rafmacalaba/data-use-mentions-extended huggingface dataset:
15 Indonesia recorded relatively high rates of wage growth compared with other provinces, although wage increases appear to have lagged behind those in other provinces in services and 'other' sectors. The employment data shown in Table 7 indicate a similar pattern. Manufacturing employment in particular was relatively rapid in the poorer and densely populated provinces. The data do not suggest, in general that these provinces were lagging relative to the rest of Indonesia in terms of employment and wage growth during the 1990s.

_Employment and Wages in Key Labour Market Areas._ To enable a more disaggregated analysis, the data were broken down for several major centres of manufacturing development. These included the BOTABEK region - covering the three rapidly industrialising districts surrounding Jakarta - Surabaya and surrounding districts, and Medan and other North Sumatra industrial centres.<sup>24</sup> Several other traditional centres of manufacturing employment were also distinguished: Bandung city and district, major Central Java cities (Semarang, Solo, Klaten and Yogyakarta) and the three other major Sumatran cities (Palembang, Padang and Pakanbaru).

To what extent was manufacturing employment in the period 1987-94 concentrated around Jakarta in the west and Surabaya in the eastern part of Java? Not nearly as much as might be expected. While the percentage of Indonesian manufacturing employment located in the Surabaya region rose steeply, Greater Jakarta's (Jakarta plus BOTABEK) share remained constant (Table 9). The share of manufacturing employment also fell in both Bandung region and the Central Java cities.

But much of the growth of smaller scale and cottage industry seems to have remained outside the major cities. The share of manufacturing employment increased in West and Central Java outside the major cities. Signs of flourishing small scale industry are evident in the smaller Central Java cities - such as in the north coast cities of Tegal and Kudus (a traditional centre of _kretek_ cigarettes) and in the furniture manufacturing centre of Jepara. This appears to mark a significant departure from patterns of slow manufacturing growth in the Java heartland observed in the
**Li, Jing, William H. Dow, and Luis Rosero-Bixby.** “Education gains attributable to fertility decline: Patterns by gender, period, and country in Latin America and Asia.” _Demography_ 54.4 (2017): 1353-1373.

**McFadden, Daniel.** “Conditional logit analysis of qualitative choice behavior.” In P. Zarembka, ed., _Frontiers of Econometrics,_ New York, NY: Academic Press (1973): 105-142.

**OECD.** “Education at a glance 2019: OECD Indicators” _OECD Publishing_ , Paris (2019).

**Prati, Alessandro, Massimiliano Gaetano Onorato, and Chris Papageorgiou.** “Which reforms work and under what institutional environment? Evidence from a new data set on structural reforms.” _Review of Economics and Statistics 95.3_ (2013): 946-968.

**Rodrik, Dani.** “Growth strategies.” _Handbook of Economic Growth_ 1 (2005): 967-1014.

**Roquez-Diaz, Adolfo, and Lorenzo Escot.** “Relationship between trade openness and economic growth in Latin America: A causality analysis with heterogeneous panel data.” _Review of Development Economics_ 22.2 (2018): 658-684.

**Roy, Andrew Donald.** "Some thoughts on the distribution of earnings." _Oxford Economic Papers 3_ , no. 2 (1951): 135-146.

**Sachs, Jeffrey D., Andrew Warner, Anders Aslund, and Stanley Fischer.** “Economic reform and the process of global integration.” Brookings Papers on Economic Activity 1995, no. 1 (1995): 1-118.

**Swiston, Andrew J., and Luis-Diego Barrot.** “The role of structural reforms in raising economic growth in Central America.” _IMF Working Papers_ (2011): 1-20.

**Williamson, John.** “What Washington means by policy reform.” In John Williamson, ed., _Latin American Adjustment
|**Primary**|1 to 6 years of schooling “Adult<br>literacy program” “Other literacy<br>program” “Church/Mosque<br>schooling”|“First to fifth grade”<br>“other”|“elementary”|
|---|---|---|---|
|**Secondary**|Above 6 years of schooling<br>“Tech/Vocational training”<br>“University/College”|“Sixth to ninth grade”<br>“SSC\Equivalent”<br>“HSC\Equivalent”|“Junior High”<br>“High School”|
|**Tertiary**|Tertiary was not created given<br>that the number of respondents<br>who studied past high school was<br>extremely small.|“University”|“Vocational school”,<br>“Diploma I/II”<br>“Diploma III”<br>“Diploma IV/BA”<br>“Masters – Phd”|

Note: the same education categories were used to define partner’s education and firm manager’s education

**Occupation of father of household head:** These occupational categories were defined analogously

to employment categories for individuals (self employment in agriculture, self employment in NFE and wage employment) – see above. In Sri Lanka, the option “other” was available which led to the inclusion of the extra category “others”. No question about father’s occupation was included in the Ethiopia survey.

# **<u>Household variables</u>**

**Head of household:** is the person who is acknowledged (and reported) to be the head of the household by other household members.

**Household size:** Number of individuals living in each household.

45
weighted average of the daily changes in the interest rate and the log difference of the exchange rate, as a measure of financial distress. This approach allows us to distinguish country-specific crisis periods without resorting to the use of ex-post data.<sup>28</sup> The crisis periods in the respective countries are determined as follows. First, we construct a series of EMP volatility, measured as the 15-day rolling standard deviations of the EMP. A crisis initiates when the EMP volatility exceeds a threshold level and remains above that level for at least four weeks, where the threshold is defined as the mean of the EMP volatility plus one standard deviation, computed for each country over the period covered by the sample. A crisis ends if the EMP volatility declines below the threshold and remains there for three months (in which case, the end date coincides with the date of the initial decline). The exchange and interest rate series come from Bloomberg and Datastream. The interest rates used vary according to data availability (in all cases, we verify that all available market-determined interest rates behave similarly over the sample period).<sup>29</sup> Table 3 reports the crisis periods identified by our methodology. All non-crisis periods are called tranquil periods, and are the ones used in the previous section.

# **6.2. Summary Statistics**

Figure 3 displays the evolution of the simple average of the cross-market premium of all stocks selected for each country. For the particular case of Korea, we include two graphs: one including stocks that have been subject to restrictions over the whole sample period, and one including only the unrestricted stocks. Light shaded areas indicate crisis periods, while darker shaded areas indicate control periods. Periods when crises coexist with controls (as is the case for Argentina, Chile, Korea, and Venezuela) are considered as control periods. Thus, in what follows, crisis periods specifically refer to periods of

> 28 The weights are equal to the reciprocal of the standard
.9 days in Model 3a; and 18.2 versus 14.1 days in Model 3b).

76. The observations from the administrative data are further corroborated by the perceptions of the times as reported by surveyed procuring entity officials. PE officials reported similar decreases in processing times (a detailed analysis has been given in Appendix 9.10). Moreover, 22% of PE officials mentioned the lower turn-around time required for processing tenders as one of the two main advantages of the e-procurement system. Similarly, 59% of bidders reported preparation times of tenders as one of the main drawbacks of the paper-based system that is alleviated by the transition to the electronic system.

27
Yet, when considered together, the studies in the four groups provide a series of _working hypotheses_ that are worth exploring more thoroughly. First, the overall quality of World Bank knowledge work is high, but it is heterogeneous both between and within groups of products. Second, the volume of resources available helps, but only to a point, and the growing availability of TFs may in fact reduce overall quality. Third, timeliness is a weak aspect of World Bank analytical work, without indications that knowledge products that take longer to prepare are of better quality. Fourth, the competence of the teams in charge of producing knowledge work matters for its quality, but it can be undermined by management processes that attend to other objectives. Fifth, within the World Bank, DEC is stronger on technical rigor, but possibly weaker on communication clarity and policy relevance. And sixth, the overall quality of World Bank knowledge work is higher when it is conducted in greater proximity to its intended users, hence with a better understanding of their needs and context.

# 3. A database of knowledge products

An empirical assessment of the six working hypotheses drawn from previous studies requires a database of knowledge products that is comprehensive both in its coverage and in the information set available for each entry. Specifically, data is needed on all the analytical work produced by the World Bank during a specific period, on the quality and reach of each piece, on the competence of the team in charge, on the budget resources devoted to it, on the time it took to complete it, on whether DEC was involved, and on the proximity between the preparation process and the ultimate client.

A comprehensive database along those lines was assembled for this paper by matching information available in three independent digital platforms:

- The _Image Bank_ (IB) is a document filing system where all entries are classified based on their sensitivity, which determines who inside or outside the World Bank can retrieve them. The criteria to decide which documents are filed have some fluidity; only
the best). Date Range: 1984 to 2014. Bars show average over time and, in the case of EM7, across countries Below we look at India’s growth record since the early 1990s more granularly, in order to reconcile its positive long-term growth trend with the perceived growth deceleration in recent years.

# **3. Three Phases in India’s Growth Trajectory Since the Early 1990s**

> 20 Domestic credit provided by the financial sector includes all credit to various sectors on a gross basis, with the exception of credit to the central government, which is net. The financial sector includes monetary authorities and deposit money banks, as well as other financial corporations like finance and leasing companies, money lenders, insurance corporations, pension funds, and foreign exchange companies.

16
- 51 -

GDP at Market Prices and wage bill estimates are from Ian Lienert (IMF) and relate to 1995. Accordingly, GDP per capita, Wages and salaries as percent of GDP are based on this data Average central government wage is taken from IMF Report No. SM/96/276 of November 1, 1996 and relates to 1995.

Djibouti Central Govemment Employment, Education and Health employment data are taken from IMF Staff Country Report No. 96/44 of June 1996 and relate to 1995. State-owned enterprise employment data are also taken from IMF Staff Country Report 96/44 of June 1996 but relate to 1992.

Ethiopia Unemployment data are taken from the ILO's Yearbook of Labor Statistics 1995, refer to 1993 and source IlIl, Code 1: _Employment office statistics_ and include people who are registered on the books of the employment office. This estimate may be underestimating the number of unemployed persons in Ethiopia.

General Government employment is taken from WB Report No. 12992 ET, Public Expenditure Review of October 21, 1994 and relates to 1994. Employment is State Owned enterprise estimates are taken from IMF Report No. SM/94/234 of August 31, 1994 and relates to 1994. Following the declaration of independence by Eritrea in April 1993, the estimated strength of the Ethiopian armed forces is some 120,000. Most are former members of the Tigray Liberation Front, with perhaps 10-15,000 from the Oromo Liberation front. Estimated numbers in service must be taken with caution.

Gabon Non-agricultural employment is from IMF Report SMI/95/242 , Background Paper of September 19, 1995 and relates to 1993.

Central Government, Non-Central Government and State-owned enterprise employment is taken from IMF Background Paper No. SM 95/242 of September 19,1995 and relates to 1993. Education data are supplied by Herbert Bergmann AF3PH and are for 1995. Education employment is made up of 6,530 teachers, of which 4,412 are for elementary schools, and 2,118 for secondary schools.
to respond the survey.<sup>37</sup> For Senegal, we explore whether after controlling for observable characteristics, there are significant differences in average technology sophistication in GBFs between firms that required a larger number of attempts to be contacted (top quartile) and those that did not. Table A.19 shows that there are no statistically significant differences in technology sophistication between the two groups.

Table A.19: Comparison of technology sophistication between high and low number of attempts

|VARIABLES|Senegal|Senegal|
|---|---|---|
|Top quartile of attempts (4 or more)|-0.021<br>(0.020)|-0.027<br>(0.019)|
|Observations|1,753|1,666|
|_R_<sup>2</sup>|0.377|0.437|
|Controls:|||
|Sector FE|Y|Y|
|Size-group FE|Y|Y|
|Region FE|Y|Y|
|Age||Y|
|Exporter||Y|
|Foreign owned||Y|

Note: *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. Data are from the Senegal FAT survey with information on the number of attempts to complete interview at the firm level. Technology sophistication is regressed on a dummy for the top quartile of the number of attempts (4 or more) with controls for the stratification (sectors, size groups, and regions) and/or firm characteristics (age groups, exporter, and foreign owned). Robust standard errors in parenthesis.

Third, we compare firms that were in the first sample list provided to enumerators and those in subsequent lists. Table A.20 show that there are no statistically significant differences between the two groups.

In each of these exercises,
the OSM data set at the global scale. In locations where primary and secondary road network data is incomplete, our approach will likely overestimate the adverse impact of flood hazard on mobility.

# **References**

1. Koks, E. E. _et al._ A global multi-hazard risk analysis of road and railway infrastructure assets. _Nat Commun_ **10** , 2677 (2019).

2. He, Y., Thies, S., Avner, P. & Rentschler, J. Flood impacts on urban transit and accessibility—A case study of Kinshasa. _Transportation Research Part D: Transport and Environment_ **96** , 102889 (2021).

3. Chen, Z. & Wang, Y. Impacts of severe weather events on high-speed rail and aviation delays. _Transportation Research Part D: Transport and Environment_ **69** , 168–183 (2019).

4. Schreider, S. Yu., Smith, D. I. & Jakeman, A. J. Climate Change Impacts on Urban Flooding. _Climatic Change_ **47** , 91–115 (2000).

5. Arnell, N. W. & Lloyd-Hughes, B. The global-scale impacts of climate change on water resources and flooding under new climate and socio-economic scenarios. _Climatic Change_ **122** , 127–140 (2014).

6. Pregnolato, M., Ford, A., Glenis, V., Wilkinson, S. & Dawson, R. Impact of Climate Change on Disruption to Urban Transport Networks from Pluvial Flooding. _Journal of Infrastructure Systems_ **23** , 04017015 (2017).

7. Pregnolato, M., Ford, A., Wilkinson, S. M. & Dawson, R. J. The impact of flooding on road transport: A depth-disruption function. _Transportation Research Part D: Transport and Environment_ **55** , 67–81 (2017).

8. Wang, W
the remainder of this section, we describe each of these sources in more detail.

# **2.1 International trade flows and tariffs**

Because our goal is to estimate quality and quality adjusted prices for several products, countries, and years, we cannot rely on firm level data which is only sparsely available for a few countries. We draw therefore on export and import data from the UN Comtrade Database for the period 2012- 2019. These data contain information on bilateral export (FOB) and import (CIF) values and quantities at the HS 6-digit level. The data is more disaggregated than the information used by Feenstra and Romalis (2014) who used SITC 4-digits.

We compute the bilateral f.o.b. unit values of traded manufactured goods using reports from the exporting country, which ensures that unit values do not include shipping costs. We use importers’ trade reports to calculate c.i.f. unit values using importers’ trade reports. These unit values already include the costs of shipping and hence only tariffs need to be added. To this end, we use ad valorem tariffs associated with the most favored nation status or any preferential status from TRAINS.

Following Feenstra and Romalis (2014), we correct for measurement error by dropping trade flows below $50,000 and quantities with less than 1 kg (or missing). We further omit observations in which the ratio of the c.i.f. unit value reported by the importer and the f.o.b. unit value reported by the exporter, for a given four-digit SITC product and year, was less than 0.1 or above 10. To examine the likely effects of AfCFTA, we further use tariff data from UNCTAD, which contains information at the product level on current and expected tariffs (by 2030) after the implementation of the agreement for all country members and their partners.

# **2.2 Definition of digital goods**

Many manufactured products have digital components, and
_Ingo Borchert and Mattia Di Ubaldo_ Figure 4: Share of substantial provisions across areas in WB-DTA <!-- Start of picture text --> 0 10 20 30<br>Share subst prov in NON-SERV (%)<br>40<br>30<br>20<br>Share subst prov in SERV (%) 10<br>0<br><!-- End of picture text --> Source: Authors’ elaboration using the World Bank DTA 2.0 Database. Note: Bilateral Iceland-Faroe Islands agreement omitted (outlier).

15
For the urban subsample, the selection correction does not alter the life expectancy effects on lifetime earnings. Household size and the individual’s relationship to the household head, our identification variables in the selection equation, always turn out to be statistically significant predictors of individual labor force participation.

The life expectancy coefficient in the selection equation is positive and statistically significant, except in the rural subsample, though it is smaller in magnitude. Since we control for the life cycle stage in the selection specification, this positive association suggests that a gain in life expectancy at birth influences labor force participation marginally at any stage of the lifecycle. In the earnings equation, the negative and statistically significant inverse Mills ratios suggest a negative selection bias: individuals with jobs have lower wages compared with those for whom we do not observe wages. This is not the case for the urban subsample. Controlling for selection, the corresponding coefficient for urban residents is lower by 9%, suggesting that those urban residents with complete wage information are drawn from a relatively higher income group than those urban residents without wage information. One possible explanation for this disparity is that the urban residents without wage information include not only those whose shadow price of labor is higher than the market wage offer but also those residents who work in the informal sector or those who own large businesses. Overall, the effects of a gain in life expectancy at birth on lifetime earnings without correcting for selection bias tend to be conservatively lower using a cohort-based analysis.

# **8. Conclusion**

Using time-series data on birth cohorts from 111 countries, we find that gains in life expectancy at birth increase both investments in human capital and lifetime earnings. An individual completes an additional 0.12 year of schooling for each year added to life expectancy 29
One interesting area of future research will be to determine whether these smaller municipalities are simply more idiosyncratic and, thus, harder to predict, or whether they differ structurally in observables ways. If the latter is true, different types of data or methods might improve estimation for the smallest municipalities. In other words, is it possible to improve the estimation of these municipalities, or are there simply weaker systematic relationships between geospatial data and labor market statistics for these areas?

# **B. Area-level model**

An alternative method for combining survey data and geospatial data is to obtain municipal estimates is an area-level model (Fay and Herriot, 1979). This method is convenient and wellknown, but suffers from a few limitations in this context. The main shortcoming is that it requires aggregating the auxiliary data to the municipal level, which discards variation across AGEBs within municipalities. In addition, the method faces challenges to accurately generate estimates of municipal variance, which is an important input into the model. This is particularly true when there is a significant number of zero values, as is the case for unemployment rates in this context.

To better understand whether these potential limitations are important in practice, we examine the results of two area-level modeling exercises. Fay-Herriot models can either be assumed to be linear models or the dependent variable can be transformed prior to estimation. For ratios such as labor force participation and unemployment rates, it is common to transform the variable using an arcsine transformation (Halbmeier et al, 2019). Use of such a transformation adds additional complexity to the procedure, but ensures that predicted participation or unemployment rates lie between zero and one. In addition, using the arcsine transformation is more consistent with the AGEB-level model discussed above. For these reasons, we use the transformed model as our main results, but display the results of a linear model in Table A4.

We select a model for each outcome by applying LASSO to the full set of municipal candidate geospatial variables, again selecting lambda to minimize BIC. When
of observation being a forecaster for a country and year. Dummy variables are created to account for the different categorizations of forecasters.

Forecasters are divided into three categories based on the _institution_ sources. These include the IMF (WEO dataset), the World Bank (GEP dataset) and private forecasters (Consensus/Focus Economics datasets). January GDP growth forecasts are used for the analysis across all forecaster types. Private forecasts are obtained from two sources. Forecasts for the MENA region are obtained from Focus Economics, while the private forecasts from the rest of the world are source from the Consensus Economics dataset. Equations (1) and (2) are altered to include them as follows: A F 1 L L 2 L L L 3 A + 4 I I 5 L L L 6 7 I + ii,tt,ff ii,tt−1 ii,tt−1 ii,tt 8AAFL AAF = αα+ ββ LLII + ββ LLFF + ββ AA∆L LLFFL (3) LLFFppFFppAA ii,tt ii,tt−1 ii,tt−1 ff ββ FFppFFI IIL IIppF + ββ + ββ BBL mm + ββ FF_LLDDm yy ff tt ii,tt ββ IIAAppIIAADDAA_LLDDm yy + ττ + εε

> 11 Each indicator in the SCI is a score from 0 to 100. Each sub-component is calculated as the simple average of its indicator scores divided by 100, to adjust the scale from 0 to 1.

> 12 For a cross-section of 145 countries over the period (2004-2020): (i) the correlation between the SCI and SCI GDP Direct sub-component is 0.72; (ii) the correlation between the SCI and SCI GDP Indirect sub-component is 0.78; and (iii) the correlation between the SCI and SCI Other Data Ecosystem (Other) sub-component is 0.93.

17