rafmacalaba/data-use-mentions-extended huggingface dataset:
Policy Research Working Paper 10327

# **Abstract**

Understanding the specific factors associated with cholera outbreaks is an integral part of designing better approaches to mitigate their impact. This paper uses georeferenced case data from the cholera epidemic that occurred in Harare, Zimbabwe, from September 2018 to January 2019. The paper applies spatio-temporal modeling to understand how the outbreak unfolded and the factors associated with higher risk of being a reported case. The study highlighted a number of findings. First, using call detail records to estimate weekly population movement throughout the city, the results suggest that human movement helps to explain the spatiotemporal patterns of the cases observed. In addition, the results highlight a number of sociodemographic risk factors and suggest that there is a relationship between cholera risk and water infrastructure, with populations living in close proximity to the sewer network with high access to piped water being at higher risk. A possible explanation for this surprising observation is that sewer bursts led to the contamination of the piped water network, turning access to piped water, usually assumed to be protective, into a risk factor. Although further studies are required to test this hypothesis, if it is true, it highlights the importance of maintenance for achieving the Sustainable Development Goals of improved water and sanitation infrastructure.

This paper is a product of the Water 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 gjoseph@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
- 4 -

# **II. RECENT DEVELOPMENTS IN ENTERPRISES AND TRANSPORT INFRASTRUCTURE**

6. In Mozambique, the manufacturing sector is generally thin, while some agribusinesses exist. As of 2016, there were about 58,000 firms registered ( **Figure 2** ). Since 2014, the formal business sector has been growing rapidly. Historically, the firm distribution is highly concentrated in major urban areas. The City of Maputo and Maputo Province account for about half of the total enterprises, followed by Gaza, Manica and Niassa ( **Figure 3** ). The northern provinces generally have a fewer enterprises, meaning that few job opportunities are available to people living in those areas, except for agriculture.

7. As far as “new firms” are concerned,<sup>1</sup> the 2014/15 national firm census data indicate that the traditional major cities experienced some reduction in the number of firms during the early 2010s. New firms seem to have been emerging in some nontraditional areas, such as Sofala, Nampula and Tete Provinces ( **Figure 4** ).<sup>2</sup> Although these changes are still marginal compared with the existing business base of the traditional urban areas, it can be interpreted to mean that some firms do not prefer to be located in traditional areas, such as Maputo, possibly because of increasing congestion and high business costs. Non-traditional areas may have become more attractive because of enhanced regional integration with neighboring countries.

8. Different industries are located in different places, depending on their nature and requirements. For instance, agribusiness, which is one of the promising industries in Mozambique, is concentrated in agricultural production areas ( **Figure 5** ). Manica and Sofala Provinces are considered to have the great advantage in packaging fruits and vegetables, meat and dairy products, and processing of seafood and rice (Ministry of Industry and

> 1 With available data, it is defined by a net increase (
being exhaustive. Among others, future research could look at the impact channels in detail in order to deliver a more nuanced understanding of why and under which conditions e-procurement delivers the hoped for impacts. On the one hand, a more detailed understanding of administrative preconditions for successful e-procurement reform can be explored by combining data from the procuring entity survey with procurement records. On the other hand, a better understanding of the constraints imposed and opportunities presented by different bidding markets could be investigated by additionally drawing on bidder registration data and a tailored bidder survey.

98. While more research is needed in this area, our findings already lend themselves to a range of policy recommendations in and beyond Bangladesh: i) Introduce and expand the scope of e-procurement systems to encompass all public procurement tenders; and ii) Capitalize on the available data in the e-procurement system by developing a contract performance dashboard which enables continuous monitoring and optimization of processes in real-time.

37
in preschool availability during eligible ages—suppose that there few preschools when the child is aged 3 and the government builds more preschools when the child is aged 4.

Our annual panel affords us sufficiently frequent observations of mothers; however, our preschool data are only available roughly once in three years. Restricting our analysis to PODES years only could be problematic because we can only leverage variations as mothers transition in and out of eligibility with an older child growing out of, and a younger child reaching, preschool ages. Suppose that in year 2000, the mother only has one young child and in 2003, she has two young children. Her labor force participation calculus likely changes with the additional child. We must assume that individual preferences for work do not change over time and that variations in work participation are attributable only to variations in preschool availability. We must also assume that preschool availability does not affect individual preferences for children. Suppose that no preschools were available when a mother has a preschool-aged child and she decides not to have another child, so she can work. Since we only observe her three years later, we can only identify changes in preschool eligibility and not variations in preschool availability during eligible ages. We do not have to make these additional assumptions in DDD because we can compare across eligible mothers subjected to different preschool availabilities. We explore the validity and present the results of each strategy in the next section.

# **5. Results**

This section describes the effect of public and private preschool availability on female employment outcomes in Indonesia. We first begin by examining the extensive margin of employment: work participation. Do preschools serve as an adequate alternative to childcare, enabling women to partake in employment? We describe the results across different empirical strategies and different treatment of missing years, without preschool data in PODES. Subsequently, we explore the intensive margins, such as the types of occupations and sectors that women go into. Given that preschool only provides 3 hours of childcare per day on average, can it
resource-based industries, and the sectoral energy inputs as a share of total sector output. Last, we control for basic industry structure and scale effects by including the following controls: total industry employment; mean plant size; the Herfindahl index of firm-level concentration; the share of plants controlled by multi-unit firms; and the share of plants controlled by foreign firms. These variables proxy for sectoral differences in the size distribution of firms, for potential differences in the location patterns of multi-unit multinationals, and differences in ‘business culture’.

As suggested by our theoretical model in Section 2 and Appendix A, controlling for inputoutput links and changes in international trade costs is important. We construct two controls that proxy for the costs of international trade and the proximity to customers and suppliers. Concerning the former, we construct measures of industry-level trade exposure (exports and imports), broken down by broad country groups – nafta, oecd excluding nafta, and low-cost countries. in international trade in international Changes exposure broadly capture changes trade costs, as well as changes in foreign productivity and the competitive environment in 2. general. We provide details and descriptive statistics in Appendix B. We also discuss possible endogeneity concerns there. Turning to the proximity to customers and suppliers, we construct 11We also constructed proxies for labor market conditions using the non-production to production workers ratio and other educational characteristics of the workforce. The latter are available at a more aggregated industry level ( _L_ -level) from Statistics Canada’s klems database (e.g., the share of hours worked by all workers with a university degree, and the labor productivity index). These measures, however, are not statistically significant in the time series because they change quite slowly over time and are thus soaked up by the industry fixed effects.

13
and also a decrease in productivity due to out-migration. In areas around directly affected market/city _K_ , the autarkic region expands leading to an increase in manufacturing in areas which are farther away from _K_<sup>0</sup> and were specialized in agriculture before the trade cost shock; the home-based manufacturing displacing specialized agriculture in this case. It is clear from equation (5), that an increase in transport cost shifts the SMS inward as well as leading to a decrease in manufacturing employment in areas closer to _K_<sup>0</sup> . The overall impact on the employment shares for the entire area surround city _K_ is thus ambiguous.

The movement of population and changes in manufacturing share of employment affect the proportion of built-up areas in both the routes; the built-up density increases around _J_ and decreases around _K_ (proposition 3).

The propositions 1-3 are based on the assumption of no population growth. With population growth, proposition 3 would need to be modified. With positive population growth, the proportion of built-up areas may not decrease around _K_ but the increase around _J_ would outpace that around _K_ . For tractability, the model considers only two economic centers ( _K, J_ ) _._ In the empirical estimation elaborated later, we utilize a measure of market access proposed by Redding and Turner (2015) that takes account of access to all possible markets.

# **(5) Data**

To estimate the impacts of the trade shock caused by the civil war in Cˆote d’Voire on Mali and Burkina Faso, we utilize population census and satellite data sources. To detect sunk investment, we rely on built-up density from global human settlement data, and the employment data come from population censuses. Agglomeration is measured by concentration of skilled (higher educated) labor, and the change in provision of electricity is captured by average night-light luminosity.

Population censuses are available for 1996 and 2006 for Burkina Faso and for 1998 and 2008 for Mali. The first period in the
: (a) female resources: education, responsibility for child care, (b) female economic activity: time use, work actually done by women, whether paid or, more commonly, unpaid, and (c) female poverty: Finally there is need to enrich our knowledge and expand information on measuring poverty in terms of its multi-dimensional nature. There is very scarce reliable information on the social indicators of welfare for Nigeria. It is necessary to collect annual or periodic monitoring indicators for measuring changes in living and welfare conditions. This is fundamental in understanding the deprivations faced by the poor and in designing poverty alleviation programs. A critical analysis of coping strategies of the poor can help design safety net programs for those hardly hit. Also poverty alleviation is not feasible without a comprehensive employment policy. There is very little information about the functioning of labor markets, wage formation and labor legislation. Gender dimensions of poverty need to be understood more clearly to be able to design programs that can target the poor women and children. An integrated "food poverty" analysis together with "money-metric" poverty measures to address the nutritional and health consequences of the poor, is also a high priority.

In summary:

- a) Nigeria needs to establish a demand driven poverty monitoring system which will include a poverty monitoring unit, and a program of both quantitative and qualitative surveys;

- b) The FOS should continue to collect basic welfare indicators through an annual household survey. The current NISH can provide the basis, but a number of improvements can be made
. 2022; Khamis et al. 2021; Kugler, Viollaz, et al. 2021). For instance, Bundervoet et al. (2022) find, based on a sample of 31 countries, that more than a third of respondents reported having stopped working since the onset of the pandemic, and almost two-thirds of households reported a decrease in total income. These job and income losses were associated with significantly higher levels of food insecurity at the household level.

The early analyses conclude that the welfare impacts precipitated by the economic crisis were not evenly distributed either across or within countries. Across countries, employment and incomes dropped sharply almost everywhere, and nearly all LICs and LMICs saw poverty increase in 2020. Globally, the pandemic led to a broad shock across the global income

> 3There is a substantive literature documenting the important benefits of public health policies to flatten the curve of COVID-19 contagion through masking, social distancing, contact tracing and vaccination in the context of developing countries that is beyond the scope and focus of this paper.

3
concept of poverty, for reasons analogous to those mentioned above, it is unclear how of much of this increase could be attributed to the crisis.

As for work on the relative weights of the El Nino and financial components in the overall impact of the crisis, this has remained limited to the suggestion that the El Nifno phenomenon may have had a greater impact than the financial crisis, based on sectoral employment and output information (Ponciano et al. 1998). Such a general statement could also be surmised from the sectoral growth rates presented in Table 1, which show a greater slump in the agricultural sector. However, this is no more than a conjecture since it can hardly be presumed that El Niuo's effects were confined to the agricultural sector. Nor can it be presumed that the financial crisis affected only the non-agricultural economy. In addition, the distributional impact within each sector could also be quite diverse, which makes it difficult to assess the poverty impact of the two shocks from their contribution to the impact on sectoral output or employment levels.

# **3. Data**

# _3.1._ **_1998 APIS survey: an opportunity?_**

The 1998 APIS survey was designed to be a longitudinal survey forming a panel with the 1997 FIES. 23,150 households (59.8% of the APIS sample) were thus common to both surveys. While these data could in principle provide us with a direct measure of welfare change, the potential usefulness of the longitudinal nature of these data was seriously impaired by problems of comparability of income and consumption across the two surveys. The comparability of income was impaired because the reference period used in the APIS is limited to a six-month period (from April to September 1998), while the FIES incomes relate to the full calendar year (January to December 1997). A partial-year recall of incomes introduces unknown seasonal 6
<br>10-15 months in the South and in the West, 11-18 months in the East, 10-11 months in the North<br>Source: Calculation with ESS data<br><!-- End of picture text -->

## **Figure 1 Selected European countries by share of extended labor force employed informally, 2008-2009**

_Notes:_ Informal self-employment includes all non-professional self-employed operating solely, as well as employers with 5 or fewer workers. Informal employees are those working without a contract (or those uncertain of their contract). Extended labor force includes persons which, during the reference week, were either employed or unemployed and willing to work. The latter category includes both those unemployed who were actively looking a job and those who were not actively looking for a job. See Tables 1 and 3 for details. _Source:_ Calculation with ESS data.

13
1<br>Source: 2012 1-2-3 Survey, DRC|||||||||

The adoption of the VAT eroded the purchasing power of all households. Household aggregate food consumption decreased, with the highest losses, measured by the compensating variation, occurring among rural poor households. The relative drop in aggregate food consumption varies between 10 (for 8 percent price increase) and 12 (for 12 percent price increase) percent (results not shown).

In this sub-section, we investigated the poverty impact of the VAT reform. We first calculated counterfactual welfare aggregates by adding the welfare loss to the 2012 adjusted food consumption aggregate. Then, we generated new food poverty lines that accounted for the changes in relative prices. To do so, we calculated the welfare loss among those whose food consumptions were in a 15 percent bandwidth above and below the food poverty line, and we added the welfare loss corresponding to each scenario to the food poverty line prevailing in 2012. Finally, we derived the three poverty measures of interest (poverty headcount, poverty gap, and squared poverty gap) in each scenario using the appropriate food consumption aggregate and food poverty line.

Not surprisingly, the adoption of the VAT increased the food poverty headcount by 1.2 and 1.4 percentage points in the scenarios of 8 and 12 percent increase in prices, respectively. In both urban and rural areas of residence, the VAT reform led to worsening of the living conditions of the households based on the poverty headcount. The poverty gap and the squared poverty gap further supported this deterioration in the food poverty status due to the adoption of the VAT. The short-term effect of the adoption of the VAT, hence, contributed to the high level of poverty headcount recorded in 2012.

22
plans, ranging from computable general equilibrium models to micro-simulations tools, all with both their strengths and weaknesses.<sup>40</sup> Monitoring performance against these benchmarks poses a number of serious data challenges. There has been huge progress in collecting the primary household survey data. When the World Bank’s current global poverty monitoring effort began in 1990 the estimates used 22 surveys for 22 countries (Ravallion, et al., 1991). Today we use over 850 surveys for 125 countries—over six per country; the latest estimates use a “global” sample of 2.1 million households. However, many problems remain. There are persistent lags and uneven coverage. The surveys used here cover 90% of the population of the developing world as a whole in 2008, but this varies from 94% in East Asia to only 50% in Middle-East and North Africa. There are continuing concerns about the comparability of the surveys over time and across countries. And there are continuing concerns about under-reporting and selective compliance in household surveys; the rich are hard to interview, and that task is not getting any easier. The weak integration of “macro” and “micro” data is also a long-standing concern, warranting far more attention than it has received. Our collective success in addressing these and other data problems will determine how confident we are about both these benchmarks and how close we are getting to reaching them in the future.

> 40 A useful compendium of the tools available can be found in Bourguignon et al. (2008). On microeconomic simulation methods see Ferreira and Leite (2003).

20
(as a percentage of GDP) sharply declined from about 30% to less than 20%.

After defaulting on commercial bank loans in 1983, the country undertook a series of foreign debt reschedulings with both official and private creditors. For the next decade, Cote d'Ivoire instituted a series of trade, fiscal, and monetary reforms. First, from 1985 to 1987, the government removed a series of import quotas, in order to increase competition and reduce prices. The government removed quantitative restrictions and unified effective rates of protection across intermediate and final goods, which reduced the competitive advantage of domestic manufacturing firms vis-à-vis foreign exporters (Harrison, 1994).

> 8 Authors‟ calculation based on IMF World Economic Outlook nominal GDP data.  Cote d‟Ivoire also accounts for close to 4% of Sub-Saharan Africa nominal GDP and more than 20% of ECOWAS‟ output (authors‟ calculations based on IMF data).

> 9 From 1960 until his death in 1993, the country was led by Houphouet-Boigny.

7
use within-country variation in ...scal institutions. This approach, in e¤ect, eliminates the unobservable country speci...c ...xed e¤ects that may in‡uence budget de...cits. The problem of omitted variables is thus alleviated; however it is not eliminated.<sup>24;25</sup> Finally, if some of the variables in the analysis are not measured accurately, there is the potential for errors-in-variables bias, which usually dampens the e¤ect of interest. Although in our empirical model the primary explanatory variable, MTEF status, can be observed with a reasonably high degree of precision, there is still scope for measurement error in the other explanatory variables.

Our empirical strategy exploits within-country variation in ...scal institutions. Annual data allow us to account for the possibility that observed ...scal performance in a given year may not represent long-run equilibrium values, because of incomplete adjustment in other variables. For example, as revenues cannot be perfectly anticipated budget balance in a given year ‡uctuates around the equilibrium balance level. To allow for the possibility of partial adjustment, we use a dynamic speci...cation with a lagged dependent variable: where yi;t is a measure of ...scal performance, L is the number of lags of the dependent variable, MTFFi;t; MTBFi;t; MTPFi;t are indicators of the three MTEF phases, xi;t is a vector of covariates, and "i;t is an error term that contains country and year ...xed e¤ects: with the idiosyncratic error �i;t assumed to be mean zero.

While this dynamic panel formulation allows for a richer model of ...scal adjustment, the presence of a lagged dependent variable introduces new sources of endogeneity. First, without controlling for the ...xed e¤ects, the model in equation (5) has a built-in positive bias in the .