rafmacalaba/datause-encoder-data huggingface dataset:
owners but below median revenue per worker, irrespective of size (6%), and older firms with three or more workers, irrespective of revenue per worker or owner’s education (5%).

The EcC results for informal firms with high predicted probability of formality also highlight older 3-24 person firms that hire wage workers, irrespective of value added per worker (10% of informal firms), and those 3-24 person firms that do not hire wage workers but have high value added per worker (0.3%) as having the highest probability of formality. It further highlights older, two-person firms with value added per worker in the top quartile (3%). Their counterparts in the second and third quartile of value added per worker come next in priority (5%).

As before, cluster analysis provides a different lens to characterize the informal firms that are most amenable to formalization. According to the analysis using ELMPS data, shown in Figure 14, these include two clusters we describe as “larger professional” (8% of informal firms in ELMPS) and “larger nonprofessional” firms” (7% of informal firms). The latter cluster in particular is also disproportionately made up of firms with collective forms of ownership and relatively high capital. Both of these clusters disproportionately hire workers from outside the household. The first hires mostly non-relatives and the second hires relatives. These are followed by clusters we describe as “shops with older owners” (14%), and “workshops” (11%). These two clusters tend to skew older in terms of firm age and the latter is more likely to hire wage workers than average. The four clusters that have the lowest share of the lowest quartile of predicted probability of formality (5%-25%) still have only 14-26% of their firms in the top quartile of predicted probability.

Cluster analysis using the EcC data, shown in Figure 15, also highlights the larger professional (2% of informal firms in the EcC) and non-professional firm clusters (14%) as well as a clusters we call “larger retail” (8%) and “non-retail with 3+ workers” (10%). Like before, firms in these clusters tend to skew older than average and to be more likely to hire wage workers. Although these clusters have the lowest shares of the lowest quartile of predicted probability of formality (8-31%) between less than 1% and 13% of firms have predicted probabilities of formality in the top quartile, although 13-38% are in quartile three.

40
# 2. Data and Measurement

## 2.1. Data

This study uses nationally representative and internationally comparable panel data from the LSMS-ISA in Ethiopia and Uganda. In Ethiopia, the survey includes 3,969 households, which are representative of rural areas and small towns. These households were surveyed in 2011/12 and 2013/14. For Uganda, we use two rounds of a nationally representative survey which interviewed 2,716 households in 2010/11 and 2011/12. As this study focuses on rural areas and small towns, we exclude Kampala from our analysis.

## 2.2. Measurement

The approach adopted in this study to characterize diversication is straightforward. We classify labor allocation and income into on-farm and o-farm. On-farm refers to engagement in crop-production and animal husbandry on own farm. O-farm encompasses engagement in paid economic activities outside the domain of one's own farm, by working for wage and salary, as a casual laborer and/or by establishing and operating non-farm enterprise(s).

We adopt various measures of o-farm engagement by exploring the size and share of gross and net o-farm income; and labor-days in o-farm activities and their share in households' labor supply:<sup>1</sup> (1) Gross on-farm income includes total income from crop, livestock and their byproducts sold and used for own consumption.<sup>2</sup><sup>_,_3</sup> (2) Net on-farm

> 1The denition of each measure slightly varies by country, due to dierences in information availability.

> 2There is no data on the amount of crop harvested in the rst round of Ethiopia's LSMS-ISA, and hence we have relied on crop disposal mechanisms (sell, giving out for free, storage for consumption and/or seed) to impute on-farm income. As a result, we could not account for the amount consumed between harvest time and the time when the households were surveyed.

> 3For Uganda, we include income from livestock services such as transportation under on-farm income.

5
1 Human capital accumulation can occur through learning in school and learning at work (Lagakos et al., 2018b,a; World Bank, 2018; Rossi, 2020; Jedwab et al., 2021c). Macroeconomic events such as downturns affect the accumulation of human capital at work, with potentially persistent consequences. However, _downturns due to pandemics are different_ . Pandemic mitigation measures include not just workplace closures but also school closures. As a result, they _disrupt human capital accumulation not just at work but also at school_ , with potentially severe long-term consequences beyond those of a typical downturn. More generally, national school closures are rare.

We explore the potential long-term economic scars from pandemic-induced disruptions to human capital accumulation at school and at work through the lens of the COVID-19 pandemic, as both workplaces and schools were shut down around the world. Significant declines in employment occurred as a result of the initial lockdowns, and continued due to behavioral changes. Given that future pandemics are likely to be similarly disruptive, the COVID-19 experience is useful for calibrating the aggregate impact of different pandemic scenarios. In addition, given that the returns to education and experience are known to vary around the world, and given that the COVID-19 shocks to schooling and employment may have affected men and women differently, such an exploration would ideally distinguish between countries at different levels of development, and also study the impact on male and female workers independently.

We quantify the aggregate impact of pandemics through a development accounting framework that tracks the accumulation of human capital in the population over time. We calibrate the framework using returns to experience and returns to education by college status and gender that are consistently estimated using 1,084 household surveys across 145 countries (1990-2016). Finally, we use the framework to compute the short and long term losses from disruptions to human capital accumulation due to pandemic shocks for countries at different income levels, and for men and women separately.

We initially treat countries as though they were _symmetrically_ affected. We find that the long-term impact of a pandemic shock through human capital accumulation can be substantial, and for most scenarios is _higher in richer nations_ .

In the most severe scenario, welfare decreases in high-income countries by 4 _._ 3 percent in perpetuity, 3 _._ 0 percent in middle-income countries and 1 _._ 9 percent in lowincome countries. Given the discount rate, this is equivalent to a one-time loss of 111
measures implemented between 2009 and 2013 slowed least developed country (LDC) exports growth significantly, costing them an equivalent of about one-third of the total exports.

While many of these studies touch on important aspects of the impacts of protectionism, none of them provide a full and consistent picture at global and at country/regional level. The goal of this paper is to fill the gap in the literature. Compared to the existing works, the novelty of the paper is manifold. First, the paper relies on the latest available data on bound, MFN, preferential and effectively applied tariffs from the World Bank WITS database, as well as the most recent snapshot of the global economy represented in the GTAP 9.2 database.<sup>2</sup> Second, the paper focuses on the impacts of protectionism on consumers, which are likely to bear the costs of global increases in protectionism. Finally, this is among the first papers that quantify the value of existing unilateral, bilateral and regional preferences.

Results show that a coordinated global withdrawal of tariff commitments from all existing bilateral and regional trade agreements, as well as unilateral preferential schemes coupled with an increase in the cost of traded services would result in worldwide welfare (real income) losses that amount to 0.3 percent or US$211 billion relative to the baseline by 2020. An important part of these losses is likely to be concentrated in regions such as East Asia and Pacific (EAP) and Latin America and the Caribbean (LAC) which together account for close to one-third of worldwide welfare losses. The impact on global trade<sup>3</sup> would be much more pronounced with a decline by 2.1 percent or more than US$606 billion relative to the baseline after three years. Second, a worldwide increase in tariffs to allowed bound rates and an increase in the cost of traded services would up legally translate into global welfare losses of 0.8 percent or US$634 billion relative to the baseline by 2020. Regions such as South Asia (SAR), East Asia and Pacific and Latin America and the Caribbean would be among the most affected, together accounting for close to half of global welfare losses. The distortion to trade flows would be significant translating into a decline by 9 percent or more than US$2.6 trillion relative to the baseline after 2020.

The magnitude of the results should be interpreted with caution. First, the paper does not consider the impact of reversing more in-depth commitments such as those related to investment, competition and intellectual property (Hoffman, Osnago and Ruta 2017;

> 2Simulations in this paper rely on GTAP database 9.2 which features as a novelty the recently updated EU-28 Input-Output tables (Rueda Cantuche et al. 2016).

> 3Trade as referred to in the discussion of aggregate results labels the average change between exports and imports.

4
_(iii)_ _<u>Gender</u>_ <u>. The base case scenario does not distinguish between genders. However,</u> women generally tend to earn less than men. The gains from education therefore differ across genders. We include two alternative scenarios with expected income profiles and unemployment rates for each gender.

_(iv)_ _<u>Income tax</u>_ <u>. We use tax-rates from 1997, the mid year for the years of our labor</u> market data. Colombia's top income tax rate was 35 percent, which was payable for yearly incomes over Col$ 50,200,000 (US$ 24,000), (Price Waterhouse, 2002). The marginal rate for the average taxpayer ranged from 0.35 percent to 23 percent. For purposes of calculation of extra income a marginal tax rate of 12 percent is applied, whereas high taxation is set to 23 percent. Workers with annual income less than Col$ 48,600,000 (US$ 23,300) may choose to deduct either mortgage interest payments, or prepaid medical assistance and educational expenses.<sup>6</sup>

# **Costs from additional education**

The costs of education stem from foregone income while studying, which depends upon the length of study time, and tuition fees: _(i) Foregone income from studying_ <u>. Studying takes up time, which will reduce time for</u> work. According to the household survey, 75 percent of adult students are employed. On average, they work 6 hours less per week than adults not attending education. Annex table 4 presents the average wage per hour for studying workers and non-studying workers. This suggests that time for studying is predominantly taken out of leisure time as opposed to working time. Based on this information, the base case scenario assumes that foregone income for time dedicated to studies amounts to 14 percent of the average monthly salary; equal to 6 hours out of the average weekly 43 working hours.<sup>7</sup>

> 6 The marginal tax rate only influences the rate of return in case the tax rate for foregone income differs from the tax rate for extra income or if tuition is paid and deducted.

> 7 The cost of studying in terms of foregone income rises with age. We implicitly assume that the value of lost leisure time is zero. As an alternative scenario, we estimate the return to education for workers with no income during studies, which is applicable for fulltime students with no job or if we account for the value of lost leisure time.

10
ANSNEX 2 33

# MAJOR SECTORAL COMPONENTS OF SOCIAL DEVELOPMENT PROJEC7TS FY90 (As % of Total Base Costs)

|County|PHN|Education|Employment<br>Training<br>Skills Dev.|Sanitation|Agiculture|Other'|
|---|---|---|---|---|---|---|
|Cameroon|28.0|12.0|38.0|-|---|22.0|
|Chad|31.0|-|18.0|34.0|-|17.0|
|The Gambia|10.0|...|22.0||26.0|42.0|
|Uganda|23.0|38.0|--|9.0|-|30.0|
|Bolivia|61.0|30.0||--|-.|9.0|
|Jamaica|47.0|49.0|--|---||4.0|

1 "Other" = predominantly cross-sectoral activities such as institutional developmcnt; community development; women in development; infrastructure; IEC and project management.
# **_Version 4b_**

Residual categories, labelled with the prefix "Other", are common in the reported data (e.g. "Other Caribbean"). As the study aims to assign every migrant to a specific country, systematically these migrants, who numbered approximately 11.9 million and 9.6 million respectively under the two definitions of foreign-born and nationality, were distributed across the countries within the "other" region. This was done according to each country’s propensity to send its nationals abroad relative to those of the other countries in "Other".

- Where:   Pr/other  = Country r’s propensity to send nationals abroad relative to the other countries located in the "other" region.

Mr = The number of migrants abroad from country r.

Ms = The number of migrants abroad from countries s in the other region.

These propensities are shares, so they sum to one. The remainder categories were therefore disaggregated by multiplying the propensity shares for those countries contained within an umbrella remainder heading by the total number of migrants to be redistributed. This process relies on a specific ordering, and a strict ordering was adhered to, in which, for each successive allocation, the largest number of migrants was used to recalculate the propensity shares. Therefore the smaller, regional remainder categories (e.g. "Other Caribbean") were disaggregated prior to the larger categories (for example "Other"). As most of the remainder headings refer to continental groupings, users who choose to aggregate the data by continent would avoid most of the approximation introduced here.

An illustrative case is that of Portugal. All migrants in the 2001 Portuguese census had been designated to specific countries with the exception of 6 migrants in the heading "Other Oceania". These remaining 6 migrants had to be shared out between the countries of Oceania. Applying the equation above, the propensity to send migrants anywhere abroad was calculated for each of these countries. Table 5 contains these propensity shares and shows how many immigrants were distributed based on these numbers for each country. This was assumed to be the most pragmatic method for distributing the remainder categories, though two weaknesses are evident. First, migrants are not distributed as integers and thus rounding is required. Second, the propensity to send migrants abroad is based on the sum of the relevant row (the total number of migrants abroad from any particular country). If it happens that a specific country sends many people to a country for which no data were collected, its propensity will be underestimated.

13
-60-

|Quantitie|<br>s (Gross O|**TABLE**4.4<br>utput), Me|xico/Braz|il(1975)|||
|---|---|---|---|---|---|---|
||**at Mex**<br>|**ican**<br>**"prices**|**"**<br>|at<br>Br<br>|azilian<br>"p|rices"|
||Mexico<br>(mill.|Brazil<br>pesos)|Mexico/<br>Brazil<br>(%)|<br>Mexico<br>(mill.<br>c|<br>Brazil<br>ruzeiros)|Mexico/<br>Brazil<br>(%)|
|**Grain Mill Products**|9,123.1<br>|13,813.3<br>|66.04|4,664.4|7,428.1|62.79|
|**Sugar & Sugar Products**<br>|6,596.3<br>|18,318.8<br>|36.00<br>|<br>4,430.4<br>|12,142.4<br>|36.48<br>|
|**Malt and Malt Beverages**<br>|10,973.8<br>|7,825.2<br>|140.23<br>|5,112.3<br>|3,429.0<br>|149.09<br>|
|**Tobacco & Tobacco Products**<br>|3,847.4<br>|10,094.6<br>|38.11<br>|2,342.6<br>|6,118.4<br>|38.28<br>|
|**Textiles**<br> <br>|17,909.9<br>|48,661.2<br>|36.80<br>|11,948.1<br>|29,420.5|40.61|
|**Footwear**<br>**& Leather**<br>**Products**<br>|6,447.3<br>|23,445.4<br>|27.49<br>|2,879.3<br>|10,291.8<br>|27.97<br>|
|**Pulp and Paper**<br>|14,607.1<br>|20,367.7<br>|71.71<br>|5,723.7<br>|10,731.1|53-33|
|**Soap and Detergents**<br>|6,335.6<br>|6,548.5<br>|96.74<br>|4,716.8<br>|3,626.9|130.05|
|Paints<br> <br>|3,460.2<br>|23,485.1<br>|14.73<br>|1,221.2<br>|7,261.2|16.81|
|**Agricultural**<br>**Fertilizers**<br> <br> <br>|4,865.7<br>|9,711.9<br>|50.09<br>|6,005.9<br>|12,096.1<br>|49.65<br>|
|**Petroleum**<br>**Refining**<br>**& Products**<br>|28,463.4<br>|43,049.9<br>|66.10<br>|37,<sup>6 </sup>27,.2'|58,024.6|64.84|
|**Tires and Inner Tubes**|4,969.8<br>|21,252.0<br>|23.38<br>|1,692.2<br>|7,209.0|23.47|
|Cement<br>|5,648.6<br>|7,041.0<br>|80.22<br>|4,557.2<br>|5,688.3|80.11|
|**Bricks**|1,636.9<br>|15,178.0<br>|10.78|365.7|6,041.1|6.05|
|Iron**and Steel**|32,836.9<br>|80,838.0|40.62|24,276.1<br>|51,525.3|47.11|
|**Radio and TV Receivers**|4,854.5<br>|12,570.4<br>|38.61|2,983.3|9,003.6|33.13|
|**Motor Vehicles**|39,425.6|125,104.0|31.51|18,362.1|57,791.5|31.77|
|**Total in our sample**|202,002.0|487,315.0|41.44|138,908.6|297,828.9|46.64|

**Source: Figures in prices of the other country derived from tables** 4.2 and 4.3; **figures in national currencies derived from table** 4.1.
# **IV. Data and Methodology**

We have quarterly balance sheet and income statement data from the CBN on all 89 Nigerian banks from 2000-2005 that allows us to investigate which bank-specific, industry and macroeconomic characteristics were the main drivers for the spreads in Nigeria. We look at the efficiency of financial intermediation especially the effects of overhead costs, bank-specific liquidity and capital holdings as well as the competitive structure of the banking industry on spreads.

# **Accounting Decomposition**

As a first exercise following Beck and Fuchs (2004), we conduct an accounting decomposition of the interest rate spread. Similar to Martinez Peria and Mody (2004), we define the spread as the difference between interest income received on loans (divided by total loans) and the interest expenses paid on deposits (divided by total deposits).

Unlike Beck and Fuchs (2004), we do not have information on ex-ante contracted interest rates so both the implicitly calculated lending and deposit rates are ex-post since they are based on the ex-post balance sheet and income statement information of the Nigerian banks. Therefore, we assume that the ex-post spread already accounts for portfolio quality, that is, loan loss provisions do not appear as a factor in the accounting decomposition.<sup>3</sup> However, spreads depend on the tax rate, reserve requirements, overhead costs and the profit margin. The tax rate is calculated from the actual tax payments of the banks, that is, one minus the after-tax over the pre-tax income, and the reserve requirement is 10%. We use the overhead costs attributable to loans and identify those by calculating the share of loan interest revenue to total revenue. In other words, the relevant overhead costs are equal to the share of loan income multiplied by total overhead costs over total loans. The profit margin is a residual after accounting for all the components and can be expressed as follows (Beck and Fuchs, 2004): _Profit Margin= (1-tax rate) x (implicit lending rate- implicit deposit rate/ (1- reserve requirement)- overhead costs attributable to loans),_

> 3 In the panel estimations, we will rectify this by including loan loss provisions as an explanatory factor for the interest rate spreads.

8
**Table 3 Fixed Effect Regression of Labor Force Participation Rate (LFP) and** **<u>Weekly Working Hours (WWH) at the Cluster Level, 1995-2002</u>**

||LFP<br>I|LFP<br>II|WWH<br>III|WWH<br>IV|
|---|---|---|---|---|
|Remittance|-0.037<br>(0.018)**|-0.022<br>(0.047)**|-0.059<br>(0.300)|-0.022<br>(0.594)|
|Remittance (t-1)|-0.020|-0.012|-0.024|-0.023|
||(0.220)|(0.372)|(0.692)|(0.650)|
|Remittance (t-2)|-0.009|-0.006|0.005|0.024|
||(0.534)|(0.603)|(0.926)|(0.556)|
|Standard deviation of remittance|0.000||0.000||
||(0.247)||(0.283)||
|Skewness of remittance|-0.019<br>(0.235)||-0.117<br>(0.042)**||
|Real wage|0.010<br>(0.741)|0.007<br>(0.816)|-0.100<br>(0.364)|-0.065<br>(0.545)|
|Average education|0.020|0.009|0.319|0.321|
||(0.433)|(0.680)|(0.001)***|(0.000)***|
|Average Household Size|-0.032|-0.040|0.150|0.083|
||(0.230)|(0.066)*|(0.129)|(0.303)|
|Mean expenditure|-0.048|-0.033|-0.055|0.380|
||(0.780)|(0.824)|(0.930)|(0.501)|
|Standard deviation of expenditure|-0.106|-0.139|-0.533|-0.579|
||(0.662)|(0.511)|(0.547)|(0.466)|
|Percentage of food stamp recipients|-0.037<br>(0.701)|0.026<br>(0.771)|0.024<br>(0.947)|0.360<br>(0.277)|
|Share of male headed household|-0.025|-0.025|-0.087|-0.005|
||(0.658)|(0.587)|(0.668)|(0.978)|
|Share of poor people|-0.165|-0.156|0.057|0.263|
||(0.163)|(0.141)|(0.895)|(0.507)|
|Constant|1.417<br>(0.002)***|1.218<br> (0.000)***|4.118<br> (0.015)**|3.335<br>(0.009)***|
|Observations|206|222|206|222|
|Number of cluster|120|123|120|123|
|R-squared|0.285|0.239|0.285|0.239|

p values in parentheses * significant at 10%; ** significant at 5%; *** significant at 1% In summary, the remittance income has a negative impact on the labor market participation both in the cross-section analysis at the individual level and the panel analysis at the cluster level. However, the effect of remittances is not strong for the labor supply of employed workers. The empirical analysis reports that people with higher remittances are more likely to have a higher reservation wage, resulting in moving out of labor force, or being less enthusiastic about finding jobs.

15
mechanisms such as cash transfers targeting the poor seems to be a valid option. In other regions, like Segou for example, where vulnerability seems to be driven as much by high consumption volatility as by low mean consumption, introducing insurance mechanisms may also be a viable policy to consider.

**Figure 7**

<!-- Start of picture text --> Vulnerability Incidence Among the Poor<br>By Region<br>Idiosyncratic Risk Covariate Risk<br>Source: ELIM 2010.<br>The horizontal line represent the average vulnerability incidence among the poor (56.4%).<br>Figure 8<br>Vulnerability Incidence Among the Non-Poor<br>By Region<br>Idiosyncratic Risk Covariate Risk<br>Source: ELIM 2010.<br>The horizontal line represent the average the vulnerability level among the non-poor (32.7%).<br>Kayes Koulikoro Sikasso Ségou Mopti Tombouctou Gao Kidal Bamako<br>Kayes Koulikoro Sikasso Ségou Mopti Tombouctou Gao Kidal Bamako<br>80<br>60<br>40<br>Vulnerability Incidence (%) 20<br>0<br>40<br>30<br>20<br>Vulnerability Incidence (%) 10<br>0<br><!-- End of picture text --> Figure 9 suggests that poverty and vulnerability in Sikasso are equally high in all districts. Plotting the poverty rate against the vulnerability incidence at the district (cercle) level, we 20