up to 2022. Also, PISA students are much older: 15 years compared to an average age of 10 for PIRLS. Studies investigating student self-regulation skills have shown that in general older students are better able to seek assistance, set goals, plan, monitor, and organize their learning. Hence, it may be that younger students and boys may be more strongly impacted by the school closures compared to older students (Schuurman et al. 2023).

In addition to the global studies, there are several national studies. Review of such studies find an average loss of 0.10 to 0.19 SD (Betthäuser et al. 2023; Di Pietro 2023; Donnelly and Patrinos
society. Given the growing trend of globalization and increased migrant movements, these links need to be better understood, particularly in major migration corridors.

India has been the largest country of origin for international migrants since the turn of the 21<sup>st</sup> century (IOM 2019), as well as being the world’s top recipient of remittances (World Bank 2019). <mark>Migration to industrialized countries from India began to increase with the liberalization of immigration laws in those countries during the 1960s. Since the 1980s, most of this migration has been permanent and comprised of highly skilled and well-educated migrants, with return migration being short-term in nature (Jain, 2013). Furthermore, migrants to industrialized countries are richer and well educated compared to the short-term migrants to the Gulf and Saudi Arabia (Rajan and Amuthan, forthcoming). With the increase in oil prices in the mid-1970s, temporary m</mark> igrant flows surged from India to the Gulf Cooperation Council (GCC) countries: Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and the United Arab Emirates <mark>(Kurian 1979).</mark> Migration to the GCC was partly driven by severe shortage of skills and large-scale dependence on skilled and unskilled foreign labor in these countries.<sup>2</sup> In 2015, 7.8 million Indian migrants out of a total of 11.4 million, or about 68 percent, were destined for the GCC (Chanda and Gupta 2018).

Many of the Indian migrants in the GCC are from the South Indian state of Kerala, although there has been an increase in the number of low-skilled workers from northern states in recent years. Kerala has been exposed to international trade for thousands of years,<sup>3</sup> but emigration on such a large scale is a relatively recent phenomenon. The first Kerala Migration Survey reported that nearly 1.4 million migrants from Kerala were in GCC countries in 1998 and that
much higher USD 60,056 for the firms in HICs. The latter is 2.4 times the former, and the difference between the two is statistically significant at the 1 percent level.

In the regressions that follow, we use the log of labor productivity defined at the firm-level as our dependent variable as it helps to minimize the impact of outliers and extreme values. The productivity gap or the mean difference in labor productivity between UMICs and HICs can be computed in two ways. First, it equals the mean value of the log of labor productivity of firms in the UMICs minus the same for the HICs. This is the productivity gap in log points. Second, we can compute the percentage difference in the mean level of labor productivity (in USD levels). This is obtained as the exponential of the log difference obtained in the first step minus one and multiplied by 100 to convert to percentage figures. Unless stated otherwise, all productivity gaps in percentage terms are as described here.

The mean value of the log of labor productivity equals 10.96 (log points) in HICs and 10.1 (log points) in UMICs. Thus, labor productivity in the UMICs is approximately 57.5 percent lower than in the HICs. This productivity gap is significant at the 1 percent level. Results are qualitatively similar when looking at the median level of labor productivity.

Apart from labor productivity, there are other significant differences between firms in UMICs and the HICs (see Table 2). One important difference relates to firm-size as measured by the log of the number of workers at the firm at the end of the last fiscal year ( _Employment_ ). Firms in UMICs tend to be relatively large as compared to firms in the HICs. The mean difference in employment level (in logs) equals 0.169, implying that employment in firms located in UMICs is almost 18% higher than in firms in the HICs; the difference is statistically significant at the 1 percent level. In terms of the sectoral
Ethiopia conducted twelve rounds of HFPS between April 2020 and June 2021. Round-on-round attrition was high due to (i) enumerator and respondent fatigue, (ii) challenges with network connectivity, particularly in rural Ethiopia; and (iii) a conflict that erupted in the North of Ethiopia and that prevented calls among households in the Tigray regions after the seventh round. Round seven, which included a SWIFT module, was conducted between October 19 and November 10, 2020, and interviewed 2,534 households (715 rural and 1,819 urban). HFPS round seven (HFPS7) data is the main data set used for poverty and inequality analysis and profiling of the poor.

The sample of the HFPS7, similar to all other rounds of the Ethiopia HFPS, is subject to both sampling bias and non-responses bias. The selection of phone owners from the ESS4 reference survey leads to sampling bias, and the random digit dial process as part of the HFPS4 further leads to nonresponse bias. Therefore, by construction, this subsample is subject to both types of biases.

# <u>Results</u>

In this Ethiopia example, the poverty estimate from the reference sample is 23.5 percent, and the poverty rate among the subsample of phone owners without any weight adjustment is 16.6 percent (Figure 5). The difference of approximately seven percentage points between the actual rate of the reference survey and that of the phone survey is not statistically significant at the five percent level.

Like previous experiments, three types of weights were calculated to adjust for the sampling bias in the subsample of ESS4, namely, PSW (Lee), PSW (Inverse), and PSW & non-PSW. The first subfigure in Figure 5 compares the poverty rates of the phone owner sample by applying the abovementioned weights to the consumption data. The poverty rates among the biased HFPS subsample applying original weights, PSW (Lee) weights, PSW (Inverse) weights, and PSW & non-PSW weights are 16.6%, 22.
We assume a flexible exchange rate with fixed foreign savings. This is a common specification in real trade models. It is assumed that any changes in the trade balance are determined by macroeconomic forces working mostly in asset markets which are not included in the model.

# **4. The Data**

The South Africa SAM is based on 2001 data including:

- SSA IO 1971-1993

- SSA SUT 1993-1998

- SAM 1998

- SARB published and unpublished data 1970-2001

- SSA industry censuses and surveys

- 1970-1996 population census

- OHS 1994-1999

- LFS 2000-2002

- HH Income and expenditure survey 2000

- McGregor BFA 1970-2001

- ASSA 2000 Demographic model

- RSA Standardized Industry Database developed by Quantec.<sup>11</sup>

The SAM consists of 49 commodities at industry level as well as 49 activities. The government produces six of the 49 activities. There are four factors of production, capital, high-skilled, semiskilled and unskilled labor. The households are divided into the 10 income deciles. Due to the magnitude of the 10<sup>th</sup> decile it is further divided into 95 percent, 96.25 percent, 97.5 percent and 98.75 percent.<sup>12</sup> Elasticities used in the model are from Gibson(2003), Van Heerden and Van der Merwe (1997), the CGE model of Lewis (2001) and Thurlow and Van Seventer (2002).

From the data in the SAM, we compute effective tax rates. These are the tax rates that change when we evaluate tax reforms. In South Africa, there are indirect taxes levied on either products or production and direct taxes levied on households and firms. Indirect taxes on products include value added tax, fuel levies, excise duties, and tariffs, while indirect on production would include payroll taxes.

# **5. Simulations and Results**

First, we evaluate the current tax structure. We remove the VAT and consider the following revenue neutral tax changes: (1) a
0 C **o** co O A m m **_C_ 0** =1 R w **0** OQ **0 0** a

**.**

|Table B6. Informal Ownership<br>from Informal Sector Surveys conducted by the author|<25 hours per<br>e<br>week<br>Taxicabs|45 hours<br>per week<br>Taxicabs<br>|Entire<br>Sample<br>Taxicabs<br>|Entire<br>Sample<br>Kiosks<br>|Entire<br>Sample<br>Stre<br>Markets<br>|Entire<br>Sample<br>et Services &<br>Vendors<br>|
|---|---|---|---|---|---|---|
|Number of Observations (actual number may vary depending<sup>on the</sup>|99|101|308|245|137|80|
|question)|||||||
|Officially Registered?|||||||
|Yes|20.2|74.0|44.6|100.0|33.6|51.3|
|Taxicab License Numbers|||||||
|Personal|71.7<br>|75.0<br>|74.8<br>|na|na|na<br>|
|Organization|28.3|25.0|25.3|na|na|na|
|Taxicab License Numbers|||||||
|Ulaanbaatar|96.9|99.0<br>|97.7<br>|na|na|na|
|Other|3.1|1.0|2.3|na|na|na
a logistic regression. The characteristics included in the logit response probability module are presented in Table 3.2 and were selected due to observed bias in the interviewed sample along these dimensions. Full results of the logit model are provided in Appendix Table 3.1. The predicted probability of response from the model parameters was obtained for each household the inverse of which was the basis for the bias adjustment factor. However, in order to prevent extreme weights due to the correction factor, the predicted probabilities were sorted into deciles and the mean inverse response probability within each decile taken as the final weight adjustment factor.

In Nigeria, the bias adjustment was performed using the calibration approach whereby the weights were adjusted (minimizing the distance between the original and calibrated weights) to achieve the same weighted estimates across selected characteristics in the GHS-Panel sample. Table 3.2 contains the list of characteristics that were used in the calibration model. These characteristics were selected for inclusion in the calibration due to observed bias in the interviewed sample across these dimensions. Adjusting the weights to match the same profile of characteristics obtained from the fully representative GHS-Panel sample will counteract the bias along these dimensions. However, one drawback to the calibration approach is that it can be difficult to consider a large set of characteristics and achieve convergence in the calibration model.

8
paper reviews a set of household surveys, focusing on World Bank Living Standards Measurement Study (LSMS) surveys, to show their potential in providing insights on multiple transport-related questions. It then discusses the limits of such surveys and suggest a standard set of guidelines to design and harmonize transport related questions to improve future household surveys. The paper first reviews all World Bank LSMS surveys conducted since 2010 to list and categorize all transport-related questions. LSMS surveys include transport-related questions in multiple modules, which makes it difficult to have a comprehensive view of all transport-related questions for the whole survey collection. In addition, there is no consistency in the inclusion of transport-related questions in LSMS surveys across countries and over time. A main contribution of the paper is to list all transport-related questions in LSMS surveys for further work and categorize them. The paper also reviews non-LSMS surveys for comparison. The paper then shows how recent efforts to harmonize survey questions on consumption patterns and ownership of durable goods can bring interesting insights on expenditures across as well as within countries. Patterns differ significantly across countries based on their level of development, and 2
|**Table 1: Summary Statistics**<br>Observations<br>2,707,562|
|---|
|Share of men<br>47.8%|
|Average age<br>36.0|
|Share of married individuals<br>46.7%|
|Average years of education<br>9.6|
|_Size of locality (share of individuals)_|
|More than 100,000 inhabitants<br>60.4%|
|Between 15,000-99,999 inhabitan<br>11.7%|
|Between 2,500-14,999 inhabitant<br>12.0%|
|Less than 2,500 inhabitants<br>15.9%|
|Weekly hours<br>43.1|
|Hourly wage<br>32.5|
|_Labor force composition_|
|Employed<br>61.8%|
|Unemployed<br>2.7%|
|Out of labor force<br>35.5%|
|_Employed status_|
|Employee<br>69.8%|
|Employer<br>5.0%|
|Self-employed<br>20.0%|
|Unpaid worker<br>5.1%|
|_Type of displacement_|
|Plant Closing<br>8.4%|
|Being Fired<br>5.5%|
|Quitting<br>25.2%|
|Closed Own Business<br>2.7%|
|Retired<br>2.6%|
|Other<br>2.2%|
|Non displaced workers<br>53.3%|
|_Measures of informality status_|
|Formal Employment<br>47.5%|
|With access to health<br>41.5%|
|With a secondary job<br>4.2%|

Source: Own calculations based on ENOE 2005-2019 There
and experience dummies, and their interaction. The variance in sector-occupation is the variance of the log real hourly wage that is explained by a full set of sector-occupation dummies. Similarly, the variance in sector-occupation-formal status is the variance of the log real hourly wage that is explained by a full set of sector-occupation-formal status dummies. Variable definitions are provided in supplementary online appendix S1. Cells contain the share of the total variance explained by each decomposition element (shown in percent). The total variance explained by each decomposition element is given in parentheses.

# **6. Wage Inequality among the Formal and Informal Workforce**

Historically, Latin America and other low- and middle-income regions have been characterized by high levels of informality (Perry et al. 2007). In 2015, 45 percent of total employment in Latin America was informal (figure 7, panel a).<sup>14</sup> However, the 2000s were a period of important growth in the number of formal jobs. Between 2002 and 2015, the share of formal employees in total employment increased from 47 to 55 percent. This resulted from a reduction in the share of self-employment, from 24 to 20 percent, and in the share of informal

> 14The first part of this section uses harmonized household survey data across countries in Latin America. Workers are formal employees in the household survey if they indicated that they have access to a pension through their job. This paper alludes to informal workers when it refers to two groups of workers: self-employed and wage workers without access to a pension (informal employees). The information to differentiate between formal and informal employees is only consistently available for a handful of countries during 1995-2002. Hence, the analysis in this section is carried out only for 2002-2015. The countries included in this section are Argentina, Bolivia, Brazil, Chile, Costa Rica, Mexico, Nicaragua, Peru, Paraguay and El Salvador.

17
**Figure 6:  Consumption and Growth in Mean Consumption by Percentiles/Quintiles, 1995-2002**

<!-- Start of picture text --> 1339 The Pen's parade:<br>7.1<br>consumption by percentile<br>1130<br>6.9 1994<br>953 1998<br>6.7<br>804 2002<br>6.5<br>679<br>572 6.3<br>483 6.1<br>407 5.9<br>344 5.7<br>290 5.5 percentile<br>0 10 20 30 40 50 60 70 80 90 100<br>PLN / month / eq. adult<br><!-- End of picture text --> <!-- Start of picture text --> Consumption growth for selected<br> quintiles<br>1<br>5%<br>2<br>4% 5<br>3%<br>2%<br>`<br>1%<br>0%<br>-1%<br>-2%<br>-3%<br>1995 1996 1997 1998 1999 2000 2001 2002<br><!-- End of picture text --> _Source:_ Staff calculations based on GUS data.

**Figure 7: Growth Incidence Curve**

<!-- Start of picture text --> Growth incidence: consumption<br> increase by percentile<br>15%<br>10%<br>5%<br>0%<br>-5%<br>1998/1994<br>-10% 2001/1994<br>2001/1998<br>-15%<br>0 10 20 30 40 50 60 70 80 90 100<br>Note : The 2002/1994 change is
Table A.7 – Treatment effects, accounting for attrition (women’s empowerment and wellbeing)

||||KL bounds|+/- .25 SD|Behag|hel et al. bo|unds|
|---|---|---|---|---|---|---|---|
||(1)<br>Main<br>specifcation|(2)<br>IPW|(3)<br>Lower<br>bound|(4)<br>Upper<br>bound|(5)<br>Using<br>relative date|(6)<br>Using inten|(7)<br>sive tracking|
||||||Exact<br>trimming|Lower<br>bound|Upper<br>bound|
|**Women agency index**|-0.045|-0.036|-0.089**|-0.011|-0.047|-0.094**|-0.026|
|**Women fnancial index**|(0.038)<br>0.408***|(0.039)<br>0.414***|(0.035)<br>0.338***|(0.035)<br>0.478***|(0.038)<br>0.438***|(0.037)<br>0.141***|(0.038)<br>0.448***|
||(0.088)|(0.091)|(0.080)|(0.080)|(0.093)|(0.044)|(0.093)|
|
. This is true across specifications and datasets. For other countries the results are different whether we look at COMTRADE or SIPRI data. Using COMTRADE data I find arms imports from China to be particularly responsive to giant discoveries, while arms from France and Germany react strongly to discoveries but only in times of high oil prices. SIPRI data on the other hand suggest that it is Russian arms that react most to giant discoveries. Overall these results suggest that different discovery countries might import arms from different countries.

In Table 6 I look at how different types of discoveries affect arms imports. I focus on three dimensions, i.e. oil vs gas, onshore vs offshore, and by field distance to the nearest border. The top panel shows results when I include separate dummies for oil and gas discoveries 20
on prescription and treatment outcomes from patients at the clinic, as well as patients’ true malaria status. Our main analysis uses data from three sources.

**In-Clinic Survey.** On clinic observation days, enumerators approached all acutely ill patients at clinic intake and requested consent to participate in the study. We classified a patient as “acutely ill” if they were feeling sick and exhibited any of the following symptoms: fever, chills, excessive sweating, nausea, vomiting, diarrhea, poor appetite, headache, cough, weak-

20