Emergency Loans and Consumption: Evidence of COVID-19 in Iran
Around the world, the COVID-19 crisis has hit the poorest segments of the population hardest, especially in developing markets (Furceri et al. 2020). Working in the informal economy, mostly in services, most low-income workers are unable to work from home or enjoy the protection of employment benefits from large formal companies. The high degree of informality also makes public health-focused lockdowns and enforcement less effective, while limited fiscal space and limited access to international financial markets make economic support policies harder to implement ( Djankov and Panizza 2020). Nevertheless, many governments in developing countries have implemented support programs for households and businesses and it is therefore important to assess whether these programs have succeeded in reaching the most affected in the economy and to what extent the payments of support were used. In a recent article, we offer such an assessment for emergency household loans in Iran (Hoseini and Beck 2020).
Our study is part of a rapidly growing literature on consumption that uses transaction data for the assessment of the impact of COVID-19, most of which concerns advanced countries, notably Portugal (Carvalho et al. 2020 ), Denmark (Andersen et al. 2020), Japan (Watanabe and Omori 2020), United Kingdom (Hacioglu et al. 2020), United States (Baker et al. 2020) and Mexico (Campos- Vazquez and Esquivel 2020).
COVID-19 in Iran and emergency loan program
Iran was the first country in the region to be affected by COVID-19, with the first confirmed case reported on February 19, 2020. In response to the pandemic, the government announced on February 22 the cancellation of all cultural and religious events as well as closure of schools and universities in the affected provinces, extended to all provinces on March 4. However, it was not until March 21 (just before the start of the Persian Nowruz holiday) that the government announced a ban on travel between cities as well as the closure of shopping malls and bazaars across the country, to except pharmacies and grocery stores.
As the number of new cases began to decline, restrictions were gradually eased from April. Furthermore, in April, the government announced that eligible households could apply for an emergency loan (≈ 54% of the minimum wage). This IRR 10 million loan is based on eligibility for a monthly cash transfer that the government pays to every Iranian over the age of 18 supported by oil revenue, except the top 5 percent. The loan is to be repaid from future cash transfers, starting from July-August 2020. Out of 25.6 million households in Iran, 24.2 million are eligible for this monthly cash transfer and among them, 21 million have requested the loan. The loans were repaid in four waves, with 17.1 million households repaid on April 23, 2.3 million on April 30, 775,000 on May 7 and 867,000 on June 11. Thus, more than 80% of the 83.5 million Iranians are covered by the emergency loan program.
We use payment transaction data to proxy high-frequency changes in consumption patterns between provinces and between different goods and services. This follows the approach of Aladangady et al. (2019) who show that aggregating anonymized transaction data from a large e-payment technology company at the national level provides patterns of monthly consumption growth rates similar to those of the monthly consumer trade survey. Census Bureau detail.
Our monthly and daily transaction data comes from Shaparak, a company owned by the Iranian Central Bank that acts as a clearing house for all transactions made through point-of-sale (POS) and online terminals using the Iranian rial . Although we do not capture cash purchases, this only includes a slight bias because according to CBI (2018), 97% of Iranian households use e-cards as their main method of payment for their purchases. We have daily data for point-of-sale (in-store) and online transactions for each of the 31 provinces for April-May 2019 and April-May 2020. In addition to province-level data, we distinguish durable goods , semi-durable and non-durable. – durable goods, 12 different groups of goods and services and 18 different retail segments. All values are in real terms, ie we adjust the data for inflation using the monthly price index at the province level.
We also have data on the value of emergency loans for each cycle and province and use both total loans to total monthly transactions and loans per household (in IRR millions) in our regression analysis. .
In order to estimate the effect of emergency lending on consumption across different provinces and categories, we use a difference-in-differences configuration, which stacks daily transaction data at the province level for April-May 2019 and 2020. We assume that the processing days are from April 23 to May 13, between the day of the first loan repayment and six days after the third loan repayment, while the dates of April 20-22 and April 14-20 May are the control dates. We also use April-May 2019 as the control period. We saturate our model with province, day, week, and holiday fixed effects. In our regression analysis, we focus on the first wave of loans, because (i) we cannot distinguish the transactions of households that received loans in the first, second and third week and since the effect of loans consumption can go beyond a week; and (ii) the first wave of loans is by far the largest.
Our regression results show:
- Emergency lending is positively related to higher consumption of non-durable and semi-durable goods, while there is no significant effect on consumption of durable goods or asset purchases, suggesting suggests that the emergency loans were used primarily for their intended purpose.
- These results hold when we focus only on the first week after the first wave of loans as well as the first three weeks after the first wave of loans.
- Coefficient estimates suggest that two-thirds of emergency loans went to unsustainable rather than semi-sustainable consumption, with the largest absolute increase in food and beverage consumption.
- The effects were strongest in the first few days and then dissipated over time, as shown in Figure 1.
- We find effects only for in-store but not online transactions and in poorer rather than richer provinces, suggesting that it was the poorest who responded more strongly with higher consumption to loans. emergency.
Figure 1 Day Effects of the First Round of Loans
Notes: The graphs show the estimated coefficients δ2i regression log(Ypt)=∑Iδ1i +∑Iδ2i × Ready1 + Dayyou + Wdayyou + Yearyou + Holidaysyou + Provincep +ϵptwhich gives the loan effect in DI days after the first wave (April 23) of emergency loans. Bothn/a9andand 16and the days are Friday. To lend1 is the volume of loans relative to total monthly transactions in the provinces. Day, weekday, year, holiday, and province fixed effects are included in the regressions.
Our findings are consistent with theory and previous studies on the impact of temporary income shocks in the presence of credit and liquidity constraints. (see Jappelli and Pistaferri 2010 for a review of the literature), which suggest that consumers respond to negative shocks by reducing their spending, especially in the presence of liquidity and credit constraints. Iran has a high degree of financial inclusion (94% account owners and 79% adults with a debit card in 2017, according to Global Findex), but with much of the population facing financial constraints. liquidity and credit (only 38% had emergency funds available in 2017). While in 2017 (2014), 24% (32%) borrowed from a financial institution, 40% did so in 2014 from stores and 49% from friends and family. An unforeseen and symmetric negative income shock such as the COVID-19 shock can therefore lead to substantial declines in consumption even if it is considered only transitory and support payments by the government leading to an increase in consumption, even if this support takes the form of loans and has to be repaid.
Although our article provides an overview of the COVID-19 crisis and government support measures in a developing country, other important questions will arise in the near future. Firstly, since these support payments are in the form of loans, to be repaid from July-August 2020, there are fears of repayment burdens on low-income segments, which requires assessing the effect of reimbursements (on income subsidies) on consumption. patterns. Second, will there be a permanent shift to online transactions instead of in-store point-of-sale transactions? As data becomes available, we will be able to answer these questions.
Aladangady, A, S Aron-Dine, W Dunn, L Feiveson, P Lengermann and C Sahm (2019), “From Transactions Data to Economic Statistics: Constructing Realtime, High-frequency, Geographic Measures of Consumer Spending”, Working Paper of the NBER 26253.
Andersen, A, ET Hansen, N Johannesen and A Sheridan (2020), “Consumer Responses to the COVID-19 Crisis: Evidence from Banking Transaction Data”, Covid Economy seven: 88-114.
Baker, SR, RA Farrokhnia, S Meyer, M Pagel and C Yannelis (2020), “How does household spending respond to an epidemic? Consumption during the 2020 COVID-19 pandemic”, Covid Economy 18: 73-108.
Campos-Vazquez, R and G Esquivel (2020), “Consumption and Geographic Mobility in Times of Pandemic: Evidence from Mexico”, Covid Economy 38: 218-252.
Carvalho, BP, S Peralta and J Pereira (2020), “What did people buy and how did people buy during the great lockdown? Proof of electronic payments”, Covid Economy 28: 119-158.
CBI (2017), Urban household budget survey reportCentral Bank of Iran (in Persian).
Djankov, S and U Panizza (eds) (2020), Covid-19 in developing economiesa VoxEU.org e-book, CEPR Press.
Furceri, D, P Loungani, JD Ostry and P Pizzuto (2020), “Pandemics and inequalities: assessing the impact of COVID‑19”, in S Djankov and U Panizza (eds.), Covid-19 in developing economiesa VoxEU.org e-book, CEPR Press.
Hacioglu, S, D Känzig and P Surico (2020), “Consumption in the time of Covid 19: Evidence from UK transaction data”, CEPR Discussion Paper 14733.
Hoseini, M and T Beck (2020), “Emergency Loans and Consumption: Evidence of COVID-19 in Iran”, Covid Economy 45.
Jappelli, T and L Pistaferri (2010), “The Consumption Response to Income Changes”, Economics Annual Review479–506.
Watanabe, T and Y Omori (2020), “Online consumption during the COVID-19 crisis: evidence from Japan”, Covid Economy 38: 218-252.