rafmacalaba/data-use-mentions-extended huggingface dataset:
Others authors, such as Vu (2020), used Deaton’s method in comparison with alternative methods to deal with quality effects. One popular alternative is the one of Cox and Wohlgenant (1986) (CW), which is based on fewer assumptions and it is easier to use compared with Deaton’s (e.g. Park et al., 1996; Lazaridis, 2003). CW uses the deviations of unit values from regional or seasonal means as proxies of quality effects. They regress them on household characteristics and use the residulas to correct unit values, obtaining quality-adjusted prices. However, the main issue with CW is that the adjusted prices are household-specific, disregarding the fact that, in theory, nearby households share similar market prices. In order to fix this problem, Vu (2009) developed a modified version of CW using communal average unit values instead of household unit values. This modified CW is used by Vu (2020) and many others (see below), however neither CW nor its modified version deals with measurement error problems.

Mergenthaler et al. (2009) used the CW approach to analyze fresh fruits and vegetables demand in the two major cities of Vietnam, Hanoi and Ho Chi Minh City. Instead of the VHLSS, they used their own survey data collected in 2005, looking at effects of different supply chains with different quality and safety standards. They conclude that fruits and vegetables from modern supply chains with higher standards, such as supermarkets or non-traditional imports, are highly income elastic, and that income effects are prevalent over price competitiveness and supermarket penetration, suggesting once again that the effects of Vietnamese economic growth affect consumption preferences, not only in terms of quantity consumed but also in terms of food quality. Hoang (2018) used the modified CW approach (Vu, 2009) in QUAIDS concluding that, despite the growing income and urbanization levels in Vietnam, the demand for rice is still expenditure and price inelastic with respect to other foods. Moreover, Hoang (2018)
- 36 J.C. Duque, N. Lozano-Gracia, J.E. Patino, P. Restrepo / LCR Flagship Report on Cities and Productivity

Bunnel, T; Drummond, LBW; Ho, KC (2002). Critical Reflections on Cities in Southern Asia. Brill Academic Press, Singapur.

- Cao, Z; Wu, Z; Kuang, Y; Huang, N; Wang, M (2016). Coupling an intercalibration of radiancecalibrated nighttime light images and land use/cover data for modeling and analyzing the distribution of GDP in Guangdong, China. Sustainability (Switzerland), 8(2). https://doi.org/10.3390/su8020108

- Carroll, M; Townshend, J; DiMiceli, C; Noojipady, P; Sohlberg, R (2009a). Global Raster Water Mask at 250 meter Spatial Resolution, Collection 5: MOD44W MODIS Water Mask. College Park, Maryland: University of Maryland.

- Carroll, M; Townshend, J; DiMiceli, C; Noojipady, P; Sohlberg, R (2009b). A New Global Raster Water Mask at 250 Meter Resolution. _International Journal of Digital Earth_ , 2(4).

- Center for International Earth Science Information Network - CIESIN - Columbia University, International Food Policy Research Institute - IFPRI, The World Bank, and Centro Internacional de Agricultura Tropical - CIAT. 2011. Global Rural-Urban Mapping Project, Version 1 (GRUMPv1): Settlement Points. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). http://dx.doi.org/10.7927/H4M906KR.

- Cervero, R (2001). Efficient Urbanisation: Economic Performance and the Shape of the Metropolis. _Urban Studies_ , 38(10):1651-1671.

- Cervero, R; Kockelman, K (1997). Travel demand and the 3Ds: Density, Design and Diversity. _Transportation Research. Part D Transp. Environ_ ., 2(3):199–219.

-
with more efficient allocation of industry for districts between urban and rural settings. The spatial mismatch index compares the observed industry distribution of district employment across urban and rural locations to a counterfactual where plants or employment in the district are allocated (in some sense optimally) to urban and rural locations according to national propensities by industry to be in urban settings. As elaborated further below, higher mismatch values for a district indicate that plants that would have been expected to be in urban areas are in rural areas, and vice versa. Figures 2a-2b plot the trends in our spatial mismatch indices. Encouragingly, there has been an aggregate decline in spatial mismatch since 1989, primarily driven by improved allocation of the unorganized sector. Additional regressions identify that the urbanization shifts associated with better education and infrastructure have improved the spatial allocation of industry.

The plan of this paper is as follows. Section 2 discusses our data and the broad patterns of urbanization for India’s manufacturing sector. We also discuss in depth how urbanization is defined in India and issues for longitudinal consistency. Section 3 presents the decomposition of urbanization changes across districts. Section 4 considers the district-level traits associated with urbanization changes; this section also presents our interaction analysis of industry traits. Section 5 analyses spatial mismatch in industry allocations. The final section concludes and discusses implications from this work.

# **Section 2: Surveys of Indian Manufacturing--Organized and Unorganized Sectors**

This paper employs cross-sectional establishment-level surveys of manufacturing enterprises carried out by the Government of India. Our work studies the manufacturing data from surveys conducted in fiscal years 1989, 1994, 2000, and 2005. In all four cases, the survey was undertaken over two fiscal years (e.g., the 1994 survey was conducted during 1994-1995), but we will only refer to the initial year for simplicity. This section describes some key features of these data for our study.<sup>3</sup> It is important to first define and characterize the distinction between the organized and unorganized
_More limited feasibility of automation?_ : The degree to which automation is adopted depends heavily on a country’s technological absorptive capacity, the skill of the workforce, ability to mobilize resources for large capital investments, capacity for maintenance, and attention to tolerances which may make it less easy to substitute away from labor in many poorer countries.

_Skills for emerging complementary tasks?_ : To the degree the automation does occur, the likelihood of developing the kinds of upgraded jobs to complement the automation would seem lower in countries with weak educational bases in the first place. Or put differently, if a country had to raise workforce cognitive and non cognitive skills to be attractive to MNC assembly operations, this task would seem more challenging as the offered jobs are the upgraded complements to assembly operations.

The first set of estimates of the combination of these effects is modestly pessimistic. Broadly following Goos et al. (2014), WorldBank (2016), using labor force surveys argues that middle skilled occupations intensive in routine cognitive and manual skills have decreased across the developing world as well, with the exception of China, Ethiopia, Argentina and Nicaragua.

# **3 Data**

We pursue the approach of Autor (2010) of tracking job categories across time for 21 developing countries in Africa, Latin America and Asia. We use the Integrated Public Use Microdata Series (IPUMS) developed by the Minnesota Population Center which harmonizes census micro-data from around the world. The project has collected the world’s largest archive of publicly available census samples. The data are coded and documented consistently across countries and over time to facilitate comparative research.<sup>1</sup>

> 1 See `https://international.ipums.org/international/` .

5
**<u>Table A2: Data Sources</u>**

|**Variable**|**Definition**|**Frequency**|**Source**|
|---|---|---|---|
|Output|Real gross domestic product (GDP)|Quarterly|Ilzetzki, Mendoza, and Vegh (2013), OECD, Haver Analytics|
|Private consumption|Real personal consumption expenditure|Quarterly|Ilzetzki, Mendoza, and Vegh (2013), OECD, Haver Analytics|
|Private investment|Real private gross fixed capital formation|Quarterly||
|Government consumption|Real government consumption expenditure<sup>a</sup>|Quarterly|Ilzetzki, Mendoza, and Vegh (2013), OECD, Haver Analytics|
|Government investment|Real government gross fixed capital formation<sup>a</sup>|Quarterly|OECD, Haver Analytics, Eurostat|
|Real effective exchange rate|Real effective exchange rate<sup>b</sup>|Quarterly|Ilzetzki, Mendoza, and Vegh (2013), BIS|
|Current account|Current account as percent of GDP|Quarterly|Ilzetzki, Mendoza, and Vegh (2013), WEO|
|Government debt|General government debt as percent of GDP|Annual|WEO|
|Fiscal balance|Overall fiscal balance as percent of GDP|Annual|WEO|
|Government consumption-to-GDP ratio|Government consumption as percent of GDP|Annual|WDI|
|Government investment-to-GDP ratio|Government investment as percent of GDP|Annual|WDI|
|Interest rate|Short term nominal interest rate|Quarterly|Ilzetzki,Mendoza,and Vegh(2013)|

_Note:_ The main source for the quarterly series is Ilzetzki, Mendoza, and Vegh (2013). This database which ends around 2008 is extended by splicing from different sources as mentioned in the table.

a This refers to general government for most countries while for a
# **Appendix A: Pillars of the SPI**

A quick primer on names. We refer to the 5 rows in the framework in Figure 1 as pillars. We refer to the 22 cells in the framework in Figure 1 as dimensions. Finally, each dimension may be composed of multiple indicators. For instance, the dimension on censuses and surveys is made up of indicators on whether population censuses have been conducted, agriculture censuses, labor force surveys, etc.

# **3.1 Data use**

The data use pillar is segmented by user type. The tiles on the Dashboard provide an indicator of use of statistics respectively by the legislature, executive, civil society (including sub-national actors), academia and international bodies. A mature system would score well across the tiles. Areas for development would be highlighted by weaker scores in that domain enabling questions to be asked about prioritization among user groups and why existing services are not resulting in higher use of national statistics in that segment.

# **3.2 Data services**

The data services pillar is segmented by service type. The tiles on the Dashboard provide an indicator of the quality of data releases, the richness and openness of online access, the effectiveness of advisory and analytical services related to statistics and the availability and use of data access services such as secure microdata access. Advisory and analytical services might incorporate elements related to data stewardship services including ethical consideration of proposals and calling out misuse of data in accordance with the Fundamental Principles of Official Statistics.

# **3.3 Data products**

The data products pillar is segmented by topic and organized into social, economic, environmental and institutional domains using the typology of the Sustainable Development Goals. This approach enables comparisons across countries and anchors the system in the 2030 agenda so that a global view can be generated while enabling different emphasis to be applied in different countries to reflect the user needs of that country.

# **3.4 Data sources**

The data sources
Policy Research Working Paper 10196

# **Abstract**

Large-scale transport infrastructure investment can facilitate structural transformation by changing firm behavior. Although its impact is evident over the long term, an important empirical challenge is potential endogeneity of infrastructure placement. By using the dynamic panel data regression, the paper examines the impacts of massive road corridor investment under the Nurly Zhol program in Kazakhstan. The paper takes advantage of detailed micro shipping data to capture historical changes in transport connectivity over the past 10 years. While the average travel speed has slightly increased, transport costs have been nearly halved. The estimated translog cost functions indicate that local market accessibility is the most important factor to boost firm productivity. The elasticity was 0.24 in absolute terms. Inventory is found to be a major cost factor for firms. It is found that a 10-percent improvement in accessibility to large cities, such as Astana and Almaty, could allow firms to reduce their inventory by 8.7 percent. The market accessibility is found to foster firm agglomerations, but agglomeration economies do not seem to translate into higher firm productivity. This is possibly because the Kazakh economy still lacks effective forward or backward linkages across industries.

This paper is a product of the Transport Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The author may be contacted at aiimi@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
<br>analysts,<br>or<br>water<br>resource specialists.|

Source: Own elaboration based on O*NET database and Dierdorff et al. (2009, 2011).

This paper uses the International Standard Classification of Occupations (ISCO) or its adaptations to classify occupations, instead of O*NET. Previous studies have mapped the O*NET-SOC 2010 classification of green occupations into the latest ISCO-08 taxonomy to allow for more straightforward cross-national comparisons of green occupations (Hogarth, 2011; Sofroniou and Anderson, 2021). However, their mappings are produced at the ISCO three-digit level at best. More importantly, their methodologies are based on a dichotomic definition of green jobs, in which a whole ISCO category is considered either green or non-green. This paper maps the O*NET-SOC classification of green occupations into the most detailed four-digit ISCO-08 taxonomy using a probabilistic instead of a dichotomic approach. Specifically, the paper uses data on total employment at the 2-digit level ISCO for 120 countries from circa 2001 to circa 2020 (676 country-year observations). Since each ISCO code may have several O*NET-SOC occupations associated with it (not all necessarily green), it would be misleading to consider a whole ISCO occupation as green or non-green based on whether the number of O*NET-SOC associated occupations are primarily green or non-green. Therefore, instead of constructing a binary classification, the paper estimates the share of employment within an ISCO-08 category that corresponds to O*NET-SOC occupations considered green and then considers this share as the probability that an occupation with that ISCO code is green. Since the O*NET-SOC classification is based on the US structure of employment, employment shares are 6
likely to be in school than the children of women of other marital status particularly at younger and older ages. It is especially striking that these children drop our earlier (Figure 11). In contrast, at any age between 5 and 14, the resident dependents of widows have the highest probability of being in school in urban areas. Those least likely to be in school are the children of divorced women at ages under 8 or so and the dependents of previously widowed but now married women at older ages (Figure 12).

A further issue is whether daughters are treated differently from sons and whether such differential treatment is associated with a mother‘s marital situation. Figure 13 explores this question by breaking down the school enrollment data given in Figures 11 and 12 by the gender of cohabiting school aged children. The difference between the share of boys versus girls is plotted. Note however, that due to small sample sizes once disaggregation by age and gender is carried out, the children of current widows and current divorced women are not included in Figure 13. Likewise, there may be some small-sample problems with the married, previously widowed group in urban areas to keep in mind. In both urban and rural Mali, boys are overall more likely to be in school than girls conditional on a given marital status. However, the gap appears to be much less pronounced and increases only weakly with age among the children of once married women. The daughters of previously widowed or divorced women hold an advantage at very young ages in rural areas, but once they reach seven and a half or so, the gap reverses, rising quite steeply with age, particularly for the children of previously widowed women. The latter‘s pro-son gap overtakes that for the offspring of once married women from around age 10 for the children of prior widows and around age 12 for those of prior divorcees. In urban areas, the gender gap favors boys at all ages and is generally significantly larger for the children of previously widowed and divorced women.
eligibility threshold of this program<sup>34</sup> for the whole sample or when broken down by gender and location. Second, and related to the continuity checks discussed above, there is no evidence of discrete changes in the distribution of other observable dimensions beyond the probability of participation in the program that could indicate some degree of manipulation of the scoring and ranking system.

Another issue is the possibility of nonrandom migration and crossover. In theory, families in control areas could try to migrate to treatment areas to become eligible for the program. This may affect the internal validity of the analysis if, for instance, families that are poor – and perhaps systematically different from non-migrants in other dimensions – are more likely to engage in this type of migration. Similarly, parents that live in control areas may prefer to send their children to schools that serve program beneficiaries if they are perceived to be of higher quality and are within their geographical reach. There are, however, a number of reasons to think that migration and crossover effects of this sort are unlikely to invalidate the findings of this paper. First, migration from control to treatment municipalities is close to zero among the baseline households examined in the PSM approach and that were resurveyed in the two subsequent rounds of longitudinal data collected in 2003 and 2005. Second, as noted above, part of the control group used for the RD analysis is comprised of children and adolescents that are above the cutoff of eligibility but reside in the same municipalities than participating children. Third, the economic incentives induced by the amount of the transfer (around $7-14 per eligible child) are probably not enough to compensate for the direct and indirect costs of migration. Fourth, _Acción Social,_ the national agency that administers the program opens calls for inscription and enrollment into the _FA_ program only on specific dates for each municipality, making it impossible for people to join the program at other times.

Problems of misspecification of the underlying regression models may introduce bias in treatment effects, particularly in
3|
|20|Food and related products machine operators|816|0.3|0.3|0.5|0.2|
|21|Wood processing and papermaking plant operators|817|0.0|0.5|0.1|0.2|
|22|Locomotive engine drivers and related workers|831|0.0|0.0|0.0|0.0|
|23|Ships'deck crews and related workers|835|0.0|0.1|0.1|0.2|
|24|Vehicle, window, laundry and other hand cleaning workers|912|0.7|0.2|0.4|0.1|
|25|Street and related service workers|951|0.0|0.0|0.0|0.1|
|26|Street vendors (excluding food)|952|0.4|0.3|1.1|0.2|
|27|Refuse workers|961|0.4|0.5|0.3|0.3|
|||**Total**|43.0|14.0|37.1|20.3|

_Sources_ : Employment shares are from Cambodia 2020 Socioeconomic Survey, and 2017 employment surveys of Malaysia, Thailand, and Vietnam.

40
ISA) data are representative at the national, regional, and urban/rural levels and serve as a baseline for our post-COVID-19 analysis.

The HFPS are not nationally representative as participation requires that each household have (1) at least one member who owns a phone, (2) cell network coverage, and (3) access to electricity. These requirements may lead to selection bias in the survey sample. Additionally, the surveys may suffer from non-response bias if targeted households were not willing or able to participate. To address these challenges, we use survey weights provided in the HFPS data which include selection bias corrections and post-stratification adjustments. Several studies using the HFPS data have found that the use of survey weights and post-stratification adjustments substantially reduce the bias, though it does not fully eradicate the bias, and our results should be interpreted with this in mind (Ambel et al., 2021; Brubaker et al., 2022; Gourlay et al., 2021). For a detailed description of the weight calculations used in this study, see Josephson et al. (2021).

The integration of data from the post-outbreak HFPS and pre-outbreak LSMS-ISA surveys allows us to capture the variation in the effects of the pandemic across a diverse set of Sub-Saharan Africa countries and over time. Importantly, the combined data afford us the opportunity to examine the effects of COVID-19 in relation to a pre-pandemic baseline, allowing us to establish a causal relationship between our variables of interest. The surveys feature cross-country comparable questionnaires on a range of topics including participation in income-generating activities and food insecurity. In total, over 9,000 households are included in this analysis. With baseline LSMS-ISA data in all three countries plus 10 rounds of HFPS data in Ethiopia and 11 in Malawi and Nigeria, our research draws from a total of over 34,000 observations. The average numbers of households in each round of data is: 2,784 in Ethiopia, 1,611 in Malawi, and 1,943 in Nigeria, though the actual number of