rafmacalaba/data-use-mentions-extended huggingface dataset:
profits. This type of contract more closely resembles a debt contract between the parties. A fully integrated solution may not draw the same distinction between bank and mobile service provider. In this case, the distribution of surplus depends on the relative bargaining power of the bank and mobile service provider. This type of contract more closely resembles an equity contract between the parties. Equity-like contracts are likely to be more complex and therefore more difficult to negotiate than debt-like contracts, thereby presenting a potential hurdle towards the goal of increasing access.

## **6.2 The Measurement of Savings**

In the 2010 FSD-Kenya survey, respondents were asked ―Do you save any portion of your income?‖ Those who responded affirmatively were asked ―Where do you save your money?‖ The options (not mutually exclusive) were M-PESA, MKESHO/PESA PAP/ KCB Connect, Bank account, SACCO account, ASCA, ROSCA, Microfinance Institution and Other. These responses reflect each respondent‘s subjective understanding of what it means to ―save your money‖ and whether the respondent‘s use of each service constituted savings.

In the analysis presented in the following section, we define an individual to have ―M-PESA savings‖ if he or she reports saving a portion of income and lists M-PESA as one of the places for savings. We also examine the use of bank-integrated mobile savings. Respondents are defined to have bank-integrated mobile savings if they report having used M-KESHO.

# **7 Analysis**

## **7.1 Who Uses Mobile Savings?**

Both savings with M-PESA and savings with M-KESHO require the individual to be registered for M-PESA. Table 3 presents a comparison of those registered with M-PESA to the overall population. Forty five percent of individuals in the sample report being registered with M- PESA.<sup>5</sup> In terms of gender, age, and geographic distribution, there are not large differences

> 5 This figure drawn from the survey conducted October-November 2010 is substantially lower
the person-plot labor data, one might expect weekly interviews to yield higher season-total estimates than end-ofseason interviews. As the direction of the bias runs counter to this explanation, forgetting does not drive our results. Instead, we propose that the hours discrepancy arises from reliance on flawed inferences in the recall interviews. Weekly data show that people do some agricultural work in an average 11 of the season’s 26 weeks and on 46 of the season’s roughly 182 days. Over such a long period, recall NPS and recall ALT respondents are unlikely to use recall-and-count strategies in reporting total days and total weeks, respectively. Thus, rather than attempting to count actual days and weeks worked, respondents may revert to shortcuts, such as inferring seasonwide work from, say, their average workload or their most recent, intense, or otherwise psychologically salient period of work. Such inference may drive the week (recall ALT) and day (recall NPS) estimates. For more granular time units, inference accomplishes this because of the design of the standard survey: for days (recall ALT) and hours (both recall modules), a memory-based recall-and-count strategy is precluded, since the question asks respondents to provide an approximate or typical number.

Whether inference results in an accurate approximation of the true number of hours worked may depend on the degree to which work takes place in regular, predictable, or uniform patterns. For the smallholders in our study, work schedules are both variable (that is, they are different from one week to another) and irregular (that is, there is no systematic or predictable pattern to the 13
performance. These students received the program in varying degrees, but every year for 3 years. Since we did not have student-level administrative data from the beginning, we first used schoollevel administrative data and propensity score matching to create a comparable group of schools. We then designed a pre-analysis plan using survey data we collected in all program and control schools, including on student performance on a test we designed. The survey data allowed us to build a second control group from within program schools. We consider the latter as our preferred control group because it addresses potential additional biases stemming from the differences in the quality of schools after the matching at the school level. We later used students’ high-school exit exam performance to replicate the results and control for student-level characteristics. Figure A2 in the appendix shows the steps of the study design timeline.

# 3.1. Data and Descriptive Statistics

We used three main data sets for the analysis in this paper. The first source is the Gambia’s Education Management Information System (EMIS) data as of 2014. EMIS is a systemwide data set collected by MoBSE annually and covers all schools from early childhood development (ECD) to senior secondary education across the country, both public and private. EMIS data aim at facilitating better education system planning and policy dialogue. We used the EMIS data as the main data source for information about school characteristics at the baseline.

The second data source is the 2014 and 2018 WASSCE data, obtained from the West African Examinations Council (WAEC). WASSCE is a type of standardized test in West Africa. It is administered by the WAEC. It is only offered to candidates residing in Anglophone West African countries (Nigeria, Ghana, Sierra Leone, Liberia and The Gambia). Admission to university education is based on student WASSCE exam scores. The WASSCE score is graded on a 9-point scale with 1 being the best score while 9 is a fail. A satisfactory grade locally known as “credit” is a score between 1 and 6, inclusive.
Table 4.4 suggests that among the households with mortgage debt, households with a foreign currency mortgage are less vulnerable to economic shocks than those which have a local currency mortgage. Households with an FX mortgage have higher income and are better educate. It is particularly reassuring that self-employed households seem to be less likely to take on a FX mortgage than households with wage earnings.

These findings partly confirm recent evidence by Fidurmuc et al. (2011) on intended borrowing in central Europe. They examine household survey data from the EURO Survey, conducted by the Austrian Central Bank (OeNB). This survey has been conducted in 10 countries of Eastern Europe on a half-yearly basis since 2007 and elicits information on household perceptions of economic conditions (e.g. exchange rates), their current financial portfolios and their intentions to borrow in the near future. They show that among the households which intend to take a new loan, those which are younger, better educated, have with savings in euro, and have remittance income are more likely to take a foreign currency loan. In contrast to the data presented above, however, they find that richer households are not more likely to demand FX loans.

# <u>Household Debt and Crisis Impact</u>

Does the fact that credit cards and mortgages are held by the least vulnerable households imply that debt has had a negligible effect on household consumption and investment in the crisis? The recently conducted LITS 2010 survey allows us to assess the impact of household debt on household consumption and investment. In this survey households were asked whether during the crisis they reduced their consumption of goods (food, luxury goods alcoholic drinks), cut the use of services (phone, utilities, health insurance) or sold off some of their assets. In Table 4.5 we relate the consumption and investment behavior of households in the crisis to their use of bank debt (Credit card, Mortgage). We hereby control for household income, education and employment type. The LITS survey
|16<br>34202|16<br>59863|

Source: International Urban Poverty Database.

Note: *** p < 0.01, ** p < 0.05, *** p < 0.1 . Robust standard errors are in parentheses. GHSPOP 1km is used for the DB methods. The dependent variable is the log of per capita consumption expenditures, expressed in PPP and not spatially deflated. Only urban households (“Core” or “Suburb”) are included in specifications (4) and (8). The baseline category is “Other rural” in all specifications, except (4) and (8), where it is “Suburb”. Baseline categories are the following: “Married” for the marital status of the household head, “No education” for the education of the household head, and “Employed in Agriculture” for the employment of the household head. Observations are weighted using population weights.

68
- vi. the Tanzania National Panel Survey (TZNPS) 2008/09, 2010/11, 2012/13, 2014/15, 2019/20, and 2020/21 rounds.

- vii. the Viet Nam Household Living Standards Survey (VHLSS) 2010, 2012, 2014, and 2016 rounds.

The sample sizes hover around 3,000 to 5,000 households in each survey round for the LSMSISA surveys (including Nigeria and Tanzania), 5,500 to 7,000 households for the BIHSs and the ESS, 9,300 households for the VHLSSs, and over 12,000 households for the Malawi IHS. The consumption data are deflated in the same survey year’s prices and are comparable across survey rounds for each country.<sup>11</sup> The objective is to produce the imputation-based welfare estimates of interest as if we did not have consumption data and then evaluate these imputation-based estimates against those based on the actual survey data (i.e., the “true” welfare rates).

For the poverty line, we use the national poverty lines for Ethiopia, Malawi, Tanzania, and Viet Nam and the international poverty lines of $1.90 (in 2011 Purchasing Power Parity (PPP) prices) for Bangladesh and Nigeria.<sup>12</sup> The extreme poverty line is defined as US$1.25 (2011 PPP) per day per capita for Bangladesh and Nigeria and half of the national poverty line for Viet Nam and Ethiopia. For Malawi and Tanzania, we use the national food poverty lines as the extreme poverty lines.

> 11 In particular, for Bangladesh, Tanzania, and Viet Nam, consumption data are deflated to <mark>2018/19 prices, 2020/21 prices, 2010 prices</mark> respectively <mark>. For the Malawi</mark> IHPSs and IHSs, c <mark>onsumption data are deflated to 2013 prices and 2010/11 prices r</mark
diaries can complement existing surveys for populations and output categories for which existing recallbased surveys may results in considerable measurement errors.

# **4.1 Comparing diary and recall-based production estimates**

To better appreciate the strengths and potential weaknesses of the diary, it is useful to compare the diary estimates to what has been obtained through recall at the household level via a standard production questionnaire. Table 5 reports the results from comparing the share of respondents who mention having obtained any output from a given crop using each method (columns 1 and 2), the value of that production (columns 3 and 4), and the number of entries in the diary this value corresponds to. We differentiate between cash crops, seasonally or continuously harvested food crops, and fruits and vegetables.

First, for cash crops we find striking differences between diary and recall both in terms of a crop being reported and the actual quantity of output recorded. With the exception of rice, reported quantities for cash crop output and, for coffee and cotton also the incidence of a crop being mentioned, are much lower for the diary than the recall. As this is unlikely to be due to the harvest period falling outside of the window covered by the diary, the most reasonable explanation is that, for this type of remunerative crops, recall may come relatively easy and that a diary, unless properly implemented, may result in underreporting if perceived as being associated with some form of taxation or income monitoring For coffee, 22 percent in the diary, _vis a vis_ 32 percent in the recall survey, mention having any output from the crop and for those who do, reported output is about two thirds lower than for those in the recall (i.e. $ 11 compared to $ 30). The difference in reporting output is more pronounced for cotton (also with a low frequency of harvesting). While sugarcane is mentioned by more households in the diary than the recall (and, with 6.6 entries per reporting household, at a
situation is potentially dire, especially in service sectors, where most women are employed in urban areas. Gender-related dynamics mainly drive these results. The significant pay gap and employment rate of women results in a lower contribution to sector value-added. Further, lower productivity than men also makes women a less favorable substitute for other production factors (i.e., male labor or capital). Eventually, women could become easily substitutable with other factors of production during the time of crisis.

However, women in rural areas will be less affected as they work mainly in agriculture. Female-owned farms other than those producing food crops will be affected less. The advantage of women in agriculture is not because their farms are more productive but mainly due to their low reliance on modern inputs. As imports are squeezed, inputs such as fertilizer and pesticides will become inaccessible. The latter will have a higher impact on male-owned farms, making women relatively better-off. But such an effect will not make women farmers better-off compared to baseline. The disadvantage of female-owned food crop farms is their higher labor intensity.

The COVID-19 pandemic has notably impacted the income of households, particularly female-headed households, who earn their main livelihood from enterprise income. Business closure due to COVID-19 and lack of customers (which could be partially attributed to the pandemic) are the most widely reported reasons both by male and female-headed households for loss in enterprise income. Results from the phone survey provide evidence that a disproportionate share of female-headed households has incurred losses from enterprise income. This is not unexpected. Although women own 57 percent of the non-farm enterprises, they make 77 percent less profit than male-owned enterprises. Female entrepreneurs, on average, have a lower level of education and have less access to electricity, running water, machinery, or telephone for their business.

Increases in food price and illness of income-earning members (both in rural and urban areas) and job loss and business closure (more in urban areas) are the most reported shocks by households. While both male and female-headed households
instruments simultaneously at the same time each day, crossing the equator around 1:30 PM local time. NASA’s EOS-Aura OMI science team at Goddard Space Flight Center retrieves, filters, and publishes the data in easily useable format, at daily frequency up through just a few days earlier, with informative illustrations, videos, and context. At the time of writing, easily accessible city-level OMI NO2 VCDs with visualizations were available below.

### **Data and Visualizations:**<sup>5</sup> `https://so2.gsfc.nasa.gov/no2/no2_index.html`

Finally, we note that while alternative NO2 VCDs from other satellites are available back to the mid-1990s, accumulating all into one analysis leads to technical inconsistencies due to differences in instruments. Thus, the fact that OMI has at this point consistently made readings using the same instrumentation for over 15 years is the crux of why a systematic global macroeconomic study like ours is just now possible. We provide additional information, and other NO2 satellite data past, present, and future, in Online Appendix A.1.

# **3 Data**

## **3.1 Basic Characteristics**

**Summary Statistics.** We begin with some simple summary statistics for the OMI NO2 data we utilize. Table 1 Panel A compares the data set, which is comprised of country average VCDs per area, to GDP as reported in v. 10.0 of the Penn World Tables. Also included is night lights per area, formed from data joining older DMSP-OLS sensors with newer VIIRS sensors.<sup>6</sup> It is apparent that the primary differences between NO2 and lights are that NO2 has an overall lesser average growth rate, and is more coarsely gridded. With respect to the latter point, as Donaldson and Storeygard (2016) note (p. 192), though there may be a tendency to equate resolution with information value, this may
# **Micro-Level Impacts of the War on Ukraine’s Agriculture Sector: Distinguishing Local and National Effects over Time**

# **1. Introduction**

The Russian Federation’s invasion of Ukraine in February 2022 started a war that has to date displaced more than 8 million individuals and destroyed the productive capacity and infrastructure of vast stretches of territory. As Ukraine has been a major agricultural producer of grains and oilseeds, it has also given rise to concerns about effects on global commodity prices and food security that are explored by a growing number of studies. Compared to these, war impacts on agricultural production and rural livelihoods in Ukraine have received less attention. This paper aims to help fill this gap. Doing so poses two types of challenges. First, the immediate effects of the conflict, largely in terms of whether specific areas were taken out of cultivation, often occur in places that are inaccessible due to ongoing military action. Second, damages to infrastructure or closure of port facilities due to the war may affect the profitability of all Ukrainian producers through changes in terms of trade or logistics costs.

To address the first issue, we use a 2019–23 panel of remotely sensed data on areas cultivated with winter and summer crops, aggregated to the village council (VC) level for the entire territory of Ukraine, including areas beyond Ukrainian control. This is combined with an indicator of conflict-induced damage to agricultural fields constructed specifically for this purpose, to assess the direct and indirect impacts of the conflict on the area cultivated with agricultural crops. To address the second issue, we draw on public data on prices for outputs and inputs as well as a 2021–23 farm-level panel of data on areas not directly affected by the conflict, which provides information on areas allocated to specific crops, yields, inputs, and profits.

For area under cultivation, economywide conflict effects predominated initially but, consistent with adaptation by farmers outside the immediate conflict area, their relative importance declined over time. Local effects assumed greater relative
of technology sophistication pre-pandemic: _Y_ ( _T, D_ ( _T_ )). Totally differentiating this relationship with respect to _T_ breaks down the total effect on sales from increasing the treatment into a direct impact from increasing _T_ while keeping the mediator _D_ constant, and an indirect effect through the change in _D_ : The adoption or increased use of digital technologies during the pandemic can be modelled as a function of technology pre-pandemic: The vector of firm characteristics _X_ includes dummies for the age of the business (0-5, 6-10, 11-15, 16+); dummies for size (5-19; 20-99; 100+); the z-score for the quality of management practices and the measures of human capital of the manager to control for managerial ability; a dummy for whether the business is an exporter; and a dummy for whether the business is foreign owned. To control for the severity of the shock, we include in _X_ the average mobility around transit stations the 30 days prior to the interview as reported in Google mobility reports ( _1,19_ ), in addition to the country-sector-exposure fixed effects _δcse_ . These fixed effects correspond to the interaction of the dummies for country (Brazil, Senegal, and Vietnam), sector (Agriculture, Manufacturing, and Services), and the measure of pandemic exposure described above using the SBPS-US (Low, High).

The shock to sales during the crisis varies linearly with both technology sophistication prepandemic and the dummy for whether the business increased the use of digital technology to respond to the shock (with no interaction between the two): Our identification is based on the assumptions that unobservables _u_ and _v_ are not correlated 12
as harmful as rainfall scarcity and cold spells. To capture this non‐ linearity, some studies break rainfall and temperature levels into different intervals or only measure them above or below a certain threshold (Deryugina and Hsiang, 2014; Deschênes and Greenstone, 2011; Schlenker and Lobell, 2010).<sup>3</sup> To control for abnormal values, some studies measure the deviation from the long‐term mean normalized for location‐specific variability (Baez et al., 2015; Hidalgo et al., 2010; Noack et al., 2015). Weather extremes measured by minimum and maximum temperature and rainfall levels matter too (Welch et al., 2010). Very importantly, all of these forms of weather variation can have different impacts in different places depending on location‐specific climate conditions (Park et al., 2015).

In order to define recommendations for poverty eradication in face of increasing climate risks in Vietnam, this paper attempts to disentangle the current impacts of weather variation on income changes. To better understand the income effects of different types of weather variation, the analyses differentiate between annual, seasonal, abnormal and extreme weather conditions and weather events related to rainfall and temperature. They also investigate how weather impacts vary by socioeconomic group, climate zones and income activities. By covering this breadth of impacts, this study adds to the existing literature showing that income effects depend on the type of weather variation and different contexts.

These analyses can build on two novel methodological aspects. First, this study combines data from the latest Vietnam Household Living Standard Surveys (VHLSS) 2010, 2012 and 2014 and gridded weather data

> 3 Related to this approach is the concept of degree days, which assumes a piecewise‐linear function in temperatures defines as the sum of degrees above a lower threshold and below an upper threshold intervals (Schlenker and Lobell, 2010; Schlenker and Roberts, 2009) .

> .

3
San Francisco Department of Health could request that people to smother coughs and sneezes, only when enough fatalities were registered were “San Franciscans...scared enough to accept drastic measures to control the epidemic” (p.95)—and ex post, “Fear had been the enforcer of the Board of Health’s policies.”(p. 108) not the authorities themselves. When schools in San Francisco were opened, many parents kept their children home out of continuing fear. This resonates with the reports from Wuhan today of the anemic rebounding of the small restaurant sector when restrictions were released.

Viewed through this lens, restrictions may often function more like a coordinating device among increasingly predisposed individuals than a repressive measure- if we are all working from home, then I will not be viewed badly if I do; whether schools are online or in person requires a decision that individual concerned parents cannot effect. This, in turn, raises the question of the whether the impact of lock-down measures per se and their subsequent removal is overstated.

# **II. Data**

**Mobility and Economic Activity:** Using data from the Maps application on smartphones, Google generates COVID-19 Community Mobility Reports<sup>8</sup> that use aggregated, anonymized data to construct an index of how visits and length of stay at different places change compared to a baseline.  They can then follow

> 8 <u>https://www.google.com/covid19/mobility/</u>

3