rafmacalaba/data-use-mentions-extended huggingface dataset:
Taher’s (2001) study explores the determinants of private savings in Saudi Arabia. His findings assert that government spending on investment has a positive effect on private saving; alternatively, budget surplus, excess of consumer loans, and outgoing remittances observed a negative effect.

Burnside (2000) analyzes the behavior of private savings in Mexico by using quarterly data from 1980 to 1995. The correlation of private savings with various macroeconomic time series data suggests that private savings is negatively correlated with the fiscal balance of the government. In addition, private savings is more highly correlated with the level of private disposable income (which has significant cyclical components) than it is with GNP. This suggests that savings, rather than consumption, absorbs transitory movements in income. The paper uses vector autoregressions in which tax shocks are found to be the primary determinants of the ratio of private savings to GDP.

Rymes (1993) examines the fundamental determinants of savings and bubbles in stocks, bonds, and real estate prices by exploring key findings drawn from the works of Laurence Kotlikoff and Robert Shiller. Kotlikoff attributes savings to the existence of the extended family as well as intergenerational transfers; he attests that savings is determined by the individual based on their “fundamentals” (tastes, technology, endowments) as well as their intertemporal maximizing choices. In addition, he claims that the role of the government is overemphasized in the determination of rates and accumulation. Alternatively, Shiller examines the prices of stocks, bonds, and residential real estate, hypothesizing that stock markets are efficient and that no such excessive volatility exists in prices. Moreover, he argues that asset prices reflect more than fundamentals, attesting that they reflect bubbles as well. Rymes concludes that although both studies are detrimental to savings literature, he deems that the necessity to draw a line of separation between fundamentals and conventions is wrong.

The main takeaway from surveying recent literature is that the findings are consistent with the classical theory; that is, growth in income per capita has a predictive power to savings
these three channels is driving any agglomeration effects is almost never investigated in the agglomeration literature, but the overall effect can be, both because this is what is important for policy makers and because it is very difficult to separate out which channel is actually driving agglomeration economies.

Despite this limitation it is possible to make some progress in unpacking the role of agglomeration economies. Combes and Gobillion (2015) distinguish between urbanization economies and localization economies when estimating the benefits of agglomeration. Urbanization economies are the positive externalities from firms locating in a particular area, irrespective of the industry the firm is in, for example due to the density of firms or individuals or the level of general human capital in an area. Localization economies are the externalities arising from the characteristics within a location of a specific industry, such as the level of competition of the firms in an industry in a specific location or the degree to which a location’s employment is concentrated in a particular industry.

Empirical analysis examining the role of agglomeration economies began with Glaeser et al (1992), who examined the impact of several localization and urbanization economies on city employment growth in the US. The authors found that sectoral diversity mattered for employment growth in US cities, where diversity was measured as the sectoral concentration of employment, excluding the sector for which the value is being created. Duranton and Puga (2001) formalized the idea that diversified cities would generate innovation and growth. Positive effects of diversity imply that urbanization economies are important in explaining employment growth. In contrast, Henderson et al (1995) also using data on US cities, found that specialization mattered for economic growth, where specialization is measured as the share of employment in each sector relative to its share at the national level. Since specialization is a localization economy, ie it is not a characteristic of a specific sector, this meant that localization economies were found to be more important by Henderson et al (1995).

4
Policy Research Working Paper 5491

# **Abstract**

As natural disasters hit with increasing frequency, especially in coastal areas, it is imperative to better understand how much natural disasters affect economies and their people. This requires disaggregated measures of natural disasters that can be reliably linked to households, the first challenge this paper tackles. In particular, a methodology is illustrated to create natural disaster and hazard maps from first hand, geo-referenced meteorological data. In a second step, the repeated crosssectional national living standard measurement surveys (2002, 2004, and 2006) from Vietnam are augmented with the natural disaster measures derived in the first phase, to estimate the welfare effects associated with natural disasters. The results indicate that short-run losses from natural disasters can be substantial, with riverine floods causing welfare losses of up to 23 percent and hurricanes reducing welfare by up to 52 percent inside cities with a population over 500,000. Households are better able to cope with the short-run effects of droughts, largely due to irrigation. There are also important longrun negative effects, in Vietnam mostly so for droughts, flash floods, and hurricanes. Geographical differentiation in the welfare effects across space and disaster appears partly linked to the functioning of the disaster relief system, which has so far largely eluded households in areas regularly affected by hurricane force winds.

This paper—a product of the Poverty and Inequality Team, Development Research Group—is part of a larger effort in the department to understand household vulnerability to weather-related shocks. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at pflewitt@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
because of pandemic-related restrictions. Resilience-building in the face of a crisis is a crucial next step to ensure that refugees can maintain their livelihoods in times of need.

Ongoing efforts will also be necessary on the research frontier, especially in providing highquality socioeconomic data that include refugee populations and are able to address existing data gaps and contribute to the design of inclusive evidence-based measures. With the continuous spread of the pandemic, new methods of collecting data such as phone surveys need to be evaluated relative to traditional face-to-face methods and improved upon. One particularly interesting strand of future research concerns the measurement of income which is notoriously difficult to capture precisely in in-person interviews due to the complexity of income streams in developing countries as well as recall and social desirability bias. It is likely these biases are aggravated during phone interviews. To evaluate the full extent of over and underreporting in phone surveys, more research will be necessary in the future.

# **References**

UNHCR Kenya. (2021). _Dadaab Refugee Complex_ . Retrieved from https://www.unhcr.org/ke/dadaab-refugee-complex

- UNHCR Kenya. (2021). _Kakuma Refugee Camp and Kalobeyei Integrated Settlement_ . Retrieved from https://www.unhcr.org/ke/kakuma-refugee-camp

- IFC. (2018). _Kakuma as a Marketplace. A Consumer and Market Study of a Refugee Camp and Town in Northwest Kenya._

- World Bank. (2021). _How Covid-19 continues to affect lives of refugees In Kenya._

Ritchie, H., Mathieu, E., Rodés-Guirao, L., Appel, C., Giattino, C., Ortiz-Ospina, E., . . . Roser,

- M. (2020). _Our World in Data._ Retrieved from Coronavirus Pandemic (COVID-19): https://ourworldindata.org/coronavirus

- Dong, E., Du, H., & Gardner, L. (533-534.). An interactive web-based dashboard to track COVID-19 in real time. _The Lancet infectious
, that follows closely the International Standard Classification of Occupations (ISCO) 2008, to the Indonesian Classification of Occupation 1982 that is used in the Sakernas (the I</mark> ndonesian National Labor Force Survey). <mark>We work with six occupational groups: administrative and managerial workers; professionals and technicians; clerical workers; sales and services workers; agriculture, animal husbandry, forestry workers, fishermen and hunters; and production workers, transport and equipment operators, and laborers. We combine the variables capturing each type of skill in the STEP survey into a single skill measure for each occupation following the methodology applied by Acemoglu and Autor (2011). The variables used are listed in the second column of Table A1.</mark> First, for each variable associated with any of the five types of skills, we calculate the mean and standard deviation across the sample of 1,565 workers and standardize the variable by subtracting the mean and dividing by the standard deviation. Second, for each worker we obtain scores for each of the five types of skills by adding all the standardized variables obtained in the previous step associated with non-routine analytical skills (7 variables), non-routine interpersonal skills (3 variables), non-routine manual skills (4 variables), routine manual skills (1 variables), and routine cognitive skills (1 variable). We standardize each of the five scores by subtracting its mean and dividing by its standard deviation. Finally, for each occupational group, we calculate a weighted average of the value of each of the five standardized skills scores using as weights the contribution

> 8 We decided to use the STEP survey of the Philippines instead of Vietnam because for the latter country the survey only covers main cities.

54
.

- Grundke, R., L. Marcolin, T. L. B. Nguyen, and M. Squicciarini. 2018. “Which skills for the digital era?: Returns to skills analysis.” _OECD Science, Technology and Industry Working Papers_ 2018/09. Paris: OECD.

- Guerra, N., K. Modecki, and W. Cunningham. 2014. “Social-Emotional Skills Development across the Life Span: PRACTICE.” _World Bank Policy Research Working Paper_ 7123. Washington, DC: World Bank.

- Hardy W., R. Keister R., and P. Lewandowski. 2018. “Educational upgrading, structural change and the task content of jobs in Europe.” _Economics of Transition_ 26(2): 201–231.

- IFC (International Finance Corporation). 2019. _Digital skills in sub-Saharan Africa: Spotlight on Ghana._ Washington, DC: IFC.

- ILO (International Labour Organization). 2012. _International Standard Classification of Occupations 2008. Volume 1: Structure, group definitions and correspondence tables_ . Geneva: ILO.

- ITU (International Telecommunication Union). 2020. _Digital Skills Assessment Guidebook_ . Geneva: ITU.

- Lewandowski, P., A. Park, W. Hardy, and Y. Du. 2021. “Technology, Skills, and Globalization: Explaining International Differences in Routine and Nonroutine Work Using Survey Data. _World Bank Economic Review (forthcoming)_ .

- Lo Bello, S., M. L. Sánchez-Puerta, and H. Winkler. 2019. “From Ghana to America: The Skill Content of Jobs and Economic Development.” _World Bank Policy Research Working Paper_ 8758, Washington, DC: World Bank.

38
Policy Research Working Paper 5564

# **Abstract**

Natural disasters could constitute a major shock to public finances and debt sustainability because of their impact on output and the need for reconstruction and relief expenses. This paper uses a panel vector autoregressive model to systematically estimate the impact of geological, climatic, and other types of natural disasters on government expenditures and revenues using annual data for high and middle-income countries over 1975–2008. The authors find that, on average budget, deficits increase only after climatic disasters, but for lower-middle-income countries, the increase in deficits is widespread across all events. Disasters do not lead to larger deficit increases or larger output declines in countries with higher initial government debt. Countries with higher financial development suffer smaller real consequences from disasters, but deficits expand further in these countries. Disasters in countries with high insurance penetration also have smaller real consequences but do not result in deficit expansions. From an ex-post perspective, the availability of insurance offers the best mitigation approach against real and fiscal consequences of disasters.

This paper is a product of the Private & Financial Sectors Development Sector Unit, Europe and Central Asia Region. 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://econ.worldbank. org. The authors may be contacted at mmelecky@worldbank.org. and craddatz@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
0.00|
|Public utilities||0.06|0.00|0.00|
|Construction||0.09|0.16|0.04|
|**Services**||**0.59**|**0.56**|**-0.20**|
|Market services||0.37|0.47|-0.17|
|Trade services||0.14|-0.01|-0.12|
|Transport services||0.27|0.16|0.07|
|Business services||-0.04|0.31|-0.12|
|Non-market services||0.22|0.06|-0.03|
|Government services||0.19|0.03|0.01|
|Other services||0.03|0.03|-0.04|
|**Total economy**|**2.93**|**2.38**|**0.70**|**-0.15**|

**Source:** Constructed from the Groningen Growth and Development Centre 10 Sector database (Timmer et al., 2015). **Note:** Productivity growth is defined as the compound annual growth rate in average labor productivity (%). See appendix B2 for details on decomposition methodology. Growth-enhancing structural change occurs when labor moves into those sectors whose - <u>productivity is above average in either productivity levels (static gains) or growth rates (dynamic gains).</u>

The service sector absorbed most of the labor moving out of agriculture. The fact that productivity _levels_ in the former are higher than in the latter contributed 0.56 percentage points to aggregate productivity growth, mostly driven by business and transportation services (table 2)
.0344|0.0433|
|F-statistics|4.4604|4.0084|3.0946|2.4864|2.8731|2.6283|2.2600|

Source: Authors ̳ calculations using Demographic and Health Surveys microdata. Notes: Linear probability model, pooled sample. Dependent variable is a dummy variable on child's survival status. It takes value 1 if the child died in postneonatal or infant period. For categorical variables with more than two categories, the left-out categories are: Low malaria risk zone (LMRZ), Surface water, No sanitation, Mother has no education, 1st wealth quintile (poorest), Nairobi province. Full immunization variable measures fraction of children with all vaccinations at district level. Vaccination records are not available for deceased children. For definition of "all vaccinations" see Appendix. Significance levels are marked as follows: * 10%, ** 5%, *** 1%.
mentioned international activities are associated with higher relative wages for skilled workers. However, the effects on relative skilled employment are negative and signi fi cant for imported inputs and export shares and are not signi fi cant for FDI shares.

# 5.2. Industries with Comparative Advantage

The effect of international economic activities on the demand for skilled labor may depend on whether or not the plant belongs to an industry with comparative advantage. We follow Yeats (1998) in calculating revealed comparative advantage indexes for each 4-digit ISIC revision 3 industry in Brazil (for 2001) and in China (for 1999) and based on these indexes, we construct dummy variables identifying the industries with comparative advantage in each country.<sup>45</sup> We estimate a modi fi ed version of Eq. (5), where the international activity Tijr<sup>2enters separately and</sup> interacted with the dummy variable for comparative advantage industries and show the results in Tables 16-18.<sup>46</sup> In Table 16, the coefficients on imported inputs shares for Brazil are positive and close to those in Table 6. The coefficients on the interaction with the comparative advantage dummy are negative in three cases but never signi fi cant. The total effect of imported inputs on the demand for skilled labor is positive suggesting that our earlier fi nding that imported inputs allow Brazilian plants access to skill-biased technology fl ows is not counteracted by the fact that plants importing inputs in comparative advantage industries may be engaging in production activities intensive in unskilled labor. For China, the effect of imported inputs shares per se on the demand for skilled labor is negative, except for education-based skills measures. Chinese plants that import higher shares of inputs in industries with comparative advantage have an even lower demand for skilled workers. This provides support to our interpretation of the negative

> 45We use detailed trade fl ows for Brazil, China and the world taken from World Integrated Trade Solution (WITS) to compute the revealed comparative advantage indexes as detailed in
the Philippines STEP Survey. In order to check the sensitivity of our results to the use of a different data source, we follow previous literature in defining skills measures using the U.S. O*Net database. We rely on 2004 O*Net data, the year before our period of analysis, to ensure that the skills measures are not affected by technological advances taking place after that year.

There are differences and similarities between the results using the U.S. O*Net database and our previous analysis, depending on the sub-period. For 2005-2010, the relationships between the technology measure and the demand for different types of skills are similar to the results using the STEP Survey (Table 30). However, we do not confirm the previous impacts of the trade measure and the interaction term on skills composition. By contrast, for the 2011-2015 sub-period the relationship between the trade measure, as well as its interaction with technology, and skills using the U.S. O*Net database are in line with previous estimates. However, we cannot confirm the previous results for the technology variable.

We believe the different results obtained when using the Philippines STEP Survey and U.S. O*Net database can be explained by the different levels and rankings of the skills used in each occupation

> 7 First-stage results for all robustness checks show significant correlations between the instruments and the value of Indonesian exports and its interaction with the technology variables. Tables are available upon request.

18
this information upfront when refugees are first registered. Vice-versa, if poverty estimates imputed with the PG data are not statistically different from the true rates (i.e., those produced directly from the HV data), this would suggest that existing PG variables are already suitable to produce reliable poverty estimates.

We also use two alternative models to estimate regression errors: one where we assume a standard normal distribution for the error term, and another where we remove this assumption and use the (non-parametric) empirical distribution of the error term instead. If the error term is not distributed normally, our poverty estimates would be biased, and a non-parametric model based on the empirical distribution would likely perform better.

Table 1 present the summary results and Table 2.1 in Appendix 2 provides the full regression results. Table 1 shows that all the estimates using the normal linear regression model fall within the 95 percent confidence interval (CI) of the true poverty rate, for both Sample 1 and Sample 2. In other words, these estimates are not statistically significantly different from the true poverty rates reported at the bottom of the table. Estimates using Specification 2 with more variables on household assets and house characteristics are somewhat better and closer to the true poverty rate than those using Specification 1 for both samples. For example, the poverty estimate using Specification 1 (Table 1, first column) is 52.6 percent, which is 1.1 percentage points larger than the true poverty estimate of 51.5 percent. The poverty estimate using Specification 3 (Table 1, third column) is 52.3 percent, which is 0.8 percentage points less than the true poverty estimate. This is likely because imputation models that include household assets are usually found to perform better than those that do not (Christiaensen _et al._ , 2012; Dang _et al._ , 2019).<sup>10</sup>

> 10 On the other hand, adding more control variables does not necessarily lead to a
errors at that level. We use the entire sample period 1992-2018, but pool all years before 2000 in one coefficient.

We present the βy coefficients in panel B of Figure C1. While results are somewhat noisy, they do not suggest a differential trend between ULGSP and comparison before the 1st ULGSP assessment in 2013. While there is a temporary decrease in nighlights in ULGSP wards relative to comparison ones in 2014, that differential is temporary and quickly disappears in the following years.

We also present results using not only wards included in our survey, but measuring average nighlight at the entire district in Figure C3. Here we observe some degree of convergence between 2000 and 2013: the difference in mean nighlights in comparison and treatment districts falls by about 2 NTL points in the period (in 2011 the standard deviation (s.d.) of NTL across the approximately 800 wards included in study districts was 6.2, meaning that the estimated convergence in NTL was smaller than 0.3 s.d.). This is reflected in the DiD coefficients in Panel B. A

> 30In Figure C2 we present the original series and the adjusted series, as well as raw VIIRS data for the period 2012-2018.

61
to other agencies in a foreign country and, therefore, typically send all of the people of the village they visit to the same destination (Parrenas 2001; Suradji 2004). Table 6 shows the gender specific destinations assigned to each household for the whole IFLS sample and for the restricted sample of households with a migrant parent. The only significant difference between the two samples is that migrant mothers are much more likely to be assigned Saudi Arabia as counterfactual destinations than the full sample of women, this is in line with the evidence in table 2 that women who leave children behind are much more likely to migrate internationally than any other type of migrant.

**Table 5.** Migrants per Village. IFLS 2007.

||Men|Women|
|---|---|---|
|Adults pervillage|55.31<br>(40.99)|58.70<br>(42.29)|
|Migrantspervillage|15.64|9.39|
||(14.002)|(9.185)|
|%Migrantsat samedestination|.617|.622|
||(0.231)|(0.234)|

**~~Notes:~~** ~~Standard deviations reported~~ <u>in</u> ~~parenthesis.~~

15