rafmacalaba/data-use-mentions-extended huggingface dataset:
—equivalent to 4.5 to 8.2 percent of GDP—between 2015 and 2030 to meet infrastructure-related SDGs. Their cost estimates depend on policy choices, highlighting the importance of spending efficiency (i.e., the quality of spending) in achieving such goals. Infrastructure investment can have particularly large growth benefits if it connects isolated communities with input and output markets, allows companies to realize economies of scale by increasing market size, and increases competitive pressures (Égert, Kozluk, and Sutherland 2009; Calderón and Servén 2010).<sup>4</sup> 3 For example, using a panel of OECD countries, Fourier (2016) shows that a 1-percentage-point increase in the share of public investment in expenditure is associated with an increase in long-term output by 5 percent. The study emphasizes that public investment is more growth-enhancing in areas with large externalities, such as health and research and development. To the extent that debt-financed investment spending stems the slowdown in potential growth, it helps preserve the revenues required to service this debt (Fatas et al. 2019).

4 Gaspar et al. (2019) show that additional spending of about 1.3 trillion dollars in 2030 is required to meet the SDGs related to infrastructure in low-income developing economies and emerging market economies, and another 1.3 trillion dollars for the SDGs related to health and education. Similarly, UNCTAD (2014) discusses additional spending needs of 1.6 to 2.5 trillion dollars per year between 2015 and 2030 to achieve the goals related to economic infrastructure (i.e., power, transport, telecommunications, and water and sanitation). The additional annual investment needed to meet health-related SDGs in low- and middleincome countries is estimated at about 370 billion dollars (Stenberg et al. 2017).

5
level.<sup>9</sup> The majority of coefficients in columns 1–3 are negative and statistically significant. There is only one positive and significant slope coefficients, on cooking bananas (column 2), but it is statistically indistinguishable from zero when we look within vendor (column 3).

For comparison purposes we estimate similar regressions using the diary data. Columns 4 and 5 of Table 3 show the slope coefficients. These estimates are not directly comparable with those in columns 1–3, because for some items the quantity support is different across the two data sets. Our interest is primarily in the signs of the estimated coefficients.

In columns 4 of Table 3, all but two estimated slope coefficient are negative, and none are positive and statistically significant. Looking within household (column 5), all coefficients are negative and 16 of 19 are statistically significant.<sup>10</sup> Taken together, the clear pattern in the market survey and diary data sets is that unit price is decreasing in quantity for many items. This finding holds within village-day, within-vendor, and within-household.<sup>11</sup> Having established that bulk discounts exist, we turn to the non-parametric approach of Section 2 to estimate expenditure schedules. The focal-point approach is less susceptible to measurement error than one based on parametric approximations of price schedules, and it reflects the reality of shopping in these markets. We designate a quantity as focal if it accounts for at least 5% of all observations at the item-district level.<sup>12</sup> By this definition there are 1-8 focal quantities per item-district, with 3.4 on average. Overall, 69% of purchases are at focal quantities. We use the median unit price at the focal quantity to estimate the focal price.

To provide a visual example of the estimated schedules, Figure 2 depicts expenditures and unit prices by quantity for 702 purchases of kerosene in one of the study districts. The

> 9For these descriptive
access in isolation. However, for localities connected to fast internet, individuals who are in locations in which a main road becomes one kilometer closer are 1.6 percentage points more likely to work in very highly skilled professions. It is very interesting to note that, while road and internet accessibility complement each other to further reduce the creation of unskilled jobs by about 2 percentage points, they further increase the creation of skilled jobs by an average of 2 percentage points. This makes it possible to conclude unequivocally that road and digital communication infrastructure investments act as complements to allow migration of employment from unskilled sectors of activity to those requiring a certain level of qualifications.

These results are consistent with those obtained when using the WBES data relating to the number of full-time employees with a high level of qualifications. Roads and the internet play complementary roles in increasing the number of qualified full-time workers in companies. Internet-connected firms that moved 10 kilometers closer to the nearest main road had 0.980 more high-skilled, full-time employees at the end of the last fiscal year compared to firms that had experienced an improvement in their accessibility to only one of the two types of infrastructure. The results also show that the combination of the two infrastructures additionally improves employment, even for the suppliers of low-skilled labor. Indeed, we show that firms in the second group have 0.447 more unskilled workers than those in the first group. The combination of these two results (means that the firms in the first group have a statistically higher demand for labor, both for high-skilled and low-skilled workers, compared to the second group of firms, which demands fewer workers of either skill level. Roads and the internet together enhance job creation; at the same time, the results do not contradict the result of labor migration from unskilled to higher-skilled employment. Unfortunately, because of the significant difference in the number of responses, it is difficult to determine whether, among the WBES responding firms, the joint effect of the presence of these two types of infrastructure is greater on
# **Table 1. Return Migrants as Share of Village Population and as Share of Migrants**

||Return Migrants as Share of<br>Village Population|Return Migrants as Share of<br>Migrants|
|---|---|---|
|1991-2000|7.73%|20.28%|
|2001-2007|7.94%|20.09%|
|2008-2012|7.82%|20.86%|
|2013-2018|7.41%|19.28%|
|2019(January-June)|5.42%|12.85%|

Data Source: Village level data from the Thousands of People and Hundreds of Villages Survey (TPHV), conducted by Remin University, Beijing, China. The survey covers 125 villages in 30 provinces.

41
relevant stakeholders by making information on policies publicly available. We see this database as providing not a definitive picture of services trade policy, but a step in enhancing the availability of information which might hopefully evolve – through feedback from various interested parties –into a collectively created public good.

The paper describes, in Section 2, the scope of the data, the structure of the Database, the data collection process, and compares this new endeavor with other databases. Section 3 describes the Services Trade Restrictions Index (STRI), which builds on the previous World Bank STRI, and Section 4 presents the Services Trade Policy Database (STPD), which is accessible through the WTO’s Integrated Trade Intelligence Portal (I-TIP) interface. The final section discusses implications for future work, including within the overall joint project between the World Bank and the WTO Secretariat.

# **2  SERVICES TRADE POLICY DATA**

# **Scope and organization of the database**

# _Scope and definition_

Trade in services is defined, as is now customary, to include the supply of a service through various modes: on a cross-border basis, through establishing a commercial presence, or through the presence of a natural person.<sup>4</sup> Accordingly, the Services Trade Policy Database (STPD) contains information on policies and regulations that affect international trade in services across all these modes of supply.

We consider policies and regulations applied by the importing/'host' economy (i.e. the jurisdiction where the services consumer is located), which affect the ability of foreign suppliers to supply

> 3 For various examples of studies on the impact of services trade policies, see Borchert et al. (2017); Deardorff and Stern (2008); Findlay and Warren (2010); Fink et al. (2001, 2002); Francois and Wooton (2008); Gönenc and Nicoletti (2000); and Mattoo and Payton (2007).

> 4 While we do not cover, strictly speaking
fiscal system has a high potential for poverty reduction in Grenada (table 1), with the highest impact being associated with in-kind transfers. After adjusting the disposable income, the poverty rate in Grenada for 2018 reached 22.77 percent under the definition of consumption per equivalent adult (equivalent to the definition of disposable income in this document), using the national poverty lines as defined by the CSO (EC$6,782 per year per person, 2017–18). Departing from this measure, the model builds all the other income aggregates as described in the previous section. In the absence of fiscal interventions, such as PIT and cash transfers, 25 percent of the population would live in poverty, with an estimated poverty gap of 8.24 percent. Moreover, after accounting for all taxes and direct transfers, the poverty headcount would be 26.35 percent. In sum, four relevant aspects can be noted. First, the direct transfers (SEED and School Feeding Programme) reduce the incidence of poverty by 2.2 percentage points; these are also reflected in other indicators sensitive to changes in income distribution among the poor, such as poverty severity and the Sen and Watts indexes. Second, the PIT does not change the poverty measures, because it is paid by the population with incomes above the national poverty lines. In fact, based on our estimates, only around 10 percent of the employed population has earnings high enough to be subject to the PIT. Third, indirect taxes such as the VAT (and excise taxes) increase poverty by about 3.6 percent; this is explained by their being structured to be neutral taxes.

11
of banking and the legal and regulatory environment for digital economic activity have been important contributors to DFS usage, but that the factors most closely related to the capabilities of users and connectivity (to electricity, the internet, and mobile telephony) were primarily responsible for increasing DFS usage because of the COVID-19 shock. One interpretation is that governments and the private sector were able to work around underdeveloped banking systems, and poor laws and regulations of the digital economy, but only if there was an adequate level of digital infrastructure and connectivity and a high share of the population that understood and could make use of digital payments. We return to this discussion later in the paper.

The rest of the paper is organized as follows. Section 2 describes the data sources for DFS usage and its drivers and the specific variables that we use in the empirical analysis. Section 3 describes our estimation approach in greater detail, while section 4 summarizes our empirical results. Section 5 discusses the empirical patterns that we find and potential policy implications. Section 6 offers concluding remarks and lays out potential directions for future research.

# **_2. Data_**

For the empirical analysis, we rely on country-level datasets with wide global coverage that summarize DFS usage, banking regulation, human capital development, and regulation of digital commercial activity and data sharing. In this section, we briefly describe the sources of those data and the specific variables that we rely on from each source.

# **Global Findex Database**

The Findex database provides information on how adults around the world use financial services, from payments to saving and borrowing, and how they manage financial events.<sup>8</sup> The

> 8 For details on Findex, including links to the data and questionnaires, and documentation on methodology, see https://www.worldbank.org/en/publication/globalfindex/Data.

8
.2% of students were admitted to the higher education institutions of ІІІ-ІV level of accreditation on the basis of the EIT certificates<sup>21</sup> .

So far there has been little analysis of the EIT data. Besides the simple descriptive statistics in the annual EIT report, there are three studies that use EIT data. Kovtunets et al (undated) provide some analysis of whether the EIT exam results predict performance during the first year at university. Muravyev and Talavera (2010) use the EIT to see how an announced (but not implemented) language policy change has affected the subject choice and performance of students at minority language schools. The closest to the current paper is Coupé et al (2010) who use a sample of 300 schools to link EIT scores to a wide set of input variables. They find a

> 21 Analytical note on results of the admission campaign in 2008 and major tasks for the organization of the admission process in 2009 (Annex to the decision of the board of the Ministry of Education and Science of Ukraine of 31.10.2008 No 12/2-4). Some of the tests were not developed in 2008 therefore the exams were conducted. For example, tests in foreign languages – English, German, French and Spanish languages – were introduced in 2009. Other changes encompassed, for example, wider choice for participants (up to 5 subjects instead of only up to 3 in 2008) with reduced number of available subjects for testing (8 disciplines instead of 11).

13
# **FIGURE 1: TRADE WAR TARIFFS AND EXPORT GROWTH**

<!-- Start of picture text --> Panel A Panel B<br>Bystanders' Export Value to US Bystanders' Export Value to China<br>2015-17 2017-19 2015-17 2017-19<br>.2 .3<br>.2<br>.1<br>.1<br>0<br>0<br>-.1 -.1<br>0 .05 .1 .15 .2 0 .05 .1 .15 .2<br>∆ ln T(US,CH) ∆ ln T(CH,US)<br>Pre-period: β =-0.19 (0.10). Post-period: β =0.31 (0.10). Pre-period: β =0.07 (0.18). Post-period: β =0.01 (0.19).<br>Panel C Panel D<br>Bystanders' Export Value to RW Bystanders' Export Value to RW<br>2015-17 2017-19 2015-17 2017-19<br>.2 .2<br>.1 .1<br>0 0<br>-.1 -.1<br>-.2 -.2<br>0 .05 .1 .15 .2 0 .1 .2 .3<br>∆ ln T(US,CH) ∆ ln T(CH,US)<br>Pre-period: β =-0.14 (0.08). Post-period: β =0.20 (0.08). Pre-period: β =0.11 (0.08). Post-period: β =0.29 (0.08).<br>ln X(i,US) ln X(i,CH)<br>∆ ∆<br>ln X(i,RW) ln X(i,RW
sense of whether recent reforms and improvements in welfare have concerned most dimensions of welfare or only a few dimensions. We therefore rely on the same data used by other studies including the National Survey on Consumption and Expenditure carried out between 2000 and 2001 and including 14,243 households (henceforth NSCE 2000/01) and the National Survey on Living Standards carried out between 2006 and 2007 and including 7,062 households (henceforth NSLS 2006/07). Both surveys are representative at the national, urban-rural and regional levels and they are fully comparable across all variables used in this paper.

Table 1 lists all variables used in the first step of the MCA exercise where we use a large set of variables to test how these variables polarize around the different axes. These variables are grouped in eight broad areas including employment, education, health, housing, utilities, durables, nutrition and expenditure. The choice of these initial variables is largely normative but based on decades of research on poverty in 8
pipeline regional infrastructure projects across SAR. C. Latest data available during 2011-15. Blue bars denote range of unweighted regional averages across EMDE regions. Health expenditure per capita in purchasing power parity terms, unweighted averages of 199 EMDEs, 34 AEs, and 6 SAR economies. Access to improved sanitation facilities (in percent of population), unweighted averages for 150 EMDEs, 33 AEs, and 8 SAR economies. Access to improved water sources (in percent of population), unweighted averages for 148 EMDEs, 34 AEs, and 8 SAR economies.

D. Latest data available during 2011-15. Blue bars denote range of unweighted regional averages across EMDE regions. Government expenditure per primary student (in percent of per capita income), unweighted averages of 87 EMDEs, 32 AEs, and 5 SAR economies. Pupil-teacher ratio in primary education (headcount basis), unweighted averages for 165 EMDEs, 31 AEs, and 8 SAR economies.

# **VI.3. Which policies can help address infrastructure needs in the SAR region?**

The alleviation of some longstanding obstacles to growth would help increase the level and productivity of investment of all forms. A more targeted, multi-pronged, policy strategy could also encourage investment by increasing returns to investment, and by expanding the financing envelope (Henckel and Mckibbin, 2010; Nataraj 2007).

42
**Appointment dates of Suharto appointed mayors:** data on the appointment dates of the Suharto appointed mayors are taken from Martinez-Bravo et al. (2013). We supplement the data they have made publicly available with data we collected ourselves from the Government of Indonesia’s Official Directories at Cornell University.

**Entry regulation:** We also construct new data on entry regulation from presidential decrees issued in 1993, 1995, and 2000. We create a (stringent) entry regulation indicator variable that equals 1 if an industry is completely closed to investments or closed unless the firm in question meets certain conditions and zero otherwise. Industries that are reserved for small businesses are not considered as regulated.<sup>25</sup>

Appendix A provides a detailed description of all the variables and how the firm-level data were cleaned. We do not consider industries that are not present over the entire sample period, that produce less than 10 million USD in 2005 prices worth of output annually over the sample period, or that do not have enough firms to compute price–cost margins and profit elasticities over a reasonable range. Eliminating these industries leaves 200 five-digit industries.

# **A.2 Definition of Firm-Level Variables**

**Export** is the share of exports in sales (not available after 2000).

**Exporter** is a dummy variable that takes the value 1 if the firm exports and 0 otherwise.

**Firm age** measures the age of the firm in years (current year – year of establishment + 1).

**Foreign ownership** is the share of ownership of a firm held by foreigners.

**Labor** is the total number of paid workers.

**Legacy:** is a measure of how long the last Suharto appointed mayor remained in office after Suharto’s fall. It is defined as ( _Appointment year of the last Suharto appointed mayor_ +5)-1998 (since Suharto left office in 1998 and
of exporting ...rms. We address this issue by incorporating the methodology of Rajan and Zingales (1998) into the framework of survival analysis. This enables us to look for a speci...c channel through which ...nancial development promotes export survival while controlling for country, ...rm and product ...xed e¤ects.

Third, we contribute to the food safety standards and trade literature. Most of the works have focused on the quantitative assessment of the e¤ects of standards on trade essentially by including standards–count of noti...cations–as explanatory variables in gravity equations (Otsuki et al. 2001, Moenius 2006, Disdier et al. 2008). We distinguish ourselves from this literature relying on an innovative measure of product sanitary risk. Our measure captures the need at the product level for ...nance to comply with the food safety regulation.

The remainder of the paper is organized as follows. The next section brie‡y discusses some key features of the data that motivated our empirical framework. Section 3 introduces the empirical strategy. Section 4 presents our African ...rm-level data and gives some descriptive statistics. Section 5 reports the main estimation results. Section 6 shows some robustness checks. And ...nally, section 7 concludes.

# 2 Motivation

## 2.1 Access to Finance and Trade Duration

The theoretical literature shapes a role for ...nance on trade mainly at the time of entry, when access to ...nancing is the most critical (Das et al., 2007; Berman and Hericourt forth). The prominence of ...rms entry and subsequent exit from export markets suggests that initial entry costs are not as high as theoretical models predict. The high proportion of failures in trade relationships, past the ...rst two years of exporting, can be explained by the high costs of sustaining in the export market (Besedes 2007). This calls for a role
Corruption|KKZ|.746|.140|.530|South<br>Africa|1.000|Finland|
|_Aggregate Index_|||.689|.139|.353|Venezuela|1.000|Finland|
|**Information Technology**<br>**Infrastructure**|Speed and Costs of Internet<br>Access|GCR|.629|.162|.348|Vietnam|1.000|Finland|
||Effect on Internet on<br>Business|GCR|.719|.102|.481|Greece|1.000|Finland|
|_Aggregate Index_|||.674|.121|.482|Mauritius|1.000|Finland|

Source:, Mann and Otsuki (2005).

Data are from the World Economic Forum, Global Competitiveness Report, 2001-02 (GCR), IMD Lausanne, World Competitiveness Yearbook 2000 (WCY), and Kaufmann, Kraay and ZoidoLobaton (2002) (KKZ). All survey data in GCR comes from the World Economic Forum’s Executive Opinion Survey. A total of 4022 firms were surveyed. “In order to provide the basis for a comparative assessment on a global basis, it is essential that we interview a sufficient number of senior business leaders in individual countries and that the sample in each country is not biased in favor of any particular business group. We have taken a number of steps to ensure this. First, we have asked each of our partner institutes, the organizations that administer the surveys in each country, to start with a comprehensive register of firms. From this, they were asked to choose a sample whose distribution across economic sectors was proportional to the distribution of the country’s labor force across sectors, excluding agriculture. They were then asked to choose firms randomly within these broad sectors (for example, by choosing firms at regular intervals from an alphabetic list), and to pursue face-to-face interviews, following up for clarifications
*|0.00589|
|(0 to 100)|(2.06)|(1.69)|(0.94)|
|Time trend|-0.0794|-0.314***|0.0053|
|(=0 in 2000)|(-0.43)|(-3.15)|(0.11)|
|n|288|265|254|
|R-squared|0.741|0.809|0.884|
|RMSE|3.60|1.88|0.891|

_Note:_ Figures in parentheses are robust t-statistics. *, **, ***: significant at 10, 5, and 1 percent respectively. Annual data from 1996–2013 for low- and middle-income countries with fuel > 25 percent of total exports. RMSE = root mean square error, or sample standard deviation.

21