rafmacalaba/data-use-mentions-extended huggingface dataset:
trends in employment, unemployment or labor force participation. On the other hand, results by Malaeb and Wahba (2018) show that previous immigrants to Jordan were more likely to work informally, work fewer hours and had lower wages after the influx of Syrian refugees. In Jordan’s segregated labor market, Syrian refugees seem to be closer substitutes to immigrants than to natives. El-Mallakh and Wahba (2018), however, show that the probability of Jordanians migrating out of the regions with higher numbers of Syrian refugees increased. While confirming no significant effect on labor market outcomes of salaried Jordanians, Rozo and Sviastchi (2018) find negative effects of refugee exposure on self-employment.

Two papers looked at the impact of Syrian refugees on prices in Jordan. Alhawarin et al. (2018), find no evidence of impacts on predicted rental prices on average. Depending on the data set used, the impact on rental prices in regions closer to the Syrian border was insignificant or positive, and in regions distant from the border insignificant or negative. They do, however, find a negative and significant impact on a housing quality index. Rozo and Sviatschi (2018) confirm these results, finding larger expenditures on housing for individuals living closer to refugee camps, at the expense of other types of expenditures, and higher rental and property income in these areas. These latter authors do not find any evidence of impacts on overall consumption expenditures.

Overall, the evidence on the Syrian crisis points to no visible overall effects on the labor market in neighboring countries although there is evidence of competition between former immigrants and informal workers on the one side and the new wave of Syrian immigrants on the other side. Most studies show a positive impact on formal employment of natives in Turkey, also providing evidence for professional upgrading. The absorption of Syrian refugees in the Turkish labor market seems facilitated by an increase in the number of new Syrian-owned businesses (Altindag, Bakis and Rozo 2018; Akgündüz, van den Berg, and Hassink 2018) with firms substituting capital
dummy variable, _fincrisis_ , if any of those crises is reported.<sup>10</sup>

# 3.2.3 Trade and FDI variables

_Trade openness_ is conventionally captured as the sum of exports and imports over GDP. To account for the higher trade levels of smaller countries, irrespective of trade policies, we calculate _trade_resid_ as the residual of a regression of exports and imports over GDP on log of exporter population and global GDP, which acts as a demand shifter. We then take the log of the residual, which first requires transformation to positive values. This is done as: _ltraderesid_ = ln(trade_residmin(trade_resid) + 0.01). All the respective variables are taken from the PWT 10.0.

For _export diversification_ , we rely on the International Monetary Fund’s export diversification index (Papageorgiou, Spatafora, and Wang 2015), which is a Theil index. A higher value hence indicates lower diversification.<sup>11</sup> The variable is logged, _lEDI_ipol_ . Moreover, we take the square of the variable because a nonlinear or U- shaped relationship is expected (see Imbs and Wazciarg 2003; Papageorgiou, Spatafora, and Wang 2015, figure 3).

To account for positive and negative shocks due to changes in international price levels, we include _terms of trade changes_ , _d.ltot_ , calculated as changes in the log of the export price level relative to the import price level ( _pl_x/pl_m_ ), both taken from the PWT 10.0.<sup>12</sup> _Foreign direct investment_ acts as a proxy for the activity of multinational corporations, their investment and productivity spillovers. We take inward FDI stock as a percentage of GDP from the United Nations Conference on Trade and Development (UNCTAD).<sup>13</sup> The variable is logged, _lFDIstock_ip_ .

# 3.2.4 Infrastructure

_Infrastructure_ is strongly correlated with income levels, but it is difficult to measure, particularly over a
assumptions. By contrast, firms in the Orbis data provide 2 digit standardized SIC codes. We are thus able to match RZ external finance dependence measure calculated from the Compustat data precisely with firm-level Orbis data.

# **IV. Results**

# **_A. Access to finance and employment growth_**

We find a strong positive correlation between firm access to finance and the rate of employment growth. Table 3 presents estimates of the association between the firm’s access to a loan and subsequent employment growth (equation 1) for all the firms in the sample, as well as over the MSME, SME, and large firm subsamples. The first four columns show estimations using country and year fixed effects along with time varying country-level controls, while the next four columns replace the time varying country-level variables with survey (country-year) fixed effects.

Column 1 shows that firms with access to a loan have an annual employment growth that is 3.29 percentage points larger than for firms with no access. Columns. (2) to (4) show the association between access to finance and employment growth to be strongest in the case of MSMEs and SMEs<sup>20</sup> (3.59% and 3.06% respectively) compared to large firms (1.18%) with point estimates more than three times larger than for large firms. To statistically test how the estimates for SMEs and MSMEs differ from those of large firms, we estimate the two subsamples as Seemingly Unrelated Regressions (SUR) and conduct a t-test for whether the coefficients are equal. We reject the null hypothesis of their equality resoundingly at the 1%

> 20 We follow the IFC and EU size categorization and classify MSME as firms with less than 250 employees. We also ran the regressions using different categorization of MSME’s as firms with less than 100 employees suggested by others and find that the results hold.

19
apprenticeship traditions. But the apprenticeship only starts at the later stages of secondary education. The program has 45 percent private funding from companies. In addition, it is not only flexible but also selective and prestigious. It is not unheard of that one starts out in an apprenticeship program but becomes a professor later (Atkins 2017).

Figure 2.10 Percentage of vocational program enrollment in total upper secondary enrollment <!-- Start of picture text --> 90.0%<br>80.0%<br>70.0%<br>60.0%<br>50.0%<br>40.0%<br>30.0%<br>20.0%<br>10.0%<br>0.0%<br>2005 data 2015 data<br>Czech Republic Austria Slovak Republic United Kingdom Belgium Netherlands Switzerland Finland Luxembourg Australia Italy Norway Germany France Sweden OECD average Denmark Poland Spain Turkey Iceland Ireland Portugal Korea Hungary Mexico United States China<br><!-- End of picture text --> _Source:_ OECD 2007, 2017.

Even in countries where tracking starts relatively early, students tend to follow the same common core curriculum for a large share of their upper secondary education. In Germany, students follow entirely similar curricula for the first two years so that the selection of specific branch of study can be deferred. In the Netherlands, students follow a common core curriculum, usually for the first two to three years of the secondary education in different streams. The common core curriculum specifies the minimum skills that should be acquired by all students, although the level of study may vary depending on the type of school concerned. The three types of lower secondary school in Liechtenstein also offer the same basic common curriculum but in the Realschule or Gymnasium, the curriculum includes additional elements.

In the United States where upper secondary education focuses on general skills, there is evidence that students can benefit from having meaningful
firm-level data, the program was found to have increased the sales and ability to export of large buyer firms since two years after the enrollment to the program (Arráiz, et al., 2013).

The little experimental evidence that exists on pure subsidy on inputs or rationing of inputs as instruments for improving access to intermediate inputs do not seem to have enhanced firm performance. In fact, in Ghana electricity rationing led to significant productivity losses (Abeberese et al., 2017).<sup>13</sup> Improved access to electronic and digital services could also boost firm performance, but there are large gaps in realizing the potential benefits. For example, in developing countries, small trading activities are generally based on self-employment, and retail firms could spend substantial time and costs on sourcing from wholesale market. In a field experiment in Bogota, retailers were randomly provided an electronic centralized distribution service to aggregate orders. These orders were then delivered to them directly from farmers. The services decreased the time and the costs of travel, as well as the purchase prices of the targeted products for the treated retailers. In fact, there were also spillover effects in the sense that untreated firms in treated blocks also reduced their prices of the same products. Nevertheless, treated retailers saw a decline in total sales because of a drop in sales from other products (Iacovone and McKenzie, 2019).<sup>14</sup> This evidence suggests that even though more efficient service delivery model is desirable, it takes more than partial adoption to make it successful. More exploration is needed to understand the larger scale effects of improved access to intermediate digital services, especially in the context of COVID-19.

# **3.4.Interventions improving firm capabilities**<sup>**15**</sup>

There is huge heterogeneity in business training programs, because the content of training program and the selection of participants, relative to other firm interventions (e.g. finance programs), vary
, as the OSM set could be a randomly drawn subsample of the total population of hotels. Unfortunately, in the absence of a “true” dataset of all hotels, it is impossible to know which hotels are present in the OSM subset and which are not. Some biases might arise from the fact that the data is crowd-sourced; if the contributors are mostly Europeans, for example, the dataset could overrepresent hotels frequented by international tourists. Another potential bias might arise from differing spatial distributions in ground data and the OSM subset. Between provinces, the distribution of hotels seems to be fairly accurate; the correlation coefficient of the provincial tourism GDP in 2016 and the number of hotels per province from OSM is 0.77. However, the spatial representativeness of hotels within a province cannot be tested without more granular data.

Despite these caveats, the OSM hotel dataset remains the only large and geolocated dataset of tourist hospitality locations in Vietnam. Using it as the basis for the assessment of the exposure of the tourism sector to hazards such as flooding and coastal erosion can provide new insights into the natural risk environment of the Vietnamese economy.

3.6. Health care facilities and schools Insights into the exposure of health care facilities are based on a dataset provided by a collaboration between the Government of Vietnam, the World Bank, and the World Health Organization. The dataset contains 1,583 geocoded public hospitals and district health centers. Not included are regional polyclinics, commune health centers, and private sector facilities (Government of Vietnam, WHO, & World Bank, 2019).

Schools are vital to every community, enabling the future prosperity of its members. If they are destroyed by a natural disaster, the impact on the livelihoods of the children inhibited in their education can persist 13
to explain the low coverage.<sup>3,13</sup> However, there is a conspicuous lack of systematic, cross-country comparable, and up-to-date evidence on these issues. Using data from five national phone surveys, we provide new evidence on vaccine hesitancy, uptake, last-mile delivery barriers, and possible promoters of vaccine demand in SSA. Our contributions are fourfold. First, unlike many existing studies that examine specific population groups or regions within countries, our data is national in scope and was collected by the respective national statistical agency of each country we study (Burkina Faso, Kenya, Malawi, Nigeria, and Tanzania) with samples drawn from large, nationally representative sampling frames. Second, data collection efforts were harmonized to a high degree between countries. Our insights are therefore cross-country comparable and representative of a population of approximately 369 million people, 2
in TFP growth has been observed in many advanced and developing countries since the 2008 global crisis (Ball, CHN 2014; Foda, 2016; Adler et al., 2017; Ollivaud and Turner, 2014). In the PWT 9.1 data, world TFP growth (excluding China) decreased from 1.2 percent in 2000–07 to 0.4 percent in 2010-17 (Figure 7). For advanced countries, the deceleration in TFP growth predates the crisis (Foda, 2016), -10 2000-2007 annual average TFP growth-5 0 5 10 pointing to a declining effect of the information and communication technology Source: PWT 9.1; authors’ calculations. boom, an aging workforce, slower human Note: An observation below (above) the 45-degree line capital accumulation, and slowing global indicates that a country’s TFP growth was lower (higher) trade integration, including the maturing of post-GFC compared with the pre-GFC period. OECD countries in orange. China’s integration into world trade (Adler et al., 2017). Albeit less relevant in the case of China, other factors further reduced global TFP growth: overheating and resource misallocation before the crisis (Ollivaud and Turner, 2014; 7
|(0.058)|(0.065)|
|Root MSE|1.8761|2.0460|3.8393|2.9874|3.6423|3.6560|
|R-squared|0.8135|0.7867|0.6383|0.7895|0.5080|0.5234|
|# obs[# countries]|35|[14]|35|[14]|35|[14]|
|.|<br>Descriptive|<br>statistics on de|<br> pendent varia|<br>bles|||
|Mean|5|.14|9.|77|1|2.28|
|Std. Dev.|3|.80|5.|58||4.54|
|Min||1.2|3|.1||3.7|
|Max|1|8.7|2|6.2||23|

_Notes:_ Countries covered: CZ (2004, 2009); SK (2004, 2006, 2008); PL (2004, 2006, 2008); HU (2005, 2006, 2009); EE (2004, 2006, 2008); LV (2007, 2009); SI (2004, 2006, 2008); BG (2006, 2009); RO (2008); RU(2006, 2008); UA (2005, 2006, 2009); ES (2004, 2006, 2008); PT (2004, 2006, 2008); GR (2005, 2009). See Annex 1 for details on the explanatory variables. Constant terms are not reported.

*, **, *** -  coefficient is significantly different from zero at 10%, 5%, 1% level, respectively.

_Sources:_ Dependent variables: Calculations with ESS data (see Table 1 for details). Explanatory variables - see Annex 1
In our opinion, these differences can be explained the by more global concept captured by our indicator, which aims at reflecting the FDI that cannot be explained by the size of the local market and the availability of natural resources. As mentioned earlier, it reflects not only the policy and political environment in a host country but also a series of factors such as the geographical location, infrastructure and the stock of human capital. The ICRG and II indexes capture only two of these multiple elements: the political and financial risks in each country. Another major difference is that these indexes are built with investors' surveys, mainly intemational banks, and thus are more subjective and forward-looking than our indicator that is constructed by using actual FDI flows and economic data. These differences can be illustrated by the cases of Zimbabwe and South Africa. Although Zimbabwe appears to be a country with low political (fourth out of 24 countries) and financial (third) risks, the fact of the matter is that most foreign investors have been reluctant to invest there. Their prudence may be explained by the weak growth performance over the past few years and numerous barriers against FDI, especially when Zimbabwe is compared to market-oriented neighbours such as Zambia, Uganda and Mozambique. Those obstacles are not captured by the ICRG or II index. The South African economy has benefited from large inflows of FDI in the recent years, but they have been mainly due to the privatization process, the return of companies based in neighbouring countries during the apartheid period and the interest of investors in the large domestic market (about three times greater than the second largest African country, i.e. Nigeria). Those factors are not related directly to the business climate, which remains quite problematic. The trade liberalization process remains timid with the exclusion of some important industries and relatively long transition periods. The economic growth performance in recent years has proved to be too modest to convince foreign investors, which is reflected in our indicator but not clearly in the ICRG or II index.

It
Policy Research Working Paper 9903

# **Abstract**

Evidence of public expenditure on infrastructure is extremely sparse. Little is known about the trends and patterns of infrastructure expenditure, and there is no real basis for assessing the adequacy and efficiency of infrastructure spending. Drawing on the World Bank’s novel BOOST database, this paper provides a first relatively disaggregated picture of infrastructure spending trends and patterns for a large sample of more than 70 developing countries covering 2010–18, drilling down into expenditure by sector for roads as well as electricity, and distinguishing operating from capital expenditure. Complementary sources of data are tapped to allow comparison between expenditure patterns on and off budget. The study finds that on-budget expenditure on infrastructure has been low both in absolute terms (1 percent of gross domestic product) and relative terms (5 percent of total public spending), as well as declining over time. Overall, infrastructure spending declined by about one-third over 2010–18 (with the road sector bearing the brunt of the decrease), and now lies well below estimates of the required levels, except in a handful of cases. There is evidence that low-income countries, despite lower spending envelopes, attach greater priority to public investment and infrastructure spending than their middle-income counterparts. Econometric analysis suggests that infrastructure spending in low- and middle-income countries has been historically procyclical, although to a lesser degree than total expenditure. In the transport sector, road funds are shown to play a substantial role in funding road maintenance, appearing to improve the adequacy of funding, while attenuating pronounced capital biases in road sector spending, but there is little evidence of efficiency improvements over time.

This paper is a product of the Infrastructure Chief Economist Office. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The author may be contacted at vfoster@
reasons make the Japanese market particularly suitable for this analysis.

First, Japan is the most important regional trading partner for Indonesia (excluding China) and is among Indonesia’s top three export destinations in our sample period. Exports to Japan represent about 10% of all export value for Indonesia.

More importantly, Indonesian firms face a high degree of competition from Chinese firms in Japan as China is Japan’s top trading partner in terms of imports, with over 20% of Japanese imports originating from China in 2018.<sup>11</sup> Indeed, Japan is also among the top 3 export destinations for China. A total of 8,353 Indonesian firms exported to Japan between 2014-18 and, among these firms, a total of 5,425 imported and exported.

A further reason to focus on Japan is the high quality of the data. We are able to obtain information on total Japanese imports and imports from China into Japan at monthly frequency at the HS 9-digit product level from the Ministry of Finance in Japan. As our empirical analysis uses demand shocks faced by Indonesian exporters in Japan that are driven by fluctuations in exchange rates of yuan per USD, in section 3.4 below we show the extent of exchange rate fluctuations during our period of analysis.

# **3.4 Exchange rates**

We use data from the International Monetary Fund (IMF) International Finance Statistics (IFS) for our exchange rate data. The data is the monthly average of bilateral exchange rates between the yuan and the USD. We also use IFS as a source for the Consumer Price Index (CPI) for the real exchange rate calculation. Figure 1 plots the log value of real exchange rate between yuan and USD over the period of analysis, 2014-2018. While there have been significant short-term fluctuations in the yuan/ USD exchange rate during this time period, which we will exploit in our analysis, the overall trend has been towards depreciation.

In all our specifications, we use the log real exchange
in the 2010 Census consisting of parents and children between 19 and 24 years old, where all members of the household are Native or non-Native. This excludes families whose children do not reside with them. This selection is smaller than in other countries, as children in Argentina tend to remain in the household of their parents for years after reaching adulthood: in 2010, 60 percent of all people between 19 and 24 years old still resided with their parents.<sup>9</sup> We focus on households in which all members are Native or non-Native for simplicity.<sup>10</sup> For each household, we calculate the average years of education of the parents, in case more than one is present, and the average years of education of the children.

Figure 3 shows the distribution of years of education of the parents disaggregated by ancestry (Natives and non-Natives). While there is great overlap in the distribution, nonnative parents have on average 1.20 more years of education than native parents. For the next generation, the difference in education between Natives and non-Natives is smaller (0.85 years) but still large.

Figure 3: Education by Generation and Ancestry <!-- Start of picture text --> Parents Children<br>Average Education Average Education<br>Natives: 8.05 Natives: 10.50<br>Non-natives: 9.24 Non-natives: 11.35<br>1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20<br>Years of Education Years of Education<br>Natives Non-Natives Natives Non-Natives<br>30 30<br>20 20<br>Percent Percent<br>10 10<br>0 0<br><!-- End of picture text --> _Notes:_ Households with children between 19 and 24 years
<br>20<br>Services trade restrictiveness index<br>0<br><!-- End of picture text --> Source: Borchert, Gootiiz and Mattoo (2010).

**Figure 4: Existing commitments, Doha offers and actual policy by region**

Source: Borchert, Gootiiz and Mattoo (2010).

As they stand today, Doha offers on services do not offer any liberalization of actual policy. Furthermore, two of the currently most protected areas in both industrial and developing countries, transport and professional services, are either not being negotiated at all or not with any degree of seriousness. In 2008 the chair of the Trade Negotiations Committee held a “signaling exercise” among a group of ministers, at the time that “modalities” in agriculture and non‐agricultural market 7
**<u>Table 1: Descriptive statistics based on administrative data</u>**

||<br>**Total**|<br>**1981-2009**|**2010-2013**|**2014-2019**|
|---|---|---|---|---|
|**First time registration**|||||
|<br>No. of leases issued|90,251|16,308|49,803|24,140|
|per year|2,314|562|12,451|4,023|
|Plot area (m<sup>2</sup>)|1,211|1,452|1,062|1,393|
|Sporadic|37,548|16,308|6,437|14,803|
|Systematic|52,703|-|43,366|9,337|
|Sporadic/year|963|562|1,609|2,467|
|Systematic/year|1,351|-|10,842|1,556|
|_Approach and land use type_|||||
|<br>Sporadic registration|0.416|1.000|0.129|0.613|
|Systematic registration|0.584|0.000|0.871|0.387|
|Residential land|0.834|0.818|0.863|0.786|
|Commercial land|0.062|0.141|0.038|0.057|
|Agricultural land|0.004|0.004|0.001|0.012|
|<br>Other Use|0.099|0.038|0.098|0.145|
|_Subject of registration_|||||
|Ownership male/female