rafmacalaba/data-use-mentions-extended huggingface dataset:
exports should not only depend on downstream trade costs (export distance), but also on upstream trade costs (import distance), and the interaction of both. Using China’s bilateral processing trade data, we have found empirical support for this complex impact of trade costs on intra-GPN trade.

A key theoretical prediction by Yi (2003) is that intra-GPN trade should be more sensitive to changes in trade costs than regular trade since vertical specialization leads to products crossing borders many more times before reaching the final consumer. Rubin and Tal (2008) and Rubin (2009) have built on this theory to conjecture that rising oil prices will increase trade costs, thus leading to a major slowdown in the growth of world trade and especially intra-GPN trade. To test this conjecture, we have used the panel data structure of the processing trade data to analyze the sensibility of intra-GPN trade to changes in oil prices. We have found evidence that China’s processing exports indeed have become more sensitive to both upstream and downstream trade costs in times of rising oil prices. Furthermore, in line with Yi’s (2003) theoretical prediction, China’s processing exports have been found to be more sensitive to oil price movements than non-processing exports.

Finally, we have used the processing trade data to analyze the role of GPNs in the large trade collapse during the recent Great Recession. In line with the predictions of many trade economists, we found that the Great Recession led to a disproportionate drop in China’s processing exports compared to non-processing exports, thus suggesting that GPNs played a key role in the trade decline. Part of this was a compositional effect. The sectors that contributed most to China’s exports collapse were those where processing exports were more prevalent. However, even within highly disaggregated industries, we have found that processing exports consistently dropped more than non-processing exports _,_ thus suggesting that intra-GPN trade is more sensitive to business cycle fluctuations than regular trade. Furthermore, in line with the bullwhip effect, we showed
**In Turkey, the servicification of manufacturing firms is happening, albeit slowly.** Turkish firm‐level data<sup>9</sup> allow for an analysis of which manufacturing firms show servicification and which only produce goods. It does so by recording the secondary services activities for each manufacturing firm using the revenue generated from services affiliates. The data therefore give a good indication of the degree of servicification of Turkish goods firms, particularly the production and output of services (see the center and right‐hand elements in Figure 3.1). Services inputs in manufacturing, which is the left‐hand element of Figure 3.1, are partially captured through the indirect domestic services value added in exports discussed in the previous section. (In other words, while service inputs are an important element of the servicification phenomenon, they are not covered by the analysis in this section due to data limitations.) The services affiliates are recorded using their 4‐digit NACE code, as is the manufacturing activity of the firm. A share of the revenue of the entire firm is assigned to each affiliate based on the number of employees in that affiliate. Over time, manufacturing firms in Turkey have seen a slow shift away from generating revenue from goods toward more services (Figure 3.2). When only manufacturing firms with a services affiliate are considered, average revenue from the services affiliate increased from about 20 percent in 2006 to 21.5 percent in 2014.

**Figure 3.2: Servicification of manufacturing firms: 2006‐2014**

<!-- Start of picture text --> In-house servicification of manufacturing firms<br>2006 2007 2008 2009 2010 2011 2012 2013 2014<br>Services (left) Manufacturing (right)<br>15 100<br>13<br>95<br>11<br>90<br>9<br>85<br>7<br>Share revenue generated from goods<br>Share
Figure 1: Average Real Income, Total Expenditures, and Education Spendings for Iranian Households <!-- Start of picture text --> 160 6<br>Sanctions Iran<br>140 Deal<br>negotiatian 5<br>120<br>4<br>100<br>80 3<br>60<br>2<br>40<br>1<br>20<br>Rafsanjani Khatami Ahmadi Nejad Rouhani<br>0 0<br>Real Income Real Expenditure Real Education Expenditure<br>Millions Rials (2011 Prices) Million Rials (2011 Prices)<br>Real Income & Expenditure Real Education Expenditure<br>1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016<br><!-- End of picture text --> _Source: Author’s calculations from HEIS data._ Note: Figure displays the decreases in average real annual income, total expenditures, and education spendings for Iranian households over the years of economic sanctions.

The analysis of this paper reveals two main findings. First, among children who ever been between the ages 6 to 24 during years 2006-2013, sanctions decreased the years of schooling significantly by 0.1 years (0.3 years among children ages 15-24 years) and the probability of attending college (any post-secondary programs) by 4.8 percentage points. This effect on children’s education is more than two times larger than previous estimates of the effect of family income on attending college (e.g., Acemoglu and Pischke (2001); Blanden and Gregg (2004); Hilger (2016)) likely because of the persistent shock and lack of adjustment possibilities.<sup>7</sup> Also, these negative effects are larger for children who were exposed longer to the sanctions and children at crucial ages (high school dropout age and matriculation at a university). In particular, the years of
## **7.1.2 Household survey outcomes**

Panel A of Table 15 reports estimates of the effect of the savings default on total spending in the week following the transfer for male-headed households, for households with any bank activity before the experiment, and for those above the median for the other three characteristics; Panel B reports the corresponding estimates for female-headed households, for those without bank activity before the experiment, and those below the median for the other characteristics. For example, the average male-headed household that received a cash transfer spent MK 16,132 in the week following the survey. The savings default increased spending for male-headed households by MK 959. The average female-headed household that received a cash transfer spent MK 13,472, and savings default recipients spent MK 1,227 less. Neither effect is statistically different from zero. In fact, of the ten coefficients reported in Table 15, none is significant. Moreover, in each case, the magnitude of the coefficient is small relative to the mean for cash recipients.

We reject the null hypothesis that savings defaults affected pairs of subgroups equally for one pair. Savings defaults decreased total spending for households that had previously used their accounts and decreased it for households that had not (column 2), so while neither coefficient is significantly different from zero, the p-value for the test that they are equal (reported in the bottom row of the table) is 0.063. The p-value for the joint test that savings defaults affect all ten subgroups equally is 0.459 (not shown).

We analyze spending on each of the four categories of consumption in a similar fashion.<sup>15</sup> In total, we estimate 40 coefficients, and only two are individually significantly different than zero. In 20 tests of equal effects between subgroups, we reject the null only once: spending on fees and transfers increases for patient respondents and falls for impatient respondents, but in either case, the effect is less than three percent
22

# F.BLIOGRAPHT

Barnes, B. 'Papanicolaou Cervical Smears for Screening in Asymptomatic Women." Primaly Care, 8 (1): 131 140, March, 1981.

Barnum, H. 'Cost Savings from Altenr.tive Treatments for Tuberculosis." World Bank Technical Notes Series, Rej%rt No. 86-1, February, 1986.

Bernard, R.P., De Watteville, H., Kessel, E., Ravenholt, R.T., and Kendall, E.N. "International Maternity Care Monitoring: Results of a Pretest." International Journal of Gynaecolonv and Obstetrics, 17 (1): 24-39, 1978-79.

Berry, L.G. 'Age and Parity Influences on Maternal Mortality: United States 1919-1969." Demozra2bv, 14 (3): 297-310, 1977.

Bhatia, J.C. Maternal Mortality in Anantapur District. India: Preliminary Findings. Background paper prepared for the Meeting on Prevention of Maternal Mortality, Geneva, WHO, November 11-15, 1985.

Carrasco, E. 'Contraceptive Practice." In World Fertility Survey Comnarative Studies: Cross National Summaries, No. 9, International Statistical Institute, Voorburg, The Netherlands, 1981.

Chen, L.C., Gesche, M.C., Ahmed, S., Choudhury, A.I., and Mosley, W.H. 'Maternal Mortality in Rural Bangladesh." Studies in Family Planning, 5 (11): 334-341, 1974.

Gorosh, N.E. and Wolfers, D. "Standard Couple-years of Protection." In 3hA Methodology of Measuring the Inmact of Family Planning Programmes on Fertility, New York: United Nations, 1979.

Gray, R.H. Effects of Reproduction and Fertility Control on Women's Health. Prepared for the Workshop on Health Consequences of Contraceptive Use and Controlled Fertility, National Research Council, Committee on Population, unpublished draft, August, 1986.

Grimes, D., Peterson, H.B
the ICRG indicators did not prove to be significant either. Hence, these results are not reported in any of the tables.

To investigate the possibility of slope heterogeneity across countries, we followed the approach used in Haque, Pesaran, and Sharma (1999). Specifically, the slope coefficients in each country are assumed to be fixed over time, but varying across countries linearly with the individual sample mean of GDP per capita. The final results (Tables 15 and 16) only include the statistically significant interaction terms, in order to avoid co linearity arising from the correlation between original explanatory variables and the auxiliary variable capturing the interaction of these with the sample mean of GDP per capita. Hence the estimated model is: _VRSTEit_<sup>=</sup> _f_ ( _WAGEit_ , _GOVEXPit_ , _GDPPCit_ , _URBAN it_ , _AIDSit_ , _GINI it_ , _WAGEGit_ , _GOVGit_ , _GINIGitCONS_ ) where _VRSTEit_ = Variable returns to scale DEA efficiency score for single output and multiple output cases

- _WAGEit_ = Wages and salaries (% of total public expenditure)

_GOVEXPit_ = Total government expenditure (% of GDP) _PUBTOTit_ = Share of expenditures publicly financed (public/total) _GDPPCit_ = GDP per capita in constant 1995 US dollars _URBAN it_ = Urban population (% of total) _AIDSit_ = Dummy variable for HIV/AIDS _GINI it_ = Gini Coefficient _CONS_ = Constant

* _WAGEGit_ = _WAGEit GDPPCi_ * _GOVGit_ = _GOVEXPit GDPPCi_ * _GINIGit_ = _GINI it GDPPCi_ − 1 _T GDPPCi_ = _T_ ∑ _t_ = 1 _GDPPCit_

Results show that the interaction terms are significant, especially for the health regression, implying that there is a heterogeneous response of efficiency scores to the different explanatory variables. This confirms Greene’s (2003) results on the WHO data. One of the key results of this section is that the negative association between the size of government expenditure and efficiency is stronger in countries with higher per-capita GDP. Similarly, this happens with the wage variable. Results are somewhat similar to those of the homogeneous slopes, though statistical significance of many of the
represents the simulation of GDI under a specific fiscal rule (BBR, SSR, BBR-HR, and SSR-HR) and submodel (Default and External Balance). Likewise, panels C, E, and F display incremental GDP, public and private investment, respectively. Finally, panel D shows incremental GDP growth _,_ defined as the difference in growth rates between scenario and baseline, in percentage points. Below we briefly discuss the impact of oil prices in each variable. Appendix Table 6 through Appendix Table 8 provide a detailed summary of the results.

> 45 These assumptions are based on the estimated forecast distribution of oil prices, using the World Bank’s Commodity Markets Outlook data from 1960 to 2019. The price of $80 is the 75<sup>th</sup> percentile of the distribution and $50 is the unconditional mean. See Appendix Figure 5 for details.

33
# **Figure 7 Employment costs as a share of revenues by fully owned and partially privatized SOEs, by sector**

<!-- Start of picture text --> 200<br>188<br>150<br>110<br>100<br>72<br>50<br>18 21 19<br>15<br>0<br>Power Airlines and airports Roads Railways<br>Partially privatized SOEs Fully owned SOEs<br>Employment costs as percent of revenues<br><!-- End of picture text --> _Source_ : Authors, based on data from the World Bank Infrastructure SOEs Database. _Note_ : Data are for 2009–18. 0 denotes partially privatized firms and 1 denotes fully owned SOEs.

# **5 Comparison with Private Companies**

This section presents the results of a matching exercise that compares the performance and fiscal risk of SOEs with that of private firms.

# **_5.1 Data and Methodology for Matching Comparisons_**

To compare the SOEs with private firms, we follow a multistep process. First, we use the tools in Standard & Poor’s Capital IQ to create a comparison set. We search for firms in the same sector and same region, restricting the search to firms that have similar total assets in US dollars. In particular, we compile financials for firms that are within +/–20 percent of the assets of the firms in our sample as well as in the same industry and region. We end up with a comparison set of 10–20 firms for each of the SOEs in our database.

Second, we eliminate companies that are easily identifiable as SOEs in the comparison set, leaving a sample of over 600 firms to use in the matching exercise. We focus on the period 2009– 18, for which the World Bank Infrastructure SOEs Database has better coverage.

One concern we had was whether our database of private firms included state-owned firms, despite our efforts
<!-- Start of picture text --> Figure 5: Regional<br>Figure 4: Regional<br>representation of sample<br>representation, 2009 Census<br>Kidal<br>5%<br>Kidal<br>14%<br>Timbuktu<br>Gao Timbuktu 49%<br>42% 53% Gao<br>37%<br><!-- End of picture text --> In addition to the 500 households and 180 refugees, 50 local authorities were interviewed. Of those, 18 are based in Goa, 22 in Timbuktu and 10 in Kidal. Included were 38 village chiefs, 11 mayors and 1 local notable.

**Table 3: Authorities interviewed by function and by region (%)**

||**Gao**|**Kidal**|**Timbuktu**|**Total**|
|---|---|---|---|---|
|**Village chief**|13|8|17|38|
|<br>**Mayor**|5|1|5|11|
|<br>**Local notability**|0|1|0|1|
|**Total**|**18**|**10**|**22**|**50**|

The authorities interviewed are all men aged between 30 and 86. Forty-four percent are either just literate or have no education at all. In Gao, authorities have a higher level of education compared with Kidal and Timbuktu. The majority (almost 39%) of respondents in Gao have a high school education, 22% are literate and 17% have primary education. In Kidal, 80% of respondents have no education at all. In Timbuktu, about 32% of the surveyed authorities are literate, 27% have a
Figure 4 continued: Educational attainment profiles for ages 15-19 by gender and wealth (Vertical axis is proportion of children who have completed grade).

<!-- Start of picture text --> Central America and Caribbean<br>Dominican Rep. 1996 . Guatemala 1995 Haiti 1994-95 Nicaragua 1998<br>0.8 - 0. - 0.8~ 0.83-<br>086 - 0' 06 5 0.6 -<br>0.4 - \ 0.4 0.41 0.4-<br>0.2- 0.2 0.2 0.2<br>o 1, ,,, 0 0<br>1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9<br>Grade Grade Grade Grade<br>South America<br>Bolivia 1997 Brazil 1996 [Brazil, Northeast 1996] Colombia 1995<br>1 3 1 - 2 . 1 - 9 I 1 9<br>0.8 0.8 0.8 0.8 -<br>0.6 0.6 0.6 0.6<br>0.2 -0.2 -0.2 -0.2-<br>0 0 0 ,.,, , ,<br>1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9<br>Grade Grade Grade Grade<br>East Asia and Pacific<br>Peru 1996 Indonesia 1997 Philippines 1998<br>1 1- 1 - a +<br>0.8 - 0.8 -c0.8em<br>0.6 -60.6 - 0.6 -<br>0.4 - 0.4 - 0.4M a<br>0.2 - 0.2 - 0.2 -<br>1 3 5 7 9 1 3
financial aspects of health services) rather than the discipline (viz. economics) characteristically applied to understanding and explaining phenomena in health and health care (Culyer, 1981; Williams, 1979).

Our EconLit metadata include author, title, journal, the year of publication, author‘s institution, country of focus, keyword, and three-digit JEL code. EconLit does not include any citation data; for 78 percent of records in our dataset we were able to obtain citation data from Google Scholar. In addition to getting at influence through citations, we also want to say something about the topics that health economists work on. The JEL sub-categories are not
choice of DOU (absolute) or DB (relative) approach does not matter much. The most striking result is that while rural areas accommodate more than half of the poor populations in many countries, the mass of poverty is more concentrated in urban areas than previously thought. By switching from the official urban definitions to the DOU and DB approaches, the number of SSA countries with more than 80 percent of poor populations living in rural areas decreases from 13 to 8 and 2 countries, respectively. Furthermore, in 4 of 16 SSA countries more than half of the poor populations reside in urban areas. We also find that, unlike poverty rates, the choice of DOU or DB approach to delineating urban areas makes a critical difference to the spatial distribution of poor populations.

The remainder of the paper is structured as follows. Section 2 provides a brief background on the framework of global poverty measurement and urban delineation—the variation in urban definitions across countries and pros and cons of different urban delineation methodologies. Section 3 describes the data used in this paper. Section 4 presents the empirical approach to integrating the new globally consistent urban classifications into the global poverty framework. Section 5 reports the results, followed by a discussion and conclusions in Section 6.

# **2 Global poverty measurement and urban delineation**

## **2.1 Global poverty measurement**

Global poverty is measured using individual or household welfare proxies, such as consumption expenditures or income, along with international poverty lines and price indexes to account for price differences.<sup>7</sup> To measure poverty, household budget surveys are used to aggregate consumption expenditures for each household (Deaton and Zaidi, 2002; Mancini and Vecchi, 2022). These expenditures are then converted to per capita consumption expenditures by dividing them by the number of household members. The World Bank defines different thresholds for international poverty lines, including the extreme poverty line ($2.15 per capita per day in 2017 PPP terms), the lower-middle-income poverty
local language. After the training, interviewers were selected on the basis of an exam and simulated interviews. The questionnaire was then tested during a pilot study in a colline that was not included in our sample. We assigned teams of five interviewers, each including a team leader and at least two women. Each interviewer did two interviews per day on average. The questionnaires were then checked for accuracy and entered in a CS-PRO program by data entry agents. We checked the accuracy of entry ex-post, too.

Community Data. During the 2010 survey, enumerators also undertook a community survey in each colline. This survey included data on past violence, public services, and community initia-

> 11There are four administrative levels in Burundi: the province, the commune (translated _“municipality”_ ), the colline (translated _“hill”_ ) and the sous-colline (translated _“sub-hills”_ ).

> 12The QUIBB survey used the same sampling strategy as the Multiple Indicator Cluster Survey (MICS), collected in September 2005 by UNICEF. The MICS survey mostly focuses on health and gender issues, and contains little useful information about economic outcomes. The sampling weights of the MICS survey will accounted for in the empirical analysis (results do not change without taking into account these sampling weights).

> 13There were three collines in which we could not trace households, all located in Bujumbura Rural. In two collines, the villagers reported not to know the households, either because they had migrated or were invented by 2005 interviewers. The remaining colline was not secure enough to conduct the survey.

10
**2 The Study Area and the Survey**

The study area, which is located in Bolangir and Kalahandi Districts, is a patchwork of small river basins and often densely forested hills. The population is highly heterogeneous, with scores of different tribal groups and Hindu communities. What makes this remote rural area particularly interesting for present purposes is its poverty and notorious proneness to drought (Sinha, 1999). In six years of the decade 1996-2005, for example, precipitation was deficient, untimely, or both (van Dillen, 2008). The most important sources of household income are cultivation, animal husbandry, nontimber forestry, government employment schemes and earnings from various forms of migration, where the latter are often a response to drought.

The original survey was carried out in 2001-02 (see van Dillen [2008] for an extensive account). Four administrative blocks (Titlagarh, Saintala, Muribahal and Bongomunda) were selected in Bolangir, and Kesinga, the most north-eastern block of Kalahandi. These five, contiguous blocks have a combined area of approximately 1,900 sq. km and a population of about half a million. Employing a stratification aimed at ensuring a fairly even spatial distribution at the second stage, six clusters of villages were defined in each block and one village was drawn at random from each cluster. Eight households were then selected from each village, yielding 240 households in all. Again, only spatial stratification was employed, households being selected at random from different parts of the village settlement area. Detailed quantitative information was collected on each household’s activities and income portfolio in each of three consecutive six-month periods, namely, for the entire crop year 2000-01 and the _kharif_ season of 2001-02. Also canvassed were the details of the household’s demographic features and wealth. In addition, the team of investigators collected qualitative information in the form of household ‘histories’ of what decisions had been made in periods of drought-induced crises in the past, and why. Data on infrastructure and land use at various
., pp. 17-31.

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Dustmann, C. & Okatenko, A., 2024. Out-migration, wealth constraints, and the quality of local amenities. _Journal of Development Economics,_ Volume 110, pp. 52-63.

Du, X. & Wong, B., 2019. Science career aspiration and science capital in China and UK: a comparative study using PISA data. _International Journal of Science Education._ Esipova, N., Pugliese, A. & Ray, J., 2018. _More than 750 Million Worldwide Would Migrate if They Could._ [Online] Available at: https://news.gallup.com/poll/245255/750-million-worldwide-migrate.aspx Evans, R., 2010. Children’s Caring Roles and Responsibilities within the Family in Africa. _Geography Compass,_ Volume 4, pp. 1477-1496.

Favara, M., 2017. Do Dreams Come True? Aspirations and Educational Attainments of Ethiopian Boys and Girls. _Journal of African Economies,_ p. 26(5): 561–583.

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