rafmacalaba/data-use-mentions-extended huggingface dataset:
al., 2015; Ceballos et al., 2020). Acceptable survey length varies depending on the topic, the country context, whether there is a previous relationship with the respondents, and on the respondents themselves. Glazerman et al. (2020) recommend interviews should not exceed 20 minutes while Mathur (2020a) find that the ideal survey length in that context was roughly 10–15 minutes. The LSMS-supported HFPS were designed to be around 20 minutes on average, though there was some variation across rounds and between countries, some of which averaged around 30 minutes (Gourlay et al., 2021). Survey designers will need to pilot questionnaires to determine what survey length is feasible without jeopardizing data quality and nonresponse.

In addition to length, questionnaire complexity is a limiting factor in phone surveys, which is thought to increase response fatigue and, in turn, affect data quality and attrition. Excessively complex questions should therefore be avoided in surveys of any mode. However, key concepts in agricultural statistics are inherently complex. Some complexity is necessary for survey questionnaires to reflect this adequately as oversimplification comes at a loss of information that can reduce the value of the data. In in-person surveys, interviewers rely on visual aids to make difficult questions more palatable. This is not possible with phone surveys.<sup>7</sup> Given these limitations in terms of length and complexity, certain topics of agricultural data cannot be collected over the phone. There are, however, some experiences with simplifying questionnaires to make

> 7 An alternative to be explored for the future may be the use of smartphone applications that provide visual aids and other survey support. An early example, Daum et al. (2019), used a pictorial smart-phone app to collect time use and nutrition data in rural Zambia.

14
An important issue during the current pandemic has been the relationship between Covid-19 and global value chains, and particularly whether GVCs absorb or transmit Covid-19 shocks (Baldwin and Tomiura, 2020; Javorcik, 2020; Miroudot, 2020). We find that while GVC participation increased an exporter’s vulnerability to foreign shocks, it reduced vulnerability to domestic shocks. The disruption of production in input source countries more adversely affected export growth in sectors that relied more strongly on imported inputs from these source countries. Similarly, a disruption of production in an exporter’s partner countries more adversely affected its export growth in sectors with high shares of imported inputs. But the negative impact of a disruption in domestic production in exporting countries themselves was mitigated by a sector’s higher reliance on imported inputs in export production.

We then perform two robustness tests and extend our analysis in two directions. First, we use growth in industrial production rather than changes in mobility as a measure of the Covid-19 shocks and find that all findings remain unaltered. Second, we estimate the regressions for the sub-periods February to April 2020 and April to June 2020, to separate the two phases of the first wave of Covid-19. As intuition would dictate, we find that the mitigating effects of remote labor mattered more during the more severe lockdown of the first three months. Similarly, during these early months exports were also most affected by shocks in upstream countries. Third, we include China in the sample of exporters. All results are confirmed except that we now find that supply shocks in third countries provided opportunites to expand exports. As a final extension, we use mirror export data, to expand the set of exporters to 64 developed and developing countries. While we find that remoteness plays a smaller role in explaining differences in trade patterns across sectors, perhaps due to the weaker diffusion of internet in developing economies, all results continue to hold.

Our study relates to the growing body of literature on the economic effects of Covid-19 (Baldwin and Weder di Mauro, 2020)
GVCs by strengthening IO linkages and fostering business interactions.

Second, this paper connects to emerging research on firm IO linkages and their effects on sourcing decisions. Most studies have relied on industry IO tables to measure these linkages. Amiti and Koning (2007), using Indonesia’s industry IO table, demonstrate that lowering upstream input tariffs leads downstream firms to increase imports and boost productivity. Similarly, Javorcik (2004), drawing on Lithuania’s IO data, find that downstream FDI generates significant positive spillovers for upstream domestic firms. More recent work, leverages granular data to trace firm-to-firm connections. Amiti and Weinstein (2011) use a unique dataset to link Japanese firms to their banks to study the effects of the health of banks providing trade finance and growth in a firm’s exports relative to its domestic sales. Kee (2015) identifies firm IO linkages between downstream exporters and local suppliers by matching names and addresses. The study reveals significant positive horizontal spillovers, known as sharedsupplier spillovers, when both FDI and domestic firms rely on common local input suppliers. AlfaroUreña et al. (2022) match tax IDs of firms in Costa Rica and show that domestic firms experience significant performance gains when they become suppliers to FDI firms. Alfaro et al. (2024) match shipment-level import data with the U.S. credit register to obtain information on importer-supplier relations at the firm level and credit relationships at the bank-firm level. Arnarson et al. (2024) analyze labor division along the supply chain for generating scale economies and productivity growth, by linking buyers and suppliers in Denmark and Sweden. Finally, a closely related study by Bernard et al. (2019) examines how HSR promotes domestic outsourcing among Japanese firms, using a detailed dataset of supplier-customer links across 800,000 firms. Complementing existing approaches, this paper introduces a straightforward method to capture firm-level IO linkages by connecting exporters to potential domestic suppliers. Moreover, the simple two-sector model provides an easy theoretical treatment of firm IO linkages through relative input prices.

This
<!-- Start of picture text --> Slovenia DASHBOARD Formal enploynent:$37.34K<br>Total domestic SOEs: $93<br>Presence of SOEs Economic role in the market (domestic SOEs only)<br>aDsa a7 Domesticformal employmentSOEs share(%)in Domestic(%SOEs GDP)revenues<br>SOE density Presence of Siovenia SOEs - ~ —_<br>(SOEs per 100k inhabitants) in 42 countries ‘ = ia as<br>0 me oe aaa 40m " =<br>ws TA, , ex 27<br>U ye<br>? J<br>Corporate Governance (Domestic SOEs only) By sector (contestability degree) Domestic SOEs<br>Number Domestic SOEs % SOEs corporatized Distribution of domestic SOEs by Distribution of SOE employment<br>om, I contestability contestability<br>B Dwecty Owned MM inarecty Owned —Crcate Stover Latvia Evtoras i& CompotitiveContestapie [Natural Monopoly 1© CompetitiveContestapie Ml Natural Monopoly<br><!-- End of picture text --> <!-- Start of picture text --> SOEs financial performance<br>% of SOEs operating"<with profittoss<br>1 SOEs operating with profits<br>B® MissingSOEs operatingprofit datawith losses<br>=<br>Domestic SOE profitoss<br>(SOE reporting data)<br>g 1,000,000<br>| 500,000<br>2 SSS<br>Profit O68 Neo’<br><!-- End of picture text -->

<!-- Start of picture text --> SOEs by government participationa<br>Distribution of SOEs byWe government participation<br>-<br>BB Majonty-owned (>50%) I Minority-owned 25-50% [i Minority-owned 10-25% fl Missing<br><!-- End of picture text --> <!-- Start of picture text --> Municipal vs
. and Antoine, P. (2013). The power of the interviewer: a qualitative perspective on African survey data collection. Demographic research, 28 (27). 763-792.

Reese, S.D., Danielson, W. A., Shoemaker, P.J., Chang, T. and Hsu, H. (1986). Ethnicityof-Interviewer effects among Mexican-Americans and Anglos. Public Opinion Quarterly, vol. 50 (4), 563–572 Schaeffer, N. C. (1980). Evaluating race-of-interviewer effects in a national survey. Sociological Methods and Research. 8: 400-419.

Schaeffer N.C., Dykema, J., Maynard, D.W. (2010). Interviewers and interviewing. In Handbook of Survey Research, Ed. Marsden, P.V. and Wright, J.D., pp. 437–70. Bingley, UK: Emerald Group Schuman H. and Converse J.M. (1971). The effects of black and white interviewers on black responses in 1968. Public Opinion Quarterly, vol. 35. 44–68 Serneels, P. M., Beegle, K. G., and Dillon, A. S. (2016). Do returns to education depend on how and who you ask? Policy Research Working Paper Series 7747, World Bank.

23
<!-- Start of picture text --> Figure 3: Expected Learning‐Adjusted Years of School<br>Expected Learning-Adjusted Years of School<br>SGP<br>CUBSRB RUSHRVKAZLVAPOLHUNPRTESTLTUCYPSVNCZEISRNZLESPJPNITAKORFRAFINGBRCANAUSTWNBELDNKDEUSWEISLAUTNLDHKGUSACHEIRLNOR MAC<br>VNM UKRMNGCHNBGRMUSCHLGRCSYCSVKMLTBHR ARE LUX<br>BDI MWI COMTGOHTIZWEBENKIRTLSSLBNPLLSOCMRVUTKENTJKBGDKHMTUVKGZGHAZMBCOGPSEHNDNICMDATONINDMMRLAOGTMGUYPHLMARJAMSWZSLVPRYARMXKXGEOJOREGYTUNIDNECUBIHLKANAMALBPERZAFCOLDZALBNBRAMKDDOMVENGABTHAAZECRIMNEBWAMEXIRNARGTURURYPANROUMYSTTOOMNSAUKWT QAT<br>SLEMOZCODMDGETHGINGMBBFAAFGUGAYEMTZASEN CIVPNGSDNSYRPAKNGA AGO IRQ<br>RWA<br>MRT<br>NER MLITCD<br>LBR SSD<br>6 8 10 12<br>Log Real GDP Per Capita at PPP<br>Note: Dataset version 21 Sept 2018<br>12<br>10<br>Years 8<br>6<br>4<br>2<br><!-- End of picture text --> Notes: Learning‐adjusted years of school are measured as expected years of school (top panel of Figure 2) multiplied by the ratio of each country’s harmonized test score (bottom panel of Figure 2) to a benchmark score of 625, corresponding to the threshold of advanced attainment set by TIMSS. Real GDP per capita adjusted for differences in purchasing power parity is taken from the Penn World Tables (Feenstra et. al. (2015)) , with missing countries filled using data from World Bank estimates of GDP at PPP. Graph shows the most recent data for all countries.

12
such judgments invite controversy and compromise the credibility of the whole program. When all is said and done the results are often disappointing. As the program lacks an explicit equalization standard, its generosity is overwhelming for its Northern Territory , Tasmania and South Australia and the program is punitive for Victoria and New South Wales. The program, however is not equitable and grant allocations vary directly with most macro fiscal capacity indicators (see Shah, 2004, for a detailed critique of the Australian Program and suggestions for simplification). The Indian formula is less complex but uses arbitrary factors and weights. Curiously enough all recent commissions have insisted on using 1971 state population figures for calculation of grant shares. The rationale presented is that India adopted a population control policy in that year. This is not defensible as the state population has experienced major changes due to migration.

# **6. Concluding Remarks**

Comparing institutional arrangements across different countries is a daunting task. The success of these arrangements depends upon a multitude of factors. The success of 15
the precision of these estimates is increased when the gaps in time that need to be bridged are reduced (i.e. when the frequency of NSS surveys is increased) -- as the assumptions underlying the imputation methods come under increasing pressure when the most recent household consumption survey becomes increasingly outdated.

# **References**

- Abraham, Rosa and Shrivastava, Anand (2019). How comparable are indias labour market surveys. Report. Centre for Sustainable Employment.

- Atamanov, Aziz, Lakner, Christoph, Mahler, Daniel Gerszon, Tetteh Baah, Samuel Kofi and Yang, Judy (2020). The effect of new ppp estimates on global poverty. Global Poverty Monitoring Technical Note 12. The World Bank.

- Bank, World (2018). Piecing together the poverty puzzle. Poverty and Shared Prosperity Report: 2018. World Bank.

- Bank, World (2020). Reversal of fortunes. Poverty and Shared Prosperity Report: 2020. World Bank.

- Basole, Amit, Abraham, Rosa, Lahoti, Rahul, Kesar, Surbhi, Jha, Mrinalini, Nath, Paaritosh, Kapoor, Radhicka, Mandela, Nelson, Shrivastava, Anand, Dasgupta, Zico, Gupta, Gaurav and Narayanan, Rajendran (2021). State of working india 2021: one year of covid-19. Report. Centre for Sustainable Employment, Azim Premji University.

- Beegle, Kathleen, De Weerdt, Joachim, Friedman, Jed and Gibson, John (2012). Methods of household consumption measurement through surveys: Experimental results from tanzania. _Journal of Development Economics_ , **98,** number 1, 3–18.

- Beyer, Robert CM, Franco-Bedoya, Sebastian and Galdo, Virgilio (2021). Examining the economic impact of covid-19 in india through daily electricity consumption and nighttime light intensity. _World Development_ , **140,** 1–13.

- Bhalla, Surjit, Bhasin, Karan and Virmani, Arvind (2022). Poverty, inequality, and growth in india:
**25.7**|31.5|
|Paved road traffic|AADT|1,341.1|**944.9**|3,797.7|
|Unpaved road traffic|AADT|38.5|**30.8**|74.7|
|Paved network condition*|% in good or fair condition|86.2|**61.0**|82.0|
|Unpaved network condition*|% in good or fair condition|55.8|**37**|57.6|
|Perceived transport quality|% firms identifying roads as major business<br>constraint|27.6|**27.4**|18.2|
|Overengineering|% of main road network paved relatively to low traffic|29.6|**19.3**|18.4|
|Underengineering|% of main road network paved relatively to high<br>traffic|13.5|**0**|20|

_Source_ : Gwilliam and others 2008. Derived from the AICD national database (www.infrastructureafrica.org/aicd/tools/data). _Note:_ *2009 data (AL).GIS = geographic information system; AADT = average annual daily traffic.

Good connectivity has been matched by a good selection of road standards in Senegal. Decisions related to whether a road should be paved or not seem to be rooted in observed traffic patterns. In fact, contrary to most African countries, there is no indication of underengineering of roads in Senegal and not even 20 percent of the roads can be classified as overengineered (this is well below the road overengineering levels observed across African countries and cited as typical examples of fiscal mismanagement) (figure 5).<sup>1</sup>

> 1 Overengineered roads are paved although traffic levels are below 300 vehicles per day—the threshold level that justifies paving
as a result!<sup>9</sup> The responses provided by Ulaanbaatar's kiosks, taxicabs, boot repairmen, petty traders, and others suggest that Mongolian informals, generally, do not consider themselves to be employed, and are therefore not counted as employed in official statistics. Only 26% of taxicab drivers report to the _duureg/soum_ that they are "employed."<sup>2 0</sup> Among kciosk operators, only 16% consider themselves employed, and among the informals populating the markets and streets of the city, the numbers are generally smaller yet - on average only 13% consider themselves to be employed?' Thus, roughly 75-85% of Ulaanbaatar's informals are not reflected as employed in the official statistics.

24. The household survey of income and expenditure, conducted by the SSO, adds further support to the contention that most informals do not consider themselves to be employed, and therefore are not reflected as such in statistics?<sup>2</sup> (This household survey will be discussed in greater detail in the next section.) Although the household survey is mainly concerned with the respondents' household budget, it also includes several control variables, such as the type of organization for which the household head works, and the number of employed and unemployed people within each household. As displayed in Box 1, participation in the informal sector is correlated with perceptions of unemployment. Not surprisingly, households in which the head is "unemployed" receive 55% of their monetary income from informal sources, whereas households in which the head is employed receive an average of only 30% of income from informal sources.
Table E7: Share of Significant Temperature Coefficients in Ethiopia

||MERRA-2|ERA5|CPC|
|---|---|---|---|
|Mean Daily Temperature|0.67|0.75|0.50|
|Median Daily Temperature|0.67|0.58|0.50|
|Variance of Daily Temperature|0.33|0.58|0.50|
|Skew of Daily Temperature|0.58|0.25|0.67|
|Growing Degree Days (GDD)|0.17|0.08|0.33|
|Deviation in GDD|0.08|0.25|0.33|
|Z-Score of GDD|0.17|0.25|0.33|
|Maximum Daily Temperature|0.33|0.42|0.42|
|Observations|96|96|96|

_Note:_ The table presents the share of statistically significant temperature coefficients in Ethiopia.

Table E8: Share of Significant Temperature Coefficients in Malawi

||MERRA-2|ERA5|CPC|
|---|---|---|---|
|Mean Daily Temperature|0.92|0.83|0.67|
|Median Daily Temperature|0.75|0.83|0.83|
|Variance of Daily Temperature|0.50|0.42|0.58|
|Skew of Daily Temperature|0.17|0.25|0.67|
|Growing Degree Days (GDD)|0.83|0.33|0.42|
|Deviation in GDD|0.58|0.42|0.33|
|Z-Score of GDD|0.58|0.58|0.42|
|Maximum Daily Temperature|0.58|0.67|0.67|
|Observations|96|96|
Spanish fish market.<sup>15</sup> Price data are daily for 2006 and 2007 and weekly during the period 2001-2005. Probably because the calamari is frozen and it can be stored, these prices do not vary significantly within a week and sometimes remain constant even within a month.<sup>16</sup> In addition, boat owners and their wives were surveyed in 2005 and 2007. The survey included questions on a wide variety of topics, such as socio-demographic characteristics, other sources of income, inventory of assets, income shocks, and savings along with a module about income targets.

We also collected data on the determinants of catches, including daily weather data from the closest Indian Meteorological Department station in Tuticorin, a town located approximately 20 kilometers away from the closest village, as depicted in Figure 2.

Finally, we collected data on the price of kerosene. Time series data on local prices are not available and so we rely on the real daily price at which kerosene is traded in Singapore, the closest international fuel market.<sup>17</sup> Table 1 depicts boat owners’ descriptive statistics using 2007 survey data. On average, boat owners are 42 years old, have approximately 5.6 years of education, and live with four other household members. Since all are married, one of their family members is their wife. In most cases, the other relatives are children, but few boat owners have extended family members living with them, such as in-laws, parents, or siblings. In all households, the boat owner’s earnings are the main source of income. However, there is usually another family member who works as well, typically their spouse, selling the fish in the local fish market that their husbands are unable to sell to merchants.

Boat owners’ daily gross average earnings are INR 1,076. Since the survey includes a few questions about the amount of their daily expenses on kerosene and laborers’ wages, we can
3-kilometer buffer thus captures 90.1 percent of cropland gains over the period (119 million hectares). We then proceed to a similar 2- stage approach for our assessment of the drivers of cropland loss. In the first stage, we run a regression similar to specification (1) where  Yi is now a dummy variable equal to 1 if pixel _i_ (in country _c_ ) transitioned into cropland between 2003 and 2018 and is equal to 0 if the pixel remained non-cropped. We consider the same set of explanatory variables Xik as before. The country-fixed effects are then recovered and regressed on the same national-level characteristics specified in (2).

# **Samples and descriptive statistics**

For the first stage regression of cropland losses, we consider the universe of 48.3 million pixels that were initially under cropland in 2003. Appendix Table A3.1 shows global summary statistics for the five explanatory variables associated with these pixels, including distance to a city, a coast, and a river, the number of drought months over the studied period, and land suitability for agriculture. The figures in the table show the existence of large global variations for these variables. For the first stage regression of cropland gains, we consider the 30.3 million pixels located within 3 kilometers of a cropland pixel in 2003 but that were not cropland at the time. Appendix Table A3.1 also shows descriptive statistics for this sample for the same five explanatory variables. As expected, the latter sample is more “peripheral” and

> 7 - The Gini index is from the World Development Indicators (https://databank.worldbank.org/source/world <u>development-indicators). Cropland per capita is constructed using MCD12Q1 and population from the Word</u> Development Indicators. The Quality of land administration index ranges between 0 and 30 and measures the reliability, transparency, and coverage of land administration, as well as protection against land disputes and equal access to property rights.

9
At the household level, we again estimate that virtually all households should gain from WTO accession.

There are, however, differences in the distribution of gains among households, the standard deviation of the estimated gains is somewhat larger and the Gini coefficient slightly increases, rather than slightly decreases. As a result of the national accounts based reconciliation of the factor shares, several sectors are considerably more capital intensive, and among them are three key expanding export intensive sectors--ferrous metals, non-ferrous metals and chemicals. Consequently the return to capital expands considerably more than in our central model based on the household budget survey data. The percentage change in skilled wages, unskilled wages and return on capital in the **national accounts based** (central model) is **4.7%** (5.3%), **2.5%** (3.7%) and **4.4%** (1.8%), respectively. Then, since rich households depend more on capital income, we estimate slightly regressive results with the national accounts based model, as opposed to very slightly progressive results with our central model based on the household survey based data reconciliation.<sup>54</sup> We conclude that the aggregate results and the shape of the distribution of gains across all households are not significantly affected by the principal choice of data reconciliation. Although virtually all households gain due to the Dixit-Stiglitz variety effect, which households gain more or less, however, is affected by the choice of how we reconcile the data.

# **Piecemeal Sensitivity Analysis**

In table 5 of Rutherford and Tarr (2008) we present the impact on welfare of varying the value of key parameters. The results are extremely close to the results presented in table 3.4 so are not reproduced here. We have verified that in the household model as well, the same three parameters

> 54 Results, based on the national accounts model, for EV