rafmacalaba/data-use-mentions-extended huggingface dataset:
this scope for COVID-19 specifically) for assessing the level of and supporting national health systems to further bolster their preparedness during the pandemic.

Guidance for health systems, highlighted in this WHO manual, centers on the objective of minimizing morbidity and mortality caused by the pandemic by ensuring that health services are kept functioning for as long as possible. Of the 18 tasks identified across the four categories for maintaining essential health services, the most recent SDI surveys provide some evidence to support planning for seven tasks concerning procurement, coordination and management related to health facilities, personnel, and supplies (Appendix Table 1). The existing SDI data do not inform any activities related to management of excess mortality resulting from the pandemic.

For instance, the SDI data allow estimation of numbers of health care workers by professional group at the facility level, which can be extrapolated to gauge availability at the national, provincial or local level. These data can also inform coordination of patient flow between health care facilities by providing insight on resources available across facilities, including provision of emergency transport and seamless communication. Furthermore, the SDI can shed light on availability of basic, yet critical, medicine, equipment and supplies for infection prevention, treatment and control, especially during respiratory pandemics. These include availability of a functioning thermometer, stethoscope and sphygmomanometer for diagnosis and monitoring; unexpired antibiotics for treatment of patients; and personal protective equipment like gloves and masks to protect frontline health care workers. This report delves into the details of what can be learned about these pandemic preparedness indicators from the SDIs.

# Ready or not? Findings from the SDIs on facility preparedness for a pandemic situation

The demand for health care increases unexpectedly and usually exponentially during disease outbreaks and pandemics. The same is true for COVID-19. Leading epidemiological models indicate that despite stringent suppression measures, the spread of COVID-19 will overwhelm most health care systems. Careful planning involving systematic assessment of the capacity of available facilities, and their utilization for treating and supporting patients based on the severity of the case, is crucial for
due to any reason. We describe this method and our implementation in more detail in Appendix 1, Part B.

# **4.2. Data Description and Construction of Variables**

We bring together various data sources for the empirical analysis. Our main data set is the Rural-Urban Migration in China (RUMiC) survey, which consists of three independent modules: a migrant household module, an urban household module, and a rural household module. It collects rich data on the socioeconomic characteristics of rural-urban migrants and their left-behind children, including information on co-residence status, schooling status, and household expenditures on various types of school fees for their children. We restrict our sample to households that have at least one school-age child (age 6-16) as we focus on the impacts of school fees on child migration. This leaves us with a working sample of 1,349 households. We provide a more detailed description of this survey and other data sources in Appendix 2.

While we analyze two rounds of the RUMiC data set, we focus in this paper on the 2008 (first) round for two main reasons. First, as discussed earlier, local governments typically relied on raising revenues through school fees before 2008 to compensate for the lack of funding transfer from the central government to pay for the education of migrant children. The abolition of school fees in 2008 resulted in local governments being no longer able to collect revenues this way, at least in theory. Thus there could be (almost) no correlation between schools fees and budget deficits, which violates the relevance condition of our IV for 2009. Second, the linkage between shocks to public education spending and school fees is also likely to be weakened during an economic crisis. This is because local governments would typically be constrained by competing spending priorities in such times, thus would unlikely have total discretion over their education budget. For example, they might not be able to spend the surplus from the education budget generated in the previous year on education in the following year,
debt constrains policy makers’ ability to support their economies through future shocks and makes countries vulnerable to financial market disruptions. Indeed, during past sovereign debt crises, fiscal deficits tightened sharply and, yet, this tightening was not accompanied by significantly improved fiscal sustainability.

More than two-thirds of EMDEs are currently experiencing a government debt boom. Ongoing government debt booms have been longer than booms that ended before the pandemic. Macroeconomic outcomes have been weaker in booms currently underway and fiscal balances and sustainability gaps have deteriorated more in comparison with earlier such episodes. Half of such debt booms in EMDEs ended in financial crises in the past. Historical booms suggest that macroeconomic outcomes, including output, investment, and consumption growth, are significantly weaker when debt booms are associated with financial crises.

Debt is likely to rise further as governments and central banks continue supporting the recovery. Policy makers need to act to prevent liquidity constraints from derailing the recovery in activity and to provide space to assess debt sustainability, as well as to consider

> 14 When data for 2020 are excluded from the calculation for ongoing booms, macroeconomic outcomes for EMDEs tend to be significantly stronger than in previous booms. In addition, changes in debt sustainability are no longer statistically significant, unlike those based on ongoing booms including 2020. These results highlight the large negative impact of the COVID-19 pandemic on debt dynamics.

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, that explains job dissatisfaction. No conclusive evidence is derived regarding job satisfaction.

# **Informal employment and health**

Next, we explore the relationship between informal employment health outcomes. We are able to define a measure of a worker’s health status based on information on self-assessed overall health which is captured through a question that asks how the respondent would rate her general health on a five-point scale. Population health is by definition complex and difficult to measure, and the reliability of one’s self-assessment of could vary by population subgroup -see (Lundberg, 1996); (Zajacova, 2011). Nevertheless, self-assessed health status is widely used to study empirical trends in population health. In the RLMS, respondents are asked how they would evaluate their health. Possible responses include very good, good, average, bad and very bad. We define a binary measure of “poor health” that includes workers that rate their health as bad or very bad. We also consider another binary variable that defines “good health” which includes the top two responses, i.e., good or very good health. With this, we attempt to capture whether there are health inequities arising from differences in one’s informality status.

The distribution of original responses shows that formal and informal workers are in fact not very different in their self-assessed health measure. The distribution is fairly similar between formal Informal Employment and Worker’s Wellbeing in Russia 22/67
spends a greater fraction of the time in formal employment (53 vs. 24 percent), and has a higher monthly wage (US$1,031 vs. US$876) conditional on being formally employed. Finally, Table A10 describes the full set of students who started an SCP in 2014 in Brazil, of whom only 30 percent graduated within three years.

# **4 Empirical Strategy**

## **4.1 Cross-Country Estimations Using Program-Level Data**

### **Data-driven selection of quality determinants**

The WBSCPS has the fascinating advantage of providing a large set of variables that proxy for program quality determinants. However, this large number of variables poses some challenges. The first is to select the “right” set of explanatory variables. Using too few controls—or the “wrong” ones—may create omitted variable biases while using too many may overfit the model. A second empirical challenge is the potential for researcher’s confirmation bias—selecting the variables in a way that confirms her hypotheses. We address these issues with our two-stage estimation approach. First, we use a data-driven method to select the parsimonious set of quality determinants that best fit the outcome data. Second, we estimate the association between the selected program quality determinants and the outcomes of interest.

To select quality determinants in the first stage, we use a supervised machine learning approach: the Least Absolute Shrinkage and Selection Operator (LASSO) technique. LASSO chooses a parsimonious set of controls that provide the best possible fit of the data and discards those that contribute little to the fit. For a given outcome in program _j_ and country _c_ , we estimate the following linear specification using LASSO: 21
0.010 <u>p for endogeneity</u> Source: Village Fund Survey 2010 and SES 2009. Robust standard errors in parentheses. Variables in first column are included based on OLS backward stepwise with p=0.2 cutoff. Regression in second column includes provincial dummy variables (not shown). * p<0.05, ** p<0.01, *** p<0.001.

# **8. Cost-Benefit Analysis**

Do the benefits of the Village Fund outweigh the costs, from an economic point of view? That is the issue we address in this section.

The costs of VF lending may be separated into administrative and financial components. The most important administrative cost is the time spend by VF committee members; to some extent this is compensated in the honoraria that committee members pay themselves, which comes to 1.1% of the value of loans disbursed, and may even be overly generous in some cases. It may be appropriate to add the imputed value of volunteering – time spent working for the VF that receives no, or below-market, compensation. We have valued this additional time at 300 baht/day, in line with the average rural wage in 2009, although a case can be made that since the time was freely offered, this adjustment might not be needed.

Village Funds also make social contributions, such as helping pay for funeral expenses, that represent 0.5% of lending; we treat this as a cost, viewing it as the price of ensuring local goodwill. Other costs at the village level – stationery, even use of a computer – are very small. We do not have information on the cost of running the VF headquarters or regional centers, but this appears to be a relatively modest operation; if it costs $2 million annually, that would represent just 0.1% of the value of lending. Overall, administrative costs come to about 2% of total lending.

There is a
per country in the 2023 round. For this analysis, we used a subset of 30 countries surveyed in both rounds of the LiTS.<sup>2</sup> We capture support for tax-financed investments in climate change actions by examining responses to the question “ _Would you be willing to give up part of your income or pay more taxes if you were sure that the extra money was spent to combat climate change?_ ” We create a dummy with a value of 1 if the respondent responded yes and 0 if he or she responded no or don’t know. This dummy variable is the main dependent variable of our analysis. Our data also contain information on age, gender, education, marital status, religion, and type of residence, which we use as controls in our empirical analysis.

We supplement the data from the LiTS with the Google Trends index, which is calculated by taking the total number of searches for a specific term at a given location and within a given time frame, dividing it by the total number of all searches conducted at that location and during the period, and then scaling this ratio with respect to the highest value in the series. This normalization process precluded us from comparing search interest indexes over time (i.e., <u>Mavragani and Ochoa 2019). To overcome this problem, we present the comparisons of the changes in the index with the</u> changes in the search index for the topics we consider relevant and comparable with the issue of climate change.

We also use administrative data on several environmental risks at the primary sampling unit (PSU) level,<sup>3</sup> capturing means, standard deviations, ranges of temperature and precipitation, information on air pollution, and the severity of flooding. We use PSU-level statistics on the risk factors for the seven years before each survey. We derive a weighted average of each risk factor with weights linearly inversely related to the time from the survey date (the further
/mark>_ <mark>model as a latent variable model and</mark> assume that there exists a random variable _SS_<sup>*</sup> such that: where  represents the error term, and _SS_<sup>*</sup> indicates whether this latent variable is non-zero: <mark>We denote our dependent variables as (net) sudden stops (</mark> _<mark>SS</mark>_ <mark>), inflow-driven sudden stops (</mark> _<mark>iSS</mark>_ <mark>), outflow-driven sudden stops (</mark> _<mark>oSS</mark>_ <mark>), and the mixed cases (</mark> _<mark>mSS</mark>_ <mark>). The definition of these binary variables is summarized in Section 3.1 while the variables that comprise the</mark> _<mark>X</mark>_ <mark>matrix are described in Section 3.2. Note that all explanatory variables are lagged one period to avoid reverse causality issues.</mark>

# **4.2 Baseline Regression**

> 11 We collected annual data for 185 countries on capital flows and the determinants of sudden stops from 1970 to 2007. We excluded from our sample countries with less than 9 consecutive observations of capital flows and small countries (i.e. countries with population less than 1 million in 2007 and gross national income per capita lower than 2,000 US dollars). Those restrictions along with other data availability problems in the set of forcing variables rendered an effective sample of 82 countries.

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paths to prosperity_ . Mit Press.

- Hidalgo, C. A. and R. Hausmann (2009). The building blocks of economic complexity. _Proceedings of the national academy of sciences 106_ (26), 10570–10575.

- Kalemli-Ozcan,<sup> ̈</sup>  ̧S., B. E. Sørensen, C. Villegas-Sanchez, V. Volosovych, and S. Ye ̧silta ̧s (2024). How to construct nationally representative firm-level data from the ORBIS global database: New facts on SMEs and aggregate implications for industry concentration. _American Economic Journal: Macroeconomics 16_ (2), 353–374.

- Korn, T. and H. Stemmler (2025). The persistence of trade relocation from civil conflict. _Journal of Development Economics 172_ , 103376.

- Korovkin, V. and A. Makarin (2023). Conflict and intergroup trade: Evidence from the 2014 Russia-Ukraine crisis. _American Economic Review 113_ (1), 34–70.

- Korovkin, V., A. Makarin, and Y. Miyauchi (2024). Supply chain disruption and reorganization: Theory and evidence from ukraine’s war. _MIT Sloan Research Paper No. 7068-24_ .

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Policy Research Working Paper 5447

# **Abstract**

As an instrument of trade and investment policy, special economic zones have played a catalytic role in processes of industrialization, diversification, and trade integration in many countries, particularly in East Asia. However, in the African context, anecdotal evidence suggests the experience has been disappointing on the whole. Among the reasons why many zones underperform may be that they fail to establish a high quality investment environment—this is, after all, one of the main promises that economic zones hold for investors. Drawing on original survey research, this paper presents a systematic analysis of the outcomes and the investment climate of economic zones programs in six African countries and four developing countries outside the region. The analysis finds that although performance across zones is mixed— with Ghana and Lesotho in particular performing well on some measures—African zones programs on the whole are underperforming in terms of attracting investment, facilitating exports, and creating jobs. Economic zones in Africa offer an improved business environment relative to what is available to firms based outside the zones; however, in comparison with the non-African countries in the survey, both absolute investment climate performance and relative improvements fall well short.

This paper—a product of the International Trade Department, Poverty Reduction and Economic Management Network—is part of a larger effort in the department to study the role and performance of special economic zones and other instruments of trade and investment policy in developing countries. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at tfarole@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
in a fixed location and annual poll taxes (on both the owner and the shop, called census taxes). A security fee is also imposed even though the security is rarely provided. HEs report that they often are not given a receipt for payment of taxes and fees to local governments. Some HEs that reported in the surveys not to be registered did report paying taxes and fees. This indicates _that the common usage of „registration‟ as an indicator of the relationship between the enterprise and government can be quite misleading_ . Rather than being the dichotomous relationship implied by criteria such as ones proposed by the ILO (2011), the enterprise‟s relationship with the state is more complex, and can differ by level of government and by sector (e.g. health, trade and industry, justice and security, etc.).

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Policy Research Working Paper 10669

# **Abstract**

When it comes to mental health, do social protection programs matter more in times of crisis? Using panel data from the Philippines around the onset of the COVID-19 pandemic, this study compares depression rates among beneficiaries of an existing conditional cash transfer program to those of non-beneficiaries of similar socioeconomic status. Depression rates were almost identical for the two groups in late 2019, but significantly lower for conditional cash transfer beneficiaries by July 2020, after the initiation of strict quarantine measures and a large emergency cash transfer program. One interpretation of the increased importance of the conditional cash transfer program during the pandemic is that these transfers have larger protective effects in times of vulnerability. Another possible reason is that the existing infrastructure of the program, by allowing for more timely distribution of the emergency cash, enhanced the effectiveness of the government’s pandemic response for conditional cash transfer beneficiaries. This paper finds evidence supporting both explanations.

This paper is a product of the Social Protection and Jobs 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 ycho1@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
, and the degree to which the activity is suitable for moonlighting. Kiosks and taxicabs, chosen for their visibility and as representatives of two sectors in which there seemed to be much informal activity, were included in numbers large enough to estimate contribution to aggregate value added. (iii) Current sample surveys can tell much about the informal sector as it exists now, but tell little about the trends over time, or the aggregate impact of all informal activity. In an attempt to understand these issues, data compiled by the State Statistical Office (SSO), particularly employment statistics and the monthly household survey of income and expenditure, were analyzed. (iv) It is impossible to study the informal sector without addressing the legal environment faced by informals, and that faced by their formal counterparts? Of particular interest to this study are the laws and regulations pertaining to taxation, to labor, and to social insurance.

# **Outline of the Report**

10. In Section 2 we take up the question of the size of the informal sector and how it has grown in the 1990s. Mongolia's employment statistics reveal trends that can only plausibly be explained by expansion of the informal sector. At the same time, the SSO's Household Survey demonstrates the decline of the importance of formal income in the aggregate and for the average household. Monetary aggregates tell a complementary story of expansion of the segment of the economy transacting in currency rather than commercial banks, a segment that is described in the literature as the "hidden" economy. Each of these approaches and bodies of data, together with the informal sector surveys conducted by the author, will be used to generate estimates of the size of the informal sector. Despite the diversity of approaches, the estimates are surprisingly consistent, each suggesting that 30-35% of economic activity and/or employment resides in the informal sector. In Section 3 we will endeavor to explain the reasons for informal sector growth. Several contributors will be addressed, including the release of labor