rafmacalaba/data-use-mentions-extended huggingface dataset:
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# **Appendix Simulation with Regional Poverty Rates Backwards from 2001 Data**

The analysis presented thus far involves simulating changes in poverty forwards from the 1991/92 survey data. Although Datt and Walker do not discuss the possibility in their work, it is also possible to project changes _backwards_ from the 2000/01 data. In terms of the notation used in Section 4, this involves recursive calculations for household consumption and the weights as follows: and For Tanzania, the backwards analysis has one advantage over the forward analysis. Because the 2000/01 HBS is representative at the regional level, regional poverty rates going back in time can be simulated. The earlier HBS is only representative at the stratum level, so it cannot be used to produce region-level estimates.

Year-by-year simulated headcount rates at the regional level are shown in Appendix Table A9, and headcount trajectories are plotted for a sample of regions in A1. The table also shows the standard errors on the 2001 estimates, which are calculated from the actual survey data. Overall, most but not all regions show a pattern mirroring that of changes in the national poverty rate: increases in poverty in the first part of the decade followed by declines in recent years.

These regional poverty rates should be interpreted with great care. In addition to the caveats already mentioned, the standard errors on the regional predictions are large because the regional sample sizes are small. As discussed in the previous section, growth estimates for some regions in certain years (in particular 1999 and 2000) appear to have been obtained by very rough approximations based on national growth rates. In Mtwara, simulated poverty incidence drops at an implausible rate, from 64% in 1997 to 40% in 1999. As there is no baseline with which to compare the results of the regional predictions, it is very difficult to assess their accuracy. There is one region for which this can be done, Dar es Salaam. The backwards simulation implies that the headcount rate for Dar was 10.0% in
volatility. The basic methodology used was to quantify the spread of minimum and maximum prices and the coefficient of variation (standard deviation divided by average prices for each year).

The purpose of the spatial variation analysis was to determine if prices within Poland showed any significant variation between regions. This has implications from a risk management standpoint as large variations between regions would suggest that there are variations in the basis risk that individual firms might encounter. Measuring the spatial variation in prices also gives insight into the level of integration of the Polish wheat market. If the market is operating efficiently, there should be a relatively stable price relationship between regions in Poland. The general method of looking at spatial variation was to observe minimum and maximum prices for each month throughout the 49 voivodships of Poland and to create a ratio of maximum versus minimum prices.

# 2.1. Variation over Time

As is shown in Table 1, there is a great deal of variation observed over time in wheat prices in Poland. Prices tend to be very volatile especially in years of shortages when supply of wheat is considerably lower compared with average years. As can be seen in the Polish wheat balance table in Table 3, the 1992/93 and 1994/95 production years were characterized by low production due to drought and low ending inventories. These years correspond to greater than normal price volatility.

" As noted later in the paper, LIFFE wheat futures contract prices are influenced by the EU's agricultural policies (CAP) for grains.

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<u>Table 1: Precision of Aid Locations</u>

|Precision|code|Percentage of locations|
|---|---|---|
|Precision|1: specifc place|40.9|
|Precision|2: within 25 km of specifc place|2.4|
|Precision|3: municipality (ADM1)|25.7|
|Precision|4: province (ADM2)|19.7|
|Precision|5: imprecise|1.9|
|Precision|6: country-wide projects|4.4|
|Precision|7: unclear|0|
|Precision|8: state or national capitals|5|

_Source_ : Authors’ summary based on AidData, World Bank geocoded research release, version 1.3. _Note_ : The sample is composed of 1,900 projects in Africa.

province). They do _not_ refer to imprecisely georeferenced point locations. Precision 5 locations are imprecisely geocoded, so only approximate coordinates are reported. Precision 7 locations are “unclear” in the sense that it was only possible to identify the country in which the project is located. Finally, precision 8 locations correspond to capital cities (both national and local) and also include projects aimed at government institutions (such as a ministry or the central bank). The distribution of location precision categories is reported in table 1.

Most projects are implemented across several locations (around 40 on average), often belonging to different precision categories. Consider, for example, a project that aims to build a road connecting two towns in two different provinces. In this case, at least four locations are assigned to the project: the two towns as precision 1 locations and the two provinces as precision 4 locations, plus any province crossed by the road as an additional precision 4 location, and any town crossed by the road as an additional precision 1 location. For the purpose of the analysis, only locations of precision 1 or 2 are
derived from the standard normal cumulative density function , with estimated expectation and variance of h and h respectively. ΦΦ (. ) We apply this method primarily to derive aggregate income thresholds that separate the XX ββ̂ XX θθ<sup>�</sup> We apply this method primarily to derive aggregate income thresholds that separate the following three groups: the poor and vulnerable, the aspiring middle class, and the middle and upper class.<sup>12</sup> Note that the grouping in this scenario differs considerably from the classification in the previous scenario. Let us call the threshold between the first two groups in this scenario the Chaudhuri vulnerability line, and the line dividing the last two groups the middle class line. The Chaudhuri vulnerability line is the average predicted per capita income of the group of households that have a certain probability of being poor in the future, 10 percent in our application. Building on this, the middle class line is the average predicted income of households with a certain probability of being vulnerable, again 10 percent in our application. Households are then classified into the three groups according to these two lines.

# 2.4. Relative mobility

limitation of such absolute mobility analysis is that what happens once households move comfortably beyond such thresholds is out of our analytical scope. Analyzing relative mobility across the five quintiles of the income distribution enables us to take a broader view and to consider movements along the entire income spectrum. Quintile thresholds are determined based on the cross-sectional income distribution in a given year, so they differ from year to year. The synthetic panel is then employed to estimate the probability that a household has made a particular transition from one quintile to another over adjacent survey rounds. We obtain a 5 x 5 transition matrix once estimates are aggregated.

For the point estimates of relative mobility, we apply the method as described above, substituting the quintile thresholds for the poverty lines in the bivariate standard normal cumulative distribution function above. For the bounds, we take a different approach
Economies included in the Global Findex survey and database

||||**Data collection**<br><br>||**Design**<br>|**Margin**<br>|**Mode of**<br>||**Over-**<br>||
|---|---|---|---|---|---|---|---|---|---|---|
|**Economy**|**Region**<sup>**a**</sup>|**Income group**|**period**<br>|**Interviews**|**effect**<sup>**b**</sup>|**of error**<sup>**c**</sup>|**interviewing**|**Languages**|**sample**<sup>**d**</sup>|**Exclusions and other sampling details**|
|Malaysia|EAP|Upper middle|Jul 4–Aug 4|1,000|1.38|3.6|Face to face|Bahasa Malay,<br>Chinese,<br>English|||
|Mali|SSA|Low|Oct 23–Nov 4|1,000|1.26|3.5|Face to face|French,<br>Bambara||The sample excludes the northern part of<br>the country because of inaccessibility and<br>nomadic population. The excluded area<br>represents approximately 10% of the total<br>adult population.|
|Malta|n.a.|High|Apr 7–Apr 18|1,004|1.27|
reweighting for unit non-response- to weight up those who respond with amounts so that they represent themselves and the other respondents who responded in the bracket in which the amount reported falls. This improves on single imputation methods (such as mid-point or even hotdeck) because it allows for the uncertainty in the true value, which other single imputation methods do not.

# Imputing the income distribution from consumption data

Household surveys in many LMICs collect only consumption data, since income data is assumed to be less reliable, particularly for measuring poverty. Chancel et al. (2023) attempt to estimate income inequality levels and trends in Africa between 1990 and 2010 for countries covering 60% of the population (and 80%-90% of the population from 1995-2010). But because of the complete lack of income data or harmonized and publicly available consumption microdata for most of these countries, Chancel et al. (2023) used the World Bank’s Povcalnet consumption data on shares of consumption by decile and then data for five countries where both income and consumption microdata were available was used to impute the income distribution in the rest of the countries. This lack of income data implies that estimating the income distribution in many African countries, let alone the top end of this distribution, is impossible with the data that currently exists and is publicly available. As we discuss below, there are other African countries with surveys that asked about income and consumption that were not used by Chancel et al. (2023). We also note that limited income data collection is not prevalent in all LMICs - Latin America has long had income data collected in household surveys (Lustig, 2020).

Using survey data and parametric distributions estimated from survey data Imputation for item non-response and reweighting for unit non-response are specific solutions for specific problems. A more general solution to the more general problem of missing top income recipients (whether due to sparsity, measurement error, non-random unit non-response etc.) is to replace the incomes at the top of the distribution with values
# 1. Introduction

For many developing countries, boosting agricultural productivity and incomes of small food producers are key policy objectives in the effort to alleviate poverty and improve food security. The international community has also embraced those goals, making them prominent targets in the Sustainable Development Goals (SDG) monitoring agenda under SDG 2, Zero Hunger.<sup>1</sup> High-quality, reliable farm output and input data are indispensable both for measuring progress towards the SDGs and for effective policy and program design and analysis.

Household and farm surveys are the most important source of agricultural data, rivalled in some countries by administrative data. In all measurement, survey-based or otherwise, there is some amount of measurement error. In surveys, the necessary reliance on imperfect respondent recall is a common source of error. Measurement in agricultural surveys in low-income countries is particularly difficult because agricultural operations are complex and seasonally variable, respondents are often illiterate, familiarity with standard units of measures is the exception rather than the norm, and a host of other factors. When data are of poor quality, and particularly when they are affected by systematic biases, policies based on that information may turn out to be less effective, if not misguided.

Methodological research shows that survey design matters and that conscious efforts to adopt improved design and survey administration choices can contribute significantly to minimizing measurement error, including recall error,<sup>2</sup> and improve the quality of agricultural survey data in developing countries (Carletto et al., 2015). This paper contributes to these efforts by focusing on the length of the recall period and how it affects the reliability of agricultural survey data. The length of the recall period is thought to be associated with recall decay, in which survey respondents forget details of events or forget events entirely, leading to reporting inaccuracies (Sudman and Bradburn, 1973). In agricultural surveys, recall length also often differs across the various activities recorded because farming activities, such as planting, plot maintenance, or harvest,
- d. Has a business facebook page/account

- e. Has a business twitter profile

- f. Has a business instagram profile

- g. Has a business snapchat account

- h. Has a business Tumblr account

- i. Has a business Whatsapp account

- j. Has a business Youtube account

- k. Has a business Pinterest account

**Overall business practices index of all forty-one business practices** , averaging the binary outcomes, so that this aggregate index ranges between 0 and 1.

Note that the baseline survey conducted during the induction workshop had a different set of questions on business practices than the more detailed follow-up survey. These questions were used to construct a marketing index, accounting index, and HR index, as well as an overall score out of 10. Ancova estimation of the treatment effects on business practices will control for this baseline score out of 10, except for the marketing and sales practices index above, which will control for the baseline marketing measure; the accounting and finance index above, which will control for the baseline accounting index; and the HR and operations index above, which will control for the baseline HR index. An advantage of Ancova estimation is that it only controls for these proxy measures to the extent that they are correlated with outcomes in the follow-up survey.

# **Table 5:**

**Inverse hyperbolic Sine of Total sales in the last month** , in Naira, winsorized at the 99<sup>th</sup> percentile. For firms not answering the exact value, the midpoint of the sales range they give, for those answering sales are in range above 10,000,000, sales are coded as the median of firms with sales in this range, or 12,000,000 if no other firms are in this range.

**Inverse hyperbolic Sine of Total sales in the last year** , in Naira, winsorized at the 99<sup>th</sup> percentile. For firms not answering
spanning intermittent years from 1995 to 2011 from GTAP and employment data from ILO.<sup>6</sup> The input-output tables in the GTAP data set allow one to exploit a form of social accounting data—a variation on the social accounting matrix (SAM) where incomes are shown in the rows of the SAM and expenditures are shown in the columns (see Hertel (2013) and McDougal (2001)). The structure of the underlying social accounting data provides a comprehensive and consistent record of national income accounting relationships between different sectors, including intermediate and final demand linkages. These are used to construct country-specific measures of the contribution of labor to the value added contained in a given country’s domestic production and exports.<sup>7</sup> The resulting data set covers 24 sectors (6 services sectors, 3 primary sectors and 15 manufacturing sectors), over 100 countries, and intermittent years between 1995 and 2011. As such, the data allow to examine the labor content of South Africa’s exports at the aggregate and sectoral levels, including exports associated to trade traditionally governed within GVCs.

Specifically, in order to obtain these labor value added measures, two intermediate multiplier matrixes need to be calculated from the social accounting and other aggregate data. The first is the Leontief inverse matrix, which measures the inputs contained in a unit of final output, and includes both direct and indirect inputs

> 6 GTAP represents a massive combined effort of international institutions and universities. Over time, the data set has grown to include more countries and more sectors. The latest version, GTAP 9, has data on 129 countries/regions and 57 sectors (Narayanan 2012). To maintain backward compatibility, Calì et al. (2016) start with the 1997 structure of regions and sectors, and carry this forward in aggregation of more recent iterations of the data set. The GTAP website also provides extensive documentation on the underlying data structure, its sources, and the GTAP model structure for each release
1996. “Sea surface temperature fields associated with West African rainfall anomaly types.” _Journal of Climate,_ 9 (11), pp. 2935-2940.

- HESTON, A. R. SUMMERS and B. ATEN (2010). Penn World Table Version 7.0, Center for International Comparisons of Production, Income and Prices at the University of Pennsylvania: <u>http://pwt.econ.upenn.edu/php_site/pwt_index.php</u> (accessed May, 2011).

- INTERNATIONAL COTTON ADVISORY COMMITTEE Database, Washington D.C. <u>www.icac.org/</u> (accessed May, 2011).

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; Bui et al., 2014; Thomas et al., 2010). More work, however, is needed to understand household‐level income effects of more subtle variation in rainfall and temperature conditions and gradual changes to this variation.

A growing number of studies in a variety of contexts investigates how observed weather conditions affect economic outcomes, mainly in an attempt to quantify the potential economic impacts of future climate change (Auffhammer et al., 2013; Dell et al., 2014).<sup>2</sup> Yet a quantification of climate change impacts remains challenging even when the current income effects of weather variation are understood. Firstly, how weather conditions will alter due to climate change is highly uncertain and even existing Global Circulation Models (GCMs) bring diverging forecasts for some regions and countries. Moreover, income 1 In line with existing literature, this paper defines weather variation to describe shorter‐run temporal variation (variation between and within years), while climate is used for longer‐term variability of conditions and changes decades (Dell et al., 2014).

2 Many of these studies are based on cross‐country analyses comparing country income and production and weather conditions at various points in time (e.g. Burke et al., 2015, 2011; Dell et al., 2012; Hsiang, 2010; Schlenker and Lobell, 2010). Another strand of the literature focuses on the impacts on agricultural profits mostly using US data at the county level (Deschênes and Greenstone, 2012, 2007; Fisher et al., 2012; Mendelsohn et al., 1994; Schlenker et al., 2006; Schlenker and Roberts, 2009). Other studies use weather variables to explain household income in order to test income effects on other variables (Feng et al., 2010; Hidalgo et al., 2010; Yang and Choi, 2007). Interesting new studies estimate current weather impacts on incomes and living conditions (Baez et al., 2015; Noack
instruments for FDPs as they would allow to track the same individuals over time, understand their livelihood and residency trajectories, and understand how FDPs take decisions. However, classic panel surveys typically rely on home addresses and they are particularly difficult to administer when populations are highly mobile. This has encouraged scholars working on FDPs to develop new instruments to track people over time. Etang and Hoogeveen (2020) developed a survey known as the “Listening to Displaced People Survey (LDPS)”, a survey that tracked living conditions of displaced people over time in Mali with a face-to-face baseline survey complemented by monthly follow-up mobile phone interviews for a period of 12 months. These data have been used by Hoogeveen, Rossi and Sansone (2019) to study patterns of return of the displaced and understand the factors that contribute to return.

Phone interviews have also increased in popularity with the COVID-19 pandemic, which made it necessary to conduct interviews without face-to-face contact. During this period, the UNHCR and World Bank have launched bi-monthly monitoring surveys of the impact of COVID-19 on the well-being of refugees in several countries across the MENA, SSA and Latin America regions using phone interviews. These resulted in panel surveys that now offer the possibility to assess the impact of COVID-19 on refugees over time and across countries in a comparable manner. Vintar et al. (forthcoming), for example, provide an example of how to use these data to understand the differential labor impacts of COVID-19 on refugees and nonrefugees (see also the report “Answering the Call: Forcibly Displaced During the Pandemic”<sup>i</sup> ). UNCHR’s proGres database includes phone numbers for refugee family heads that can be utilized as a sampling frame. However, data privacy concerns need to be addressed if the phone survey is conducted by a firm. A possible solution is sending text messages to selected respondents asking for permission to share phone numbers with a contractor.

Comparisons between FDPs and host populations are also an essential exercise to conduct in the context of FDP poverty
‐year. This very stringent specification is possible since our variable of interest, ������� varies by country‐sector‐year and only variation in the data from this perspective will be picked up in the regressions. Last, ����� is the residual. Regressions are estimated with standard error clustered by country‐sector‐year. Finally, for all regressions we take the years 2006‐2013, which at first sight may be affected by the global financial crisis but we are not too much concerned about this since extremely restricted time fixed effects are applied, in combination with our country and sector dimension. However, in subsequent robustness checks we also correct for later time periods post‐2008.

Lastly, equation (3) could also control for other policy influences. In previous works such correction was comprised of a variable that measures the within industry tariff and input‐tariff reductions, the latter following similar strategy of multiplying input‐output coefficients as in our case of input services. Furthermore, another control variable present in previous works is one that measures, over time, the

> 16 This is different compared to Fernandes and Paunov (2012) who use firm‐specific services inputs coefficients. Although further endogeneity issues may be solved by using input‐output tables from the first year in our analysis, i.e. 2008, these were unfortunately not available. Similarly, one could also think of applying completely exogenous input‐output tables from, for instance, the US. Two problems may actually arise with such approach. One, the US economic structure may be entirely different compared to some of the EU economies which are still categorized as upper‐middle income countries, which in particularly may affect their services input reliance for each sector. Second, reclassification schedules from the US system of sector classification, namely NAICS, to the European NACE scheme may result in an imprecise consistency across the two schedules. All former studies take country‐specific input‐output tables.

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