rafmacalaba/data-use-mentions-extended huggingface dataset:
nonstate sector, where the foreign enterprises account for an overwhelmingly large proportion, is closely associated with the upward trend of foreign investment in Shenzhen.

Also a study based on the 1993 data indicates that, in the Shenzhen SEZ, foreign firms, as well as those Hong Kong, Macao, and Taiwan, China invested firms, are generally more efficient than their domestic counterparts (Ge 1999). The data on sector output after 1993 were no longer segregated by type of enterprise ownership, so it is difficult to conduct a similar type of analysis; but a comparison of productivity growth between two sectors—the primary sector with very little FDI and the transportation, postal, and telecom sector where FDI is very heavy—shows that FDI is still very positively linked to the sectoral productivity improvement after 1993 (see figure 1.7).

**Figure 1.4    Output and Foreign Investment in Shenzhen’s Secondary Sector, 1979–2006**

<!-- Start of picture text --> 70 Output 3.5<br>60 3.0<br>50 Foreign 2.5<br>40 Investment 2.0<br>30 1.5<br>20 1.0<br>10 0.5<br>0 0.0<br>ource:ce:e:: Shenzhen Statistics Bureau,Statistics Bureau,Bureau,eau,, various years.years.ears..<br>Note: FDI = foreign direct investment.= foreign direct investment.foreign direct investment.oreign direct investment.reign direct investment.eign direct investment.n direct investment. direct investment.investment.vestment.estment.<br>Figure 1.5 Output and Foreign Investment in Shenzhen’sShenzhen’s’ss Tertiary Sector, 1979–2006 ector, 1979–2006 ctor, 1979–2006 tor, 1979–2006 or, 1979–2006 , 1979–2006 1979–2006<br>70 Output 2<br>
tasks (e.g. in the Democratic Republic of Congo by Vaillant et al., 2020).

Our interest in this paper is to advance this nascent literature. We devise innovative behavioral games to examine social dynamics to supplement the survey data from a randomized controlled experiment of a gender norms intervention for adolescents in Somalia. For norms to be _social_ they must be shared by other people and partly sustained by their approval and disapproval (Elster, 1989). The behavioral games were designed to measure normative social influence, drawing insights from the field of social psychology which has long argued for the importance of social contexts and interactions for decision-making. We build on a series of conformity experiments developed by Asch (1951, 1952).<sup>1</sup> Using a novel lab-in-the-field experiment we modify Asch’s experiments to measure conformity in attitudes among adolescent peers who are asked to state their gender beliefs both in _private_ and in _public_ . The experiments allow us to study whether and how gender attitudes differ if expressed privately or publicly; and provide a causal link between the gender norms program and the observed behaviors among the study sample, albeit in a lab-in-the-field setting.<sup>2</sup> We also examine the marginal effect of a complementary gender norms intervention to parents on adolescent attitudes and behaviors.

In economics, conformity has been modeled in theory by Jones (1984); Bernheim (1994); Akerlof (1997), and shown to exist in educational settings (e.g. in Bursztyn and Jensen

> 1Asch provided provocative experimental findings of conformity by showing how easily individuals’ judgment can be manipulated in a group setting using a line-match exercise (Asch, 1951, 1952).

> 2 We use the experiment to observe actual behaviors and to address concerns that self-reported survey findings may reflect some social desirability bias.

2
.05; ***p _<_ 0.01 In terms of the non-classical measurement errors that were uncovered in the ERIVaS data, similar patterns emerge from the EACI sample. Table 11 shows the means of alternative yield and harvest measures for pure stand and intercropped sorghum plots. Similar to ERIVaS, the mean differences between SR and CC measures are statistically significant. However, in the nationally-representative sample, the sign of the difference is opposite and we find that the mean SR yields are underestimated vis-`a-vis CC yields (i.e., SR - CC _<_ 0). Figure 5 allows us to better understand why this is the case by showcasing the mean difference between SR and CC yields across quintiles of plot area, quantity of seeds per hectare and days of household labor per hectare. SR yields are overestimated vis-`a-vis CC yields in the first plot area quintile, while, in each of the remaining quintiles, SR yields are underestimated. On

- 17An expanded set of descriptive statistics computed with both samples can be found in Table A4.

28
design and the response rate for the survey.

The survey sampled 8,615 households, with everyone responding to a short questionnaire that collected information on household demography, education, employment and other core non-monetary indicators of well-being (“short-form”). A random subset of 1,500 sampled households also responded to a set of questions on household food consumption and non-food expenditure (“long-form”). While the short form sample was representative for each governorate, the long-form sample was representative only for four regions: Kurdistan, North, Center, and South. The consumption modules included food items purchased in the market as well as those received through the PDS.

The survey also provides information on the displacement status of household members. Internal displacement has a well-defined status in Iraq. The Ministry of Migration and Displacement (MoDM) is the government agency with the remit of registering internally displaced persons. Registered households are issued an identification card recognizing their status as internally displaced, and the card makes them eligible for assistance issued by the government or other parties. The survey asked if any member in the household is an IDP and, if so, how many. Additionally, the survey respondents were asked about number of refugees in the household and whether the household experienced forced displacement since January 2014. We categorized a household as displaced if any of its members are IDPs or refugees or if the household was forced to relocate from its place of origin. Using the criterion, 1,849 households in the sample are classified as displaced units.<sup>8</sup> Table 2 (columns 1 to 4) reports descriptive statistics by displacement status. While almost all (97 percent) of the non-displaced families in Iraq received transfers through the PDS in the 12 months preceding the survey, only 86 percent of the displaced families received the assistance. Compared to the non-displaced families, the displaced have significantly greater household size and dependency ratio, mostly due to having more children. Other characteristics such as age, marital status and educational
<!-- Start of picture text --> 6000<br>5000<br>4000<br>3000<br>2000<br>1000<br>0<br>Flow<br>J-88 J-89 J-90 J-91 J-92 J-93 J-94 J-95 J-96 J-97 J-98 J-99 J-00 J-01 J-02 J-03 J-04 J-05 J-06 J-07 J-08 J-09<br><!-- End of picture text --> Figure 3 : The Zambezi values of runoff (m3/s per month, y-axis) and time (month / years--, January 1988 through August 2009) display seasonality with both the observed gauging station data (blue) and the estimated values from the Zambezi runoff model (pink) (see Equation 4). The dashed line represents no SSM/I data.

52
.1<br>Social Sustainability Index<br>0<br>.1<br>1<br>.8<br>.05<br>.6<br>CCDF<br>.4 Difference<br>0<br>.2<br>0 -.05<br>.4<br>.3<br>.2<br>.1<br>Social Sustainability Index<br>0<br><!-- End of picture text --> _C9. Change in the indicators included for each dimension, one indicator removed at a time. Baseline scenario: All indicators included. Alternative scenario: Confidence in government institutions indicator removed (weights within the Social Cohesion dimension were scaled from 6.25% to 8.33% so all weights add up to 1)._ South Africa C9.1. FOSD: Counting Vector C9.2. Dominance Analysis 57
## **Figure 20.  Unemployed by Gender and Job Search Methods, 1996-2002**

<!-- Start of picture text --> Unemployed males by main search method Unemployed females by main search method<br>100% 100%<br>90% 90%<br>80% 80%<br>active<br>70% 70%<br>60% friends 60%<br>50% read ads 50%<br>40% Private ES 40%<br>30% 30%<br>Public ES<br>20% 20%<br>10% 10%<br>0% 0%<br>1996 1997 1998 1999 2000 1996 1997 1998 1999 2000<br>job search methods: males job search methods: females<br>80.0 80.0<br>70.0 70.0<br>60.0 60.0<br>50.0 50.0<br>40.0 40.0<br>30.0 30.0<br>20.0 20.0<br>10.0 10.0<br>0.0 0.0<br>2001 2002 2001 2002<br>SES Private ES<br>SES Private ES read ads<br>read ads ask friends<br>ask friends contact employers place ads<br>contact employers place ads<br>try to start business<br>try to start business<br>percent percent<br><!-- End of picture text --> _Source:_ Calculation based on LFS.

# **E.  RELATIVE WAGES**

One of the most important features of a labor market is its wage structure. Are human capital related workers’ characteristics – education, general, and firm specific experience - rewarded in Latvia in similar ways as in established market economies? Empirical evidence from other transition countries suggests
functions, and procedural innovations, which consists of changes to routines, processes, and operations of a company. Thus, these innovations change or implement new procedures and processes within the company, such as simultaneous engineering or zero buffer rules.

Marketing innovation is a change made to incorporate the advances in marketing science, technology or <u>engineering to increase the effectiveness and efficiency of marketing, to gain competitive advantage.</u> In order to examine the innovative behavior of firms in the South Asia region, we use the World Bank Enterprise Survey and its specific innovation module implemented in the region during the 2014 and 2015 years. The survey, which compiles data from face-to-face interviews, uses a stratified sampling method where firms are stratified by industry, size, and location. Firm-size levels are 5-19 (small), 20-99 (medium), and 100+ employees (large-sized firms). Since in the South Asia region most firms are small and mediumsized, the Enterprise Survey oversamples large firms. Overall, the data set has around 5,500 observations unevenly distributed between the four South Asian countries. For instance, the number of firms surveyed in India surpasses what was surveyed in the other three countries jointly (see Table 1). Regarding the sector composition of the sample, more than 4,000 firms belong to the manufacturing sectors, while only 1,266 firms operate in services. The survey targeted formal firms only and excludes micro firms, although few firms in the survey are de facto micro as they had fewer than 5 employees.

Table 1. Sample composition

||**M**<br>**small(<20)**|**anufacturing**<br>**Medium**<br>**(20-99)**|**Large**<br>**(100 and**<br>**over)**|**Ser**<br>**small(<20)
# _Implementation design and sampling frame_

The SDS implementation design was adjusted to the local context in Vietnam in several ways.

- <u>Respondents. The SDS relied only on job incumbents as respondents and did not use</u> occupation experts. O*NET, in contrast, uses occupational analysts to fill in the skills questionnaire given the abstract nature of skills concepts. However, occupational analysts are not widely available in Vietnam. Given the minimal differences in ratings between analysts and incumbents in the United States and between experts and incumbents in the Indonesia pilot and given the fact that the SDS simplified the questionnaire language, job incumbents were used as respondents (Alatas, Granata, and Posadas 2020).<sup>13</sup> Respondents were selected based on their occupation as stated in previous rounds of the Vietnam Labor Force Survey.

- <u>Number of respondents. The SDS targeted 20 respondents per occupation.</u><sup>14</sup> O*NET’s minimum acceptable sample size per occupation is 10, although this varies widely depending on the questionnaire and respondent type. The SDS aimed to have a balanced sample of respondents from the most economically active areas in the country (Ha Noi, Ho Chi Minh City, Da Nang, Vinh Phuc, and Dong Nai). <mark>In addition, to address the tendency of female underrepresentation in the previous pilots, the SDS designed a sampling frame in which the share of female respondents approximated the share of employment in the occupation at the national level.</mark>

- <u>Data collection strategy. The SDS was a supplement to the December 2021 round of Vietnam’s</u> Labor Force Survey, which is administered by Vietnam’s General Statistics Office, which also oversaw collection of the SDS. This created significant administrative and cost benefits because separate survey collection infrastructure did not need to be created.

- <u>Sample.</u> <mark>Adding
this technology to understand insight in the urban mobility pattern in the city of Freetown, capital of Sierra Leone.

Among research question under investigation in this study are:

- (1) what are the impacts of rainy season on urban mobility?

- (2) where are the locations in the city that attracted most trips? and

- (3) is there a correlation between poverty and longer trips?

Additionally, in this paper, we fill some knowledge gaps by sharing experiences, takeaways, and best practices in operationalizing mobile network data in an ongoing World Bank (WB) transport infrastructure project. Among others, these lessons learned can support governments and administrations using these data to monitor the results of lockdown and mobility restrictions to reduce the spread of COVID-19.

# **LITERATURE REVIEW**

There is no doubt that mobile network data have transformed urban mobility analytics. The revolution has occurred mainly in developed countries, where smartphone penetration, use of social media, and internet access are widely spread. In these countries, Location Base Services (LBS), such as GPS location collected and generated by mobile phone applications, have been used in transport planning [9] supplanting some initial proof of concepts of the use of Call Detail Records, and improving some of the biases that this last data set incurred [10].

Call Detail Records or cellular network data are defined as a single incoming or outgoing call event and SMS, which is represented as a single row in the data set and contains information on the time, and cell tower that this event occurred. This type of data had initially been proven useful to estimate urban mobility pattern and flows [3]–[6], demonstrating that CDR could generate origin and destination matrices. However, due to the widespread availability of location based data and the publication of some studies that raised concerns on the biases of CDR, this type of data was relegated to a second place.

In most of the global South, smartphone penetration and access to reliable internet are
39 Breheny, M., 1992. Sustainable development and urban form. Pion, London.

Burchfield M, Overman H, Puga D, Turner M (2006) Causes of sprawl: A portrait from space. _Quarterly Journal of Economics_ 121: 587–633

- Cao, Z., Wu, Z., Kuang, Y., Huang, N., & Wang, M. (2016). Coupling an intercalibration of radiance-calibrated 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

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

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

- Champion, A.G., 1992. Urban and regional demographic trends in the developed world. Urban Stud. 29 (3/4), 461–482.

- Cheng, Y., Zhao, L., Wan, W., Li, L., Yu, T., & Gu, X. (2016). Extracting urban areas in China using DMSP/OLS nighttime light data integrated with biophysical composition information. _Journal of Geographical Sciences_ , _26_ (3), 325–338. https://doi.org/10.1007/s11442-016-1271-6

- Cheshire, P., 1995. A new phase of urban development in Western Europe? The evidence for the 1980s. Urban Stud. 32 (7), 1045–1063.

- CEC, 1992. Commission of the European Communities. DG Regional Policy. Urbanisation and the Functions of Cities in the European Communities. European Institute of Urban Affairs
spatially dependent inequality outcomes. Households located in physical proximity to mining districts in Peru tend to experience the largest gains in living standards, consumptions and literacy, while consumption inequality increases in all districts of a resource-producing province.

Although many empirical studies have investigated the link between natural capital, growth and inequality, very limited research is available on how all three factors interact. Moreover, much of the existing literature on the subject fails to adequately account for the role of wealth stocks. A thorough review of the literature would seem to indicate that the quantitative relationship between the exploitation of natural resources, economic growth and income inequality has been studied only by Gylfason and Zoega (2002) and Alessandrini and Buccellato (2009).

The following analysis tests the existence of a relationship between the dependence on nonrenewable natural resources (hereafter dependence) and per capita income and income inequality. The analysis is conducted in the spirit of Sachs and Warner (1995; 2001) and uses panel data for 43 countries for which data is available from 1980 to 2012 to examine, under different specifications, a system of two equations in which the dependent variables are per capita GDP and the Gini index and the independent variable is the dependence on nonrenewable natural resources. While Gylfason and Zoega (2002) and Alessandrini and Buccellato (2009) use a recursive model and focus on crosssectional data, this analysis attempts to account for the endogeneity problem generated 3
in the Developing Economy: Impact on the Labor Market and the Role of Geography and Policy.</mark> _<mark>Available at SSRN 4786527</mark>_ <mark>.</mark>

**Min, B., O’Keeffe, Z.P., Abidoye, B., Gaba, K.M, Monroe, T. Stewart, B.P., Baugh, K., Sánchez-Andrade Nuño, B.** 2024. Lost in the dark: A survey of energy poverty from space, _Joule_ , Volume 8, Issue 7.

**Maslej, N., Fattorini, L., Perrault, R., Parli, V., Reuel, A., Brynjolfsson, E., Etchemendy, J., Ligett, K., Lyons, T., Manyika, J., Niebles, J.C., Shoham, Y., Wald, R., and Clark** , **J.** 2024.

27
check robustness to use KCI or KCO, or on selected asset categories.

> 5We refer to the IFS World Notes to determine the exact maturities of each interest rate variable for each country. For instance, when there are no series explicitly defined as “3M Tbills”, we search for descriptions like “91-day” or “3-month”, if these do not exist either, we further try “6M”, “9M” etc. We examine robustness to a sub-sample of countries for which only short-term rates with maturities under 1-year are available.

> 6To enlarge sample size, we use interest rate series with the most sufficient data. In subsection 6.3, we also look at specific “policy rate” following the definition of De Leo et al. (2022).

> 7Relaxing this criterion up to 12 enlarges the sample only by retaining small islands or countries with annual GDP less than 20 bil.$; while further restricting the cutoff to below 5 would drop some Latin American countries like Argentina and Chile with interesting capital control histories.

4
may be somewhat arbitrary, and thus poverty researchers go to considerable lengths to use objective basis on which to develop a poverty line. Summarizing the general approach to estimating  a nutritionbased poverty lines, the analyst must first estimate the cost of the underlying food requirements for the basket of foods that might be available for someone near poverty are first estimated. To this cost, costs of other durables and housing for someone near poverty are then added to the nutrition cost, and this value is used to determine the poverty line. Researchers working in the poverty field have thought very carefully about the objective calculation of poverty lines and a useful introduction may be found in Ravallion (1996). We use the nutrition-based poverty line calculated for rural China over the period of the study by Ravallion and Chen (2007), who have long experience working with China’s National Bureau of Statistics (NBS) to develop objective, nutrition-based poverty lines for rural and urban China.

18
(valid<br>poverty rate is t|mates shown in<br>r models and<br>ation). For mu<br>he estimate dir|boldface or wit<br>Random Forest m<br>ltiple imputation,<br>ectly obtained fro|21<br>(2.<br>h a “<sup>**a**</sup>” res<br>odels are<br>we use fi<br>m the sur|.0<br>2)<br>pectively fall wi<br>trained in the fi<br>ve nearest neigh<br>vey data.|thin the 9<br>rst round<br>bors with|5% confidence<br>when the data i<br>the predictive|interval or one<br>s split into two<br>mean matching|

Note: One-standard-error rule was used to select lambda. Estimates shown in boldface or with a “<sup>**a**</sup> ” respectively fall within the 95% confidence interval or one standard error of the true poverty rate. Lasso, Elastic net linear models and Random Forest models are trained in the first round when the data is split into two subsets: 50% used for training and 50% used for testing (validation). For multiple imputation, we use five nearest neighbors with the predictive mean matching method. 50 simulations are implemented. True poverty rate is the estimate directly obtained from the survey data.

64
appendix. These results confirm the prediction in the previous paragraph. That is, the triple interaction term is negative, large and statistically significant at the 5 percent level. This holds when using the overall corruption measure as well as the two petty corruption measures. This helps raise our confidence against endogeneity problems with our main results.

# 4.5 _Falsification test_

The next check against endogeneity is based on the idea that while having money to pay bribes can result in a quid pro quo situation alleviating some of the negative effects of corruption, the quid pro quo is much less or non-existent with other elements of the business environment correlated with corruption. Thus, if the corruption-finance nexus found above is indeed about corruption then we should not find any significant difference in how other correlated elements of the business environment affect firms that face high versus low financial constraints. However, if our corruption measure is a mere proxy for other correlated elements of the business environment, it should be the case that the correlated elements of the business environment impact high and low financially constrained firms differently like we found for corruption.

We focus on three elements of the business environment which include physical infrastructure availability, regulatory burden experienced by the firms, and the quality of enforcement of the laws. We proxy these by CI cell averages (for all firms excluding the firm in question) of total hours of power outage ( _Power Outage_ ), percentage of senior management’s time spent in dealing with business regulations ( _Time Tax_ ), and severity of the lack of proper functioning courts as an obstacle to firms’ operations ( _Courts: Obstacle Severity_ ). All these variables have been discussed above. The regression exercise involves repeating the baseline regression (as in Table 1) but substituting the corruption and financial constraint interaction term 25