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, mainly in services, most low-income workers are unable to work from home or benefit from the protection of employment benefits of large formal enterprises. The high degree of informality also makes public health-focused lockdown and enforcement less effective, while limited fiscal space and limited access to international financial markets make economic support policies more difficult to implement ( Djankov and Panizza 2020). Nonetheless, many developing country governments have implemented support programs for households and businesses and it is therefore important to assess whether these programs have been successful in reaching those most affected in the economy and for what payments. support were used. In a recent article, we propose such an assessment for emergency household loans in Iran (Hoseini and Beck 2020).
Our study is part of a rapidly growing consumer literature that uses transaction data to assess the impact of COVID-19, most of which relates to 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 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 an inter-city travel ban as well as the closure of shopping malls and bazaars across the country, to l except pharmacies and grocery stores.
As the number of new cases began to decline, restrictions were gradually eased from April. Also, in April, the government announced that eligible households can apply for an emergency loan (â 54% of the minimum wage). This IRR 10million loan is based on eligibility for a monthly cash transfer the government pays to all Iranians over the age of 18 supported by oil revenues except the richest 5%. The loan must be repaid by future cash transfers, from July-August 2020. Out of 25.6 million Iranian households, 24.2 million are eligible for this monthly cash transfer and among them, 21 million have applied for the loan. The loans were paid in four waves, with 17.1 million households paid 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 assess high frequency changes in consumption patterns between provinces and between different goods and services. This follows the approach of Aladangady et al. (2019) which show that the aggregation of anonymized transaction data from a large electronic payment technology company at the national level provides patterns of monthly consumption growth rates similar to those of the monthly trade survey of detail from the Census Bureau.
Our monthly and daily transaction data comes from Shaparak, a company owned by the Central Bank of Iran that acts as a clearinghouse for all transactions made through point of sale (POS) and online terminals using the rial. Iranian. Although we do not capture cash purchases, this only includes a small bias because according to CBI (2018), 97% of Iranian households use electronic cards as the primary means of payment for their purchases. We have daily data for POS (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 between durable, semi-durable and not sustainable. – durable goods, 12 different groups of goods and services and 18 different retail segments. All values ââare in real terms, i.e. 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 versus total monthly transactions and loans per household (in millions of IRRs) in our regression analysis. .
In order to estimate the effect of emergency loans on consumption in 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 payment and six days after the third loan payment, while April 20 to 22 and May 14 to 20 are the days of the loan payment. control dates. We also use April-May 2019 as the control period. We saturate our model with province, day, day of the 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 on consumption could go beyond a week; and (ii) the first wave of loans is by far the largest.
Our regression results show:
- Emergency loans are 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, which suggests that emergency loans were mainly used for their intended purposes.
- These results are valid when we focus only on the first week after the first wave of loans as well as when we consider the first three weeks after the first wave of loans.
- The coefficient estimates suggest that two-thirds of emergency loans were spent on unsustainable rather than semi-sustainable consumption, with the largest absolute increase in food and drink consumption.
- The effects were strongest in the first few days, then wore off over time, as shown in Figure 1.
- We find effects only for in-store transactions but not online and in poorer rather than wealthier provinces, suggesting that it was the poorest who responded most strongly with higher consumption of emergency loans.
Figure 1 Daily bills of the first round of loans
Notes: The graphs show the estimated coefficients Î´2i regression logâ¡ (Opt) = âI??1i + âI??2i Ã Loan1 + Dayt + Dayt + Yeart + Holidayst + Provincep +pt, which gives the effect of a loan in DI days after the first round (April 23) of emergency loans. The twosd, 9e, and 16e the days are Friday. To lend1 is the volume of loans in relation to the total monthly transactions in the provinces. The fixed effects of day, day of the week, year, statutory holiday and province are included in the regressions.
Our results 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), which suggest that consumers react 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% of account owners and 79% of adults with a debit card in 2017, according to Global Findex), but with a large portion 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 families. An unforeseen and symmetrical negative income shock such as the COVID-19 shock can thus lead to substantial declines in consumption even if it is only considered to be transitory and support payments by the government leading to increases in consumption, even if this support takes the form of loans and has to be repaid.
While our paper 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. First of all, as this aid is in the form of loans, to be repaid from July-August 2020, there are concerns about the repayment charges on the most modest segments, which requires evaluating the effect of reimbursements (excluding income subsidies) on consumption. patterns. Second, will there be a permanent shift to online transactions instead of point of sale in-store transactions? As data becomes available, we will be able to answer these questions.
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