# **3. Data and descriptive statistics**

Household data show that, although continued access to non-agricultural incomes, access to public social support and the scope to draw on informal safety nets provided short-term support, the war led to a dramatic drop in long-term perspectives. In the agriculture sector, continued high demand for renting in or buying land together with low willingness to sell land points to strong sectoral fundamentals. Size-biased access to credit that is reinforced by traditional agricultural subsidy policies may, however, limit access to working capital finance even for investments that would be advantageous in the short term and, together with a complete lack of long-term finance, may limit farmers’ ability to take advantage of opportunities for long term investment and diversification into higher value crops in due course.

# **3.1 Sample, household characteristics, coping strategies, and conflict effects**

To obtain the sample that could form the basis for a phone-based farm survey, we exclude conflict affected areas and define four farm size strata (< 50 ha, 50-120 ha, 120-500 ha, and > 500 ha) for a stratified random sample.<sup>14</sup> To assess representativeness, panel A of table 1 compares median and mean farm size between SAR and Form 29, the frame currently used by SSSU, in each stratum, pointing towards a near perfect match in the 50-500 ha category, no difference in the median for farms in the > 500 ha category (but better coverage of large farms in Form 29) and a much better coverage of small farms in SAR as compared to official statistics. A graphical representation of the sample’s geographic coverage in figure 1 shows it covers most of the rayons in areas not affected by conflict but coverage is scant in the East and the North.<sup>15</sup> At the farm level, the survey asked about total output and market participation as well as input costs for key inputs (hired labor; fertilizer and chemicals; machinery and fuel) by crop. If respondents could not provide information by crop, inputs were recorded at a more aggregate level. For owner-operated farms (82%), information on household composition, key income sources, and coping mechanisms is also included. As far as possible, questions were asked for before and after invasion or the 2022 and the 2021 cropping season. In tables 2-6, we present information by region (columns 2-6) and farm size group (columns 7-10).

Key data on household composition in table 2 suggest that Ukraine’s small and medium farm owners are about 50 years old, well educated, and linked to banks as well as the internet.<sup>16</sup> The head almost always has a bank account, higher than secondary education in 89% of cases, and a Facebook account in 67% of farms.

> 14 To allow use of the sample as a basis for evaluating the PSG cash grant, farms in the below 120 ha category are split equally into PSG applicants and non-applicants.

> 15 Appendix table 1 shows that, with less than 21% of the sampled farms resulting in a successful interview (66% in the first, 22% in the second, and 12% in the third attempt), non-response (45%) and refusal rates (34%) were high. As indicated there, large part of the non-responses can be attributed to war conditions with cellphones out of range or power the most frequent reason.

> 16 The high level of digital literacy might be accelerated by the shift towards digital service provision associated after the invasion. As opening a bank account is one of the most expeditious ways of obtaining an electronic signature for individuals who do not yet have one, the share of heads with a bank account may be biased upwards though this does not apply to other household members.

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