Impacts of COVID-19 on households in CAREC countries

Azhgaliyeva, Dina; Mishra, Ranjeeta; Trinh Quang Long; Morgan, Peter J. Working Paper Impacts of COVID-19 on households in CAREC countries ADBI Working Paper, No. 1298 Provided in Cooperation with: Asian Development Bank Institute (ADBI), Tokyo Suggested Citation: Azhgaliyeva, Dina; Mishra, Ranjeeta; Trinh Quang Long; Morgan, Peter J. (2022) : Impacts of COVID-19 on households in CAREC countries, ADBI Working Paper, No. 1298, Asian Development Bank Institute (ADBI), Tokyo This Version is available at: https://hdl.handle.net/10419/264158 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by-nc-nd/3.0/igo/ ADBI Working Paper Series IMPACTS OF COVID-19 ON HOUSEHOLDS IN CAREC COUNTRIES Dina Azhgaliyeva, Ranjeeta Mishra, Trinh Long, and Peter Morgan No. 1298 January 2022 Asian Development Bank Institute The Working Paper series is a continuation of the formerly named Discussion Paper series; the numbering of the papers continued without interruption or change. ADBI’s working papers reflect initial ideas on a topic and are posted online for discussion. Some working papers may develop into other forms of publication. Suggested citation: Azhgaliyeva, D., R. Mishra, T. Long, and P. Morgan. 2022. Impacts of COVID-19 on Households in CAREC Countries. ADBI Working Paper 1298. Tokyo: Asian Development Bank Institute. Available: https://www.adb.org/publications/impacts-of-covid-19-on- households-in-carec-countries Please contact the authors for information about this paper. Email: [email protected] Dina Azhgaliyeva is a research fellow, Ranjeeta Mishra is a project consultant, Trinh Long is a project consultant, and Peter Morgan is a senior consulting economist and vice chair of research, all at the Asian Development Bank Institute, Tokyo, Japan. The views expressed in this paper are the views of the author and do not necessarily reflect the views or policies of ADBI, ADB, its Board of Directors, or the governments they represent. ADBI does not guarantee the accuracy of the data included in this paper and accepts no responsibility for any consequences of their use. Terminology used may not necessarily be consistent with ADB official terms. Working papers are subject to formal revision and correction before they are finalized and considered published. Asian Development Bank Institute Kasumigaseki Building, 8th Floor 3-2-5 Kasumigaseki, Chiyoda-ku Tokyo 100-6008, Japan Tel: +81-3-3593-5500 Fax: +81-3-3593-5571 URL: www.adbi.org E-mail: [email protected] © 2022 Asian Development Bank Institute ADBI Working Paper 1298 D. Azhgaliyeva et al. Abstract The impacts of the COVID-19 outbreak have heavily affected CAREC member countries, which include Afghanistan, Azerbaijan, Georgia, Kazakhstan, the Kyrgyz Republic, Mongolia, Pakistan, the People’s Republic of China (PRC), Tajikistan, Turkmenistan, and Uzbekistan. The COVID-19 crisis and the resulting falls in demand and supply due both to uncertainty and policy measures such as lockdowns, “social distancing,” and travel restrictions are having a severe impact on CAREC member countries. In order to better understand these impacts, computer-assisted telephone (CATI) interviews of households were conducted in ten countries from the CAREC region (excluding the PRC). This paper estimates the impact of COVID-19 on income declines, expenditure changes, and financial difficulty in December 2020 compared with June 2020. Keywords: COVID-19, CAREC, Central Asia, household survey, household income, employment JEL Classification: D14, G51, H12, H84, I10, I24, J6 ADBI Working Paper 1298 D. Azhgaliyeva et al. Contents 1. INTRODUCTION ....................................................................................................... 1 2. LITERATURE REVIEW ............................................................................................. 1 3. SPREAD OF COVID-19 AND GOVERNMENT RESPONSES ................................... 2 3.1 COVID-19 in CAREC ..................................................................................... 2 3.2 Government Responses ................................................................................ 4 3.3 Financing COVID-19 Responses ................................................................... 6 4. ADBI HOUSEHOLD SURVEY IN CAREC COUNTRIES............................................ 9 5. COVID-19 IMPACTS AND COPING STRATEGIES ................................................. 10 5.1 Determinants of Having Income Decline Due to COVID-19 .......................... 10 5.2 Determinants of Having Expenditure Increase Due to the COVID-19 Pandemic .......................................................................... 15 5.3 Determinants of Having Financial Difficulty .................................................. 17 6. CONCLUDING REMARKS ...................................................................................... 20 ANNEXES A Databases of COVID-19 Policy Responses and Measures by International Organizations...................................................................... 22 B Income Groups ............................................................................................ 23 REFERENCES ................................................................................................................... 24 ADBI Working Paper 1298 D. Azhgaliyeva et al. 1 1. INTRODUCTION The impacts of the COVID-19 outbreak have heavily affected CAREC member countries, which include Afghanistan, Azerbaijan, Georgia, Kazakhstan, the Kyrgyz Republic, Mongolia, Pakistan, the People’s Republic of China (PRC), Tajikistan, Turkmenistan, and Uzbekistan. The crisis has caused falls in demand and supply due both to uncertainty and policy interventions such as lockdowns, “social distancing,” and travel restrictions, which are having a severe impact on CAREC countries. These negative impacts manifest through several channels, including: loss of employment or reduced working hours, loss of sales and income of the household business, restricted travel to work, increased need to stay at home to look after small children or sick household members, higher prices and/or lack of availability of staple items, etc. (Morgan and Trinh 2021). In order to develop appropriate policy responses, it is necessary to understand the current situation of households. As part of the Asian Development Bank’s overall strategy to deal with the current crisis, the Asian Development Bank Institute (ADBI) has been tasked with carrying out surveys of households to better understand the size, aspects, and incidence of impacts on vulnerable people. Assessing the magnitude of these challenges and deploying effective policy responses will play a critical role in determining the CAREC region’s potential to efficiently recover and proceed with economic development and regional integration, in accordance with the CAREC Strategy 2030. The main contribution of this paper is that it provides empirical evidence on the impact of the COVID-19 crisis on households in the CAREC region. Computer-assisted telephone (CATI) interviews were conducted in ten countries from the CAREC region, namely Afghanistan, Azerbaijan, Georgia, Kazakhstan, the Kyrgyz Republic, Mongolia, Pakistan, Tajikistan, Turkmenistan, and Uzbekistan. The PRC, which is also part of CAREC, was excluded from the household survey and analysis compiled in this study. Face-to-face surveys were impractical due to the lockdowns being implemented in response to the pandemic and risks of spreading COVID-19. The surveys were carried out from mid-May to the end of August 2021. The surveys mainly cover the period from June to December 2020. Representative samples of 1,000 households in each country were surveyed. We compare June 2020 and December 2020 (both periods during the COVID-19 pandemic) in order to see how households were able to cope with COVID-19 six months after a big spike in the number of cases and large lockdowns in CAREC member countries (excluding PRC). In particular, we assess how household income, expenditure, and financial difficulty changed from June to December 2020. 2. LITERATURE REVIEW This research is inspired by, and closely related to, Morgan and Trinh (2021). They carried out computer-assisted telephone interviews of households in eight Southeast Asian countries: Cambodia, the Lao People’s Democratic Republic (Lao PDR), Indonesia, Malaysia, Myanmar, the Philippines, Thailand, and Viet Nam in 2020. A nearly identical (with modifications to facilitate understanding for the households from the CAREC region) survey questionnaire was used for this study. ADBI Working Paper 1298 D. Azhgaliyeva et al. 2 Morgan and Trinh (2021) demonstrated that various household characteristics, including pre-COVID-19 household income class, household demographic factors, and COVID-19-induced factors such as having at least one person who lost their job or being located in lockdown areas, all affected the likelihood of a decline in income. Having at least one person who lost their job or had reduced working hours increased the likelihood of a household experiencing financial difficulties. The gender of the household head also has a significant impact on financial difficulties and income decline due to COVID-19. For low-income groups, income in female-headed households was found to decline significantly more than in male-headed households due to the pandemic. In addition, female-headed households had more financial difficulties due to COVID-19 than male-headed households. Literature studying the impact of the COVID-19 crisis in the CAREC region is limited. Compared with 2019, CAREC and ADB (2021) found for CAREC in 2020 that there were 40 million fewer airline passengers, 46 million fewer airport passengers, $7 billion lower passenger revenues, one million travel and tourism jobs at risk, an $11 billion reduction in international visitor spending, a $27 billion reduction in travel and tourism contribution to gross domestic product, 33 million fewer visitor arrivals, and 5.5 million fewer visitor arrivals by air. Thus, restrictions on travel could affect households through loss of jobs and reduced working hours and income, especially those sourcing income from hospitality, travel, and tourism. The recent book published by ADBI (Beirne, Morgan, and Sonobe 2021) titled COVID-19 Impacts and Policy Options: An Asian Perspective provides crucial insights into the economic effects and policy implications of the COVID-19 pandemic in the region. This book demonstrates the disproportionately negative effects on low-income households, particularly in poor and vulnerable countries. Lower employment and incomes have increased household financial distress. The book also provides policy recommendations for supporting vulnerable households, such as enhancing social security protection during the pandemic in order to limit the effects of unemployment. Temporary social protection measures should also be extended to vulnerable migrant workers. Holzhacker (2020) suggested in early 2020 that potential economic implications of the COVID-19 outbreak in the PRC have a potentially negative impact on the economies of the CAREC region mainly through commodity prices, travel, and trade. 3. SPREAD OF COVID-19 AND GOVERNMENT RESPONSES 3.1 COVID-19 in CAREC Figures 1–3 show the progress of COVID-19 in nine CAREC countries. They show daily net changes in cases of COVID-19, deaths, and vaccination doses. Unfortunately, data on COVID-19 cases, including deaths, from Hale (2020) do not include Turkmenistan, and vaccination data available from Bloomberg are available only for five out of ten CAREC countries (excluding the PRC). Cases of COVID-19 and deaths were greater in the PRC only in early 2020. Later it spread to other CAREC countries. Despite a smaller population, Georgia had more cases of COVID-19, even more than in Pakistan. Deaths were higher in Georgia, Pakistan, and Afghanistan. Vaccination started at the end of 2020 in the PRC. Vaccine doses remain high in the PRC. ADBI Working Paper 1298 D. Azhgaliyeva et al. 3 Figure 1: COVID-19 Cases (Daily Net Changes) Data source: Hale et al. (2021). Figure 2: COVID-19 Deaths (Daily Net Changes) Data source: Hale et al. (2021). ADBI Working Paper 1298 D. Azhgaliyeva et al. 4 Figure 3: COVID-19 Vaccine Doses (Daily Net Changes) Data source: Bloomberg. 3.2 Government Responses CAREC countries have implemented various measures, including school closures, lockdowns, social distancing requirements, and border closures. However, the times at which each country implemented these measures, the duration, and the stringency of these policies vary across countries. Figures 4–7 show stringency indices of the measures that CAREC countries have adopted to contain the spread of COVID-19, as calculated by a team at Oxford University (Hale et al. 2021). Table 1 provides a description of the stringency Index of Government Nonpharmaceutical Intervention Measures and Figures 4–7 demonstrate these indexes. The Blavatnik School of Government from the University of Oxford provides the Oxford COVID-19 Government Response Tracker (OxCGRT) (Hale et al. 2021). In order to make data comparable across countries, they measure policy responses as indices on a scale of 0–100: (i) overall government response index; (ii) containment and health index; (iii) stringency index; (iv) economic support index; (v) risk of openness index. It measures how many of the relevant indicators a government has acted upon, and to what degree, using simple, additive, unweighted indices. Other data sets of policy measured are provided in Annex A. Table 1: Policy Indices Policy Indices Description Overall government response index Response of governments to COVID-19 Containment and health index “Lockdown,” closures, testing policy, contact tracing, short-term investment in healthcare and vaccines Stringency index Strictness of “lockdown style” policies that restrict people’s behavior Economic support index Income support and debt relief Source: Hale et al. (2021). ADBI Working Paper 1298 D. Azhgaliyeva et al. 11 Figure 13: Change of Household Income, by Source of Income, % of Households Source: Authors’ calculation based on ADBI’s database. Figure 14: Change in Income, by Income Level, % of Total Subgroup Source: Authors’ calculation based on ADBI’s database. Figure 15 presents a decline in income across income sources for each country. Household business income fell the most in all countries except Afghanistan and Turkmenistan, where wage income showed the highest decline (29% and 51%, respectively). ADBI Working Paper 1298 D. Azhgaliyeva et al. 12 Figure 15: Sources of Income Decline, by Country, % of Households Source: Authors’ calculation based on ADBI’s database. We examine the effects of the COVID-19 pandemic on household income. Following Morgan and Trinh (2021), we estimate the following equation: 𝑖𝐷𝑒𝑐𝑙𝑖𝑛𝑒𝑖= 𝛼0+ 𝛼1𝑆𝐸𝐶𝑖+ 𝛼2𝐻𝐻𝑖+ 𝛼3𝐶𝑂𝑉𝐼𝐷𝑖+ 𝜖𝑖 (1) in which 𝑖𝐷𝑒𝑐𝑙𝑖𝑛𝑒𝑖 is a dummy variable that takes the value of one if household i experienced a decline in income during the COVID-19 period; 𝑆𝐸𝐶𝑖 is a set of dummy variables indicating the socioeconomic class that household I belongs to; 𝐻𝐻𝑖 is a set of household characteristics including sources of income, household head’s education, age, and gender, household location (i.e., rural vs. urban areas), and household size (total number of household members); 𝐶𝑂𝑉𝐼𝐷𝑖 is a set of variables reflecting COVID-19-induced effects such as whether the household was located in a lockdown area or not; and 𝜖𝑖 is the error term. We estimate the above equations for pooled data on 11 countries (with country dummy being controlled) and separately for each country in our sample. Table 3 presents our estimation results. The first column reports the results using pooled data and the subsequent columns are results for Afghanistan, Azerbaijan, Georgia, Kazakhstan, the Kyrgyz Republic, Tajikistan, Turkmenistan, Uzbekistan, Mongolia, and Pakistan, respectively. We found that on average, the socioeconomic class of household on average is not related to the likelihood of experiencing a decline in income, suggesting that the COVID-19 pandemic affected the income of all households relatively equally regardless of their economic status before the pandemic. This is also found in ASEAN households (Morgan and Trinh 2021). However, we find the COVID-19 pandemic may have different impacts on different countries. For example, in Afghanistan, households in the second SEC class are less likely to suffer from income decline than households in the poorest SEC, while there is no difference between richer groups (SECs 3 and 4) and the poorest groups. Or in the case of Azerbaijan, while there is no difference among households in SECs 1, 2, and 4, households in the middle upper class (SEC 3) are more likely to experience a decline in income than the poorest group (SEC 1 as our reference group). The situation is similar in Tajikistan where middle-income households (in SECs 2 and 3) are more prone to income decline than households in the poorest groups. Among these ten ADBI Working Paper 1298 D. Azhgaliyeva et al. 13 countries, we only find that richer households are less likely to suffer income decline than the poorest households. Different sources of income may also have different effects on the likelihood of decline in income. On average, households with income from wages tended to experience a decline in income while those with income from agricultural production or from household businesses or self-employment were not different from households in the reference group. This is different from the situation in ASEAN economies, where households with income from agricultural production and from household businesses or self-employment were more likely to experience a decline in income (Morgan and Trinh 2021). The difference may be due to the structure of the economy between ASEAN and CAREC countries. ASEAN economies seem more dependent on small-scale agricultural production and household businesses. While the effect of income source on the likelihood of experiencing income decline is quite similar among ASEAN economies, we observe a large difference among CAREC countries. For example, those households with income from agriculture experienced a decline in income in the Kyrgyz Republic but were less likely to have a decreased income in Afghanistan. Households with wages as income sources were more likely to experience income declines in Afghanistan, Georgia, Kazakhstan, the Kyrgyz Republic, Mongolia, and Pakistan, but less likely to do so in Azerbaijan and there were no effects in the other three countries. Similarly, Afghan and Mongolian households with income from household businesses or self-employment were more likely to experience income declines but less likely to experience them in Tajikistan and Uzbekistan. In other countries, households with income from household businesses or self-employment were not different from those households without such sources of income. The household head’s education level on average was negatively associated with the likelihood of experiencing an income decline. For example, a household head with a high school diploma has a lower probability of experiencing an income decline by 16.1 percentage points than those who have a lower qualification than a high school diploma (i.e., secondary school and below). The figure for those who have a higher education level than high school is 27.6 percentage points. This result is consistent with that among ASEAN households (Morgan and Trinh 2021). Similarly to the case of ASEAN households, the role of a household head’s education level is not observed in all countries in our sample. For example, in Afghanistan, households with a higher education level have a much higher likelihood of experiencing an income decline than those with a lower education level. The same situation is also observed in the Kyrgyz Republic and in Mongolia, but only for households whose household head has a higher education level than high school. We do not observe the relationship between household head education and the likelihood of income decline in other countries. Our results also suggest that female-headed households are less likely to experience a decline in income than their male counterparts. However, the results also vary by country. For example, we only observed a negative relationship in three countries (Georgia, Uzbekistan, and Mongolia), while there is a positive relationship in Turkmenistan and no relationship in other countries. ADBI Working Paper 1298 D. Azhgaliyeva et al. 14 Table 3: Determinants of Income Decline Due to the COVID-19 Pandemic Variables All Afghanistan Azerbaijan Georgia Kazakhstan Kyrgyz Republic (1) (2) (3) (4) (5) (6) HH head female –0.110* –0.620 0.195 –0.286* 0.0294 –0.105 (0.057) (0.381) (0.195) (0.150) (0.140) (0.156) HH head education • below high school –0.007*** –0.019*** –0.003 –0.013** –0.004 –0.003 (0.00157) (0.00492) (0.00586) (0.00515) (0.00541) (0.00524) • high school –0.161** –0.612*** 0.209 0.365 0.240 –0.323 (0.073) (0.188) (0.621) (0.419) (0.426) (0.240) • above high school –0.275*** –0.702*** –0.0574 0.332 0.0874 –0.626*** (0.071) (0.166) (0.618) (0.405) (0.414) (0.237) Located in lockdown area 0.265*** –1.436*** 0.960*** 0.527*** 0.286* 0.288* (0.058) (0.266) (0.211) (0.143) (0.162) (0.149) Income group (base: SEC1 poorer) • SEC2 –0.016 –0.428** 0.147 0.195 –0.398* 0.223 (0.065) (0.186) (0.231) (0.230) (0.225) (0.191) • SEC3 –0.020 –0.214 0.454* –0.018 –0.502** 0.123 (0.0674) (0.202) (0.236) (0.254) (0.229) (0.199) • SEC4 (richer) –0.078 –0.907*** 0.132 –0.029 –0.648*** 0.417** (0.065) (0.250) (0.216) (0.233) (0.207) (0.200) Income source • agriculture –0.0029 –0.318** 0.101 0.021 –0.276 0.251* (0.049) (0.154) (0.176) (0.165) (0.218) (0.149) • wage 0.298*** 0.303* –0.573*** 0.477** 0.704*** 0.424*** (0.053) (0.164) (0.150) (0.218) (0.209) (0.147) • business –0.045 0.398*** –0.202 –0.197 –0.200 0.064 (0.049) (0.149) (0.166) (0.172) (0.178) (0.144) Rural –0.097** –0.392** –0.114 0.313* 0.149 –0.136 (0.047) (0.160) (0.176) (0.163) (0.147) (0.149) Constant 0.713*** 3.362*** –0.198 –0.878 –0.313 –0.182 (0.130) (0.397) (0.705) (0.536) (0.548) (0.357) Observations 10,252 1,064 1,000 1,024 1,066 1,024 Variables Tajikistan Turkmenistan Uzbekistan Mongolia Pakistan (7) (8) (9) (10) (11) HH head female –0.117 0.437* –0.594*** –0.373* 0.219 (0.175) (0.237) (0.217) (0.222) (0.285) HH head education • below high school –0.011** 0.004 –0.012** –0.032*** 0.017*** (0.00452) (0.00539) (0.00550) (0.00737) (0.00532) • high school –0.019 –0.224 –0.308 –0.372 0.130 (0.232) (0.226) (0.367) (0.234) (0.205) • above high school –0.202 –0.104 –0.373 –0.926*** 0.309 (0.229) (0.219) (0.348) (0.260) (0.223) Located in lockdown area 0.426*** –0.488 0.652*** –0.039 (0.135) (1.017) (0.192) (0.265) Income group (base: SEC1 poorer) • SEC2 0.631*** –0.093 –0.024 0.145 –0.251 (0.183) (0.233) (0.204) (0.241) (0.276) • SEC3 0.574*** 0.085 –0.051 –0.094 –0.578** (0.186) (0.234) (0.213) (0.317) (0.257) • SEC4 (richer) 0.127 0.035 –0.047 0.128 –0.283 (0.205) (0.228) (0.200) (0.258) (0.239) Income source • agriculture 0.012 0.081 –0.166 –0.336 –0.070 (0.145) (0.136) (0.163) (0.291) (0.173) • wage 0.218 0.097 0.261 1.239*** 0.530* (0.172) (0.141) (0.190) (0.204) (0.275) • business –0.392*** 0.020 –0.641*** 0.762*** –0.198 (0.140) (0.238) (0.154) (0.211) (0.185) Rural –0.048 –0.132 –0.042 –0.642*** –0.230 (0.153) (0.131) (0.152) (0.244) (0.170) Constant –0.088 0.379 0.623 0.091 1.087** (0.357) (0.435) (0.441) (0.478) (0.459) Observations 1,011 1,000 1,001 1,006 1,056 Note: Standard errors in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1. ADBI Working Paper 1298 D. Azhgaliyeva et al. 15 Similarly to the case of ASEAN households, being located in a lockdown area on average increases the likelihood of experiencing an income decline. This is also observed in most countries, except for Afghanistan, Turkmenistan, and Pakistan. While living in a lockdown area does not have an effect on the likelihood of experiencing an income decline in Turkmenistan and Pakistan, it reduces the likelihood of income decline in Afghanistan. This finding is rather different from other countries. According to Morgan and Trinh (2021), this negative relationship could be because those living in lockdown areas receive a subsidy from the government to enable them to keep their income stable. Households in rural areas were also less likely to experience income declines, but this relationship is only observed in two countries (Afghanistan and Mongolia). Meanwhile we find that Georgian rural households were more likely to suffer income declines than their urban counterparts. 5.2 Determinants of Having Expenditure Increase Due to the COVID-19 Pandemic Half of the households (50%) in our sample reported that their expenditure increased (Figure 16). Only 16% reported that their expenditure declined, which was much lower than the 45% of households that experienced a decline in income (Figure 13). Figure 16: Changes in Household Expenditure, % of Households Source: Authors’ calculation based on ADBI’s database. We examine the effects of the COVID-19 pandemic on household expenditure using the following equation: 𝐸𝑥𝑝𝑖= 𝛼0+ 𝛼1𝑆𝐸𝐶𝑖+ 𝛼2𝐻𝐻𝑖+ 𝛼3𝐶𝑂𝑉𝐼𝐷𝑖+ 𝜖𝑖 (2) in which 𝐸𝑥𝑝𝑖 is a dummy variable that takes the value of one if household i experienced an increase in expenditure during the COVID-19 period; 𝑆𝐸𝐶𝑖, 𝐻𝐻𝑖, and 𝐶𝑂𝑉𝐼𝐷𝑖 are similar to Equation (1) and 𝜂𝑖 is the error term. As before, we estimate the ADBI Working Paper 1298 D. Azhgaliyeva et al. 16 above equations for pooled data of 11 countries (with country dummy being controlled) and separately for each country. Table 1: Determinants of Experiencing an Expenditure Increase Due to the COVID-19 Pandemic Variables All Afghanistan Azerbaijan Georgia Kazakhstan Kyrgyz Republic (1) (2) (3) (4) (5) (6) HH head female –0.0875 –0.149 0.361* –0.145 –0.0218 0.0597 (0.0540) (0.383) (0.194) (0.133) (0.128) (0.153) HH head education • below high school education –0.00495*** –0.0186*** –0.00223 –0.00665 –0.00300 –0.00313 (0.00150) (0.00496) (0.00615) (0.00462) (0.00494) (0.00518) • high school education –0.0980 –0.0588 0.785 –0.349 –0.374 –0.399* (0.0703) (0.185) (0.800) (0.359) (0.387) (0.239) • above high school education 0.108 0.225 1.330* –0.0452 0.00486 –0.233 (0.0683) (0.156) (0.795) (0.345) (0.374) (0.235) Located in lockdown area –0.166*** –1.043*** 0.578** 0.0494 –0.278* –0.539*** (0.0559) (0.200) (0.227) (0.130) (0.153) (0.151) Income group (base: SEC1 poorer) • SEC2 0.0185 0.0790 0.330 0.140 0.265 0.152 (0.0618) (0.176) (0.240) (0.206) (0.217) (0.188) • SEC3 0.000358 0.422** –0.244 0.174 0.313 0.0502 (0.0649) (0.190) (0.250) (0.225) (0.219) (0.195) • SEC4 (richer) 0.145** –0.00737 0.255 0.265 0.465** 0.203 (0.0621) (0.244) (0.228) (0.208) (0.198) (0.198) Income source –0.00139 –0.327** 0.0609 –0.108 0.0403 0.112 • agriculture (0.0476) (0.153) (0.183) (0.150) (0.197) (0.147) –0.0159 0.462*** –0.858*** –0.219 –0.0761 –0.0128 • wage (0.0519) (0.156) (0.156) (0.209) (0.203) (0.147) 0.0926** –0.126 0.147 0.0516 0.138 0.255* • business (0.0466) (0.145) (0.179) (0.154) (0.168) (0.143) –0.0442 –0.163 0.0697 –0.120 –0.00117 –0.206 Rural (0.0456) (0.153) (0.186) (0.143) (0.135) (0.147) 0.0237 1.408*** –2.062** 0.554 –0.0998 0.152 Constant (0.126) (0.328) (0.877) (0.471) (0.503) (0.353) –0.00139 –0.327** 0.0609 –0.108 0.0403 0.112 Observations 10,252 1,064 1,000 1,024 1,066 1,024 Variables Tajikistan Turkmenistan Uzbekistan Mongolia Pakistan (7) (8) (9) (10) (11) HH head female –0.326* 0.00392 –0.253 –0.422** 0.293 (0.172) (0.252) (0.192) (0.173) (0.252) HH head education • below high school education –0.00672 0.00714 –0.00635 –0.000733 8.18e–05 (0.00439) (0.00615) (0.00504) (0.00550) (0.00422) • high school education –0.0634 0.109 –0.0800 0.173 0.0185 (0.230) (0.253) (0.358) (0.188) (0.183) • above high school education 0.0980 –0.0791 –0.00123 0.298 –0.324* (0.226) (0.242) (0.341) (0.199) (0.183) Located in lockdown area 0.129 –0.160 0.0675 (0.131) (0.189) (0.230) Income group (base: SEC1 poorer) • SEC2 –0.366** –0.368 –0.0222 –0.0220 –0.103 (0.178) (0.288) (0.194) (0.187) (0.234) • SEC3 –0.228 –0.670** 0.239 –0.162 –0.209 (0.180) (0.283) (0.200) (0.253) (0.221) • SEC4 (richer) 0.0673 –0.512* 0.443** –0.0240 0.00314 (0.195) (0.280) (0.188) (0.208) (0.196) Income source • agriculture –0.0740 –0.175 0.275* 0.199 –0.0456 (0.142) (0.153) (0.151) (0.215) (0.151) • wage 0.0720 0.158 0.298 0.116 –0.0307 (0.169) (0.161) (0.182) (0.180) (0.208) • business 0.175 –0.245 0.410*** 0.0452 –0.197 (0.136) (0.288) (0.149) (0.169) (0.161) Rural –0.112 –0.194 –0.0148 0.144 –0.0638 (0.150) (0.149) (0.142) (0.183) (0.146) Constant 0.277 1.645*** –0.473 –0.917** 1.139*** (0.349) (0.514) (0.425) (0.378) (0.391) Observations 1,011 996 1,001 1,006 1,056 Note: Standard errors in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1. ADBI Working Paper 1298 D. Azhgaliyeva et al. 17 Table 4 presents our estimation results. The structure of this table is similar to that in Table 3. We find that on average, only the richest households experienced an increase in expenditure. More specifically, the likelihood of having an expenditure increase among the richest households was 14.5 percentage points more than that of the poorest households (SEC 1, our reference group), while the likelihood of having an expenditure increase was not different among other SEC groups. However, in five countries, namely Azerbaijan, Georgia, the Kyrgyz Republic, Mongolia, and Pakistan, we did not find any difference among households across SEC groups. In other countries, richer households (either in SEC 3 or SEC 4) were more likely to have expenditure increases than poorer households (either in SEC 1 or SEC 2), except for the case of Turkmenistan. Our results show that richer households (SECs 3 and 4) in Turkmenistan were less likely to experience an expenditure increase than poorer households (SECs 1 and 2). With regard to sources of income, our results show that on average, households with income from household businesses or self-employment were more likely to increase their expenditure than households that do not have such income. Meanwhile, there is no difference for households with agricultural or wage income from the reference group. The effect of income sources on household expenditure also differs by country. For example, while Afghan households with income from agriculture are less likely to experience an increase in expenditure than households without such a source of income, their Uzbekistani counterparts are more likely to have higher expenditure. Our results also show that on average, education level and gender of household head have not affected the changes in household expenditure during the pandemic. The evidence also suggests that households located in a lockdown area have tended to experience an expenditure decline during the pandemic. 5.3 Determinants of Having Financial Difficulty Financial difficulty is defined as a lack of financial resources for at least a week. About 76% of households in our sample reported that they had experienced financial difficulties (Figure 17). However, the share of households that reported financial difficulties varies across countries from 40% in Azerbaijan to 96% in Afghanistan. In nearly all countries, with Azerbaijan being the exception, more than half of the households reported financial difficulties. Nearly all (above 80%) households reported financial difficulties in Kazakhstan (81%), Pakistan (84%), Turkmenistan (93%), Georgia (92%), and Afghanistan (96%). These numbers are higher than in SEA (Morgan and Trinh 2021), which means that more households reported financial difficulties in CAREC than in SEA. In SEA there are five countries where more than half of the households did not report financial difficulties (Cambodia, the Lao PDR, Malaysia, Myanmar, and Viet Nam) in 2020. Also, in SEA there were only two countries where nearly all households (above 80%) reported financial difficulties (Indonesia (84%) and the Philippines (85%)). ADBI Working Paper 1298 D. Azhgaliyeva et al. 18 Figure 17: Households Experiencing Financial Difficulty during the Pandemic, % of Total Households Source: Authors’ calculation based on ADBI’s database. This section examines which households are more likely to be financially vulnerable to the pandemic (or any expected shocks) than others. Following Morgan and Trinh (2021), we estimate the following equation: 𝐹𝑖𝑛𝐷𝑖𝑓𝑓𝑖= 𝛽0+ 𝛽1𝑆𝐸𝐶𝑖+ 𝛽2𝐻𝐻𝑖+ 𝛽3𝐶𝑂𝑉𝐼𝐷𝑖+ 𝜂𝑖 (3) in which 𝐹𝑖𝑛𝐷𝑖𝑓𝑓𝑖 is a dummy variable that takes the value of one if household 𝑖 experienced financial difficulties (which is defined as a lack of financial resources for daily expenditure if all the income sources disappear for a week) during the COVID-19 period; 𝑆𝐸𝐶𝑖, 𝐻𝐻𝑖, and 𝐶𝑂𝑉𝐼𝐷𝑖 are similar to Equation (1) and 𝜂𝑖 is the error term. As before, we estimate the above equations for pooled data of 11 countries (with country dummy being controlled) and separately for each country. Table 5 presents our estimation results. The first column reports the results using pooled data and the subsequent columns are results for Afghanistan, Azerbaijan, Georgia, Kazakhstan, the Kyrgyz Republic, Tajikistan, Turkmenistan, Uzbekistan, Mongolia, and Pakistan, respectively. We found that, on average, households in the lowest socioeconomic class (i.e., the poorest group) had a higher likelihood of getting into financial difficulty than households in the highest socio-economic class (i.e., the richest group) by about 94.6 percentage points. This is consistent with the results Morgan and Trinh (2021) found among ASEAN countries. However, we also find a wide difference across countries. In Afghanistan, Georgia. and Tajikistan, there was no different across households in different SECs, while in Uzbekistan, Mongolia, and Pakistan, only households in the richest group were less likely to have financial difficulty than the poorest group while households in other SEC groups were not different. Meanwhile in Kazakhstan and the Kyrgyz Republic the two upper income groups were less likely to have financial difficulty. Only in Azerbaijan did we find that all three richer household groups were less likely to suffer from financial difficulty. ADBI Working Paper 1298 D. Azhgaliyeva et al. 19 Table 2: Determinants of Financial Difficulties Due to the COVID-19 Pandemic Variables All Afghanistan Azerbaijan Georgia Kazakhstan Kyrgyz Republic (1) (2) (3) (4) (5) (6) HH head female 0.0944 –0.167 –0.00370 0.0911 0.670*** (0.0666) (0.232) (0.253) (0.164) (0.211) HH head education • below high school education –0.0118*** 0.00388 –0.00991 –0.0173** –0.00147 –0.00282 (0.00186) (0.0119) (0.00706) (0.00879) (0.00629) (0.00627) • high school education 0.0735 –0.815 –1.241 0.706 0.221 0.0223 (0.0904) (0.499) (0.826) (0.550) (0.470) (0.294) • above high school education 0.122 –0.529 –0.900 0.883* 0.498 –0.0635 (0.0893) (0.473) (0.823) (0.520) (0.454) (0.290) Located in lockdown area 0.138* –0.262 –0.451* 0.263 –0.120 0.0704 (0.0706) (0.652) (0.249) (0.252) (0.191) (0.182) Income group (base: SEC1 poorer) • SEC2 –0.193** 0.949 –2.219*** –0.151 0.110 –0.209 (0.0808) (0.728) (0.340) (0.416) (0.313) (0.251) • SEC3 –0.442*** –0.725 –2.318*** –0.0990 –0.493* –0.677*** (0.0837) (0.612) (0.341) (0.464) (0.295) (0.247) • SEC4 (richer) –0.946*** –2.192*** –4.459*** –0.539 –0.570** –0.842*** (0.0785) (0.592) (0.353) (0.410) (0.272) (0.248) Income source • agriculture –0.0567 –0.152 0.260 –0.327 0.0217 –0.269 (0.0600) (0.407) (0.204) (0.269) (0.256) (0.176) • wage 0.0857 –0.890** 0.128 0.0275 0.252 0.105 (0.0645) (0.376) (0.180) (0.390) (0.270) (0.174) • business 0.232*** 0.527 0.0816 –0.151 –0.388* 0.112 (0.0576) (0.374) (0.203) (0.296) (0.234) (0.173) Rural –0.109* 1.275*** –0.111 –0.0455 0.173 –0.241 (0.0577) (0.365) (0.209) (0.277) (0.174) (0.182) Constant 3.730*** 3.678*** 4.096*** 3.074*** 1.681*** 1.938*** (0.210) (0.943) (0.956) (0.824) (0.638) (0.442) Observations 10,252 1,031 1,000 1,024 1,066 1,024 Variables Tajikistan Turkmenistan Uzbekistan Mongolia Pakistan (7) (8) (9) (10) (11) HH head female 0.0567 0.572 –0.0261 0.0348 0.407 (0.173) (0.682) (0.202) (0.173) (0.341) HH head education • below high school education –0.00171 0.0136 –0.0135** –0.0324*** –0.0171*** (0.00439) (0.0156) (0.00532) (0.00592) (0.00510) • high school education 0.286 –0.380 –0.231 0.285 –0.124 (0.230) (0.650) (0.403) (0.188) (0.230) • above high school education 0.306 –0.769 –0.153 0.249 –0.531** (0.226) (0.644) (0.387) (0.199) (0.224) Located in lockdown area 0.355*** –0.0663 0.328 0.430* (0.132) (2.197) (0.208) (0.256) Income group (base: SEC1 poorer) • SEC2 0.0808 –1.106 –0.207 0.138 0.706* (0.180) (0.817) (0.210) (0.196) (0.364) • SEC3 –0.179 –1.129 –0.338 –0.167 0.156 (0.182) (0.825) (0.219) (0.266) (0.305) • SEC4 (richer) –0.163 0.375 –0.673*** –0.442** –0.536** (0.198) (0.850) (0.203) (0.223) (0.242) Income source • agriculture –0.238* 1.295*** –0.226 –0.224 –0.267 (0.142) (0.420) (0.158) (0.222) (0.186) • wage 0.150 0.387 –0.175 1.036*** –0.0837 (0.172) (0.431) (0.191) (0.224) (0.246) • business –0.233* 4.778*** –0.489*** 0.306* 0.171 (0.138) (0.402) (0.162) (0.182) (0.197) Rural –0.380** –0.321 –0.298** –0.612*** –0.129 (0.154) (0.379) (0.151) (0.187) (0.185) Constant 0.423 –0.545 2.442*** 2.257*** 2.455*** (0.352) (1.233) (0.483) (0.406) (0.480) Observations 1,011 1,000 1,001 1,006 1,056 Note: Standard errors in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1. ADBI Working Paper 1298 D. Azhgaliyeva et al. 20 We also find some evidence on the effects of income sources on the likelihood of getting into financial difficulty. On average, households receiving income from household businesses and/or self-employment were more likely to suffer from financial difficulty, while there is no difference between households with and without income from agricultural production and from wages. This may be due to the fact that households with income from household businesses tend to experience an increase in expenditure (as shown in Table 5). As with other results, we also find a large difference across countries. For example, households in Turkmenistan with income from agricultural production were more likely to experience financial difficulty, while their counterparts in Tajikistan were less likely to do so. Similarly, households in Afghanistan with income from wages are less likely to have financial difficulty than those without this source of income, but in Mongolia, these households are more likely to have financial difficulties. Different patterns were also found across households with income from household businesses across countries. Our empirical results show that the education level of the household head on average was not associated with the likelihood of getting into financial difficulties. This result is different from that found in ASEAN economies where household heads with a higher education level were less likely to experience financial difficulty. We find a negative relationship between education and the likelihood of getting into financial difficulty in Pakistan, while in Georgia the relationship was positive, i.e., households whose heads have an education level higher than high school were more likely to experience financial difficulties. The estimation results also suggest that being located in a lockdown area increased the likelihood of getting into financial difficulty on average. But the effects seem to be weak and differ by country. Only in Tajikistan and Pakistan did we find a positive relationship between living in lockdown areas and the likelihood of getting into financial difficulties, while the relationship is negative in Azerbaijan and there is no relationship between the two variables in other countries. We also find that rural households are less likely to experience financial difficulty, but this relationship is only statistically significant at the 10% level. This variable is only statistically significant in four countries. Rural households in Tajikistan, Uzbekistan, and Mongolia were less likely to have financially difficulties than their urban counterparts, but in Afghanistan urban households were less likely to experience financial difficulty than rural households. 6. CONCLUDING REMARKS The impacts of the COVID-19 outbreak have heavily affected CAREC member countries, which include Afghanistan, Azerbaijan, Georgia, Kazakhstan, the Kyrgyz Republic, Mongolia, Pakistan, the PRC, Tajikistan, Turkmenistan, and Uzbekistan. The COVID-19 crisis and the resulting falls in demand due both to uncertainty and policy measures such as lockdowns, “social distancing,” and travel restrictions are having a severe impact on CAREC member countries. In order to better understand these impacts, computer-assisted telephone (CATI) interviews of households were conducted in ten countries from the CAREC region (excluding the PRC) over the period May–August 2021. The samples were representative of the income classes and the rural and urban population in each country.