rafmacalaba/datause-encoder-data huggingface dataset:
immediately obvious given the differences between innovation activities and the more conventional industrial production activities discussed above.<sup>32</sup> In building up the industrial clusters, Singapore was catering to the (known) requirements of the multinational corporations, exploiting the shift in production bases over the course of the product cycle. It was using existing technology without having to “push the frontiers”. Innovation businesses, on the other hand, require considerable amount of frontier pushing and entail a great deal of uncertainty in terms of the ingredients needed to create the necessary and sufficient pre-conditions for success. The outcome of any government initiatives will naturally be much less predictable and the costs of the ventures potentially much greater.

In general, one may argue that industrial policy would be most effective when it is applied to strengthen a country’s known comparative advantage, and that it should be avoided in areas where the country enjoys no natural comparative advantage. As things stand, Singapore is well recognized for its comparative advantage in two areas: its strategic geographical location (a given comparative advantage) and the strength of its efficiency infrastructure (a created comparative advantage). That the government is embarking on large scale construction of the innovation infrastructure reflects a serious effort to remedy one perceived weakness of the economy. There are two other major weaknesses that Singapore suffers as a small sovereign city-state economy: the limited size of the domestic market and the political and social constraints it faces in re-engineering the economy.

Wong et al (2004b) survey 40 firms in three segments (high-tech manufacturing, KIBs and creative contents) of the innovative sector. The results largely confirm the perceived strengths/weaknesses of Singapore as a potential innovative hub. For example, the two main reasons firms cited if they were to relocate away from Singapore are “market factors: other locations provide access to large emerging markets” and “more conducive business environment in other locations”. At the same time, the main factors that keep the firms in Singapore are “the hub location”, “strong business infrastructure” and “government support and incentives”. For

> 32   Even in area where Singapore has succeeded, there was a certain element of luck involved.  In a study on Singapore’s investment in airport infrastructure, Phang (2003) argues that such an “over-investment” outcome would have resulted had the economy not grown more strongly than expected and the volume of world trade not exceeded the original official estimates.  Strong growth in the region helped turn what could have been “over capacity” and wastage into an effective deterrence for other potential entrants to the market of air transportation and logistic services.

22
Table A1

|Time Period|Tarif Defnition|
|---|---|
|1974-1991|Tarif + Statistics rate (1)|
||Extra-MERCOSUR tarif +|
|1992-2001|Statistics rate +|
||ad-valorem equivalent of DIEM (2) +|
||Convergence factor (in 2001) (3)|

Notes: (1) The Statistics rate is an additional ad-valorem levy which the government adds to certain goods. Its purpose is to finance the collection of statistical data. It was originally instituted by Executive Decree 6123/61. (2) DIEM (Derechos de Importación Mínimos Específicos, minimum specific import taxes) originated as anti-dumping measures for certain import categories (including textiles). An “average import price” was calculated for each category, and the corresponding tariff was applied to each price. The actual tariff paid was the higher of this “minimum import levy” and the ad-valorem tariff applied to the actual price. (3) The convergence factor was established by law in June 2001. It was calculated as cf = 1 −<sup><u>1+</u></sup> 2<sup><u>e</u>,whereeisthedollarpriceof1Euro.</sup> ISIC classification is coarser, we constructed an aggregate indicator for NADI categories corresponding to a single ISIC code. This resulted in two series - one for 1974-1991, another for 1992-2001. At a second stage, we produced a uniform classification for the whole period. Each stage is described in detail below.

Official conversion tables between 8—10 digit NADI classification and 5-digit ISIC (Revision 2) are available starting from 1980 (when 1000 new categories were added to the NADI classification). Backward compatibility with data from 1974-1979 is also possible, but at the 5-digit level of aggregation of the NADI classification. Starting in 1991, we used official conversion tables between NADI and ISIC Revision 3. Table A2 describes the sources used in this process.

Given that 5-digit ISIC is a coarser category than the NADI, we created two versions of each tariff measure. In one, we treated each NADI category as a unique observation (this is version 1, or v1 below). In the other one, version 2 v2, we kept one instance of each unique 5-digit ISIC/tariff combination, eliminating duplicate entries.

Once we had the 5-digit ISIC code for each NADI category, we aggregated them into 3-digit measures. Starting from the 5-digit classification, we computed the median tariff level 18
_"Among the demographic phenomena determining the size and structure of a population, migration Is undoubtedly the most complex."_ Hania Zlotnick (1987a:v)

# INTRODUCTION

This report reviews the World Bank's latest international migration statistics for every country in the world for each five-year period from 1980-2000. The estimates and projections of net international migration during this period will be used as input statistics for the forthcoming edition of the World Population Projections. This review is based partially on international migration statistics published by national immigration agencies, statistical organizations, and other official and unofficial sources. Statistics on international labor migration come from sources in the labor-importing and labor-exporting countries. In addition, I have consulted country population estimates and projections produced by international and regional agencies (such as the United Nations, OECD, and SOPEMI), national census and statistics offices, other government organizations, inter-agency task forces, and research institutions. Where appropriate, information on international migration intentions has been taken into account, sucb as the intentions data incorporated in Survey Research Hong Kong's Confidence Index. Some of the major sources consulted for worldwide data are listed below. Sources relevant to a single country or region are listed in the references.

(1) World Bank, 1988, World PoRulation Projections. 1987-88 Edition: Short- and Long-Term Estimates, (2) Information from previous World Bank reviews of international migration data, (3) United Nations, 1986, World Population Prospects: Estimates and Projections as Assessed in 1984, (4) United Nations, 1984, Population Projections: Methodology of the United Nations, (5) United Nations review of national policies toward immigration and emigration, contained in United Nations, 1987, Global PoRulation Policy Database, (6) United Nations, 1988, World Population Trends and Policies: 1987 Monitoring Resort, (7) U.S. Bureau of the Census, 1983, World PoDulation: Recent DemograRhic Estimates for the Countries and Regions of the World.

(8) U.S. Bureau of the Census, 1981, DemograRhic Estimates for Countries with a PoRulation of 10 Million or More,
# **2. Wage Bill Dynamics in Europe and Central Asia**

The size of the public wage bill varies significantly across countries, corresponding to differences in the total workforce and public pay policies.<sup>2</sup> If measured as a percentage of GDP, the level of wage bill expenditures varies between 4 percent of GDP in Kazakhstan to 14 percent in Montenegro (see Figure 1). While cross-country public employment data are not readily available for the entire set of countries, available data suggest similar variation in employment numbers, ranging from 5 percent of the labor force in Ukraine to about 20 percent in Lithuania. This variation is a reflection of the different roles and functions assigned to the public service, rooted in different traditions and institutional legacies, preferences, and social contracts. Despite these differences, wage bill expenditures absorb a significant share of public spending across most countries. If measured as a share of public spending, the majority of countries spent between 20-30 percent of total spending on compensation of the public workforce in 2011 (although Azerbaijan only spends 14 percent, while Montenegro spends 33 percent of consolidated expenditures).

**Figure 1. Size of the Public Wage Bill**

<!-- Start of picture text --> Wage Bill Expenditure, 2011 Public Employment, 2010<br>34 EST MON Lithuania<br>Serbia<br>32 LIT<br>Hungary*<br>30 Estonia<br>TAJ KOS BH Russia**<br>28 KYR<br>MOL Latvia<br>SLV<br>26 ROM BUL CRO Azerbaijan<br>TUR POL<br>Moldova<br>24 BY<br>KZ RF SER Slovenia<br>22 ARM HUN Romania**<br>20 UKR LAT Croatia**<br>GEO SLO Slovak Republic<br>18 Bulgaria*<br>16 MAC CZ Czech Republic**<br>Turkey<br>14 AZ Poland<br>Ukraine<br>12<br>2 4 6 8 10 12 14 0 5 10 15 20 25<br>% of GDP General Govt (% of labor force)<br>% of Govt Exp<br><!-- End of picture text --> Source: ECA Fiscal Database, ILO Laborsta, Country sources. *2009; **2008 Over the past decade, countries have experienced very different patterns of wage bill growth over time. Before the crisis, many countries experienced strong economic growth, which supported an expansion of wage bill expenditures across the region. The average annual growth of wage bill expenditure was 9.4 percent in real terms between 2000 and 2008, driven by expansionary income policies in public sector wages. This general trend, however, masks

> 2 For the purposes of this paper, the public wage bill is confined to the direct budgetary costs of general government employment, and excludes publicly owned enterprises and corporations. This definition comprises all levels of government (central, state, local), and includes ministries and agencies directly financed and controlled by government. Non-monetary benefits, such as free health services, housing, or cars, as well as intangible benefits, such as higher job security or prestige, are omitted due a lack of systematic data.

4
Davidson, R., and J. MacKinnon, (1993) _Estimation and Inference in Econometrics,_ Oxford: Oxford University Press.

- Djajic, S., (1986). “Migrants in a Guest-Worker System: A Utility Maximization Approach,” Seminar Paper No 386, Institute for International Economic Studies, Stockholm

- Docquir, F., and H. Rapoport, (1998) “Are Migrant Minorities Strategically SelfSelected?” _Journal of Population Economics_ , Vol. 11: 579–588.

- Du Y., Park, A., and S. Wang, (2005) “Migration and Rural Poverty in China,” _Journal of Comparative Economics_ , Vol. 33: 688-709

- Do, T., and L. Iyer, (2007) “Poverty, Social Divisions and Conflict in Nepal.” Mimeo, The World Bank.

- Dustmann, C., and O. Kirchkamp, (2002) “The Optimal Migration Duration and Activity Choice After Re-migration,” _Journal of Development Economics_ , Vol. 67: 351– 372.

- Efron, B., (1981) “Nonparametric Estimates of Standard Error: The Jackknife, the Bootstrap and Other Methods,” _Biometrika,_ Vol. 68 (3): 589–599.

- Fafchamp, M., and F. Shilpi (2003) “The Spatial Division of Labor in Nepal,” _Journal of Development Studies_ , Vol. 39 (6): 23–66.

- Funkhauser, E. (1992), “Mass emigration, Remittances and Economic Adjustment: The Case of El Salvador in the 1980s,” in G. Borjas and R. Freeman Eds., _Migration and the Workforce: Economic Consequences for the United States_ , University of Chicago Press, Chicago, IL.

- Gustafsson, B., and N. Makonnen (1993) “Poverty and Remittances in Lesotho,” _African Economies_ , Vol. 2 (2): 49–73.

- Jha, H., (1999) _Status of Informal Sector Workers: The Other Side of Economy in Nepal._ Katmandu.

- Heckman, J., and G. Sedlacek, (1990) “Self-Selection and the Distribution of Hourly Wages,” _Journal of Labor Economics_ , Vol. 43 (1 Part II): S329–263.

- Hildebrandt, N., and D. McKenzie, (2004) “The Effects of Migration on Child Health in Mexico”, _Economia_ , 6 (1): 257–289.

- Hoddinott, J., (1994) “A Model of Migration and Remittances Applied to Western Kenya,” Oxford Economic Papers, Vol. 45: 135–151.

- Kanbur, R., and H., Rapoport, (2005) “Migration Selectivity and the Evolution of Spatial inequality,” _Journal of Economic Geography_ , Vol. 5: 43–57.

- Kumar, B., (2003) “Migration, Poverty and Development in Nepal,” paper presented for an Expert Group Meeting on Migration and Development, Economic and Social Commission for Asia and the Pacific, Bangkok.

- Low, A., (1986) _Agricultural Development in Southern Africa: Farm Household Economics and the Food Crisis_ , London: James Curry.

- McKenzie, D., (2005) “Beyond Remittances: The Effect of Migration on Mexican households.” Mimeo, World Bank, Washington, DC.

- McKenzie, D., and H., Rapoport, (2005) “Migration Networks, Migration Incentives, and Education Inequality in Rural Mexico,” Paper presented at the Inter-American Development Bank “Economic Integration, Remittances, and Development” Conference.

29
# Figure 20. SAR: Policies to raise potential output growth

A. Potential output growth and contributions

# B. Reform scenarios

<!-- Start of picture text --> Percent Percent Social benefit reform<br>7 Baseline Demographic trends Other factors 8 Labor market reforms<br>Education and health improvements<br>Investment surge<br>6 6 Baseline<br>5 4<br>4 2<br>2011-21 2022-30 Reform 2011-21 2022-30 Reform<br>3 impact impact<br>2011-21 Factor 2022-30 2011-21 Factor 2022-30<br>EMDE SAR<br>EMDEs SAR<br>C. Climate change scenarios D. Female labor force scenarios<br>Percent Investment boost (climate change) Percent<br>8 Disasters (climate change) 8 Baseline Female LFPR improvement<br>Baseline<br>6 6<br>4 4<br>2 2<br>2011-21 2022-30 Reform 2011-21 2022-30 Reform<br>impact impact<br>0<br>EMDE SAR<br>2011-21 2022-30 Reform<br>E. Share of agriculture sector F. Climate risk<br>Percent Occurences per year Millions 2021 US$<br>60 Share of GDP Share of employment Occurences<br>50 250<br>Cost per event (RHS)<br>50<br>40 200<br>40<br>30 150<br>30<br>20 20 100<br>10<br>10 50<br>0<br>0 0<br>Pre-2000s Post-2000s<br>Sources: EM-DAT database; Penn World Tables; UN Population Prospects; World Bank; World Development<br>Indicators database.<br>2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020<br><!-- End of picture text --> Note: EMDEs = emerging market and developing economies; SAR = South Asia. GDP weights are calculated using average real U.S. dollar GDP (at average 2010-19 prices and market exchange rates) for the period 2011-21. A.-D. Period averages of annual GDP-weighted averages. Potential growth estimates based on production function approach.

A. Sample includes 53 EMDEs, of which 3 economies are from SAR region. “Other factors” include trend improvements in human capital, and stable investment growth relative to output growth.

- B. Sample includes 53 EMDEs, of which 3 economies are from SAR region.

- D. LFPR = labor force participation rate.

- E. Sample includes eight SAR economies.

- F. Based on data from 1980-2021. “Cost per event” in current 2021 US dollars. Occurrences of natural disasters.
3 The tiird objection, and the most difficult objection to answer, is that the existence of an association does not imply causation. While causality is nearly impossible to establish, we present three arguments. First, we use instrumental variables estimation which allows the identification of the impact of exogenous changes in participation, eliminating the effect on the participation estimates of reverse causation or simultaneity. Second, data on the timing of participation shows that participation at early stages improves project performance at every stage, from implementation to maintenance. Third, we describe case studies in which exogenous changes in participation in ongoing projects had strong impacts on performance.

Even for this limited set of projects.. this paper is not intended as a comprehensive account. We focus on a narrow set of econometic issues involved in drawing inferences from project data. Narayan (1994), drawing on these data and more, discusses in more detail the relationship between performance and participation, the determinants of project success besides participation, the detenninants of participation, and the mechanisms whereby participation increases overall effectiveness.

# 1) Data on rural water projects and basic results

The systematic case review method (Finsterbusch 1990) is used to transform varied, qualitative evaluations of a set of related phenomena into data suitable for statistical evaluation. To illustrate this approach, consider a hypothetical study of effectiveness in higher education. A researcher wants to use statistical techniques to determine the most important factors that contribute to undergraduate leaming. She does not have the time or resources to design and implement a new survey, but does have access to myriad evaluations by other
13 comorbidity is likely to increase the probability of severe disease. To incorporate this into this study would require estimates of odds ratios for each risk factor and their interactions as well as an understanding of the correlation between risk factors as these are likely to not be independent. For example, obesity has been linked to hypertension (Aneja, 2004). What the model seeks to do is provide a framework into which more data on risk factors can be added as and when it becomes available.

Fourth, we make the assumption that the impact of improvements in access to handwashing on prevalence of respiratory illness occurs over a timescale relevant to the pandemic. While previous research from Kenya suggests that the impact can be observed 2-9 months after intervention (Hoekstra _et al._ , 2012), if this timescale is longer in Zimbabwe, or if the improvements themselves take a long time to be implemented, we may not see the desired effects. One potential way to maximize impact would be to prioritize districts for improved handwashing according to their current handwashing risk. Intervention impacts could be maximized by targeting districts or communities with highest handwashing risk first. Outputs from ongoing spatially discrete agentbased modeling, informed by human movement data, could also be used to identify those districts most vulnerable to transmission.

Finally, this paper has only focused on underlying risk factors in the population and has not considered risk of transmission due to e.g. population mobility. The outputs of this work will likely therefore be used as part of a larger agent-based modeling exercise to identify those districts with highest probability of severe disease when transmission is also taken into account, in a separate, forthcoming paper.

In conclusion, this study shows the spatial disparity in severe COVID-19 risk across Zimbabwe. Second, it shows that there is a relationship between access to handwashing facilities and respiratory illness, a known risk factor for severe COVID-19. In addition to any direct effects on transmission of SARS-CoV-2, improving access to handwashing may therefore reduce the probability of severe disease among the population which could in turn reduce the number of individuals requiring hospitalization, critical care or dying from the disease.

# References

Aneja, A. (2004) ‘Hypertension and Obesity’, _Recent Progress in Hormone Research_ , 59(1), pp. 169–205. doi:10.1210/rp.59.1.169.

Ansari, S.A. _et al._ (1991) ‘Potential role of hands in the spread of respiratory viral infections: studies with human parainfluenza virus 3 and rhinovirus 14.’, _Journal of Clinical Microbiology_ , 29(10), pp. 2115–2119. doi:10.1128/JCM.29.10.2115-2119.1991.

Bagepally, B.S. _et al._ (2021) ‘Cost-effectiveness of surgical mask, N-95 respirator, hand-hygiene and surgical mask with hand hygiene in the prevention of COVID-19: Cost effectiveness analysis from Indian context’, _Clinical Epidemiology and Global Health_ , 10, p. 100702. doi:10.1016/j.cegh.2021.100702.

Brady, M.T., Evans, J. and Cuartas, J. (1990) ‘Survival and disinfection of parainfluenza viruses
reliable than reported consumption in LMICs, where self-employment and/or agriculture predominate, and which makes accurate reporting of incomes difficult (Deaton, 1997). The concerns about the reliability of income data collected in poor countries pertain mainly to measuring poverty. It is possible that income data is more reliable at the top end than at the bottom because agricultural income is likely to be less important at the top. However, self-employment income is still likely to be important, although the extent to which more accurate record keeping in formal or at least larger businesses translates into better income data in surveys is unclear.

# _Lack of common support_

All the challenges discussed thus far can limit the number of observations with incomes close to the top of the true income distribution. At worst, this can result in a lack of common support between the top of the distribution in the household survey and the true distribution, meaning that there are simply no individuals in the surveys with reported incomes near the top of the true income distribution (Lustig, (2020), Ravallion, (2022)). This implies the surveys will not accurately represent the top of the income distribution. But it is also a concern because some of the solutions to the measurement challenges that we discuss below require at least some individuals in the surveys at the top of the distribution.

Table 1 summarizes the challenges described above, in a rough order of their importance when measuring the top tail of the income or wealth distribution. However, we should stress that the order is likely to vary in different countries and/or time periods and depend on the level of development of the country concerned. In the section below outlining the research agenda, we further discuss which challenges are more important and deserve further investigation.

Table 1: Measurement Challenges for the Top tail of the Income and wealth distributions

|**Challenge**|**Description**|
|---|---|
|Unit non-|High Income or Wealth Individuals or Households do not respond to the survey|
|response|at all|
|Item non-<br>response|High Income or Wealth Individuals or Households respond to the survey but do<br>not respond to income or wealth questions|
|Measurement|High Income or Wealth Individuals or Households respond to the survey but|
|Error<br>Sparseness|under-report their incomes or wealth<br>The survey sample size is relatively small, and few or no high income or wealth<br>individuals are sampled|
|Lack of<br>Common<br>Support|The challenges above mean that there are no individuals with income or wealth<br>in the top tail of the distribution|
|Data|The survey organization creates measurement error that makes the measured|
|Processing|top tail unreliable|

10