rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 10366

# **Abstract**

This paper examines the impact of Covid-19 on a host of labor market outcomes in India, using a continuous household survey. Cross-sectional trends of headline indicators like unemployment and labor force participation indicate a quick recovery after the unprecedented shock of the pandemic. However, women, marginalized social groups, and youth were more adversely affected, largely due to the nature of their employment before the pandemic. Next, a fixed-cohort analysis with a difference-in-differences regression framework is used to follow the labor market trajectories of a Covid-affected cohort and a pre-Covid cohort for comparison, over a span of 12-16 months. A story of downward transitions emerges, with movements out of the labor force, into more informal and lower-paid types of work in agriculture and rural areas. Higher education and being located in dense, urban areas partially mediated the adverse shocks of Covid-19. Lastly, India’s extensive vaccination campaign had significant positive impacts on employment recovery.

This paper is a product of the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at heyiming23@gmail.com; anarayan@worldbank.org; polinto@worldbank.org; ssinharoy@worldbank.org; nsarma@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
seasonal gaps across locations, ranging from 13.6 percent to 46.5 percent for maize and between 3.0 percent and 22.0 percent for rice (Figure 2).

Seasonality in the international prices is estimated at 10.7 percent for SAFEX white maize and 4.7 percent for Bangkok rice. These findings underline that some degree of price seasonality is to be expected, even when markets function efficiently. Second, seasonality of international maize prices is also about twice as high as this of rice, providing confidence in the domestic findings. Finally, the Tanzanian maize and rice seasonal price gaps are on average 2.5 to three times higher than those observed in the international markets, with the gap substantially above the international one in virtually all markets. This points to substantial excess seasonality in Tanzania’s staple markets.

Finally, the seasonal _R_<sup>2</sup> statistics (Table 2, column 7) show that only 13.1 percent the monthly variation in maize and rice prices is attributable to seasonality. For rice, this is about half (7.1 percent).<sup>16</sup> Seasonality does not appear to be a major contributor to staple price volatility. While there will benefits from reducing seasonal price fluctuations of staple foods to enable households to better smooth their consumption expenditures, policies aimed at this objective will not substitute for policies addressing the overall level of food price volatility.

# **4 Staple price induced seasonality affects poorer urban households and net food sellers**

The data on consumption and other household characteristics are from the three available Living Standard Measurement Study‐Integrated Surveys on Africa (LSMS‐ISA) waves for Tanzania (2008‐09, 2010‐11 and 2012‐13). These are panel datasets in which considerable effort is made to recontact households which relocate between waves as well as to track the components of the households which break up or combine, generating in effect a panel of individuals. See <u>www.worldbank.org/lsms‐isa for details. The analysis is confined to the</u>

> 16 When using the dummy variable approach, the shares are about twice as high (22.6 and 15.2 percent for maize and rice respectively), overestimating the contribution of seasonality to price volatility by a factor two.

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and adults with an account at a post office. Second, we compare adults with an account at the post office only to those with no account. We find that individuals with an account at the post office only are relatively more likely be male, richer, older, reside in urban areas, more educated and employed by an employer. They are less likely to be unemployed or self-employed. This suggests that the postal service may provide financial services to vulnerable parts of the population if we restrict ourselves to the universe of account owners. However, relative to the unbanked, adults with a post office account only may not represent the most vulnerable segment of society.<sup>7</sup>

# **2.2 Country-Level Data**

We combine individual-level data from the Global Findex database and the Gallup World Poll with country-level information on the post offices’ adopted business model and the types of financial services provided. The data on the different business models pursued by posts with regard to providing financial services was collected by the Universal Postal Union (UPU), a United Nations specialized agency that is the primary forum for cooperation between governments and postal sector players.<sup>8</sup> Appendix 2 provides an overview of the included business models. First, we identify dummy variables (0/1) on whether posts have a postal bank license, provide unlicensed postal savings (usually referred to as post office savings bank), or offer financial services through various partnership models with other financial institutions.<sup>9</sup> Because few countries have fully licensed postal financial services, we combine the licensed and

> 7 For the most vulnerable segment of society owning an account might not make economic sense due to costs related to opening and maintaining an account. However, those individuals may use the post for transactional financial services such as sending remittances.

> 8 See Berthaud and Davico (2013) for a description of the database.

> 9 Licensed in this context refers to the fact that the post has been issued a banking licenses by the banking supervisor (typically the central bank) and falls under its supervision. Unlicensed here refers to the fact that the postal savings bank does not fall under the supervision of the banking supervisor. Instead, it falls under the supervision of its line ministry or the postal regulator.

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parameter values). As a result, the country is self-sufficient in the steady state and no trade takes place.

**Table 1. Parameterization**

|Parameter|Economic interpretation|Assigned value|
|---|---|---|
|_β_|Annual discount factor|0_._95|
|_η_|Income elasticity|0_._5|
|_α_|Own-price demand elasticity|_−_0_._4|
|_γ_|Commodity budget share|0_._15|
|_Y_|Income|6_._67|
|_d_|Normalization parameter of demand function<br>|0_._39|
|_θ_|Parameter defning risk aversion|_−_2_._62|
|_k_|Physical storage cost|0_._06|
|_τ_|Trade cost|0_._2|
|_μ_|Normalization parameter of world yield distribution|1|
|_ε_<sup>_H_</sup>, _ε_<sup>_w_</sup>|Probability distribution of yield|_B_(2_,_2)_·_0_._5 + 0_._75|

An annual interest rate of 5% is used for discounting. Based _inter alia_ on Korinek and Sourdin (2010), we set trade costs to 20%. This is more than the average cost cited in this study for agricultural products, reflecting our focus on grains for poor countries.<sup>4</sup> Seale and Regmi (2006) estimate elasticities for food consumption for 144 countries. From their research, we choose cereal elasticities typical of low-income countries: _−_ 0 _._ 4 for price elasticity and 0 _._ 5 for income elasticity. We assume that consumers spend, at the steady state, _γ_ = 15% of their income on the staple, a value intermediate between what is observed for rice consumption in poor and affluent households in Asia (Asian Development Bank, 2008). Since steady-state consumption and price are equal to 1, income, which is assumed to be constant, is equal to the inverse of the commodity budget share, 1 _/γ_ . We assume at the steady state a relative risk aversion parameter of 2, implying _θ_ = _−_ 2 _._ 62.

We follow Brennan (2003) and assume a per-unit storage cost of 6% of the steady-state price (i.e., _k_ = 0 _._ 06). Combined with the opportunity cost, this physical storage cost entails an overall storage cost at steady state equal to 11.3% of the steady-state price. There is no evidence on which to base the choice of a particular probability distribution for yield at a country level. Thus, we assume that the random productions, _ε_<sup>_H_</sup> and _ε_<sup>_w_</sup> , follow a beta distribution. The beta distribution has the advantage of being empirically supported and popular in stochastic yield modeling at a local level (see, among others, Nelson and Preckel, 1989, Babcock and Hennessy, 1996), and of having a bounded support, which is computationally convenient. We assume the distribution to have shape parameters 2 and 2, which makes it unimodal at 0.5, and symmetric. The distribution is translated and rescaled to vary between 0.75 and 1.25, implying a coefficient of variation of 11.2%.

> 4Noteworthily, international trade also frequently entails beyond-average domestic transport costs.

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While the focus of our analysis so far has been on overall mobility, one could also bring in a “directional” element to analyze upward and downward mobility. For example, what are the prospects for individuals to be better educated than their parents? How prevalent is downward mobility? We operationalize this measure and compare upward and downward mobility in Armenia, Azerbaijan and Georgia with the rest of the ECA countries in the LiTS sample in Figure 16 and Figure 17.

Figure 16: Countries in the South Caucasus have experienced lower prospects for upward mobility...

_<u>(proportion of people who have done strictly better than their parents)</u>_ <!-- Start of picture text --> 0.70<br>0.60<br>0.50<br>0.40<br>0.30<br>0.20<br>0.10<br>0.00<br>Romania Cyprus Lithuania Greece Albania Mongolia Turkey Moldova Kosovo Estonia Poland Bulgaria Serbia Latvia Croatia Ukraine Italy Slovenia Hungary Georgia Armenia Russia Tajikistan Belarus Germany<br>Bosnia and... FYR Macedonia Montenegro Slovak Republic Kazakhstan Kyrgyzstan Uzbekistan Czech Republic Azerbaijan<br><!-- End of picture text --> _Source_ : Tiwari et al (2017) The results suggest that prospects for upward mobility have been slightly lower in the South Caucasus relative to the average for the entire region. Upward mobility prospects are especially low in Azerbaijan. The prospects for downward mobility, on the other hand, are among the highest in the sub-region with Armenia leading the group. What these results suggest is that there is considerable churning (upward as well as downward mobility) even in educational attainment across generations. Unlike measures such as income or earnings that are unbounded at the top, educational attainment is bounded so the results here have to be interpreted with the caution that countries with high average level of attainments can plausibly have lower “room at the top” whereas countries starting out from a low base have greater scope for upward mobility.

Figure 17: ...and higher downward mobility _<u>(proportion of people who have done strictly worse than their parents)</u>_ 34
Urdal & Hoelscher: Urban Youth Bulges and Social Disorder

# **Appendix C: Coding of city population**

For each city year from 1960 to 2006, _United Nations Demographic Yearbooks_ were consulted. These sources provide population information for capital cities and those over with over 100,000 residents, and are drawn primarily from national census data, but also include estimates and measurements from sub-national bodies. As with all disaggregated measurements, there are issues measuring between cities using different methodologies to determine city size and what constitutes a city’s geographic bounds. The UN Demographic Yearbooks (UNDYB) where possible provide measures of both City Proper (i.e. the Metropolitan Area) and the Urban Agglomeration. Here these are defined as: _City proper_ is a large locality with legally fixed boundaries and an administratively recognized urban status which is usually characterized by some form of local government.

_Urban agglomeration_ has been defined as comprising the city proper and also the suburban fringe or thickly settled territory living outside of, but adjacent to, the city boundaries.

The UNDYB data has been supplemented by two online sources of urban population measurement: The World Gazzetteer (WG) <u>(www.world-gazetteer.com) and City Population</u> (CP) (www.citypopulation.de). These sites provide census and estimate data as well as that provided by other less verifiable sources. Each data point has been coded according to which source it was derived from, namely:

1. UNDYB, relevant year (1960-2006), City Proper

2. UNDYB, relevant year (1960-2006), Urban Agglomeration 3. World Gazzetteer, relevant year (1960-2006), City Proper 4. World Gazzetteer, relevant year (1960-2006), Urban Agglomeration 5. City Population, relevant year (1960-2006), City Proper 6. City Population, relevant year (1960-2006), Urban Agglomeration

# <u>Variable Construction:</u>

Due to the greater scope, and likely reliability of UN data over other online population sources, priority was given to UNDYB census data. Where these values were missing, WG and CP sites were consulted. Encouragingly, the data provided by these two sites very often conferred with values for UNDYB data, indicating they are drawn from similar data sources and may at least provide some similar level of valid measurement of urban population. When constructing the dataset, we gathered data on city size which would reflect the area which incidents of urban violence/protest would be recorded, and which best reflect the size and scale of the city. In many cases, the metropolitan area would certainly underestimate the size of the area and population which would be reported for. A good example of this is Manila. In many cases also, estimates for urban agglomeration would overreport the area and population, such as in Tokyo, whose agglomeration with Yokohama stands at around 37 million, considerably larger than the 12 million in the Tokyo agglomeration. For some cities measures of the metropolitan area and urban agglomeration from different sources were used. This was done to increase the number of 25
**Figure 5: Instruments Covered by the Debt Management System**

<!-- Start of picture text --> Commercial Public DMS In‐house<br>Cross Currency Swaps<br>Interest Rate Swaps<br>Fees<br>Receivables<br>On‐lent Debt<br>Guarantees<br>Project finance loans<br>Inflation‐linked Instruments<br>Floating<br>Bonds (Amortizing)<br>Bonds (Bullet)<br>Bills<br>0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0<br><!-- End of picture text --> Source: World Bank Survey on Debt Management Systems For each instrument, possible answers were fully (F), partially (P) or unable (N) to capture with scores assigned to each where F =1, P =0.5 and N=0. A score approaching 1 implies a DMS fully able to process that instrument. As an example of the methodology, all nine institutions using an in-house DMS indicated they are fully able to process bills as instruments. On the other hand, five of the countries using a commercial solution indicated they are fully able and one indicated they are able to partially process the bills in their DMS (yieldinscore of >0.9).

The survey primarily evaluated the ability of the DMS to handle back, but also middle and front office functions. Figure 6 below shows the breakdown across functions. In-house and commercial solutions scored better on average across a variety of front-office functions covered by the survey, such as integration with auction platform to the debt database, interface with electronic platforms as well as market data provider. The users of these solutions also indicated a better setup to check whether a transaction violates any compliance or limit requirements. Results were more mixed in recording transactions not yet committed where approximately 90 percent of in-house, close to 50 percent of public solution and only one-third of commercial solution users indicating they are able to do so (See Appendix 1 for more results).

Commercial systems fared better in capturing middle-office functions stated in the survey compared to other solutions. Connecting to other software, such as Excel and Matlab scored high among especially among commercial systems. The same goes for generating risk indicators and providing analytical tools to perform market risk and scenario analysis. On the other hand, decomposition into composite currencies (i.e. from SDR to original currency) scored on average low among all users. Also, not many users indicated that their current solution supports credit risk management functions except for commercial solution users.

The survey respondents also generally seem satisfied with the overall ability of their DMS to capture back office operations (See Figure 6). In general, the systems seem to have no issues capturing the critical components, such as monitoring of the instruments during the entire lifecycle and checking calendar for holidays at the time of deal entry (see Appendix 1, part 4). Overall, support of STP scored higher than no support. However, other desired features, such as generation of SWIFT message and external events alerts generally received low marks, especially among users of public 24
Figure 8: State- Level Education Deprivation <!-- Start of picture text --> child 15-17<br><!-- End of picture text --> <!-- Start of picture text --> child 10-14<br><!-- End of picture text --> Source: Authors’ estimates based on MICS 2016/17.

Note: Individuals with missing age were dropped from the analysis. Total refers to all individuals with known age. States are sorted by the highest deprivation rate of the oldest child category.

Figure 9: State-Level Education Deprivation by Region <!-- Start of picture text --> a) b) c)<br>Educational Deprivation - by State Educational Deprivation - by State Educational Deprivation - by State<br>in North Central - MICS in North East - MICS in North West- MICS<br>70%<br>80% 80%<br>70% 60% 70%<br>60% 50% 60%<br>50% 40% 50%<br>40% 30% 40%<br>30% 30%<br>20% 20%<br>20%<br>10% 10%<br>10%<br>0% 0% 0%<br>child 5-9 child 10-14 child 15-17 Total child 5-9 child 10-14 child 15-17 Total child 5-9 child 10-14 child 15-17 Total<br>Benue FCT Abuja Kogi Kwara Adamawa Bauchi Borno Jigawa Kaduna Kano Katsina<br>Nasarawa Niger Plateau Gombe Taraba Yobe Kebbi Sokoto Zamfara<br>d) e) f)<br>Educational Deprivation - by Educational Deprivation - by State Educational Deprivation - by State<br>State in South East - MICS in South South MICS in South West- MICS<br>80% 80% 80%<br>70% 70% 70%<br>60% 60% 60%<br>50% 50% 50%<br>40% 40% 40%<br>30% 30% 30%<br>20% 20% 20%<br>10% 10% 10%<br>0% 0% 0%<br>child 5-9 child 10-14 child 15-17 Total child 5-9 child 10-14 child 15-17 Total child 5-9 child 10-14 child 15-17 Total<br>Abia Anambra Ebonyi Enugu Imo Akwa Ibom Bayelsa Cross River Ekiti Lagos Ogun Ondo Osun Oyo<br>Delta Edo Rivers<br><!-- End of picture text --> Source: Authors’ estimates based on MICS 2016/17 Note: Individuals with missing age were dropped from the analysis. Total refers to all individuals with known age.

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where ����,���� is the Purchasing Power Parity conversion factor in year 2011 for country _c_ ; Π�,���� is the cumulative percent increase in the CPI from time _0_ to year 2011; Π�,� is the cumulative percent increase in the CPI from time _0_ to time _t_ ; and �����������,� is the correction factor for country _c_ in time _t_ , estimated from equation (9).

# **IV. Data**

The Engel curve method requires detailed monthly CPI data on food and non-food items at regional levels and comparable consumption surveys in multiple years. However, data in Sub-Saharan Africa are challenging in terms of availability and quality. Of the 48 countries in the region, only half has two or more HBSs since the early 2000s (Beegle et al 2016). Among these, only a limited number collect consumption data using the same (or at least similar) survey design and methodology, thereby rendering the consumption aggregates comparable over time. Obtaining detailed CPI series adds another layer of difficulty. In some of the countries with comparable HBSs, detailed CPI data are either unavailable during the periods of interest or the continuity of the series has been disrupted due to political unrest or other factors, as, for example, in the case for Sierra Leone.

For the purpose of this study we obtain household consumption and CPI data for 16 countries in Sub-Saharan Africa that account for 70 percent of the regional population.<sup>10</sup> These countries are Burkina Faso, Cameroon, Côte d’Ivoire, Democratic Republic of Congo, Ethiopia, Ghana, Madagascar, Mauritius, Mozambique, Nigeria, Rwanda, Senegal, South Africa, Tanzania, Togo, and Uganda. For Nigeria and Tanzania we use panel data from the Living Standards Measurement Study–Integrated Survey on Agriculture (LSMS-ISA), partly to address quality concerns with other consumption surveys (World Bank 2014, 2015).<sup>11</sup> Only household survey series considered as comparable over time (in terms of the broad data collection method for consumption data and

> 10 We use consumption rather than income because it is generally thought to be a better measure of permanent income and is also easier to measure in economies with large agricultural or other informal sectors (see Deaton 1997).

> 11 See LSMS-ISA (database), World Bank, Washington, DC, <mark>http://go.worldbank.org/BCLXW38HY0.</mark>

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