Abstract
The Global community has been battling the COVID-19 pandemic for two years, and Bangladesh is no exception. Literature shows that severe crises like the COVID-19 are likely to leave medium-term effect on potential output. Bangladesh aims to become a developed country and eliminate poverty by 2041, which is a challenging task even without the pandemic2 Exploring the effect of the pandemic on key factors of production is important to mitigate the medium-term impact of the crisis and to develop policy priorities to boost potential to achieve their development aspirations.
The Medium-Term Effect of Covid-19 in Bangladesh1
The Global community has been battling the COVID-19 pandemic for two years, and Bangladesh is no exception. Literature shows that severe crises like the COVID-19 are likely to leave medium-term effect on potential output. Bangladesh aims to become a developed country and eliminate poverty by 2041, which is a challenging task even without the pandemic2 Exploring the effect of the pandemic on key factors of production is important to mitigate the medium-term impact of the crisis and to develop policy priorities to boost potential to achieve their development aspirations.
A. Context
1. Bangladesh has been hit hard by the pandemic. Before COVID-19, growth has been impressive in Bangladesh, thanks to the successful transformation to a more manufacturing and service-based economy and robust external demand. However, the global outbreak of the pandemic hit Bangladesh hard through multiple supply and demand shocks. On the supply side, nationwide lockdowns caused factories to cut production, while the drop of external demand and disruption of global supply chain exacerbated production losses. On the demand side, increased unemployment, and loss of income dampened household consumption. With idle capacity amid significant uncertainty around the evolution of the pandemic, businesses have remained hesitant in investing, as seen in continued subdued private credit growth. As a result, Bangladesh economic growth in FY20 fell to a historical low, though it has fared better than regional peers benefiting from external demand recovery, stimulus to export-oriented sectors, and a surge in remittance.
Real GDP Forecast Revision After the Pandemic
(Jan 2020 WEO vs. Oct 2021 WEO forecasts, 2019 Real GDP-100)
Citation: IMF Staff Country Reports 2022, 072; 10.5089/9798400204128.002.A002
Sources: World Economic Outlook.Note: Bangladesh is excluded from the South Asia group for comparison.Real GDP Forecast Revision After the Pandemic
(Jan 2020 WEO vs. Oct 2021 WEO forecasts, 2019 Real GDP-100)
Citation: IMF Staff Country Reports 2022, 072; 10.5089/9798400204128.002.A002
Sources: World Economic Outlook.Note: Bangladesh is excluded from the South Asia group for comparison.Real GDP Forecast Revision After the Pandemic
(Jan 2020 WEO vs. Oct 2021 WEO forecasts, 2019 Real GDP-100)
Citation: IMF Staff Country Reports 2022, 072; 10.5089/9798400204128.002.A002
Sources: World Economic Outlook.Note: Bangladesh is excluded from the South Asia group for comparison.2. Severe crisis like the COVID-19 is more likely to leave a medium-term effect. Once the virus is under control, containment measures will be lifted, and idle production capacity can be utilized again. But this does not necessarily mean the economy will automatically rebound to its pre-crisis track. Plenty of evidence since the 1980s suggest that deep recessions are likely to cause medium-term effect, i.e., cyclical fluctuations affecting the steady growth path.3 This is because the shocks from temporary recessions have forced changes on the development paths of key factors of growth such as capital, labor, and productivity, which will in turn affect long-run growth. On the other hand, the medium-term effect of crises also suggests that policy measures targeted at smoothing cyclical fluctuations could play a role in upholding trend growth – if implemented in a timely and appropriate manner to prevent prolonged impairment of the factors of growth.
B. Methodology
3. Various methods have been used to measure the medium-term effect of the pandemic. For country groups, cross-country data on past deep recessions and health crises have been used to measure the impact on capital, employment, and total factor productivity (TFP) using panel regressions, which is then used as a reference point on what might be expected for the impact of the COVID-19 (IMF 2021; World Bank 2020a and 2021a). For individual countries, as data of real recessions or health crises is insufficient, the most straightforward way is to compare the projections of potential output before and after the pandemic, with the difference being the effect of the COVID-19. Methods differentiate depending on how to measure potential output. Among others, growth accounting has been used by many given its clear advantages of combining economic structure and the strength of filters.4 This note uses the growth accounting framework to measure the medium-term effect of COVID-19 in Bangladesh.
4. The growth accounting framework. A simple Cobb-Douglas production function is used to analyze the growth path of the economy before and post-pandemic, where Yt is output, At is TFP, Kt is total capital stock, ht is human capital index, Lt is total employment, and β is capital’s share of income.
Data on Y, K, h, L, and β are taken from the authorities, the Penn World Table 10.0, United Nations, and International Labour Organization (ILO). Based on ILO modeled estimates, 1-β, or labor’s share in income for Bangladesh has gradually declined during 2004–17 and this exercise will take an average value of 0.44. 5 TFP is calculated as a residual. See more details on data in Annex Table I.
5. Scenario analysis is used to estimate the medium-term effect of COVID-19 for FY21–25. In each scenario, assumptions on the growth of actual Y, K, h, and L are made, and TFP is calculated as a residual. Then, the Hedrick-Prescott (hp) filtered h, L, A and the projected actual K will be used to calculate the potential output in each scenario. 6 The differences in potential output between the two pandemic scenarios and the no-pandemic scenario is considered the pandemic’s medium-term effect.
6. Major assumptions about the scenarios are: (see more details in Annex Table 2).
No-pandemic scenario. The growth rate of real GDP and capital stock is consistent with January 2020 World Economic Outlook (WEO). Labor force participation is assumed to gradually increase over time, while unemployment rate is to gradually decline, following the developments before the pandemic (see text figure). Human capital, proxied by an index of average years of schooling, is assumed to keep the pre-crisis annual growth rate of 1.29 percent7
Pandemic-baseline scenario. The growth rate of real GDP and capital stock is consistent with October 2021 WEO. The capital depreciation ratio is assumed to be lower than pre-crisis level during FY20–22 to reflect the closure of factories during lockdowns. Labor force participation and employment will deteriorate in FY20, then gradually get back to pre-crisis level in FY23, when vaccines are expected to be widely available in Bangladesh and GDP growth picks up.8 From there, labor market situation is assumed to continue improving. Human capital growth is expected to slow down only as students affected by school closures during the pandemic enter labor market9 For example, people aged 24 in FY20 will enter the job market in FY21, while primary students today will start to enter the job market in FY33. Remote learning is considered to have mitigated 30 percent of the loss on years of schooling, as a highly simplified assumption.10 As a result of the staggering entrance into the labor market, school closure will show its full impact on growth during the long run.
Pandemic-optimistic scenario. The growth rate of real GDP and capital stock is consistent with the authorities’ estimates and the 8th Five-Year Plan (FYP). Labor force participation path is the same with the pandemic-baseline scenario until FY23, before attaining the higher level assumed in the 8th FYP. Assumptions on depreciation ratio, unemployment rate, and human capital are the same as in the pandemic-baseline scenario.
Assumptions on Employment
Citation: IMF Staff Country Reports 2022, 072; 10.5089/9798400204128.002.A002
Sources: UN, ILO, and IMF staff estimates.Note: The unemployment rates are assumed to follow the same path since FY20 for both the pandemic-baseline and pandemic-optimistic scenarios.Assumptions on Employment
Citation: IMF Staff Country Reports 2022, 072; 10.5089/9798400204128.002.A002
Sources: UN, ILO, and IMF staff estimates.Note: The unemployment rates are assumed to follow the same path since FY20 for both the pandemic-baseline and pandemic-optimistic scenarios.Assumptions on Employment
Citation: IMF Staff Country Reports 2022, 072; 10.5089/9798400204128.002.A002
Sources: UN, ILO, and IMF staff estimates.Note: The unemployment rates are assumed to follow the same path since FY20 for both the pandemic-baseline and pandemic-optimistic scenarios.7. It is worth acknowledging great uncertainties around these projections due to the nature of the pandemic. First, the growth accounting methodology has a highly simplified economic structure and some of its inputs are difficult to be precisely estimated or interpreted. The pandemic also prevented timely national survey on labor market. Second, some assumptions in the scenarios are subject to large uncertainty. Capital stock may not grow as quickly as expected if balance sheet burdens of the financial system cannot be properly addressed or more equity investment cannot be mobilized. Human capital loss could increase if remote learning is less effective or more students drop out of schools after the pandemic. Additionally, the unprecedented nature of the COVID-19 crisis and its uneven impact on individual countries means that past experiences or peer country data cannot be easily used as reference points. Therefore, the results of this exercise should be interpreted with caution, and more in-depth research should be conducted when data is available.
C. Results from Growth Accounting
8. In the pre-pandemic period, capital accumulation is the key driver of increasing potential growth, while TFP growth also contributed. The growth rate of capital stock has reached an average of 8.7 percent during FY15–19. Combined with a relatively high capital share of income, the increase in the speed of capital accumulation explained most of the rise in potential GDP growth. This is also consistent with the fact that Bangladesh has a higher capital formation-to-GDP ratio compared to its peers. The contribution of trend TFP growth, though negligent during FY00–04, has gradually picked up afterwards. The contributions of trend labor and human capital are relatively stable, with the growth of labor quality to some extent offsetting the decline of labor quantity growth in recent years.
Bangladesh: Growth Accounting (2000–2019)
Bangladesh: Growth Accounting (2000–2019)
| Contributions to growth (percentage points) | |||||
|---|---|---|---|---|---|
| Potential GDP growth | Capital | Labor (trend) | Human capital (trend) | TFP (trend) | |
| FY00–04 | 5.5 | 3.8 | 1.0 | 0.5 | 0.0 |
| FY05–09 | 6.0 | 4.5 | 0.9 | 0.6 | 0.1 |
| FY10–14 | 6.3 | 4.7 | 0.8 | 0.6 | 0.2 |
| FY15–19 | 6.8 | 4.9 | 0.8 | 0.6 | 0.6 |
Bangladesh: Growth Accounting (2000–2019)
| Contributions to growth (percentage points) | |||||
|---|---|---|---|---|---|
| Potential GDP growth | Capital | Labor (trend) | Human capital (trend) | TFP (trend) | |
| FY00–04 | 5.5 | 3.8 | 1.0 | 0.5 | 0.0 |
| FY05–09 | 6.0 | 4.5 | 0.9 | 0.6 | 0.1 |
| FY10–14 | 6.3 | 4.7 | 0.8 | 0.6 | 0.2 |
| FY15–19 | 6.8 | 4.9 | 0.8 | 0.6 | 0.6 |
9. In both pandemic scenarios, the COVID-19 crisis will leave medium-term effect on potential growth. In the pandemic-baseline scenario, average potential growth rate during FY21–25 is around 1.1 percentage points lower compared to the no-pandemic scenario. In the pandemic-optimistic scenario, if the assumptions on real GDP, investment, and employment growth taken from the 8th FYP can be fully realized by FY25, this negative growth effect could be largely mitigated, leaving the average potential growth rate during FY21–25 to be only 0.4 percentage point lower than without the pandemic.
A Comparsion: Potential Output Projections
(FY2021 no-pandemic potential output=100)
Citation: IMF Staff Country Reports 2022, 072; 10.5089/9798400204128.002.A002
Sources: WEO; Bangladesh 8th FYP; UN; ILO; PW10.0; and IMF staff calculations.A Comparsion: Potential Output Projections
(FY2021 no-pandemic potential output=100)
Citation: IMF Staff Country Reports 2022, 072; 10.5089/9798400204128.002.A002
Sources: WEO; Bangladesh 8th FYP; UN; ILO; PW10.0; and IMF staff calculations.A Comparsion: Potential Output Projections
(FY2021 no-pandemic potential output=100)
Citation: IMF Staff Country Reports 2022, 072; 10.5089/9798400204128.002.A002
Sources: WEO; Bangladesh 8th FYP; UN; ILO; PW10.0; and IMF staff calculations.10. Decomposition shows that productivity has the most significant effect on potential growth loss. In both pandemic scenarios, most of the decline in average potential output growth rate is due to lower trend TFP growth rate. In the pandemic-baseline scenario, capital and labor also dragged down potential output growth rate to some extent, while in the pandemic-optimistic scenario, fast rebound of capital and labor played a compensating role. Only a fraction of potential output loss is caused by losses in human capital. However, it is likely because most of the impact of human capital is spanned in the longer run, when students (aged 5–19) affected by the pandemic today enter the labor market during FY26–40.
Decomposition of Production Factors
(Contribution to the loss of potential GDP, simple average of FY2021-25)
Citation: IMF Staff Country Reports 2022, 072; 10.5089/9798400204128.002.A002
Source: IMF staff calculations.Decomposition of Production Factors
(Contribution to the loss of potential GDP, simple average of FY2021-25)
Citation: IMF Staff Country Reports 2022, 072; 10.5089/9798400204128.002.A002
Source: IMF staff calculations.Decomposition of Production Factors
(Contribution to the loss of potential GDP, simple average of FY2021-25)
Citation: IMF Staff Country Reports 2022, 072; 10.5089/9798400204128.002.A002
Source: IMF staff calculations.11. Sensitivity checks are conducted with different assumptions on TFP growth. TFP has been calculated as a residual in the three scenarios. Now we consider deriving TFP growth using the long-run relationship between capital accumulation and balanced growth, following the methodology proposed by Bannister et al (2020).11 In the pandemic-baseline scenario, this will lower potential growth loss to 0.77 percentage point, in which the contribution from TFP growth loss is 0.6 percentage point. In the pandemic-optimistic scenario, the potential growth loss is halved to 0.2 percentage point, in which TFP growth loss is 0.4 percentage point. Assuming unchanged capital depreciation ratio in the two pandemic scenarios will only have minor impact on output and TFP growth loss. In addition, if the effectiveness of remote learning is reduced to 7 percent, human capital’s contribution to potential growth loss will rise to 9.5 percent from the current 7.1 percent in the pandemic-baseline scenario, though still well below that of the TFP.12 These sensitivity analyses confirm that (i) the pandemic is likely to have medium-term effect, and (ii) slowdown in TFP growth is a major factor in the decline of medium-term potential growth.
12. Multiple reasons could cause the drop of TFP growth in the pandemic scenarios. On the one hand, TFP to some extent represents technology progress. Slower technology growth after a severe pandemic could be explained by less investment in technology due to weakened balance sheets of firms and financial institutions, and loss of management skills and knowhow as viable firms fall bankruptcy. On the other hand, TFP is a residual that includes all factors affecting potential output other than capital, labor, and years of schooling. The divergence between high capital growth and subdued TFP growth might suggest inefficient use of capital. Average hours worked per employee, which became extremely important during the pandemic, is not considered in labor input but will have an impact on skill accumulation of workers.13 The allocation of resources could become less efficient due to credit rationing (banks favoring large enterprises during a crisis) and displaced workers (people having to move to different jobs, especially unskilled labor).14
D. Heading for a Resilient and Inclusive Recovery
13. To revive growth potential, targeted policy measures are needed to mitigate the immediate impact of the pandemic, while not losing sight of the longer-term goals.15
Private investment should be revived to support recovery of growth. Bangladesh’s investment as a share of GDP has been high among peers, and capital stock has historically contributed to about 2/3 of potential output growth. In the pandemic-optimistic scenario, the fast rebound of investment towards the target investment-to-GDP ratio of 36.6 percent by FY25 is a major factor mitigating the loss in potential GDP growth. To realize this goal, focus should be placed on reviving investment, especially private investment. This will require improving the balance sheets of financial institutions, removing distortions in key interest rates to facilitate market-based lending, and creating a benign investment climate to attract foreign financing. Capital market development to provide long-term financing will also be critical. Public investment should focus on providing key infrastructures efficiently to remove bottlenecks for private sector development.
Boosting productivity has become a more important and urging task. The LDC graduation in 2026 calls for higher productivity, as Bangladesh will face more intense competition in its export markets. The pandemic also poses new challenges. The authorities have announced several skills development programs and planned to kick off the implementation of the Productivity Masterplan in FY22. Timely implementation and regular review of progress will be key to success. More needs to be done on technology achievements by exposing the economy to foreign technologies through foreign trade and foreign direct investment (FDI), increasing the penetration rate of traditional (energy, transportation) and modern (internet, mobile phone) technologies. On the positive side, opportunities grow out of crisis as digital platforms and payment methods flourished in Bangladesh during the pandemic. In this regard, “Digital Bangladesh”, with appropriate risk monitoring and supervision, could help increase penetration of technology and make growth more inclusive and efficient.
Making the recovery human-centered. Unemployment of female and young workers have historically been high, and the pandemic could only make it worse. 16 To achieve the 8th FYP’s projection on employment growth, increased efforts are needed to boost labor participation rate post-pandemic, especially for women (text figure) and young people. 17 Labor force survey would help diagnose legacies of the COVID-19 and facilitate policy support to the most needed. Investment should be made in the health and skills of workers, while diversifying the economy could help create more decent jobs. Based on UN estimates, working-age population growth will fall below total population growth by around 2036 (text figure). It is vital to realize the full potential of the young labor force within the next decade.
Human capital has a bigger role to play in the future. Bangladesh has made significant achievements in increasing human capital during FY1990–2010, but there is still an education gap with peers (text figure). World Bank (2021b) shows that technical and managerial expertise levels are low in most Bangladeshi firms.18 Better education is the key to help the country better absorb technology and move up the value-added chain. The prolonged school closure during the pandemic will have a negative impact on the growth rate of human capital in the long run, likely with a compounding effect, so actions are needed today to limit this impact.19 The authorities should strive to increase the share of students returning to school, adapt curriculum and teaching methods to help students catch up with peers based on student ability assessments, and continue to increase government education investment as stated in the 8th FYP.20
Labor Force Participation by Sex
(In percent of total labor force by sex)
Citation: IMF Staff Country Reports 2022, 072; 10.5089/9798400204128.002.A002
Source: ILO.Labor Force Participation by Sex
(In percent of total labor force by sex)
Citation: IMF Staff Country Reports 2022, 072; 10.5089/9798400204128.002.A002
Source: ILO.Labor Force Participation by Sex
(In percent of total labor force by sex)
Citation: IMF Staff Country Reports 2022, 072; 10.5089/9798400204128.002.A002
Source: ILO.
Working-Age Population Growth Path
(In percent, year-on-year growth rate)
Citation: IMF Staff Country Reports 2022, 072; 10.5089/9798400204128.002.A002
Sources: UN World Population Prospects 2019; and IMF staff calculations.Working-Age Population Growth Path
(In percent, year-on-year growth rate)
Citation: IMF Staff Country Reports 2022, 072; 10.5089/9798400204128.002.A002
Sources: UN World Population Prospects 2019; and IMF staff calculations.Working-Age Population Growth Path
(In percent, year-on-year growth rate)
Citation: IMF Staff Country Reports 2022, 072; 10.5089/9798400204128.002.A002
Sources: UN World Population Prospects 2019; and IMF staff calculations.
Average Years of Schooling
(Average years of schooling for population aged 25+)
Citation: IMF Staff Country Reports 2022, 072; 10.5089/9798400204128.002.A002
Sources: Barro and Lee (2013); and IMF staff calculations.Average Years of Schooling
(Average years of schooling for population aged 25+)
Citation: IMF Staff Country Reports 2022, 072; 10.5089/9798400204128.002.A002
Sources: Barro and Lee (2013); and IMF staff calculations.Average Years of Schooling
(Average years of schooling for population aged 25+)
Citation: IMF Staff Country Reports 2022, 072; 10.5089/9798400204128.002.A002
Sources: Barro and Lee (2013); and IMF staff calculations.E. Conclusion
14. Bangladesh has been hit hard by the COVID-19 pandemic, which is likely to leave medium-term effect on the potential output. A growth accounting framework and scenario analysis are used to measure this effect. The results show that in both pandemic scenarios, the COVID-19 crisis will leave medium-term effect on potential growth. However, if the assumptions on real GDP, investment, and employment growth taken from the 8th FYP can be fully realized by FY25, the negative growth effect could be largely mitigated. To revive growth potential, targeted policy measures are needed to mitigate the immediate impact of the pandemic, while not losing sight of the longer-term goals. Focus should be placed on reviving private investment, boosting productivity, and investing in education and health of the labor force.
Annex I. Data
Bangladesh: Data Description and Sources
Bangladesh: Data Description and Sources
| Indicator | Sources | Comments |
|---|---|---|
| Capital stock | Penn World Table 10.0 | Capital stock= |
| Actual real GDP*Capital stock-to-GDP ratio | ||
| Bangladesh Bureau of Statistics | Latest data as of FY20. | |
| Real GDP | Bangladesh Bureau of Statistics | Latest data as of FY20. |
| Depreciation rate | Penn World Table 10.0 | Latest data as of FY19. |
| Labor’s share of income | International Labour Organization | Average level during FY11–17. |
| Human capital | Penn World Table 10.0 | Average years of schooling for population aged over 25 years. Latest data as of FY19. |
| Working-age population | United Nations | |
| World Population Prospect 2019 | Both historical estimates and medium-variant projections are used. | |
| Labor participation rate | International Labour Organization | Latest data as of FY20. |
| Unemployment rate | International Labour Organization | Latest data as of FY20. |
Bangladesh: Data Description and Sources
| Indicator | Sources | Comments |
|---|---|---|
| Capital stock | Penn World Table 10.0 | Capital stock= |
| Actual real GDP*Capital stock-to-GDP ratio | ||
| Bangladesh Bureau of Statistics | Latest data as of FY20. | |
| Real GDP | Bangladesh Bureau of Statistics | Latest data as of FY20. |
| Depreciation rate | Penn World Table 10.0 | Latest data as of FY19. |
| Labor’s share of income | International Labour Organization | Average level during FY11–17. |
| Human capital | Penn World Table 10.0 | Average years of schooling for population aged over 25 years. Latest data as of FY19. |
| Working-age population | United Nations | |
| World Population Prospect 2019 | Both historical estimates and medium-variant projections are used. | |
| Labor participation rate | International Labour Organization | Latest data as of FY20. |
| Unemployment rate | International Labour Organization | Latest data as of FY20. |
Bangladesh: Assumptions in the Medium-Term (FY21–25)
Bangladesh: Assumptions in the Medium-Term (FY21–25)
| Indicator | No pandemic scenario | Pandemic-baseline scenario | Pandemic-optimistic scenario |
|---|---|---|---|
| Real GDP (Y) | Jan 2020 WEO projections | Oct 2021 WEO projections | Authorities’ estimates for FY21–22, 8th FYP real GDP growth projections for FY23–25 |
| Capital stock (K) Kt=l+(1–6)*Kt-1 | I: Jan 2020 WEO projections | I: Oct 2021 WEO projections | I: Authorities’ estimates for FY21, 8th FYP projections for FY22–25 |
| δ:4% (equal to 2019 level) | δ: 2%, 3.2%, 4% for FY20–22 (50%, 60%, 80% of 2019 level), 4% for FY23–25 | δ: the same with the pandemic-baseline scenario. | |
| Human capital index (h) | 1.29% (equal to 2011–19 growth rate) | 1.25%, 1.18%, 1.13%, 1.13%, 1.13% for FY21–25, respectively. | The same with the pandemic-baseline scenario. |
| Labor force participation rate (LFPR) | FY19 LFPR=59.1%. LFPR has increased by an average of 0.1 pp each year during FY17–19. The assumption is LFPR will increase by 0.1 pp each year during FY20–25. | Based on ILO estimated LFPR of 55.7% in FY20, it is assumed that LFPR will gradually pick up during FY21 -22. It will get back to 59.1% in FY23 and increase by 0.1 pp each year in FY24–25. | Based on ILO estimated LFPR of 55.7% in FY20, it is assumed that LFP will gradually pick up during FY21–22. It will get back to 59.1% in FY23 and increase according to the labor force growth rate assumed in the 8th FYP. |
| Unemployment rate (UNR) | FY19 UNR=4.22%. UNR has decreased by an average of 0.05 pp each year during FY17–19. The assumption is UNR will decline by 0.05 pp each year during FY20–25. | Based on ILO estimated UNR of 5.3% in 2020, it is assumed that UNR will gradually decline during FY21–22 but still higher than FY19. It will get back to 4.22% and stay at this level since FY23. | The same with the pandemic-baseline scenario. |
| TFP | Residual | Residual | Residual |
Bangladesh: Assumptions in the Medium-Term (FY21–25)
| Indicator | No pandemic scenario | Pandemic-baseline scenario | Pandemic-optimistic scenario |
|---|---|---|---|
| Real GDP (Y) | Jan 2020 WEO projections | Oct 2021 WEO projections | Authorities’ estimates for FY21–22, 8th FYP real GDP growth projections for FY23–25 |
| Capital stock (K) Kt=l+(1–6)*Kt-1 | I: Jan 2020 WEO projections | I: Oct 2021 WEO projections | I: Authorities’ estimates for FY21, 8th FYP projections for FY22–25 |
| δ:4% (equal to 2019 level) | δ: 2%, 3.2%, 4% for FY20–22 (50%, 60%, 80% of 2019 level), 4% for FY23–25 | δ: the same with the pandemic-baseline scenario. | |
| Human capital index (h) | 1.29% (equal to 2011–19 growth rate) | 1.25%, 1.18%, 1.13%, 1.13%, 1.13% for FY21–25, respectively. | The same with the pandemic-baseline scenario. |
| Labor force participation rate (LFPR) | FY19 LFPR=59.1%. LFPR has increased by an average of 0.1 pp each year during FY17–19. The assumption is LFPR will increase by 0.1 pp each year during FY20–25. | Based on ILO estimated LFPR of 55.7% in FY20, it is assumed that LFPR will gradually pick up during FY21 -22. It will get back to 59.1% in FY23 and increase by 0.1 pp each year in FY24–25. | Based on ILO estimated LFPR of 55.7% in FY20, it is assumed that LFP will gradually pick up during FY21–22. It will get back to 59.1% in FY23 and increase according to the labor force growth rate assumed in the 8th FYP. |
| Unemployment rate (UNR) | FY19 UNR=4.22%. UNR has decreased by an average of 0.05 pp each year during FY17–19. The assumption is UNR will decline by 0.05 pp each year during FY20–25. | Based on ILO estimated UNR of 5.3% in 2020, it is assumed that UNR will gradually decline during FY21–22 but still higher than FY19. It will get back to 4.22% and stay at this level since FY23. | The same with the pandemic-baseline scenario. |
| TFP | Residual | Residual | Residual |
References
Adler, Gustavo et al. 2017. “Gone With the Headwinds: Global Productivity”, IMF Staff Discussion Note, International Monetary Fund, Washington, DC.
Anand, Rahul, Kevin C. Cheng, Sidra Rehman, and Longmei Zhang. 2014. “Potential Growth in Emerging Asia”, IMF Working Paper, WP/14/02, International Monetary Fund, Washington, DC.
Andrabi, T., Daniels B., Das, J. 2020. “Human Capital Accumulation and Disasters: Evidence from the Pakistan Earthquake of 2005”, May 2020, RISE Working Paper Series 20/039. https://doi.org/10.35489/BSG-RISE-WP_2020/039.
Barro, Rober J. and Lee, Jong-Wha. 2013. “A New Data Set of Educational Attainment in the World, 1950–2010”, NBER Working Paper 15902.
Bannister, Geoffrey, Harald Finger, Yosuke Kido, Siddharth Kothari, and Elena Loukoianova. 2020. “Addressing the Pandemic’s Medium-Term Fallout in Australia and New Zealand”, IMF Working Paper, WP/20/272, International Monetary Fund, Washington, DC.
Bloom, Nicholas, Philip Bunn, Paul Mizen, Pawel Smietanka, and Gregory Thwaites. 2021. “The Impact of COVID-19 on Productivity”, NBER Working Paper 28233.
Caselli, Francesco. 2005. “Accounting for Cross-Country Income Differences” in Philippe Aghion and Steven N. Durlauf (eds.) Handbook of Economic Growth, volume 1A, Elsevier, Amsterdam, 679–741.
Cerra, Valerie, Antonio Fatas, and Sweta C. Saxena. 2020. “Hysteresis and Business Cycles”, IMF Working Paper, WP/20/73, International Monetary Fund, Washington, DC.
Estevão, Marcello and Evridiki Tsounta. 2010. “Canada’s Potential Growth: Another Victim of the Crisis?”, IMF Working Paper 10/13, International Monetary Fund, Washington, DC.
Fernald, John and Huiyu Li. 2021. “The Impact of COVID on Potential Output”, Federal Reserve Bank of San Francisco Working Paper 2021–09. https://doi.org/10.24148/wp2021–09
Fuchs-Schundeln, Nicola, Dirk Krueger, Alexander Ludwig, and Irina Popova. 2020. “The Long-Term Distributional and Welfare Effects of Covid-19 School Closures”, NBER Working Paper 27773.
Haltmaier, Jane. 2012. “Do Recessions Affect Potential Output?”, International Finance Discussion Papers, Number 1066, Board of Governors of the Federal Reserve System.
International Labour Organization. 2021a. “World Employment and Social Outlook: Trends 2021”, International Labour Office, Geneva: ILO.
International Labour Organization. 2021b. “ILO Monitor: COVID-19 and the World of Work (Seventh edition)”, International Labour Organization.
International Monetary Fund. 2009. “U.S. Potential Growth in the Aftermath of the Crisis”, Selected Issues Paper, IMF Country Report 09/229, International Monetary Fund, Washington, DC.
International Monetary Fund. 2013a. “How Fast Can Portugal Grow?”, Selected Issues Paper, IMF Country Report 13/19, International Monetary Fund, Washington, DC.
International Monetary Fund. 2013b. “How Fast Can Brazil Grow?”, Selected Issues Paper, IMF Country Report 13/313, International Monetary Fund, Washington, DC.
International Monetary Fund. 2014. “Estimating South Africa’s Potential Growth”, South Africa Selected Issues Paper, IMF Country Report 14/339, International Monetary Fund, Washington, DC.
International Monetary Fund. 2018. “Bangladesh Article IV Consultation Staff Report”, “Annex V Medium-Term Growth Outlook”, IMF Country Report No. 18/158, International Monetary Fund, Washington, DC.
International Monetary Fund. 2021. “World Economic Outlook April 2021”, Chapter 2 “After-Effects of the COVID-19 Pandemic: Prospects for Medium-Term Economic Damage”, International Monetary Fund, Washington, DC.
Kaffenberger, M. 2020. “Modeling the Long-Run Learning Impact of the COVID-19 Learning Shock: Actions to (More Than) Mitigate Loss”, RISE Insights Series 2020/017. https://doi.org/10.35489/BSG-RISE-RI_2020/017
Rahman, Tashmina; Sharma, Uttam. 2021. “A Simulation of COVID-19 School Closure Impact on Student Learning in Bangladesh”, World Bank, Washington, DC.
Sun, Yan. 2010. “Potential Growth of Australia and New Zealand in the Aftermath of the Global Crisis”, IMF Working Paper 10/127, International Monetary Fund, Washington, DC.
World Bank. 2020a. “Global Economic Prospects June 2020”, Chapter 3 “Lasting Scars of the COVID-19 Pandemic”, World Bank, Washington, DC.
World Bank. 2020b. “Simulating the Potential Impacts of COVID-19 School Closures on Schooling and Learning Outcomes: A Set of Global Estimates”, June 2020 Conference edition, World Bank, Washington, DC.
World Bank. 2021a. “Global Economic Prospects June 2021”, World Bank, Washington, DC.
World Bank. 2021b. “Gearing Up for the Future of Manufacturing in Bangladesh”, World Bank, Washington, DC.
Prepared by Fan Qi (APD).
See 2021 Bangladesh: Selected Issues Paper “Bangladesh in Transition” for a comprehensive analysis on the challenges Bangladesh is likely to face during its transition to higher income status.
See a comprehensive literature review by Cerra and Saxena (2020) on this effect; see also discussions about aftereffects of the pandemic in IMF (2021) and World Bank (2020a and 2021a).
Such as analyses by Fernald and Li (2021) for the United States, Bannister et al. (2020) for Australia and New Zealand, IMF (2013a and b) for Portugal and Brazil, IMF (2014) for South Africa and Anand et al. (2014) for Emerging Asia, and IMF (2018) for Bangladesh.
The ILO modeled estimates have already been adjusted for self-employment. One possible explanation to declining share of labor income could be the force of global integration, which contributed to raising the capital intensity in production. See April 2017 WEO, Chapter 3.
Using actual K instead of filtered K is common in literature, see Sun (2010) and Estevão et al (2010).
The index is calculated using a Mincer equation building on the average years of schooling for the age group 25+ published by Barro and Lee (2013) and the rates of return for education suggested in Caselli (2005).
ILO (2021a) also projects in its baseline scenario that total working hours will fall short by 3.5 percent in 2021 and by 0.9 percent in 2022, relative to the no-pandemic scenario. This means that the working hour gap in Bangladesh could close in FY23 if the economy continues to recover.
Consistent with the methodology of the Penn World Table, only people aged 25 and above are considered.
World Bank (2020b) suggests using a 30 percent effectiveness rate for mitigation measures in Lower-Middle Income Countries in an optimistic scenario (assuming school closure for 3 months). For more analysis on Bangladesh’s mitigation measures, please see Rahman and Sharma (2021).
The decomposition of growth is: Δ lnYt*=ΔlnAt*+ βΔlnKt+(1-β)Δlnht*+(1-β)ΔlnLt*, where * means trend. Assuming ΔlnKt*=Δ lnYt*, we could get: ΔlnAt*= (ΔlnKt*-Δlnht*-ΔlnLt*)*(1-β).
World Bank (2020b) suggests using a 7 percent effectiveness rate for mitigation measures in Lower-Middle Income Countries in a pessimistic scenario (assuming school closure for 7 months).
ILO (2021b) shows that the drop in working hours has been driven, to an almost equal extent, by a reduction in employment and by a reduction of the hours worked among those who remained employed.
Local thinktank survey shows that during June 2020 to March 2021, 41 percent of interviewed workers have to move to another occupation. For example, in the skilled labor group, 60 percent of people maintained a skilled-labor job, 15 percent became unemployed, while 25 percent ended with unskilled labor. The survey was conducted by Power and Participation Research Centre and BRAC Institute of Governance & Development on 6,099 households.
While the authorities’ efforts to address the economic fallout of the pandemic – including through implementation of the stimulus packages of Tk. 1.9 trillion and monetary expansion – have helped, reviving potential growth would require decisive reforms to address structural issues.
Based on the latest Bangladesh labor force survey, female and male unemployment rate (age 15–24) in 2017 is 16.8 percent and 10.8 percent, respectively, while total unemployment rate (age 15+) is 4.4 percent. See: https://www.ilo.org/shinyapps/bulkexplorer38/?lang=en&segment=indicator&id=EAP_2EAP_SEX_AGE_NB_A
Female labor force participation rate has been stagnant for years before the pandemic and much lower than male labor force participation rate. The target in the 8th FYP is to increase female labor force participation rate from around 36 percent to 43 percent.
For example, more than 75 percent of firms do not have any workers with a college degree in engineering or applied science. The mean percentage of the workforce with an MBA or master’s level degree is only 2.4 percent.
The compounding effect means that loss of earning at lower grades will reduce learning in all subsequent grades. This effect is found by many researchers, such as Andrabi et al. (2020) and Kaffenberger (2020).
The goal is to increase public education spending from 2 percent of GDP in FY2019 to 3 percent of GDP in FY25.
