rafmacalaba/data-use-mentions-extended huggingface dataset:
pletely offset by a reduction in other debt flows, finding no evidence that corporate bond financing declines after an MDB starts lending in a given country-sector pair.<sup>5</sup> Fourth, the MDB mobilization effects estimated at the sector level (within a country) could be (partially) offset if the presence of MDBs in a given sector crowds-out private bank lending to other sectors. We show that this is not the case and our results hold even when aggregating the data at the country-year level. Finally, we find differences in the mobilization effects across countries. In particular, some of these effects are weaker in low-income countries, suggesting that MDBs still face challenges with mobilizing resources in weak macroeconomic contexts.<sup>6</sup> The existing literature on mobilization effects is mostly focused on IMF lending, but its catalytic effect—the capacity to attract private investment after the provision of official assistance (Giannini and Cottarelli, 2002; Morris and Shin, 2006)—cannot be easily generalized to MDBs, given its nature of crisis lending.<sup>7</sup> A smaller strand of literature has explicitly focused on MDBs and, using aggregate macroeconomic data, finds mixed results. Rodrik (1995) tests whether net transfers from multilateral sources to a country are a predictor of subsequent net private capital inflows, controlling for past private flows. Using country data averaged over four periods of six years (from 1970 to 1993), he does not find a significant association between past multilateral lending and current private flows. With a similar framework, Dasgupta and Ratha (2000) and Ratha (2001) instead find evidence suggesting that private capital flows to a large sample of developing countries respond positively to multilateral lending.

Our contribution to this strand of literature is twofold. First, we depart from the literature on catalytic finance that has mainly discussed the catalytic role of the IMF by focusing specifically on the role of MDBs, which have been greatly overlooked to date and can instead be an important
Policy Research Working Paper 10982

# **Abstract**

The impacts of climate change on developing economies are becoming increasingly severe, creating challenges for risk management and requiring enhanced levels of resilience. This paper explores how to mitigate the effects of such climate shocks on developing economies, placing a particular focus on the role fiscal policy in creating and strengthening an economy’s resilience. Using data on natural disasters, the analysis shows that economies with constrained fiscal space experience more pronounced negative effects. In an application to a small open economy, the paper tests the presence of the non-linearity of short- and long-run disaster impacts in the World Bank’s macroeconomic and fiscal model and illustrates the importance of fiscal policy in mitigating shocks.

This paper is a product of the Economic Policy Global Department. 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 arezai@ wu.ac.at; fruch@worldbank.org; rchoudhary@worldbank.org; and jfrancois1@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
: the high-quality learning benchmark, scaling of the learning assessments, standard deviations across tests and samples, and status-quo learning trajectories.

## **5.1 High-quality learning benchmark**

We use 0.8 _σ_ as a benchmark for high-performing learning rates. As noted above, this value is an artificial high-performance benchmark chosen because it is stable (unlike benchmarks based on actual performance of leading countries) and non-political. This approach to defining high-quality learning rates is similar to the approach to defining the high-performance benchmark learning level in the World Bank Human Capital Index (Kraay, 2019). We explore three approaches to validating this high-performance benchmark: (a) average annual learning trajectories in high-performance cases; (b) policy-relevant learning changes; and (c) rules of thumb and a range of effect sizes in reviews of multiple studies.

The first approach draws on high-performance learning trajectories. Although there is surprisingly little year-on-year raw data on learning, one notable example where there is longitudinal data is from the Young Lives survey. That survey follows students in India, Vietnam, Peru, and Ethiopia over 15 years and uses learning assessments based on Item-Response Theory (IRT).

24
2008. In October 2008, December 2008, and February 2009, tracking protocols required the team to re-contact all respondents ahead of the spring 2009 follow-up survey. In the process of tracking migrants through firms, the team found that 9 percent of workplaces had closed in the 14 months following the November–December 2007 census; by matching workplaces to migrants they had employed, the team estimated that 13 percent of migrants in the 15 cities surveyed were affected by post-crisis shutdowns. If this lay-off rate were applied to an estimated 142 million long-term migrant workers in 2008, perhaps some 18.5 million migrant workers could have lost their jobs as a result of enterprise closings during the crisis period.

Not all cities were affected equally. Kong, Meng, and Zhang (2009) found that 34 percent of migrants in the export-oriented city of Dongguan (in Guangdong province) would have lost employment through enterprise closings. Across industry sectors, the study found that manufacturing (which is export oriented) was hit hard, but so too were construction and services. With large-scale layoffs in export-oriented sectors, however, it would not be surprising to find that sectors specializing in nontraded goods (construction, real estate, and services, for example) would be affected as well.

However, the Kong, Meng, and Zhang (2009) study has limitations that should lead to a healthy skepticism toward the results. First, the authors are unable to account for layoffs from firms that did not close. Second, the study makes a strong counterfactual assumption that no firms would close in the absence of the financial crisis (as a result of adjustments associated with the 2008 Labor Contract Law, for example, or the credit tightening associated with macroeconomic policies initiated in 2007). In practice, even a booming economy will have some failures, and some firms may be lost from the sample if they move locations to expand or move as a result of urban redevelopment initiatives. Furthermore, the authors do not appear to account for new firm creation or the
13 across regions (or even cities) within a country and in the quality of the good, whereas price data tend to be collected at a higher level of aggregation and for a representative (average) item in each category. But it is hard to think that these problems are so severe that choosing arbitrary weights across “dimensions” such as the availability of cooking utensils and, say, the ownership of certain means of transport, would yield a preferable metric. In this regard, Ravallion’s (2011) arguments for a greater reliance on market prices seem overwhelmingly compelling.

The true value of the analysis of multidimensional poverty (or well-being) lies in the existence of certain aspects of well-being that we deem important, but for which there can truly be no sensible estimate of relative prices. It is reasonable to include in this category items such as political and personal freedoms, health and, arguably, education. While various inputs into the production of health and education are marketable, that is not true of all of them. Health is influenced by environmental quality and a number of other public goods. “Education”, once embodied in human capital, generates so many externalities that it is difficult to think that school fees or costs are suitable approximations to its true shadow price. These things – not the material from which a ceiling is made, or what kind of stove one uses – are true _dimensions_ of welfare.

It is in these cases that multidimensional analysis becomes particularly relevant. If, for the purpose at hand, the analyst decides that the price of selecting weights is worth paying (in order to obtain a complete ordering), then the question is whether one can choose relative weights that at least attempt to represent the existing trade-off between the different components of deprivation (or well-being). There are various approaches for setting these weights, some based exclusively on the observed distribution of attributes, others based on people’s opinions, and others using both sorts of information (Decancq and Lugo, forthcoming
to 149). Another way to view the patterns by age, sex, and country, is to examine the mortality sex-ratio (M/F) for countries by tercile of GNI per capita, as shown in Figure 3. (See also Annex Table A1.) These figures show that the widening of the gender mortality gap driven by COVID-19 in 2020 was limited to higher income countries and principally among adults ages 45-54 and 55-65. Figure 4 displays the same data with individual country points. Annex Figure A2 shows the same figure with the subset of countries held constant across years.

# **Discussion**

This paper, using a global dataset covering a wide swath of middle- and high-income countries, confirms previous findings, based on more limited data, that for all age groups above age 45 and in all countries with few exceptions, men die at higher rates than women. It also identifies global variation in patterns of COVID-19 mortality by age and sex. The findings complement the finding from Demombynes et al. (2022) that the age curve of excess deaths in 2020 was flatter for middle-income countries and steeper in wealthier countries. The findings in this paper demonstrate that this difference is principally driven by the mortality patterns of men, resulting in COVID-19 amplifying the gender mortality gap in 2020 more in richer countries as compared to less-wealthy countries.

An important limitation of this study is that because it excludes excess mortality estimates not based on actual observed mortality, it does not include any low-income country.

<mark>Still, these findings point to the need to consider public policies during public health emergencies that offer more targeted protection for men over 45 with co-morbidities, for example those working in jobs exposing them to a higher risk of infections.</mark> The patterns found will hopefully inspire future country-level research using cause-of-death data to get a fuller understanding of relevant drivers of age-gender mortality patterns. It is worthwhile to explore structural socioeconomic conditions that vary with country income and sex, which would also interact with both infection exposure
of magnitude smaller. On the payment margin, we nd that 5-25% of small rms pay their taxes with signicant delay. After accounting for theses delays, however, payment is above 95% of liabilities for both the income tax and the sales tax.

We interprete this as evidence that a country like Costa Rica, with relatively high institutional capacity, achieves a satisfactory level of tax compliance on third-party reported margins. Some administrative follow-up interventions on detectable compliance gaps are necessary as deterrence, to maintain the level of compliance, but increasing investments in these administrative interventions is unlikely to yield substantial revenue gains. This does not mean that tax compliance in Costa Rica is near perfect. The Ministry of Finance estimates, based on macro data, that 30% of sales tax liabilities and 70% of corporate income tax liabilities are evaded or avoided (Hacienda 2015). A large part of these liabilities are simply not observable to the government. We thus investigate whether expanding the coverage of third-party information reporting, and of associated withholding mechanisms, holds more promises to increase tax revenues substantially.

We turn to analyze the impact of information reporting in the second part. We begin by showing

> 5We borrow the term from Kleven et al. (2011) who conduct an anatomy of compliance for wage earners and the self-employed in Denmark.

3
enterprises had a much different legal structure, and became much more, though not completely, marketoriented.<sup>4</sup> Both the productivity level and input uses of firms changed over this period. The average labor productivity of the firms in the data set increased at 2.6% annually.

> 2This data set has been used by many other authors, including Groves et al. (1994, 95).

> _3In_ the 1980s the Chinese SOEs did not face a real threat of bankruptcy.

> 4See Johnson (1990), Tidrick et al. (1987), Perkins (1994), and Jefferson and Rawski (1994) for more thorough coverage of industrial reforns and the rural reformns of China.

3
000412|-0.0415|-0.0867|
||(0.0626)|(0.0940)|(0.128)|(0.0592)|(0.0912)|(0.106)|
|_N_|36|36|36|36|36|36|
|_R_<sup>2</sup>|0.482|0.593|0.311|0.603|0.624|0.303|

Standard errors in parentheses _∗ ∗∗ ∗∗∗ p <_ 0 _._ 05, _p <_ 0 _._ 01, _p <_ 0 _._ 001 Table A.7: Regressions in first differences, with the same regressors used in the VARX of Table 8. The Financial Openness measure is a 3-year moving of the Chinn-Ito account index over average capital openness averaged advanced and emerging countries.

55
, as per regulations of the Ministry of Manpower that stipulate a minimum requirement of junior secondary school completion for some jobs.

Female out-of-school prevalence (1.7 percent) is slightly lower than male (2.2 percent). Nearly 2 percent of urban females and 1.2 percent of rural females aged 7-to-15-years-old had never attended school, compared with 1.1 percent of urban males and 1.9 percent of rural males.<sup>25</sup> In terms of repetition, the rates are higher for boys at the primary level. In 2018, nearly 1.7 percent of boys repeated grades in primary school, compared with 0.9 percent of girls.<sup>26</sup> Despite pro-poor programs such as _Bantuan Operasional Sekolah_ and _Program Indonesia Pintar (PIP)_ that have been implemented to ensure students from poor households can access free basic education, poverty is still a key determinant of school enrollment in Indonesia (ACDP, 2013). There is a large gap between the value of the PIP and total reported out-of-pocket costs for households. Households report education expenditures at IDR 1.19, 1.67, and 1.91 million (US$87, US$122 and US$139) for a single student in primary, junior secondary, or senior secondary school, respectively.<sup>27</sup> However, PIP benefits

> 22 Sakernas 2018.

> 23 <u>http://documents.worldbank.org/curated/en/855851490958133680/Could-childcare-services-improve-women-s-labormarket-outcomes-in-Indonesia</u>

> 24 _Source:_ BPS, Statistik Indonesia, 2017. _Note:_ *Figure from Judges is gathered from Berita MA (January 21, 2018) reported by Azizah (https://www.mahkamahagung.go.id/id/berita/2905/ketua-ma-hakim-perempuan-indonesia-memiliki-peran-penting- <u>dalam-pembangunan-hukum-nasional).</u>

> 25 Susenas, 2019.

> 26 Repetition rate is computed from student repeaters divided by total student last
Table 3: Wage Gaps across Formal and Informal Workers, 2002-2015

||Unadjusted|Adju|sted|
|---|---|---|---|
||(1)|(2)|(3)|
|Informal employee|-0.582***|-0.380***|-0.339***|
||(0.00438)|(0.00414)|(0.00408)|
|Self-employed|-0.795***|-0.588***|-0.434***|
||(0.00457)|(0.00436)|(0.00441)|
|2015|-0.0840***|-0.152***|-0.146***|
||(0.00348)|(0.00325)|(0.00314)|
|2015*Informal employee|0.0465***|0.0727***|0.106***|
||(0.00520)|(0.00484)|(0.00464)|
|2015*Self-employed|0.0973***|0.0930***|0.115***|
||(0.00544)|(0.00506)|(0.00488)|
|Individual characteristics|No|Yes|Yes|
|Sector-occupation dummies|No|No|Yes|
|Country fxed efects|Yes|Yes|Yes|
|Observations|620,397|619,562|619,560|
|R-squared|0.219|0.327|0.396|

_Source_ : Authors’ calculations based on data from the Socioeconomic Database for Latin America and the Caribbean, World Bank and Center for Distributive, Labor and Social Studies of the Universidad Nacional de La Plata
Taking advantage of the panel data, we formalized this approach in the following regression: hc = H h<sup>+ ∑</sup> T c T c hc hc (Equation 1) TT cc=2 cc cc hcc<sup>) + λλXX+ εε</sup> where YY denote household, commune, and time, respectively. γγ δδ + ββT TT + κκ(T TT ∗FFH δt denotes dummy variables for each year; H h<sup>is a household fixed effect,11 andXX</sup> hc<sup>are time-variant household characteristics.FFH</sup> h<sup>is a</sup> h, cc, TT dummy variable equal to one if a household head is a woman, and zero otherwise. hc includes controls for the age of the household head, the number of dependents in the household<sup>12</sup> and the level of XX education of the household head. hc captures outcomes of interest, such as household production and trade of crops and livestock, the types of crops grown, and the inputs into production, as well as measures of welfare like income, quality YY of housing, and consumption at the household level. We also explore the impact on employment at the individual level. These outcomes represent the overlap between the outcomes where we expected changes based on the literature and the data available in the VARHS. The parameter of interest in equation (1) is the coefficient on the dummy T T c which takes the value of one when the treatment is active. In contrast to the existing literature where road improvements are TT treated as permanent, given the nature of the RTP3 project, we treat improvements as temporary. Following consultation with transport specialists at the World Bank and corroboration by specialists from the DRVN and the Transport Development and Strategy Institute (TDSI) in Vietnam, we arrived at the following as an average estimate of the life span of each type of RTP3 activity:
# **Who on Earth Can Work from Home?**

Daniel Garrote Sanchez Nicolas Gomez Parra Caglar Ozden Bob Rijkers Mariana Viollaz Hernan Winkler

**World Bank**

**JEL codes:** J17, J21, J28, J48

_Acknowledgements:_ We are grateful to Ana Fernandes, Chisako Fukuda, Gaurav Khanna, Aart Kraay, Norman Loayza, Harry Moroz, Nina Rahman, Achim Schmillen and Joana Silva for useful comments and discussions, to Leora Klapper for her help with the Gallup data on internet access and to Efsan Nas Ozen for the Turkish Labor Force survey. 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 of Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the countries they represent. We are also thankful to the Knowledge for Change Program, the World Bank Research Support Budget and the Multi-Donor Trust Fund on Trade for financial support. All errors are our responsibility.
We construct employment-related indicators directly from primary data sources, including population censuses, labor force surveys, and household surveys from 2001 to 2017. We use about one hundred censuses and surveys containing representative data on employment status (summarized in Table 3<sup>6</sup> ). The targets of the exercise are individuals aged 15 to 64—that is, the working-age population. However, the results do not differ significantly if considering all individuals above 15, as the elderly constitute a minor share of total population in South Asia. First, we categorize respondents into employed, unemployed, and inactive, taking into account survey-specific differences (details below). Then, we further classify employed individuals by age categories (15-24, 25-54, 55-64), type of employment contract (regular, casual, self-employed, and unpaid), sector of activity (agriculture, manufacturing, services, construction, and mining), location (rural or urban), and educational attainment (illiterate, primary, high school, more than high-school).

As shown in Table 3, Sri Lanka and Pakistan have the most frequent and easily accessible households and labor force surveys. Sri Lanka conducts its national Labor Force Survey (LFS) annually and a national Household Income and Expenditure Survey (HIES) every three years. Similarly, Pakistan has carried out the Pakistan Integrated Household Survey (PIHS)/Pakistan Social and Living Standards Measurement (PSLM) survey and the Household Integrated Economic Survey roughly every alternate year since 2001<sup>7</sup> . For 2015/16, Pakistan’s Bureau of Statistics launched the Household Integrated Income and Consumption Survey (HIICS) containing employment data. In addition, Pakistan has conducted its LFS almost every year during the study period considered for this paper.

Data was far less frequent for Nepal and Bhutan, for which we only had three surveys. We used data from the Nepal Living Standards Survey (NLSS). Although the NLSS has three rounds, only the last two were post-2000
Reform., American Economic Review, 86(2): 1-14.

**Feldstein** M. **S.** (1978) Do Private Pensions Increase National Savings?, Journal of Public Economics, 10(3): 277-93.

**Hausman J.** A. (1978) Specification Tests in Econometrics., Econometrica 46: 125171.

**Holzmann R.** (1997) Pension Reform, Financial Market Development and Economic Growth-Preliminary Evidence for Chile?, IMF Staff Papers.

**Hubbard R.** G. (1986) Pension Wealth and Individual Saving: Some New Evidence., Journal of Money, Credit and Banking, 18: 167-78.

**IMF** International Financial Statistics.

**Impavido G.** (1998) Institutional Investors, Stock Markets and Firms' Information Disclosure., University of Warwick Working Paper No 305.
|**Table 1. Summa**<br>**Author (Date)**|**ry of studies that e**<br>**Type of**<br>**intervention**<br>**examined**|**xamine the im**<br>**Outcome of**<br>**interest**|**pact of prenatal sex**<br>**Data**|**selection on postnatal outcomes f**<br>**Empirical strategy**|**or girls**<br>**Observed impact on**<br>**female child survival**|**Observed impact**<br>**on other female**<br>**child outcomes**|
|---|---|---|---|---|---|---|
|Goodkind (1996)|Rising SRB<br>(indicator of<br>rising prenatal<br>sex selection)|Postnatal<br>survival of<br>girls|Vital registration<br>data, census data,<br>and other<br>secondary data<br>sources. Countries:<br>China, Hong<br>Kong, Japan,<br>Korea, Malaysia,<br>Singapore, Taiwan|Correlation study: Examine<br>how changes in the male-<br>female sex ratio of infant and<br>child mortality correlate with<br>changes in SRB before and<br>after the 1980s across different<br>Asian countries (with and<br>without documented son<br>preference)|Female infant and child<br>survival improved after the<br>1980s in countries or sub-<br>populations with<br>documented