rafmacalaba/datause-encoder-data huggingface dataset:
they would fail if these effects vary across time.<sup>22</sup> In this case, an instrumental approach using the types of variables described in the previous paragraph would be appropriate.

Additionally, measurement errors may also create such bias. Although Garcia-Mila, McGuire and Porter (1996) test for measurement errors using the Griliches and Hausman test on US annual state-level public capital data and conclude to no significant measurement problems, data based on some form of public investment indicators do need to be treated with caution. Indeed, issues of data comparability and measurement errors are likely to be responsible for the huge discrepancy in results across studies.

The first problem is the choice of proxy. An example from the cross-country approach makes the point clear. Barro (1991) and Easterly and Rebelo (1993) find that an increase in public investment in transport and communications leads to higher growth, while Milbourne at al. (2003) find the effect not to be significantly different from zero and Devarajan at al. (1996) find a significant negative effect. In the discussion of their results, Devarajan at al. (1996) partly attribute the discrepancies to the fact that their data differ in many dimensions from those used in other studies. They mention first that Barro (1991) uses different criteria to decide which part of public expenditures are productive or not, second that Easterly and Rebelo (1993) use measures that aggregate government expenditures at all levels and expenditures by public enterprises, while they restrict to central government data, and third that Easterly and Rebelo do not control for level effects of expenditures, thus mixing composition and level effects. This raises the question of the choice of indicators.

# _Choice of indicators_

As mentioned above, two main types of infrastructure proxies have been used in the empirical literature: Public capital (based on some monetary measure of public

> 22 Note also that first differencing destroys the long term relationships in the data (e.g. for labour and private capital) so it is unclear whether it still allows their identification (Duggal et al, 1999; Sturm et al., 1998).

22
we show that the magnitude of the negative effect of lockdown measures induced by the COVID-19 pandemic increases with children’s age. This negative impact is much more significant for children whose participation in schooling is no longer compulsory and, for this reason, is more likely to be permanent.

The rest of the paper is structured as follows. Section 2 presents the context, the data, and the measurement of variables. Section 3 discusses the empirical strategy and section 4 presents the results of the effect of the socioeconomic crisis induced by the COVID-19 pandemic. Finally, section 5 concludes.

# **2 Context, Data and Measurement of Variables**

## **2.1 Context**

Nigeria— the most populous country in Sub-Saharan Africa— is a federation of 36 states and 1 Federal Capital Territory (Abuja), distributed across six regions. The country is a setting of localized periodic natural hazards such as droughts and flooding. Additionally, it has been facing a decade-long security crisis in the North-East region in the form of frequent violent incursions by Boko Haram armed combatants. With the population aged 24 or less representing nearly 62% of its total population, Nigeria needs to harness its burgeoning youth’s potential to boost economic development, reduce widespread poverty, and steer its youth away from ongoing religious and ethnic violence, either as victims or perpetrators.<sup>6</sup> Yet, despite being resource-rich, 40.09% of Nigeria’s population still live in poverty (NBS, 2020). According to the 2020 edition of the UNDP’s _Human Development Report_ (UNDP, 2020), in 2019, Nigeria had a headcount of 46.40% for the population in multidimensional poverty, an internet use coverage of 42%, and a _Human Development Index_ of 0.539— which puts the country in the low human development category, with a rank of 161 of 189 countries and territories.

In 2019, the mean number of completed years of schooling was 6.7, up from 5.2 in

> 6See the World Factbook, Nigeria, 2021. Accessed online on March 11th, 2020, at `https://www.cia. gov/the-world-factbook/countries/nigeria/`

7
**Table 5: Pooled regression results: on new ratings**

|Dependent variable: Sovereign rating|**S&P**|**Moody’s**|**Fitch**|
|---|---|---|---|
|GDP growth (3-yr MA %)|-0.23***|-0.29***|-0.08 **|
|Log of GNI per capita|-0.66***|-0.77***|-0.51***|
|ICRG composite index|-.20***|-0.14***|-0.18**|
|Ratio of reserves to import and ST debt|-1.01***|-0.64***|-1.67***|
|Ratio of ext. debt to exports|0.66***|0.67***|0.96***|
|GDP Volatility (5 yr Std. Dev.)|0.17***|0.18***|0.29***|
|Dummy for first rating|-0.39|-0.19|-1.55**|
|Observations|520|514|374|
|Adjusted R-squared|0.65|0.62|0.68|

_* significant at 10%; ** significant at 5%; *** significant at 1%. White robust standard errors are suppressed._

**Table 6: Regression results: on very first rating**

|Dependent variable: First sovereign rating|**S&P**|**Moody’s**|**Fitch**|
|---|---|---|---|
|GDP growth (3-yr MA %)|-0.85*|-0.15|0.15**|
|Log of GNI per capita|-0.15|0.38|0.39|
|Rule of law|0.48|-0.06|-0.08*|
|Ratio of reserves to import and ST debt|-0.56**|0.2|-1.7**|
|Ratio of ext. debt to exports|0.19|0.28|0.06|
|GDP Volatility (5-yr Std. Dev.)|0.08|-0.16*|0.01|
|Existing rating (by another agency)|0.76***|0.91***|0.87***|
|Observations|36|28|38|
|Adjusted R-squared|0.89|0.9|0.92|

_* significant at 10%; ** significant at 5%; *** significant at 1%. White robust standard errors are suppressed._ 20
Figure 8. Male-Female difference in work stoppages (percentage points) Note: Each bar represents the male coefficient in a regression by country and survey round of work stoppages on a male dummy and (most recent) employment sector fixed effects. Asterisks denote levels of statistical significance. Source: HFPS In sum, while periods of stringent government containment measures may have had similarly far-reaching impacts on workers across the welfare distribution, in areas or times of recovery or relative normalcy in terms of mobility, those at the top may have been more shielded from the economic impacts of the pandemic.

The partially mixed picture of work stoppages (with unequal impacts only at times of lower mobility restrictions) contrasts what has typically been observed in high-income countries. While beyond the scope of the present note, as mentioned in section 1, this result may be due to the tendency of the poor and vulnerable in middle-income EAP countries to reside in rural areas and be engaged in agriculture, which was relatively less affected during the pandemic. Moreover, limited scope for home-based work, as well as higher engagement in self-employment activities in middle-income EAP may have influenced our results. The next section looks at household labor income losses, complementing the analysis on work stoppages and providing a more comprehensive picture of the economic impacts of the pandemic.

# 3. Household labor incomes: Looking beyond work stoppages

HFPS data on labor income losses may paint a more comprehensive picture than work stoppages of the extent of the pandemic’s impact on livelihoods for two main reasons. First, the HFPS typically only asks about the employment status of one or two members of the household, and overwhelmingly the household head in most countries (Table 2). Thus, to the extent that employment patterns and job characteristics differ between the head and other members of the household or that additional household members begin to work as the main income earner loses employment (added worker effect), these surveys may not fully capture the pandemic’s impact on employment. Second, employment status is sensitive to type of employment, for example whether one is a wage employee or a self-employed worker. Self-employed workers such as farmers and business owners have greater discretion over whether or not they stop working or lose their job compared to employees. For example, a shop owner may not have any customers but may continue to keep shop until they are forced to close their business. They may lay off employees as demand continues to decline, but only when the business folds will the shop owner be considered 19
# **Informal Sector Heterogeneity and Income Inequality: Evidence from the Democratic Republic of Congo**<sup>**1**</sup>

## **Franck M. Adoho World Bank**

## **Djeneba Doumbia Paris School of Economics World Bank**

_Keywords:_ Informal sector, income inequality, firm performance. _JEL classification:_ 017; 012; D21.

> 1 The authors gratefully acknowledge the comments of Jean-Bernard Chatelain, Kathleen Beegle, Jean-Louis Combes, Chadi Bou Habib, Tidiane Kinda, Aly Sanoh, Clarence Tsimpo Nkengne, Pierella Paci, Philippe de Vreyer, Romeo Gansey as well as participants to the 12th Doctorissimes of University Paris 1 Pantheon-Sorbonne, the International Monetary Fund African Department Seminar, and the 57<sup>th</sup> Conference of the Canadian Association for Economic Science.
Porto’s identification of an increasing skill premium in periods of trade liberalization in Argentina.

Though weaker, the pattern of results still holds when we include two kinds of skills , (SK+BS)/(NO-ED) and (SK-ED)/(BSED) in column 4 both of which enter with the expected signs (a strong endowment in (SK-ED)/(BS-ED) is associated with more inequality while the opposite holds for (SK+BS)/(NO-ED)). As to natural resources, adding mining and fuels (MF/L) in column 5 reveals a positive correlation between trade liberalization and inequality in countries well-endowed in (MF/L), results echoing those of Perry and Olarreaga (2006). This last specification seems to best capture the interactions between trade liberalization and factor endowments.

Table 5 quantifies the effects of a 5 percentage points reduction in tariffs on Gini coefficient value for different quartiles of the distribution of endowments. As, an example, tariff reduction reduces the value of the Gini coefficient by 1.3% for countries in the bottom quartile of the distribution of (K/L), while it increases inequality by 0.9% for those in the top quartile. A similar pattern holds for (SK-ED)/(BS-ED), with the strongest effect for the ratio (SK+BS)/(NO-ED)). Since countries with a high share of non-educated population are also likely to be poorly endowed in capital, the two effects will tend to cancel each other.

We now summarize the results of several robustness checks (these are available in the corresponding annex tables). Regarding macroeconomic and institutional variables, we used those in Lopez (2003) (Table A8). Results show that original results are robust when using these controls with all the macroeconomic variables having the expected sign (e.g. an improvement in civil liberties or an increase in government expenditure decreases inequality).

Not surprisingly, the results are less robust to the use the alternative indices of trade liberalization given in Table 18
Table 1: Life time Cost Estimates of Girls Dropping out of Primary and Secondary Education

|**Country**|**Life time**<br>**school l**<br>**m**|**Cost of early**<br>**eaving USD**<br>**illion**|**Cost of e**<br>**leavin**|**arly school**<br>**g %GDP**|**Cost of e**<br>**leavin**<br>**developin**<br>**adjuste**<br>**ela**|**arly school**<br>**g % GDP**<br>**g countries**<br>**d for labor**<br>**sticity**|**Education premium**|**pe**|
|---|---|---|---|---|---|---|---|---|
||**Primary**|**Secondary**|**Primary**|**Secondary**|**Primary**|**Secondary**|**Percentage increase in**||
|||||<br>|<br>|<br>|<br>**lifetime earnings, prim**<br>**over no edu**|**i**<br>**e**|
|Burundi|719|1970|24.83%|68.04%|21.96%|46.44%|24%||
|China|817|32336|0.01%|0.46%|0.01%|0.44%|21%||
|Ethiopia|2090|6803|2.98%|9.70%|2.47%|6.89%|15%||
|India|1315|10610|0.04%|0.34%|0.04%|0.34%|27%||
|Kenya|11501|27415|19.97%|47.60%|18.85%|41.04%|43%||
|Nigeria|34157|40366|10.76%|12.72%|8.18%|8.38%|23%||
|Norway|144|399|0.05%|0.14%|-|-|8%||
|Senegal|2801|4861|13.54%|23.51%|11.19%|16.77%|24%||
|Sweden|570|122|0.17%|0.04%|-|-|9%||
|Tanzania|8727|15833|17.86%|32.40%|14.76%|21.75%|24%||
|Uganda|3843|9742|13.23%|33.55%|11.7%|22.90%|24%||
|UK|705|4041|0.03%|0.19%|-|-|9%||
|US|29684|62783|0.21%|0.45%|-|-|8%||

Source: Authors’ computations based on data from ILO KILM, WDI and WB EdStats 31
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first-differenced sample. In column (4), the effect of rainfall is allowed to vary with the amount of risk in a household’s crop portfolio. The interaction term of rainfall and the risk index is positive and statistically significant at the 5% level. At the optimal risk level of 0.41, the marginal effect of rainfall is as high as 0.22 with a standard error of 0.15. Other variables, such as the amount of inputs allocated, total cultivated area and labor have the expected sign and are all statistically significant.<sup>35</sup> The robustness of these results can be gauged by comparing returns per hectare of different crops to the variability of these returns.<sup>36</sup> Plotting average returns against the standard deviation of these returns in Andhra Pradesh, reveals a clear positive relationship between average returns and their volatility, indicating again that the riskiness of crops is strongly correlated with returns to producing these crops (c.f. Figure 2).<sup>37</sup>

# **5.2 Does the NREGS support households in coping with shocks?**

Next, I estimate to which extent the NREGS helps households in coping with shocks. The theoretical argument presented in Section 2 is based on the premise that work provision within the NREGS sufficiently reacts to increasing demand in the case of a shock. This can be tested by analyzing whether deviations from mean rainfall levels, as well as households’ self-reported shocks, drive changes in the number of days households report to have worked for the NREGS. The analysis is restricted to phase one districts; thus only households who had access to the NREGS in both survey rounds are considered.

The results suggest that the number of days worked for the NREGS changes considerably with variation in rainfall levels (c.f. Table 3).<sup>38</sup> The greatest change is observed for

> 35The share of area under irrigation seems to increase output levels. In contrast, the dummies indicating whether or not a household applied fertilizer or high yielding variety (HYV) seeds are not statistically significant. This might seem somewhat surprising, but since expenditure on fertilizer and seeds is included in variable inputs, one should not attribute too much weight to this finding.

> 36These statistics are available at the state-level for major crops and for the years 1996 to 2006 from the Cost of Cultivation Statistics.

> 37Many of these commodities are traded internationally, such that risk-aversion of farmers alone can probably not explain the observed correlation between the riskiness of crops and their returns. Other reasons could be differences in the concentration of supply or demand between crops. Analyzing the reasons for the apparent relationship between risk and returns in crop portfolios is beyond the scope of this article.

> 38In the first two columns, the total number of days worked in the past 12 months is the dependent variable; in the last two columns it is the log of this variable.

21
**_<u>________________________________________________________</u>_**

WBES is designed to generate comparative measurements in areas like corruption, judiciary, lobbying, and the quality of the business environment. To provide the most transparent, flexible and user-friendly approach to access the data and prepare charts and tables, an interactive webtool has been developed to work with the WBES. In the data analysis, these business climate constraints are related to specific firm characteristics and firm performance. Some indicators related to trade facilitation include:

- Burden of customs/foreign trade regulations (no obstacle, minor obstacle, moderate obstacle, major obstacle);

- Quality and efficiency of public services (roads, water etc.) (from very bad to very good);

- Quality and efficiency of customs;

- Time taken to clear customs.

_Investment Climate Around The World: Voices of The Firms from The Business Environment Survey_ (G. Batra, D. Kaufmann, A.H.W Stone 2003) presents the core WBES questionnaire and survey findings, and confirms the significance of key country conditions on firm performance and behavior. The findings provide a basis for regional comparison, but suggest the need for caution when averaging across categories, especially in light of country conditions that can significantly affect firm-level sales and investment. (See <u>Annex A</u> for additional detail) _<u>Investment Climate Assessments (ICAs)</u>_ Investment Climate Assessments are comprehensive country reports that draw upon the results of Investment Climate Surveys and other available diagnostic tools. They identify and prioritize investment climate constraints, benchmark reform progress, provide cross-country comparisons of investment climate indicators, and help countries forge broad consensus on priority areas for reform. These assessments ultimately feed into World Bank operations and technical assistance.

Investment Climate Assessments have been completed for 18 countries and more are underway, including Chile, Mongolia, Oman, South Africa, Syria, Thailand, and Turkey.

Dollar, Hallward-Driemeier and Mengistae (2003) provide various infrastructure indicators for 10 countries based on firm-level surveys in Bangladesh, Brazil, China, Honduras, India, Nicaragua, Pakistan, and Peru. The standardized surveys highlight the monetary costs of bottlenecks and delays in infrastructure services such as power, telecom as well as customs administration (See <u>Annex C).</u> In addition, work is underway on _country level supply chain/ logistics studies_ that are diagnostic assessments of logistics costs for selected strategic products using a supply chain approach in several countries. The methodology is being built on information used for _Forging Subregional Links in Transport and Trade Facilitation (2001)_ . Nigeria, Kenya, Lesotho, Ghana, Honduras, Nicaragua are some of the countries where these studies will be carried out.

8 __________________________________________________________________________________________________________
‘Easier’ data collection in destination countries in no way means ‘easy’, as collecting information from migrants presents many challenges and shortcomings that prevent researchers from answering many important policy questions. Most of these are concerned with the impact of emigration on the families and communities that migrants have left behind in their home countries. These effects range from the poverty alleviation impact of remittances to the decline in health and education services when doctors and teachers emigrate. While censuses and administrative records in origin countries may provide clues on these issues, most relevant data come from surveys with special migration questions or modules. The main challenge is that questions about migrants need to be answered by a proxy, generally a family member, which introduces many imperfections, as we discuss below.

# **_Censuses_**

Censuses survey an entire population at a single point in time and are generally conducted decennially, even though there are plenty of exceptions. Everyone needs to be counted and the staff of the relevant National Statistics Office uses their expertise to reach everyone with the same, short questionnaire on mainly demographic variables. The main goal of a census is not to collect data on the migrant population, but on the whole population; the data on migration are generally a ‘by-product’.

An important distinction is between a ‘ _de jure_ ’ census, which aims to count all ‘usually resident’, and a ‘ _de facto_ ’ census, which targets all physically present at the time of census. _De facto_ censuses in an origin countries would generally, by definition, fail to 4
# Appendix Table 4: Shares of activity by type

||S|hare of to|tal Indian|mfg plant|s||Distri|bution by|sector||
|---|---|---|---|---|---|---|---|---|---|---|
||1989|1994|2000|2005|2010|1989|1994|2000|2005|2010|
|Organized sector, urban areas|0.4%|0.5%|0.4%|0.4%|0.4%|72%|70%|63%|60%|62%|
|Organized sector, rural areas|0.2%|0.2%|0.2%|0.2%|0.3%|28%|30%|37%|40%|38%|
|Unorganized sector, urban areas|21%|24%|30%|29%|42%|21%|25%|30%|29%|42%|
|Unorganized sector, rural areas|78%|75%|70%|70%|58%|79%|75%|70%|71%|58%|
||Shar|e of total|Indian mf|gemploy|ment||Distri|bution by|sector||
||1989|1994|2000|2005|2010|1989|1994|2000|2005|2010|
|Organized sector, urban areas|12%|13%|10%|11%|13%|69%|68%|61%|57%|58%|
|Organized sector, rural areas|6%|6%|6%|8%|9%|31%|32%|39%|43%|42%|
|Unorganized sector, urban areas|20%|25%|30%|30%|37%|25%|31%|36%|37%|48%|
|Unorganized sector, rural areas|62%|55%|53%|51%|41%|75%|69%|64%|63%|52%|
||S|hare of to|tal Indian|mfgoutp|ut||Distri|bution by|sector||
||1989|1994|2000|2005|2010|1989|1994|2000|2005|2010|
|Organized sector, urban areas|59%|54%|43%|43%|44%|68%|65%|56%|53%|54%|
|Organized sector, rural areas|28%|30%|34%|38%|38%|32%|35%|44%|47%|46%|
|Unorganized sector, urban areas|6%|10%|15%|13%|12%|46%|62%|65%|66%|66%|
|Unorganized sector, rural areas|7%|6%|8%|7%|6%|54%|38%|35%|34%|34%|

Notes: See Table 1a.
**ABBREVIATIONS**

ACIP: Advisory Committee on Immunization Policy AVEI: Antiviral Efficacy in Reducing Infectiousness AVES: Antiviral Efficacy in Reducing Susceptibility BMR: Blanket Movement Restrictions CDC: Centers for Disease Control (US) CFR: Case Fatality Rate DHHS: Department of Health and Human Services GAR: Gross Attack Rate GTAP: Geographically Targeted Antiviral Prophylaxis H5N1: Hemagglutinin‐5, Neuraminidase‐1 H2N1: Hemagglutinin‐2, Neuraminidase‐1 HCW: Healthcare Workers IATR: International Air Travel Restrictions MIDAS: Models of Infectious Disease Agents Study NPI: Nonpharmaceutical Intervention NVAC: National Vaccine Advisory Committee _R_ 0: Basic Reproductive Number RMR: Reactive Movement Restrictions ROW: Rest of the World SARS: Severe Acute Respiratory Syndrome SE: Southeast SEIR: Susceptible Exposed Infectious Removed TLC: Targeted Layered Containment TAP: Targeted Antiviral Prophylaxis VEI: Vaccine Efficacy in Reducing Infectiousness VES: Vaccine Efficacy in Reducing Susceptibility WB: The World Bank WHO: World Health Organization 2