rafmacalaba/data-use-mentions-extended huggingface dataset:
chosen, the food poverty line in each region is then established using the _basket_ of quantities of the _national_ reference group, but _region specific_ commodity prices.' We use unit values for our food price estimates obtained from dividing expenditures by reported quantities. Bidani and Ravallion (1993) and Ravallion and Bidani (1994) use separately collected price data. The main advantage of using unit price estimates is that they can be derived from the survey. Especially in a period of high inflation, it is important that the price and expenditure data correspond to the same reference time. A disadvantage is that products may not be homogenous within a commodity category. Wealthier household can consume more luxurious varieties of a commodity and therefore pay higher unit prices.

We attempt to correct for the 1product heterogeneity problems that arise from using unit prices instead of separate price data by using predicted prices at the poverty line. If households indeed switch to more luxurious varieties as they get richer, this would result in a positive significant estimate of per capita consumption in the unit price regression. By taking the predicted price at the poverty line, we use the unit prices that are relevant for the poor. We use quantile (median) regression methods because since a regression is performed for each commodity in each region sample sizes are small and median regression is less sensitive to outliers.<sup>2</sup> lOne could set multiple nutritional intake targets for the consumption basket to achieve with the gain of realism about nutritional adequacy with the loss of symphony.

**2** A similar procedure in the construction of poverty line in Indonesia is used by Alatas (1997). A quantile regression using the results to median is the same as the LAD (Least Absolute Deviations) estimate.

_5_
likelihood of corruption is higher when regulatory processes are complex and/or unclear. Furthermore, in such cases, firms are pushed out of the formal sector and start operating in the informal economy. Djankov et al. (2002) analyze firm entry regulation in 85 countries and find that in the economies with more heavily regulated business entry the level of corruption and the size of unofficial economy are considerably higher, while the quality of public and private goods is lower. The authors also conclude that opaque regulation of entry is beneficial for bureaucrats and politicians, as it can serve them as a rent extraction mechanism. Conversely, relatively light regulation of entry is commonly observed in the economies with more democratic governments.

Focusing on a similar subject, Ulyssea (2010) argues that high costs of entry are linked with higher level of informality, worse labor market performance and lower welfare of an economy. Hence, reduction in the cost of entry to the formal sector is a key element for shrinking the informal economy. Yet, firms operating informally have different reasons and incentives for doing so with regards to regulatory reforms. For instance, while some are prompted to resort to informality due to unwieldly regulations, others remain informal for a limited time - until owners find wage jobs. Moreover, Ulyssea (2018) employs data on Brazilian firms to investigate the response of informal firms to formalization policies as well as the effects of such policies on economic performance and development. The author highlights the importance of differentiation between the two margins of informality – non-payment of registration fees and payment of informal wages to workers – for policy formulation and analysis. This research demonstrates a negative relationship between enforcement and both informality and welfare creation. Entry costs reduction in the formal sector has weaker effect on the decrease of informality, yet strong positive effect on welfare gains, GDP growth and wages. Similarly, Maican and Orth (2018) analyze Swedish retail food stores data and establish that, by enhancing competition, lower entry costs increase welfare in differentiated product markets.

According to
to an annual average will result in either an over- or underestimate of total annual cases. Correcting this bias is often difficult without knowledge of seasonal variations.

In spite of all these difficulties a reasonable estimate has been made of the number of cases and prevalence of diarrhea in the population, along with the number of DALYs per 100,000 cases of diarrhea. The figures summarized in Table 4.1.

**<u>Table 4.1: Baseline Data for Estimating Health Impacts</u>**

||**Baseline**|**Source:**|
|---|---|---|
|Under-5 child mortalityrate in 2006|52-82|NFHS-3|
|Diarrheal mortality in children under 5 years (% of child<br>mortality)|14 %|Office of Registrar General<br>(2004)|
|Diarrheal 2-week Prevalence in Children under 5years|8.9-9%|NFHS-3|
|Estimated annual diarrheal casesper child under 5years|1.85-1.87|Estimated from NFHS-3|
|Estimated annual diarrheal cases per person (> 5 years)|0.37-0.56|International experience<br>(Krupnick et al,2006)|
|Hospitalization rate (% of all diarrheal cases) –children<br>under 5years|0.15%|NSS (2004)|
|Hospitalization rate (% of all diarrheal cases) –children<br>under 5years|0.3-0.6 %||
|Percent of diarrheal cases attributable to inadequate water<br>supply,sanitation and hygiene|90 %|WHO (2002b)|
|DALYs per 100 thousand cases of diarrhea in children<br>under 5|70|Estimated from WHO tables|
|DALYs per 100 thousand cases of diarrhea in persons >5<br>years|100-130||

16
sup> _p_ < 0.01. MOB indicates the Facebook mobility index with the mobility level in February 2020 as the baseline. Individual and household characteristics include age, sex, education, household size, dependency ratio, self-employment, industries, internet access, and consumption quintiles.

# **5. Discussion and conclusions**

This paper examines spatial heterogeneity of the impacts of the early days of the COVID-19 pandemic on urban households’ incomes in Ethiopia and Kinshasa. Drawing on new panel household surveys, the fixed-effects regression analysis for Ethiopia finds that households in large and densely populated towns were exposed to higher risk of labor income reductions at the beginning of the pandemic and their recovery was slower than other households. Disadvantaged groups, such as female, low-skilled, selfemployed, and poor workers, particularly suffered in large and densely populated towns. In the case of Kinshasa, we find that labor incomes are more elastic to the changes in mobility among workers— particularly female and low-skilled workers—who live in areas that are located farther from the city core area and/or highly dense and precarious neighborhoods.

Our analyses do not allow to distinguish specific paths in the COVID-19 impacts on urban households’ incomes. The disadvantaged groups may have been particularly vulnerable to mobility shocks, as they need to travel around for their jobs in self-employment, with no option to work remotely. Their employment may also be unstable due to the lack of written contracts and informality. In large and densely populated towns, people are less likely to travel during the pandemic in view of the high contagion risk. The disadvantaged group of workers who live far from their workplace must have been hit hard by mobility restrictions, further reducing their incomes. In the short term, it is important to provide direct support to those households in the form of, for example, cash transfers. In the medium term, it might be effective to build their resilience against shocks by improving their accessibility to jobs. This could be achieved by reducing the distance between jobs and residential
24 months of program exposure the illness rate of the treatment group was 39.5 percent lower than the control group, a difference that is significant at the 1-percent level.

With the help of the central project implementation office, we are able to use administrative data to add another measure of the availability of the program. The mean length of exposure is about 14 months (with a substantial standard deviation of six months). (Appendix Figure B.1 shows the distribution of the length of exposure to the program.) Table 7 shows the distribution of children, by age in years at Round 3, across four exposure categories. We restrict our estimates to children who lived in barangays that had at least four months of exposure at Round 3 because children with less than four months of exposure are unlikely to have had enough exposure to the program to show any measurable impact, particularly given initial start-up problems in modifying effectively existing programs or in introducing new ones. As shown in Table 7, this involves dropping 252 children from the analysis. Two barangays received the program inputs about a year and a half before Round 1, so we exclude these two barangays (N = 33 children); in these cases the matching variables, measured at baseline, are not likely to be exogenous to the 17
Table 7a reports the regressions based on the scale of mining operations. When we estimate the models using the USGS data, the results show statistically insignificant effects on per capita growth, population density, spatial Gini and Sen Index in treated districts with small-scale mining operations. With the exception of the significant negative effect on population density, the results in districts with small-scale mining operations remain the same for other outcome variables when we estimated the models using the World Bank’s real prices data. The table also shows that, when fitting the model using the USGS data, the estimates on treated districts with large-scale mining operations are statistically not different from zero for average annual lights per capita growth, population density, and spatial inequality, but positive and different from zero for the Sen Index. Similarly, when we use the World Bank’s real prices data, the lights per capita growth, population, and spatial inequality remain statistically insignificant, while the Sen Index remained significant, albeit at the margins. Moreover, when we use the World Bank price data the coefficients of real prices remain insignificant for all variables except the Sen Index, indicating the significant welfare impacts of changing real mineral prices. The expectation effect remains the same as before, but at the 10 percent significance level with the USGS data compared to a 1 percent significance level with the World Bank data.

Table 7b reports the regressions based on the aggregate extracted minerals’ values. When we fit the models using the USGS data, the results show insignificant effects on the average annual lights per capita growth, population density, spatial Gini, and Sen Index in the treated districts engaged in extracting the high-value minerals. Similar results hold when we fit the models using the World Bank’s real price data. Additionally, the table, using USGS price data, reports statistically insignificant impacts on average annual lights per capita growth, population density, and spatial inequality in the treated districts engaged in extraction of low-value minerals. Nonetheless, the impacts on the Sen Index are positive and
11-V _f(T_ _-t)_ where _d,_ =2 Ronn and Verma (1986) model equity as being dividend protected and therefore dividends do not appear in the previous equation. The BlackScholes formula thus defines a one-to-one mapping between the unknown asset value and the observed equity value.

The relationship between the equity and asset volatility, which can be obtained by applying Ito's Lemma to equation (3), is used by Ronn and Verma (1986) as the second restriction where<sup>**_0_**</sup> **_E_** is the standard deviation of equity retums. The derivation of equation (4) can be found in the Appendix.

Since the market value of equity is observable and the equity volatility can be estimated, two nonlinear restrictions are now in place for identifying two unknowns. Using data on total debt (deposits), bank equity, and equity volatility, equations (3) and (4) can be solved simultaneously for V and cJ. Given these values, equation (2) is used to solve for the value of deposit insurance per dollar of deposits, which we interpret as the gross safety net subsidy. In order for this approach to be valid, the time until maturity, T, of the put and call options must be the same. Ronn and Verma (1986) use Merton's (1977) assumption that the time until maturity of the debt is equal to the time until the next audit. They interpret the strike price of the put option to be equal to total debt of the bank instead of total deposits only. This assumes that all the debts of the bank are insured and that they are issued at the risk-free interest rate.

From equations (2)-(4) it follows that the implicit safety net subsidies can be expressed as a function of only three known variables: the equity volatility, the ratio of equity to deposits, and the dividend yield. Table 1 in the Appendix presents this
-4-

# 2. Data Source

The main data source for this study is the C8te d'Ivoire Living Standards Survey (CILSS), conducted between February 1985 and February 1986. The survey canvassed a nationally representative sample of 1,600 households. Information was collected on a wide array of subjects, including income, expenditures, assets, employment, and socio-economic characteristics of the household and its members. Table 1 liats the different sections of the CILSS questionnaire and their content.l/ The education module recorded self-reported literacy and numeracy, educational attainment of all household members aged five or above, educational expenses of household members enrolled in the last twelve months, and education of children who no longer live in the household. The education module is reproduced in Appendix 5.

CILSS interviews took place in two rounds, two weeks apart, which made possible the set-up of a system of in-field detection and correction of interviewer and measurement errors. To that effect, microprocessors were installed in Abidjan and in the regional statistical offices where the field teams were based. After the first round, the questionnaires were immediately delivered to the team's regional office. Since it was almost completely precoded, the information on the questionnaire could be entered directly on computer diskettes, thereby avoiding the conventional step of coding (which is often very slow and susceptible to error). Using a custom I/ A further description of this questionnaire can be found in Grootaert (1986).
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that were laid off to continue to have access to (own-farm, self) employment. As seen by the decisions of many laid-off workers, the flexibility of China’s rural economy is based in part on the fact that almost all households have access to contracted land.

By definition, however, this means that there were some that were hurt. Specifically, households/individuals that lost access to the land that they had been farming prior to the return of the unemployed necessarily were less well off. Who were these households/individuals? We do not have data on all individuals in the village so we are not able to measure the effect on everyone. However, our survey did ask about rental contracts between landlords (contractors) and tenants (contractees). While rental land accounts for almost 20 percent of cultivated area in 2008, the vast majority of the land was rented to either family members or to other households in the same village that are considered “friends.” Indeed, according to our data, in 2008 96.3 percent of rental contracts were between family members or friends. This does not dampen the negative impact on contractees when rented land was taken back. But, it almost certainly allowed for more amicable settlements and renegotiations. In addition, it should be noted that, according to our data, 92 percent of rental agreements are either specified for one season or one year. Because of this, also, there was little reason to believe that there was tension over the breaking of an agreement.

# **5. Recovery: One Year after the Crisis**

Nearly one year after the financial crisis started, the CNRS data show that China is far down the road to recovery from the financial crisis—at least in terms of rural employment (Table 4). In total, 124 individuals who were in the labor force and working 21
**A1.4 European Bank for Reconstruction and Development (EBRD)**

The EBRD is an intemational organization dedicated to fostering the transition towards open market-oriented economies and promoting private and entrepreneurial initiative in the countries of Central and Eastern Europe and the Commonwealth of Independent States (CIS). The EBRD is based in London, and as of May 1999 can be reached at http://www.ebrd.orQ.

The EBRD publishes an annual Transition Report, which includes a number of governance variables in its Transition Indicators and Survey of Legal Reforms. We use data from the 1998 edition of the Transition Report.

# **Transition Indicators**

The Transition Report The EBRD presents eight "Transition Indicators" whose goal is to represen **t** he cumulative progress in the movement from a centrally planned economy to a market economy." The indicators are based on an a subjective analysis of objective data in a number of issues (checklist). Table A1.4 presents a defailed description of these indicators.

# **Legal Reform Survey**

In 1998, the EBRD conducted a survey of local public officials, private firms, academics, lawyers, and other experts, in order to assess the progress made in financial legal reform in transition economies. The survey considered two areas of financial legal reform: banking and securities activities. For each area, two indices describing the extensiveness and effectiveness of the financial legal framework were developed, for a total of four ratings. The "extensiveness" ratings measure how closely legal r ules affecting investment follow international standards. "Effectiveness" reflects how clear, accessible and adequately-supported the legal rules are. Both are intended to provide a measure of how conducive the laws of these countries are to fostering investment. This survey covered 26 countries. Table A1.4 presents a detailed description of each of the ratings.

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