rafmacalaba/datause-encoder-data huggingface dataset:
Policy ReseaRch WoRking PaPeR 4466

# **Abstract**

Infectious disease outbreaks can exact a high human and economic cost through illness and death. But, as with severe acute respiratory syndrome (SARS) in East Asia in 2003, or the plague outbreak in Surat, India, in 1994, they can also create severe economic disruptions even when there is, ultimately, relatively little illness or death. Such disruptions are commonly the result of uncoordinated and panicky efforts by individuals to avoid becoming infected, of preventive activity. This paper places these “SARS type” effects in the context of research on economic epidemiology, in which behavioral responses to disease risk have both economic and epidemiological consequences. The paper looks in particular at how people form subjective probability judgments about disease risk. Public opinion surveys during the SARS outbreak provide suggestive evidence that people did indeed at times hold excessively high perceptions of the risk of becoming infected, or, if infected, of dying from the disease. The paper discusses research in behavioral economics and the theory of information cascades that may shed light on the origin of such biases. The authors consider whether public information strategies can help reduce unwarranted panic. A preliminary question is why governments often seem to have strong incentives to conceal information about infectious disease outbreaks. The paper reviews recent game-theoretic analysis that clarifies government incentives. An important finding is that government incentives to conceal decline the more numerous are non-official sources of information about a possible disease outbreak. The findings suggest that honesty may indeed be the best public policy under modern conditions of easy mass global communications.

This paper—a product of the Chief Economist's Office in the World Bank's East Asia and Pacific Region—is part of a larger effort in the Region to better understand the economic aspects of infectious diseases, with a view to developing more effective policies to alleviate their human and economic costs. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at mbrahmbhatt@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
Chifunde districts of Mozambique. Within each community, a random sample of 20 female farmers were surveyed along with their husbands. The survey is the baseline of an ongoing randomized controlled trial to assess the effectiveness of a human capital program targeting female farmers. The survey elicited information on both spouses’ cognitive and socio‐emotional skills, as well as their time and risk preferences. The spouses were asked these modules separately and privately.

Second, we use data from a survey conducted in June‐July 2016 across 180 villages in the Bas‐ Sassandra, Comoé, Lacs and Lagunes districts of Côte d’Ivoire. The survey is the baseline of an ongoing randomized controlled trial to assess the impact of improving rubber farmers’ access to technologies and markets. The survey elicited information on the socio‐emotional skills, and time and risk preferences of the farmers that applied to the program, as well as of their spouses. The spouses were asked these modules separately and privately.

Third, we use data from the second wave of the Malawi Third Integrated Household Panel Survey (IHPS), conducted in 2013. This is a publicly available longitudinal survey that is representative of the Malawian population. For the rural sub‐sample, the survey elicited information on the cognitive and socio‐emotional skills of up to two plot managers. These are typically the husband and the wife, which are the focus of our analysis.

In each country, we restrict the sample to couples for whom we observe the corresponding skills and preferences of both spouses, as well as the other covariates used in the analysis. This results in working samples of 2,280 couples in Mozambique, 1,407 couples in Côte d’Ivoire, and 531 couples in Malawi.

# **2.2. Measuring Skills and Preferences**

Socio‐emotional skills were measured in the three countries by asking respondents to rate their level of agreement with different attitudinal statements (see Appendix A1). In Mozambique and Malawi, these items elicit abilities commonly associated with an entrepreneurial mindset, such as perseverance, locus of control, and optimism: some of the items are identical or similar between the two countries, but not all of them. In Côte d’Ivoire, these items focus on self‐esteem 3
Page 15 The area of land developed between 2001 and 2011 has increased at a slower rate than the population, resulting in higher densities in the built-up areas in 2011 than in 2001. This increase in average built-up density is exceptional for a city of the size of Ahmedabad. Cross country analysis over cities in across the income range shows that when household income rises, built up densities go down (Angel 2011). Given that between 2001 and 2010 the nominal median household’s income has increased by 170%, **this countertrend in density land development shows a serious land supply constraint** . (It is important to note that we speak here of built up density only, not overall density. That figure is lower due to the fragmented nature of development at the periphery, see Figure III.5) This land supply constraint, however, is not caused by a lack of transport infrastructure – as it is often the case in other parts of India – Ahmedabad has developed a large number of arterial roads during the last 10 years – but by severe regulatory constraints that are discussed in a section below.

The areas occupied by slums have barely increased by 2% over 10 years, while the slum population has increased by 18%. This implies that a densification of existing slums occurred during this period. The morphology of slums allows this densification through the occupation of existing open space or the subdivision of existing dwellings. Thus contrary to some popular perceptions, slums and challis do not waste large tracts of valuable urban land. They are using this resource more and more parsimoniously as slum/challi populations expand—much more so than other urban development.
related to the fact that having two girls might have a direct effect on parental labor supply.

Initially, the first-stage regressions for the just-identified models that link the potentially endogenous fertility variable to only one instrument are the following: where the sibling sex-composition instrument is measured by three dummy variables, _same sex_ , _two girls_ and _two boys_ , indicating whether the sex of the first child is the same as that of the second child. Given the Albanian parental preference for sons, _Fijs_ is expected to be positively correlated with _same sex_ and _two girls_ , but negatively correlated with _two boys_ . Both the first and second stage regressions include fixed effects for districts _λj_ and survey years _μs_ . Also, in order to avoid potential biases in the estimation of the standard errors, an arbitrary structure of covariance is allowed by computing clustered robust standard errors at the primary sample unit (PSU) level.<sup>24</sup> Furthermore, since the _same sex_ binary variable is easily decomposed into two variables indicating the sex composition of the first two children, namely _two boys_ and _two girls_ , an overidentified model with these two instruments and one endogenous variable can be estimated by the following first-stage regression: In this specification, either _b_ 1 or _b_ 2 must be dropped from the list of covariates because _b_ 1, _b_ 2, _two boys_ and _two girls_ are linearly dependent. Thus, I chose to drop _b_ 2. It is important to clarify that the results are not sensitive to this choice, or to the elimination of both _b_ 1 and _b_ 2, as is shown in the first-stage results presented in the following section.

# **6 Results**

## **6.1 First-Stage**

Apart from the raw estimation analysis used to test the relevance condition of the instruments, this section examines in more depth this condition through the first-stage estimation. Table 7 reports the first-stage results linking sex-mix and fertility, where columns (1) to (3) show the results for the just-identified models without covariates, columns (4) to (6) show the results after adding covariates, and column (7) shows the results for the over-identified model with covariates.

> 24The primary sampling units (PSUs) are geographically defined area units, which are selected with a probability proportional to size and they are very similar to a village level unit. The PSUs are already constructed in all the LSMS surveys and also in the DHS.

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8percent of public law enforcement agencies had conttacted out t<sup>t</sup> l protection of public property.<sup>14</sup> Turnkey prisons built and operated by private firms and water supply services, large solid waste disposal and gas recovery waste-to-energy plants are other examples of new privatization activity.<sup>15</sup> There is speculation that the technology of limited access highway networks with electronic road pricing systems (eliminating the need for toll booths) might offer scope for further privatization in the tran.-portation sector. Experimental programs in Minnesota have be .anded by the state for school districts (local) to contract out to groups of teacherr the task of teaching non-core subjects such as art and music.

The privatization experiences of the U.K. and U.S.A. have several noteworthy features. First of all, in both countries a combination of severe budgetary constraints, increasing failure of the public delivery of some services, and a strong political commitment in favor of the private sector contributed significantly to the progress of privatization. The reduction in the federal support to state and local governments and the drying up of other sources of revenue were important factors in the U.S. The rising budget deficits of the goverTament and the need to reduce public borrowing requirements weighed heavily in the U.K. context. Second, the relatively fast pace of privatization in the U.K. and U.S. seems to be 14/ ICMA, The Munici2al Yearbook, 1983 (Washington, D.C.), pp. 216-217.

- 15/ Sandra Bellush, "Private Choices," American City and Country, October, 1985, p.<sup>62</sup> .
|Over Credit<br>Outlook” (S&P<br>2020)||labor force,<br>productive<br>capital, and<br>productivity –<br>is unchanged,<br>meaning that<br>output after<br>the<br>coronavirus<br>shock returns<br>to its original<br>path.||Top Global Risk:<br>Coronavirus restrictions<br>less systemic globally,<br>with sector variations (risk<br>level: elevated)|growth loss of 0.3 percentage;<br>The U.S. and Europe may<br>experience minimal reductions<br>in growth, while the impact is<br>largest in East Asia.|
|---|---|---|---|---|---|
|“The Global<br>Macroeconomic|G-Cubed<br>Multi-|-6 sectors and<br>24 countries;|Scenario 1|China Shock, Low<br>Severity,|GDP loss (2020):<br>China = -0.4%;|
|Impacts of<br>COVID-19”|Country<br>Model –|Long-run stock<br>equilibrium||Temporary. Attack rate<br>for China = 1%; Case|USA = -0.1%;<br>Japan = -0.3%;|
|(McKibbin and|Global|obtains||fatalityrate China = 2%|Korea = -0.1%|
|Fernando 2020)|hybrid<br>DSGE/CGE|through the<br>adjustment of<br>asset prices;<br>Nominal wages<br>are sticky and<br>adjust over|Scenario 2|China Shock, Middle<br>severity, Temporary.<br>Attack rate for China =<br>10%; Case fatality rate<br>China = 2.5%|GDP loss (2020):<br>China = -1.9%;<br>USA = -0.1%;<br>Japan = -0.4%;<br>Korea = -0.2%|
|||time based on<br>country-<br>specific labor<br>contracting<br>assumptions.;|Scenario 3|China Shock, High<br>severity, Temporary;<br>Attack rate for China =<br>30%; Case fatality rate<br>China = 3%|GDP loss (2020):<br>China = -6%;<br>USA = -0.2%;<br>Japan = -0.5%;<br>Korea = -0.3%|
|||Short run<br>rigidities;<br>Heterogeneous<br>households<br>and firms|Scenario 4|Global Shock, Low<br>severity, Temporary;<br>Attack rate for China =<br>10%; Case fatality rate<br>China = 2%|GDP loss (2020):<br>China = -1.6%;<br>USA = -2%;<br>Japan = -2.5%;<br>Korea= -1.4%|
||||Scenario 5|Global Shock, Middle<br>severity, Temporary;<br>Attack rate for China =<br>20%; Case fatality rate<br>China = 2.5%|GDP loss (2020):<br>China = -3.6%;<br>USA = -4.8%;<br>Japan = -5.7%;<br>Korea = -3.3%|
||||Scenario 6|Global Shock, High<br>severity, Temporary;<br>Attack rate for China =<br>30%; Case fatality rate<br>China = 3%|GDP loss (2020):<br>China = -6.2%;<br>USA = -8.4%;<br>Japan = -9.9%<br>Korea = -5.8%|
||||Scenario 7|Global Shock, Low<br>severity, Permanent;<br>Attack rate for China =<br>10%; Case fatality rate<br>China = 2%|GDP loss (2020):<br>China = -2.2%;<br>USA = -1.5%;<br>Japan = -2%;<br>Korea = -1.3%|

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# **2. Empirical Strategy**

Our ultimate goal is to estimate the impact of aid receipt on household consumption outcomes. To do that, we address the potential endogeneity of household aid receipt by relying on instrumental variable regressions and novel instrumental variables that are strong predictors of household aid receipt but that do not directly affect household consumption outcomes, except through their impact on household aid receipt. Our instrumental variables are derived from a monthly data set on humanitarian aid distribution data provided by the World Food Programme and covering the period 2012-2017. Data is provided at the traditional authority (TA)-level and includes food and cash aid recipient households.

Following the instrumental variable strategy pursued in Ravallion and Wodon (2000), we first estimate TA-level aid distribution as a function of explicit geographic targeting characteristics as well as other factors that may affect aid receipt. We do this for three reasons. The first is to generate evidence on factors affecting aid distribution, which provides important policy-relevant evidence in and of its own right. The second is to establish that included variables actually do a good job predicting placement, supporting the validity of our instrumental variable strategy. The third reason is to evaluate the potential for identifying additional instruments.

Specifically, we argue that the rainfall metrics used by the Food Assistance Response Programme (FARP) and the Malawi Vulnerability Assessment Committee (MVAC)<sup>3</sup> introduce exogenous variation in the aid distribution. Previous work on the impact of extreme weather events on crop production outcomes, using the same data set informing our analysis here, evaluated a wide range of GIS-based drought and flood measures, and determined that the best drought and flood measures were constructed using different variables and data sources than those used by the FARP (McCarthy et al., 2021). The

> 3 The FARP was called the Food Insecurity Response Programme in 2015 and 2016.  The FARP relies, in part, on inputs provided by MVAC.  MVAC is a government-led committee set up in early 2002, following the 2001-2002 hunger crisis. It is comprised of government ministries and institutions, UN agencies and NGOs, together with a small secretariat that provides technical and administrative support.

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6 mostly fixed, this indicator tends to decline over time. It should therefore not be a surprise that in mature, developed systems NRW should be showing relatively lower values, whereas systems that have been built more recently and hence have significant spare capacity are likely to have relatively higher water losses as managing a system with large overcapacity tends to be complicated. Because of the drawbacks associated with the use of this indicator, the International Water Association (IWA) has recommended that this NRW indicator not be used.

IWA is recommending alternative indicators such as the water losses per connection, water losses per main length and the infrastructure leakage index (Alegre et al., 2006; Winarni, 2009). The infrastructure leakage index is a complex indicator as it also measures pressure in the system. Yet, collecting pressure data for a utility is complicated as pressure can vary widely within the piped water system and hence is useful as an indicator when improving the system’s overall performance but cannot be easily used as an indicator to review network losses between utilities as averaging such an indicator for a utility may not provide very meaningful data except to reflect that there is an underlying problem that needs to be addressed. Hence, in this paper we will focus on the remaining indicators that are being used to measure water losses: water losses per main length and water losses per connection.

# **_Size of NRW_**

The empirical analysis is conducted using a unique database of water utilities, namely the International Benchmarking Network for Water and Sanitation Utilities (IBNET). The use of data from IBNET guarantees that the data are relatively homogeneous and comparable across countries. For each country, we have annual data on utilities’ performance. The IBNET database contains information on performance from 1,861 water utilities in 2010 serving nearly 513 million people with water in 12,480 cities and towns. This is approximately 14 percent of the total population of all households with piped water access in the world or nearly 45 percent of the urban population of developing countries. The database represents the equivalent of more than US$40 billion in annual revenue in 2010. The utilities represented in the database employ about 623,000 professional staff. As participation in IBNET is voluntary and the data collection process
# **4. Identification strategy**

We identify the effects of the reform with a difference-in-difference strategy that compares formal firm creation before and after the reform across regions based on the level of distrust in regional commercial courts. Our main regression model takes the form: Log�# new firms�,���α� �β� �δreform� ∗distrust� �ε�,�, (1) where the outcome variable is the log of the number of new firms in region _r_ in year _t_ , and α� and β� are region and year fixed effects, respectively. The variable _reform_ t is equal to 0 before 2005 and equal to 1 after (and including) 2005. The variable _distrust_ r is the measure of distrust in the court responsible for region _r_ . As described in section 3, it represents the fraction of survey respondents in region _r_ who say they have at least some distrust in courts. The coefficient δ measures the effect of the reform on the number of newly registered firms. We expect it to be positive and statistically significant, indicating that more firms registered in years after the reform compared to years before the reform and this difference was larger in regions with higher levels of distrust in courts. The number of observations in equation (1) is 108 (12 court-regions times 9 years of data). Standard errors are bootstrapped<sup>5</sup> and clustered at the region level.

The identification assumption is that, in the absence of the reform, the level of distrust in courts would not have been correlated with the change in number of new firms over time. To test whether this assumption is plausible, we estimate the following variant of equation (1) using prereform data only, i.e. data for the years 2003 and 2004: Log�# new firms�,���α� �β� �γyr2004� �δyr2004� ∗distrust� �ε�,� (2) where yr2004� is equal to 1 in year 2004 and equal to 0 in year 2003, and all variables are defined as in equation (1). If the level of distrust in courts was not correlated with the change in outcomes before the reform, the coefficient δ will be equal to zero. Columns 1 and 2 of table 6 shows the coefficient δ from estimating equation (2) for our two different measures of new firms (based either on reported year of registration or year the firm entered the database). The coefficients for both variables are not statistically different from zero, suggesting that our identification assumption is plausible.

To measure the effect of the reform on our other outcome variables of new firms (log mean employment size, 1-year and 2-year survival rates, and the 1-year employment growth rate), we

> 5 Using the vce(boot) option with 50 replications in STATA.

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Table 8: logit estimates of demographic and household structure variables'

|||**women**||**me**<br>|**n**<br>|
|---|---|---|---|---|---|
||Wife|single<br>Imother|unmarried<br>no kids|husbands<br>_|1unmarried<br>no kids|
|_Marital status_||||||
|married|-0.11 *<br>(0.058)|||-0.021<br>(0.19)||
|separated|0.82**<br>(0.31)||--|2.18<br>(1.2)||
|marry||-0.68*<br>(0.31)|-0.65<br>(0.81)||--|
|consensual union||-1.0*<br>(0.41)|2|__|1.09<br>(0.81)|
|_Children_||||||
|newborn|-0.12*<br>(0.055)|-0.35<br>(0.34)|---|-0.49<br>(0.46)||
|# children age 1-5|-0.093**<br>(0.023)|-0.9<br>(0.12)||0.46**<br>(0.14)||
|change in#<br>children age_1-5_|-0.41**<br>(0.15)|-0.18<br>(0.46)|---|-0.34<br>(0.45)||
|#childrenage 6-11|0.017<br>(0.017)|0.016<br>(0.074)||-0.16<br>(0.071)||
|change in#<br>children age 6-11|_-0.15_<br>_(0.15)_|-0.29<br>(0.34)|---|-0.003<br>(0.32)||
|# daughters age<br>12-17|0.069*<br>(0.033)|-0.04<br>(0.084)|---|-0.068<br>(0.066)||
|change in#<br>daughters age 12-<br>17|0.28<br>(0.37)|1.2<br>(1.45)|---|0.29<br>(0.67)||
|sons age 12-17|-0.0051<br>(0.031)|0.0052<br>(0.081)|---|0.09<br>(0.064)||
|change in # sons<br>age 12-17|-0.35*<br>(0.17)|0.27<br>(0.45)|---|0.027<br>(0.4)||
|# additional<br>children|-0.28**<br>(0.063)|-0.18<br>(0.22)|---|0.12<br>(0.21)||
|#fewer children|0.33*<br>(0.12)|0.43<br>(0.23)|---|-0.027<br>(0.27)||
|_Non-head adults_||||||
|# females age 18-<br>64|-0.11**<br>(0.031)|-0.063<br>(0.068)|-0.099<br>(0.29)|0.073<br>(0.043)|-0.0017<br>(0.21)|
|change in#|0.087|0.85|---|0.31|-|
|<br>females 18-64|(0.3)|(1.14)||(0.54)||
|# males age 18-64|-0.14**|-0.19*|-0.24|0.057|-0.44|
||(0.029)|(0.074)|(0.39)|(0.043)|(0.27)|

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