rafmacalaba/datause-encoder-data huggingface dataset:
Mohanan, P.C., “Differentials in the Rural-Urban Movement of Workers,” _The Journal of Income and Wealth_ , 30(1), (2008): 59-67.

- Moretti, Enrico, “Human Capital Externalities in Cities,” in Henderson, J.V. and J.F. Thisse (eds.), _Handbook of Regional and Urban Economics Volume 4_ (Elsevier, 2004): 22432291.

- Mukim, Megha, “Industry and the Urge to Cluster: A Study of the Informal Sector in India,” SERC Discussion Papers 0072, (2011).

- Nataraj, Shanthi, “The Impact of Trade Liberalization on Productivity: Evidence from India’s Formal and Informal Manufacturing Sectors,” _Journal of International Economics_ , forthcoming (2011).

- Nelson, Andrew and Hirotsugu Uchida, “Agglomeration Index: Towards A New Measure of Urban Concentration,” World Bank WDR Paper 2009 (2008).

Planning Commission, “Eleventh Five Year Plan 2007-2012,” Government of India (2008).

- Rossi-Hansberg, Esteban and Mark Wright, “Establishment Size Dynamics in the Aggregate Economy,” _American Economic Review_ , 97(5), (2007): 1639-1666.

- Schneider A., M. Friedl and D. Potere, “A New Map of Global Urban Extent from MODIS Satellite Data,” _Environmental Research Letters_ , 4(4), (2009).

- Shaw, Annapurna, and Kavita Pandit, “The Geography of Segmentation of Informal Labor Markets: The Case of Motor Vehicle Repair in Calcutta”, _Economic Geography_ , 77(2), (2001): 180-196.

- Sivaramakrishna, K.C., A. Kundu and B.N. Singh, _Handbook of Urbanization_ (New Delhi: Oxford University Press, 2005).

- Sridhar, Kala Seetharam, “Impact of Land Use Regulations: Evidence from India's Cities,” _Urban Studies_ , 47(7), (2010): 1541-1569.

- Srivastava, Vivek, Pooja Gupta and Arindam Datta, “The Impact of India’s Economic Reforms on Industrial Productivity, Efficiency and Competitiveness: A Panel Study of Indian Companies, 1980-97,” National Council of Applied Economic Research Report (2001).

- United Nations, _World Urbanisation Prospects: The 1999 Revision_ (New York: United Nations Population Division, 2001).

World Bank, “Reshaping Economic Geography,” World Development Report (2009).

- World Bank, “World Bank Urban and Local Government Strategy”, Urban Development Unit (2011a).

- World Bank, “Perspectives on Poverty in India: Stylized Facts from Survey Data,” Economic Policy and Poverty Sector (2011b).

24
13 impression that policies are becoming less distorted in this period but, because the upward trend in the NRA is caused by an increase in import taxes, the TRI correctly reveals that agricultural policies are in fact becoming more restrictive in this time period.

The WRI series for all covered products is necessarily positive and everywhere lies above the TRI series (Figure 2). The WRI series correctly demonstrates the negative welfare consequences that flow from both negative and positive price distortions. Furthermore, the WRI series provides a better indicator of the welfare cost of distortions than the average level of assistance or taxation, due to the inclusion in the WRI of the ‘power of two’. A weighted arithmetic mean does not fully reflect the welfare effects of agricultural distortions because the dispersion of that support or taxation across products has been ignored. By contrast, the WRI captures the higher welfare costs of high and peak levels of assistance or taxation. That is, the WRI reflects the disparity issue discussed in Lloyd (1974): the larger the variance in assistance levels, the greater the potential for resources to be used in activities which do not maximize economic welfare.

The aggregate African results mask country-level diversity in the TRI and WRI series. Some countries — such as Cote d’Ivoire, Ethiopia, Sudan, Tanzania and Zimbabwe — persistently restrict trade (in aggregate) throughout the period under analysis (Table 3). Other countries — such as Kenya, Zambia and Mozambique — have had periods in which policies in aggregate have expanded agricultural trade slightly. In terms of the WRI, there is less diversity across countries, since WRI measures are all necessarily positive (Table 4). The extent to which agricultural policy reduced aggregate welfare does differ across countries, however. Some countries have low reductions in welfare, including Uganda and most cotton-exporting countries. Figure 3 provides a snapshot for 2000–04 of the diversity in the WRI and TRI for each of the 19 countries, with the weighted African average in the middle.

A useful way of understanding the overall welfare reduction for Africa from agricultural policy is to compute the country contributions to the WRI for the 19 African focus countries as a whole. Contributions can be found by computing dollar values of the welfare reduction for each country (by multiplying the WRI percent by the average of the gross value of production and consumption at undistorted prices). Such contributions will therefore take account of the magnitude of national WRIs as well as the significance of each country in terms of its share of the gross value of
<!-- Start of picture text --> Electricity: Hidden Costs - Total<br>Percent of GDP<br>30.0<br>28.0<br>26.0<br>24.0<br>22.0<br>20.0<br>18.0<br>16.0<br>14.0<br>12.0<br>10.0<br>8.0<br>6.0<br>4.0<br>2.0<br>0.0<br>2000 2001 2002 2003<br>Average 2000 Average 2001 Average 2002 Average 2003<br>AlbaniaArmeniaAzerbaijan Belarus BosniaBulgaria Croatia GeorgiaKazakhstanKyrgyz RepublicMacedoniaMoldova PolandRomaniaSerbia and MontenegroRussia Tajikistan TurkeyUkraineUzbekistan<br><!-- End of picture text -->

# **_Natural Gas Sector_**

Across the Region tariffs set below cost-recovery levels accounted for 70 percent of total hidden costs, collection failures at 29 percent, and unaccounted losses at 1.0 percent. Hidden costs for natural gas remained below 2.0 percent of GDP for all years and in all countries except Ukraine (in earlier years) and Uzbekistan, where they were between 3.0 and 6.0 percent. Overall in the Region hidden costs declined from US$ 6.6 billion in 2000 to US$ 4.7 billion in 2002. In absolute values, Ukraine had the largest decrease in the three years prior to 2002 (a drop of US$ 749 million); in relative terms Poland had the largest decline—64 percent in the same three years.

- _Natural gas tariffs and cost recovery._ Region-wide, in relative terms, hidden costs due to tariffs set below cost recovery declined from 0.8 percent to 0.6 percent of GDP, despite a 13 percent increase in end-user consumption for the period ending in 2002. This shows a convergence between average cost-recovery prices and weighted average end-user tariffs. In constant 2001 US$, hidden costs from tariffs set below cost-recovery levels declined 35 percent—from US$ 5.1 billion in 2000 to US$ 3.3 billion in 2002. The largest decline in hidden costs occurred in Central and Eastern European (CEE) countries. Azerbaijan, Belarus, Kyrgyz Republic, and Uzbekistan increased over this period.

- _Natural gas—collection rates._ During 2000-02, hidden costs due to collection failures remained largely unchanged at around 0.4 percent of GDP, roughly US$ 90 million.  This is consistent with relatively small changes in collection efficiency. Other than Tajikistan,<sup>3</sup> ECA countries improved their collection rates very gradually, reflecting diminishing scope for potential revenue leakages due to poor revenue collection institutions. Marked differences existed in collection rates across the Region; Georgia’s rate remained low at 25 percent; Croatia, Moldova, Poland, and Turkey

> 3 Tajikistan’s collection rates for the first three years were in the 50-55 percent range, and spiked to 100 percent in 2003.

8
# Institutional mapping

The purpose of institutional mapping is to provide a schematic description of institutions involved in infrastructure provision, policy making, regulatory functions, and their funding. The infrastructure institutional mapping is divided into two templates:

- Template 1. Jurisdictional responsibilities in infrastructure service delivery: An outline of infrastructure service delivery as mandated to different providers (central government, stateowned enterprises [SOEs], and local governments), listed by name and country, with an indication of whether their funding is on- or off-budget.

- Template 2. Special funds financing infrastructure: An overview of special funds—(specifying if they are extrabudgetary funds)—used to finance infrastructure. For each identified fund, the overview includes the name of the administrating agency, the source of funding, as well as the objectives of the fund.

Institutional mapping should be done once per country, but be revised and validated periodically, since the universe of operators is always evolving (as operators cease to operate, change names, new comers can enter the market, divestitures can occur) Indicative sources of information for the institutional mapping exercise are as follows:

- Sectoral policy documents and reports available from the World Bank, African Development Bank, Organisation for Economic Co-operation and Development, the U.K. Department for International Development, and other donor and university Web sites.

- Acts/laws governing institutions, their roles and responsibilities, including the decentralization of infrastructure services.

- National government Web sites, in particular those of relevant line ministries.

- Acts/laws governing each special fund.

- Fund-specific Web sites.

- Interviews with experts.

The qualitative documentation of institutions and their sphere of action is the _first and arguably the most important step in the successful completion of the exercise_ . Provision of infrastructure is fragmented and increasingly decentralized. Consequently, data and information sources are varied and fragmented as well. The data will only make sense if institutional, legal, and procedural information is well understood, particularly since the aggregation and generation of spending indicators require careful processing to avoid double counting while guaranteeing comprehensive (representative) coverage. Splitting expenditures across sectors is an enormous challenge if feasible at all (as in the case of multisector water and power utilities or hydropower investments that involve power and irrigation investments).

Infrastructure institutional mapping also helps in defining the scope and depth of the spending on public infrastructure (SPI) application. It is increasingly common that a myriad institutions and subnational governments provide infrastructure and channel public funds. One hundred percent spending coverage might be not only impossible but also impractical. Establishing the scope and depth of the SPI 14
either change the way people respond, or their behavior. For example, Zwane et al. (2010) summarize the results of five experiments in which the frequency of surveying was varied. They find being surveyed more frequently lowers reported child diarrhea rates and leads to more use water treatment products and take-up of medical insurance. However, they find no effect of being surveyed on borrowing behavior. The risk is therefore that in some settings and for some outcomes, repeated surveying may serve as reminders to increase the salience of neglected actions. Finally, repeatedly asking individuals the same questions may change reporting due to fatigue. For example, individuals who are repeatedly asked about expenditure may quickly learn that they can reduce the length of the questionnaire by saying “no” to questions which when answered positively lead to lengthy detailed questions about what was spent.

**4.** _Cost considerations._ Surveying a smaller cross-section over multiple survey waves can result in a different cost than doing a larger cross-section single baseline and follow-up. Whether the cost is higher or lower will depend on country context. On one hand, a smaller cross-sectional sample with more survey waves allows a smaller survey team to be used for longer periods of time, potentially resulting in higher average quality enumerators. In addition, it can lower the costs of listing since a smaller cross-sectional sample is used. Of greatest cost savings in many experiments is that it also avoids the costs of paying for the intervention for as many units. On the other hand, the team is in the field for a longer period of time, which can raise costs. As a general rule, the higher the per-unit treatment cost, the more likely it is that doing multiple measures per unit treated is optimal.

**5.** _Learning by doing._ Even in cases where researchers have some power to detect a treatment effect with one post-treatment survey round, it can be useful to plan multiple survey rounds relatively close together in time. This allows researchers to quickly analyze the early results from the first follow-up survey, and based on these, ask followup questions in the next post-treatment survey to explore new hypotheses generated by the results.

**6.** _How do we know what ρ is?_ Power calculations already require substantial insights from researchers in terms of what the likely mean, standard deviation, and treatment effect size of interest are. Pilot surveys or existing data are often used to give some sense of likely

23
6 of its assets outstanding in Mexico.<sup>4</sup> The Brady package for Mexico's debt restructuring started a period of dramatic reversal. The package was implemented with the help of a major cash infusion by the World Bank, the IMF and Japan (cf van Wijnbergen (1991)); private capital started to return almost immediately. The Government's initial response was, to let interest rates fall rather than quantities flow in; interest rates dropped by twenty percentage points within days of the announcement of the package (Figure 4). Interest rates increased towards the end of 1989 and prior to the implementation of the deal in the first months of 1990. However, once the package was implemented, and once doubts about Mexico's ability to finance the build up of the various guarantee funds required (as well as the gap created by the re-timing of interest payments) were allayed, private money came back and interest rates resumed a sharp downward trend.

The analysis of capital flight on which we draw (Eridean et alii 1992) only uses annual data, so timing within 1990 cannot be picked up; but the analysis (cf. Figure 3) clearly shows a dramatic reversal in 1991. For the first time in the entire period covered, flight capital came back. Recorded capital inflows also shot up. commercial banks started to issue Euro CDs, DFI shot up by a factor of 5 in two years, and portfollo investment (largely in response to the very succesful privatization program) soared (Table i). over 4 Some have argued that the continued officiaL Lending shows that those institutions are uLtimately salt and their seniority status of dubious stwiding CluLow and Rogoff (1988)). This is in our vies a serious misinterpretation of events. Roth institutions Lent at such a large scate because in their vies Mexico's adjustment policies deserved a chance and were Likely to succeed; economic efficienrcy accordingly justified borrowing against the future recovery of output. Later events clearly justified that vies. And as to seniority, it was the tough commerciaL barns who saw their debts written down in 1989fl0, whitae both the IMF and borLd Bank Loans are meticulousLy serviced, even now when both have entered a period of negative tronsfers. One can entirely plausibly argue that the commerciaL banks' failure to support Mexico's reform program (beyond debt rescheduling and reLatively samiL aunomts of rnw money) was as shortsighted as their uncritical bankroLling of Lcpez-PortilLoas unsustainabLe fiscal expansion was in earLier periods.
**Figure 3: Agricultural GDP, agricultural emissions from methane and nitrous oxide, 2005**

<!-- Start of picture text --> CO2 emissions Constant 2005<br>(megatonnes) U.S. dollars (billions)<br>140 35<br>Agricultural GDP (right axis)<br>120 30<br>100 25<br>80 20<br>60 15<br>40 N2O and CH4 agricultural emissions (left axis) 10<br>20 5<br>0 0<br>Estonia Tajikistan Macedonia, FYR Moldova Latvia Kyrgyzstan Slovenia Bosnia & Herzegovina Armenia Lithuania Georgia Albania Turkmenistan Azerbaijan Croatia Bulgaria Slovakia Belarus Kazakhstan Czech Republic Hungary Ukraine Uzbekistan Romania Poland Russian Federation Turkey<br><!-- End of picture text -->

**Source: WRI (2011) and World Bank (2011).**

59
# **Higher Losses and Slower Development in the Absence of Disaster Risk Management Investments**

Stephane Hallegatte<sup>1</sup> , Mook Bangalore<sup>1</sup> , Marie-Agnes Jouanjean<sup>2</sup> 1 Climate Change Group, World Bank, Washington, DC 2 International Economic Development Group, Overseas Development Institute, London, UK

## **Acknowledgements**

The authors thank Reinhard Mechler, Junko Mochizuki, Emily Wilkinson, Swenja Surminski, and Adrien Vogt-Schilb for very useful comments on previous drafts of this paper.

## **Keywords**

Disaster risk management, resilience, development, economic losses

## **JEL Codes**

Q54, O10, D81, G11
# **3.2 Research Design**

The purpose of this paper is to identify the impact of public health insurance on households’ OOP spending for medicines and the incidence of informal insurance mechanisms used to cope with these expenditures and other financial consequences of health shocks.

In an ideal experiment, one would randomly grant NHIS access to districts and compare household responses to health shocks in districts with access to the NHIS to the counterfactual behavior of ex-ante similar households in districts without access to the NHIS. Absent this, we exploit the fact that the NHIS was rolled out simultaneously to the GLSS survey in some districts. We construct _d_ , a measure of the months between the date of interview and the NHIS implementation date in the district of residence. Normalizing the date the district adopted the NHIS to zero, the survey contains households from subdistricts interviewed at _d <_ 0, and households from subdistricts interviewed at _d ≥_ 0.

By keeping only these districts having both negative and positive (or zero) values of _d_ , we can exploit the within-district variation in the timing of the survey. If within each district, timing is truly orthogonal to EA characteristics, within-district variation in exposure to the NHIS at the time of the survey is as good as random. In this case, EAs interviewed before the NHIS implementation in the district represent suitable controls for EAs interviewed after the NHIS was available in the district. This measure _d_ is the running variable of our regression discontinuity (RD) design, which is presented in detail in the subsequent section. We model the effect of NHIS implementation on household outcomes that would be potentially affected by a health shock.

Our research design captures the reduced-form short-term impact estimates of the reform, where the first stage corresponds to the impact of the NHIS rollout on the health insurance take-up of households. Our data do not allow us to correctly identify changes in health insurance coverage due to the implementation of the NHIS. We can, however, rely on reported national enrollment rates to get an impression of the extent of NHIS coverage. Figure 2 shows that national enrollment in 2006 was around 20 percent, and increased to almost 40 percent in 2007. These figures, however, are low bounds of the real coverage rate of the NHIS, as households were frequently allowed to enroll under the NHIS after the occurrence of a health shock (Gajate-Garrido, 2013). Our results need to be interpreted keeping in mind that at the time of observation, about 20 percent of Ghanaians, situated higher on the health risk spectrum, were enrolled under the NHIS.

8
# **Annex—Tables and Figures**

**Table 1. Assets of financial institutions in Poland (PLN billion)**

|Institutions|2002|2003|2004|2005|2006|2007|2008|2009|2010|
|---|---|---|---|---|---|---|---|---|---|
|Commercial<br>and<br>cooperative<br>banks|466.5|489.0|538.5|586.5|681.8|792.8|1035.4|1057.4|1158.0|
|Credit unions|2.5|3.3|4.2|5.3|6.0|7.3|9.4|11.6|14.1|
|Insurance<br>companies|57.6|65.7|77.9|89.6|108.6|126.9|137.9|139|145.1|
|Investment<br>funds|23|33.8|37.6|61.6|99.2|133.8|73.9|93.4|121.8|
|Open pension<br>funds|31.6|44.8|62.6|86.1|116.6|140|138.3|178.6|223.3|
|Brokerage<br>entities|2.8|3.7|5.5|6.9|10.8|11.8|8.6|9.9|9.2|
|**Total**|**584.0**|**640.3**|**726.3**|**836**|**1023.0**|**1212.6**|**1403.5**|**1489.9**|**1671.5**|

Source: NBP, KNF, KSKOK.

**Table 2. Assets of financial institutions in Poland (% of GDP)**

|Institutions|2002|2003|2004|2005|2006|2007|2008|2009|2010|
|---|---|---|---|---|---|---|---|---|---|
|Commercial<br>and<br>cooperative<br>banks|57.7|58.0|58.2|59.6|64.3|67.4|81.2|78.7|81.8|
|Credit unions|0.3|0.4|0.5|0.5|0.6|0.6|0.7|0.9|1.0|
|Insurance<br>companies|7.1|7.8|8.4|9.1|10.2|10.8|10.8|10.3|10.3|
|Investment<br>funds|2.8|4.0|4.1|6.3|9.4|11.4|5.8|7.0|8.6|
|Open pension<br>funds|3.9|5.3|6.8|8.8|11.0|11.9|10.8|13.3|15.8|
|Brokerage<br>entities|0.3|0.4|0.6|0.7|1.0|1.0|0.7|0.7|0.6|
|**Total**|**72.2**|**75.9**|**78.6**|**85.0**|**96.5**|**103.0**|**110.0**|**110.9**|**118.1**|

Source: NBP, KNF, KSKOK.

26
Policy Research Working Paper 10360

# **Abstract**

While the impact of COVID-19 on Sub-Saharan African labor markets is well documented, there is suggestive evidence that urban households may have fared particularly poorly. This paper uses data from high-frequency phone surveys in 27 Sub-Saharan African countries to investigate which kinds of urban household enterprises were most affected, what coping strategies were utilized, and heterogeneity by sociodemographic characteristics in the short and medium run. Using linear probability models, the paper finds that households that relied on income from non-farm enterprises were hit particularly hard during the early stage of the crisis, with 20-26 percent reporting income declines, and women experiencing even greater losses. Few coping strategies were utilized in the short run to counterbalance the loss of enterprise income. As the crisis progressed, wage employment recovered more quickly than self-employment, with faster gains for non-farm household enterprises, less poor households, and those headed by males and adults. Women, adults, and non-poor self-employed household heads were more successful at leveraging external sources of support early in the pandemic, but these supports largely dropped off by August 2020. These results demonstrate the vulnerability of non-farm household enterprises in urban Sub-Saharan Africa to the COVID-19 shock and highlight the need to expand publicly and privately financed coping mechanisms, particularly for women, youth, and poor household heads who are self-employed.

This paper is a product of the Social Protection and Jobs Global Practice and the Poverty and Equity 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 atWcunningham@worldbank.org and dnewhouse@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