rafmacalaba/data-use-mentions-extended huggingface dataset:
local institutions.<sup>13</sup> The data set contains information for the period 1999 to 2007 on how the current district head was selected (e.g. by pre-1999 DPRDs, indirect elections by post-1999 DPRDs, direct elections, or whether (s)he is a caretaker head appointed by the central government until the next elections), the name of the elected district head, whether (s)he was preceded by a caretaker district head, whether the incumbent governor won or ran in the elections, the reason for the incumbent governor not contending, the share of votes won, the number of candidates , the political party, the date the term began and ends and some key personal characteristics such as gender, and whether (s)he is in the military.

The SIKD regional budget data from 2001 to 2006 was derived from MoF‘s Regional Financial Information System ( _Sistem Informasi Keuangan Daerah_ , SIKD).<sup>14</sup> The Fiscal Year within this time span runs from January to December. The introduction of a ―new‖ budget format according to MoHA Decree no. 29/2002 changed the budget structure since 2003, with the changes mostly affecting the classifications on the expenditure side.<sup>15</sup> To enable comparison over time, we converted the new dataset into the old format as per SIKD guidelines – by mapping the post-2003 expenditure data into the old categories of development and routine expenditures. To measure changes in real terms, we also deflated the fiscal data for 2001 to 2006 using 2000 as the base year.

The changes in fiscal variables over time and across treatment and control districts estimated in more detail by the regressions can be seen clearly from graphs showing trends for different groups. Figure 4 traces the averages of selected fiscal variables over time and separately for treatment and control groups. An implicit assumption behind the application of the DID estimator is that the time trend (or year effect) is identical between the treatment and control
the skyscrapers of many developing countries constitute white elephant projects that are economically and environmentally inefficient (Gjerlow and Knutsen, 2019; Ianchovichina et al., 2022; Jedwab and Roberts, 2023). Finally, nations and cities of similar income levels can differ in their specialization in heights or areas. These specializations likely reflect differences in land-use regulations and institutions more broadly, as shown by studies on individual regions, countries, cities (Brueckner and Sridhar, 2012; Fox, 2014; Brueckner et al., 2017; Brueckner and Singh, 2020) or skyscrapers globally (Jedwab et al., 2022; Jedwab and Barr, 2023b,a). We instead consider the whole building distribution.

# **2. World Settlement Footprint 3D: From Version 1 to Version 2**

The first version of the World Settlement Footprint 3D (WSF3Dv1) dataset, released in 2017, provides information on the building fraction (BF), height (BH), and volume (BV) at 90 m spatial resolution (Esch et al., 2022). WSF3Dv1 was created based on a global coverage of 12 m digital elevation (TDX-DEM) and Synthetic Aperture Radar (SAR) data (TDX-AMP) collected by the TanDEM-X satellite mission in 2012-2013 (Zink et al., 2014), in combination with 10 m resolution multispectral Sentinel-2 (S2) satellite imagery. Although the analyses are performed at a gridding of 12 m, the final WSF3D products can only be made publicly available with a reduced resolution of 90 m due to German data security regulations (SatDSiG) related to the TanDEM-X mission.
which probably explains the multiple failures of many initiatives in this area. Only a couple of importers may be on the side of reform.

# **2. An Analytical Demand Model – Some Theoretical Considerations**

Long dwell times (which account for a large share of containers in terminals) are one of the key issues that needs to be addressed (probably across the continent) and are related mostly to factors under the control of shippers. This confirms one of the initial hypotheses of this analysis, which is that the behaviors and strategies of shippers have an impact on dwell times in ports. Demand by importers for port dwell time beyond the time required to complete port operations and transactions seems to be related mainly to inventory management and the ―business model‖ used (including the extent of informal practices).

Due to the fact that the demand from importers seems to explain a large part of long dwell cargo, in this section we present theoretical foundations explaining current demand issues in SSA and then present some empirical analysis, based mainly on firm surveys.

The model examines cost minimization strategies and profit maximization strategies. Coupled with various market structures, it seeks to explain why behaviors that are perceived as irrational, such as leaving cargo in the port are the best option for an importer. It describes the economic foundations of rational decisions with regard to container storage in port terminals and off-dock container yards (ODCYs).

Storage operations can be defined as a subcomponent of an international logistics pathway that starts with loading containers in the supplier’s facilities and ends with unloading them in the customer’s facilities. We define the logistics pathway as ―a sequential set of logistics operations, warehousing, depot operations, port operations, trucking, and freight forwarding, which deal with the end-to-end movement of freight‖ (Magala and Sammons, 2008). In addition, we focus on containerized trade only, specifically containerized trade through international ports.

When deciding to import a certain quantity of containerized cargo, shippers have to choose either directly
**Figure 9. Types of applicants by education**

_Source_ : Authors’ calculation using the data from Rozee.pk.

_Note_ : Shown are the proportions of applicants who have different levels of education.

# **_Q. In which industries and occupations are skills more demanded and supplied, and how is industrial and technological specialization at school relevant to jobs?_**

# **_A. In Rozee.pk, the majority of the jobs are the professional level. ICT sector has the largest number of vacancies, because of which the tightness of ICT jobs is the highest, although there are many jobseekers who specialize in ICT._**

The labor supply, demand, and market tightness by occupation levels are presented in Figure 10. Occupational levels are grouped into four categories: intern, entry level, professional, and manager. The most prevalent job postings are professional jobs, followed by entry-level jobs. On the supply side too, professional-level and entry-level jobseekers are the majority.<sup>17</sup> However, entry-level jobs are more competitive than professional-level jobs because of limited availablity of job vacancies relative to the size of jobseekers, indicating that fresh gradautes face more difficulties of finding jobs than those who are already working.

By industry, jobseekers whose self-claimed specialization is either manufacturing, or professional, scientific, and technical services are the two largest groups, whereas job postings in both these industrial categories are relatively few (Figure 10). As a result, the tightness in these two industries is low. On the other hand, ICT sector has the largest number of job vacancies. Although there are many jobseekers in this sector, the tightness for ICT jobs is the highest among all sectors. Thus, jobseekers specalized in ICT may find jobs relatively easily.

> 17 Job seekers’ occupation levels are self-reported.

10
. Most of these groups had multiple companies listed on the Jakarta Stock Exchange. The total sample of firms for which we can revisit Fisman’s analysis is 51, which is lower than the original sample of 79 firms analyzed by Fisman (2001) because Bloomberg data were only available for surviving firms.

26 Recall that Bloomberg does not retain information on the stock prices of de-listed companies. This forces us to reduce our analysis to 51 firms instead of the 79 firms that Ray Fisman included in his original analysis. These 51 surviving firms do not seem to be systematically more or less connected than those included in Fisman’s original sample as is shown in Table B1 in the online appendix. Replicating key regressions from Fisman’s (2001) seminal paper using our smaller sample, as is done in Table B3, offers additional suggestive evidence that survivor bias is limited.

42
days earlier under commitment. That previous choice was made clear and salient. Then, facing the same rate of return, the participant was allowed to revise the original allocation decision. That decision (revised or unrevised) was then implemented with certainty. Surveys at both stages measured household wealth, income, and expenditures as well as the participants’ expectations for each of these variables.

We now turn to a description of the setting and its advantages for the experiment, followed by a more detailed description of the two stages of the experiment.

# **2.1 The Setting**

As a setting for experimental study of intertemporal choice, rural Malawi has a number of advantages. Most important, financial markets are thin there, especially during the rainy season when the entire experiment was conducted. As a consequence, participants lack effective methods for smoothing the large amounts of new consumption made possible by the experiment.

The farmers in our study have virtually no cash during the rainy season; and borrowing substantial cash in rural Malawi during the rainy season is not merely expensive, it often appears impossible. Similarly, short-term saving is difficult due to limited banking institutions, and familial or social demands for what appears like excess cash.<sup>15,16</sup> This lack of borrowing and saving opportunities is important because it sharply reduces the smoothing opportunities that confound efforts to elicit time preferences in developed economies. In economies with thick financial markets and low transaction costs, answers to the questions asked in typical time-preference experiments should, in theory, bracket only the market rates of return that participants face, and reveal very little about their “true” preferences regarding consumption (Fuchs, 1982).<sup>17</sup> To the extent that rural Malawi in the rainy season approximates autarchy, 15 Data from a 2008 nationally representative survey in Malawi indicates that only 26 percent of respondents use a formal financial product, and around 60 percent had never heard of a savings account (FinScope, 2008).
Three pieces of evidence lead us to believe that our results are not driven by experimenter demand effects. First, the point estimates of our treatment effect on objective and subjective indicators for HIV testing are very similar at 0.031 and 0.025, respectively (see Table 2). In the presence of experimenter demand effects, one would expect the latter coeffi cient to be larger.

Second, many relevant self-reported outcomes do not display significant treatment effects. Notably, we find no effects on self-reported condom use (Panel B of Table 3) and on five other condom-related variables (Appendix Table A16).

Third, if treated individuals were differentially affected by reporting bias, we should find that when they have a chance of pleasing the research team by choosing condoms in the experimental game played in health camps, they should be more likely to do so. Panel A of Table 4 shows that this was not the case.

Heterogeneous effects by education and language. An interesting question is whether our effects differ depending on viewers’level of education or ability to understand English (as MTV Shuga was in English). In Appendix Table A25 we interact the treatment dummy with the respondent’s years of education (odd-numbered columns) and with a dummy for whether respondent speaks English as the primary or secondary language at home (even-numbered columns). The average respondent in our sample has 11.6 years of education, and 96 percent of the respondents speak English at home as either primary or secondary language.<sup>35</sup> We find that Shuga had a stronger effect on HIV knowledge and attitudes of relatively more educated respondents, although the coeffi cient on the interaction term with education is not significant when adjusting for multiple hypothesis testing. This may seem surprising as one may expect television to be a particularly effective means of communication for audiences with low literacy, but it should be noted that ours is a uniformly highly educated sample, as is the region.<sup>36</sup> So
**Table 1. Episodes of Large Devaluations and the Timing and Magnitude of FDI and Export Surges** **<u>(quarterly data, 1990-2010)</u>**

||REER|Deva|luation|Timin<br>abov<br>(q|g of firs<br>e the 7<br>uarters|t increase<br>5 centile<br>after)|Growth rat<br>increase a<br>75 ce|e of first<br>bove the<br>ntile|
|---|---|---|---|---|---|---|---|---|
||||_Growth_||||||
|_Country_|_Year_|_Qtr_|_Rate_|_Exports_|_FDI_|_FDIfirst?*_|_Exports_|_FDI_|
|Argentina|2002|1|-0.54|1|6|C|0.19|0.69|
|Austria|2010|2|-0.02||0|A||0.61|
|Bangladesh|1996|3|-0.23|0|3|C|0.23|3.87|
|Belarus|1999|1|-0.37|1|0|A|0.16|3.04|
|Belgium|2010|2|-0.03|||D|||
|Bolivia|2009|2|-0.08|4|1|A|0.18|1.56|
|Brazil|1999|1|-0.33|1|2|C|0.23|0.58|
|Bulgaria|1996|2|-0.26|3|1|A|1.17|0.80|
|Canada|2008|4|-
’ of the household head with the corresponding household weekly consumption, which was used as proxy variable for income.

2. Individual-level data based on members within the household reporting an economic activity and corresponding income from such activity during the reference week.

The advantage of using household consumption levels as a proxy for income is that wage-based data do not include income received by self-employed individuals. Analysis on household level data was purely based on consumption, even if there are individuals within the household which reported income. For the data set based on individual respondents, individuals (15 years and above, excluding those studying full time) who had an activity during the week and reported its corresponding earnings are included.

# **4.2 Limitations**

The ISIC - HS correlation is not a full match concordance table. There are ISIC codes for which there is no corresponding HS code. Hence the analysis only includes activities in which the associated good/s can be identified with the HS code. For example, some ISIC codes which refer to construction (e.g. ISIC "4100 - Construction of buildings") do not have a matching HS code, and such observations were dropped from the calculation of the statistics.<sup>9</sup> For the

> 7 The tariffs at the HS subheading level matched with the ISIC code are trade weighted averages by value of imports from each partner. We also look at how much the tariffs faced have declined from their 1996 level for specific identified sectors. To calculate the reduction in tariffs, the same methodology was used to match tariffs in 1996 for the same partners.

> 8 The data on NTMs were extracted from the WTO's NTM database - I-TIP.  The database shows the number of NTMs by type – SPS, TBT, ADP, CV, SSG, SG, QR, TRQ and XS –  in each HS chapter.

> 9 Only observations of selected respondents who work in ISIC sectors where a correlation with HS (hence tariffs) exists (32
The estimates of the model coefficients are found to be largely coherent: (a) per capita expenditure decreases with household size with a declining marginal effect, (b) the `returns to education’ are all positive with higher returns for higher education levels (i.e. tertiary > secondary > primary education coefficient), (c) unemployment enters negatively, while waged-, self-employment and being an employer all enter the regression positively, (d) public sector employment too enters positively, while employment in the BTP sector (construction) is associated with lower standard of living, which is consistent with the BTP being a low-wage sector, (e) employment in the financial sector is clearly beneficial, but only in 2007, it was not yet significant in 2001, (f) size of the house as measured by the number of rooms per capita is strongly positively associated with total household expenditure, although the marginal effect declines for larger houses as expected, (g) similarly, households equipped with electricity, sewage, in-house clean drinking water (as well as the added durable assets and housing variables) are found to have higher total expenditure on average, (h) the significance of the interactions with the region dummy variables shows that the significance of the above mentioned variables is stronger for some areas than for others.

**Table 2 – Urban Model**

||**Wit**|**hout Addit**|**ional Assets**||**W**|**ith Additio**|**nal Assets**||
|---|---|---|---|---|---|---|---|---|
|**Variable**|**2001**||**2007**||**2001**||**2007**||
|Domain U2|-0.
39 World Bank Survey in Seven Russian Cities (1994). Similar studies were conducted in 1995 and 1996. 1

- **_EHDP Social_** _Household Survey_ was conducted in multifamily buildings in Vladimir and Volkhov and in demonstration buildings in Ryazan as part of the preparation for the World Bank Enterprise Housing Divestiture Project (Guzanova and Diachenko, 1995; Guzanova and Diachenko, 1996). A total of 508, 300 and 455 households respectively were polled using random samples in the autumn of 1995. The goal of these surveys was to determine housing conditions, as well as the households' needs, perceptions and opinions of housing maintenance and potential improvements of the energy efficiency of their buildings.

For comparative analysis, we used the findings of a survey conducted in Yaroslavl, a large cultural and industrial center, where in 1993 tenants of 1,400 apartments were interviewed. The sample included typical apartments in each of the city districts. The following paper is devoted to this problem:

- Susan G. Lehmann and Blair A. Ruble. From Sovie **t** o "European" Yaroslavl: Changing Neighborhood Preference in a Post-Soviet Russian City. Columbia University, New York, 1994.
other sectors. Overall, people in the poorer states are more likely to be working compared to those in the richer states, which are mainly concentrated in the urban areas, where there are limited opportunities to engage in agricultural activities.

# **Figure 7. Unemployment rate by state and gender**

<!-- Start of picture text --> 60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>Total Male Female<br><!-- End of picture text --> _Source:_ World Bank staff calculations based on NHBPS 2014/15.

Youth unemployment increased between 2009 and 2014, especially in urban areas. Unemployment among the population between 15 and 24 years increased by 2 percentage points from 20 percent in 2009 13
& Vigorito, Andrea. (2013). Teenage School Attendance and Cash Transfers: An Impact Evaluation of PANES. _Economía, 14_ (1), 61‐96.

- Ambler, Kate, Aycinena, Diego, & Yang, Dean. (2015). Channeling Remittances to Education: A Field Experiment among Migrants from El Salvador. _American Economic Journal: Applied Economics, 7_ (2), 207‐232.

- *Amin, Sajeda, Ahmed, J, Saha, J, Hossain, I, & Haque, E. (2016). Delaying child marriage through community‐based skills‐development programs for girls. Results from a randomized controlled study in rural Bangladesh.

- Andrabi, Tahir, Das, Jishnu, & Khwaja, Asim Ijaz. (2017). Report Cards: The Impact of Providing School and Child Test Scores on Educational Markets. _American Economic Review, 107_ (6), 1535‐1563.

- Angrist, Joshua, Bettinger, Eric, & Kremer, Michael. (2006). Long‐term educational consequences of secondary school vouchers: Evidence from administrative records in Colombia. _American Economic Review, 96_ (3), 847‐862.

- Aturupane, Harsha, Glewwe, Paul, Ravina, Renato, Sonnadara, Upul, & Wisniewski, Suzanne. (2014). An assessment of the impacts of Sri Lanka’s programme for school improvement and school report card programme on students’ academic progress. _The Journal of Development Studies, 50_ (12), 1647‐1669.

- Avitabile, Ciro, & De Hoyos, Rafael. (2018). The Heterogeneous effect of information on student performance: evidence from a randomized control trial in Mexico. _Journal of Development Economics, 135_ , 318‐348.

- Baez, Javier E, & Camacho, Adriana. (2011). Assessing the long‐term effects of conditional cash transfers on human capital: Evidence from Colombia. _World Bank Policy Research Working Paper No.5681_ .

33
important information on the severity of malnutrition that is not always represented by headcount measures.

Table 1 Changes in headcount and gap for child stunting, underweight and overweight

||Changes fr<br>20<br>|om 1993 to<br>05|Changes f<br>20<br>|rom 2000-<br>09|Changes fr<br>20<br>|om 2005 to<br>14|
|---|---|---|---|---|---|---|
||Number||Number||Number||
||<br>of<br>countries|% of<br>countries|<br>of<br>countries|% of<br>countries|<br>of<br>countries|% of<br>countries|
|_Stunting_|||||||
|<br>Headcount and gap<br>moving in same direction|24|71%|29|83%|49|83%|
|<br>Headcount and gap<br>moving in different<br>direction|10|29%|6|17%|10|17%|
|_Underweight_<br>Headcount and gap<br>moving in same direction|27|79%|29|83%|49|83%|
|<br>Headcount and gap<br>moving in different<br>direction|7|21%|6|17%|10|17%|
|_Overweight_|||||||
|<br>Headcount and gap<br>moving in same direction|32|91%|31|89%|56|95%|
|<br>Headcount and gap<br>moving in different<br>direction|2|6%|4|11%|3|5%|
# **1 Introduction**

China has made significant strides in innovation since it acceded to the WTO and opened its markets to international flows of trade, investment, people, and ideas. International integration catalyzed a period of rapid growth, with foreign firms playing a key role in facilitating the diffusion of advanced technologies and enhancing China’s innovation capacities (L. G. Branstetter et al. 2023; Fang et al. 2020; Jiang et al. 2024 and Wei et al. 2017). The share of patents filed by Chinese assignees in the United States Patent and Trademark Office (USPTO) increased from 0.2% in 2000 to 7.2% in 2022.<sup>1</sup> This remarkable growth in patent activity, driven by both technology transfers and learning by doing through supply chain relocation, demonstrates the importance of global integration and international collaboration for China’s innovation ecosystem.

Technology has, however, become a source of tension between the U.S. and China – raising the prospect of technological decoupling. The U.S. Department of Commerce has increasingly utilized its Entity List to regulate the technology transfer and exports of goods containing U.S. components to Chinese entities. The Entity List subjects selected foreign entities to licensing requirements for the export, reexport, and/or transfer of certain technologies and goods. For example, the inclusion of Huawei in the Entity List in 2019 prohibited Google from providing its services to Huawei. This restriction directly impacted Huawei’s smartphone business and led to the company developing its own operating system (Reuters 2019). The number of Chinese firms on this list has increased dramatically from 3 in 1997 to 345 in 2022, with a significant increase in 2018. Despite the extensive discussion of these U.S. sanctions in the media, there is little research exploring their effects on Chinese firms’ innovation.

This paper examines how inclusion in the U.S. Entity List affects the innovation activities of Chinese firms.