rafmacalaba/data-use-mentions-extended huggingface dataset:
# **Appendix A: Data description**

<mark>The price data were taken from the World Bank’s world commodity price data system. The sample covers 50 years, January 1970 through December 2020 (612 observations). The prices, which are reported in nominal US dollar terms, were deflated with the U.S. CPI (taken from the St. Louis Fed). Although the World Bank covers more than 70 commodity price series, this paper uses only 27 series. The selection of the commodities was based on the following criteria:</mark>

- **<mark>Substitutability</mark>** <mark>. If two commodities are close substitutes only one was included. For example, because the edible oils are close substitutes, only soybean oil is used in the analysis.</mark>

- **<mark>Importance</mark>** <mark>. Several commodities that used to be very important in the past are not included in the current study because they have lost their importance during the past few decades, often due to the development of synthetic products. Notable exclusions are wool, hides and skins, sisal.</mark>

- **<mark>Price determination process</mark>** <mark>. Prices should be determined by a market-based mechanism such as exchange (or auction in the case of tea). Notable exclusions are iron ore (until recently, its price used to be the outcome of a negotiation process among key players of the steel industry) bananas (its price reflects quotations from a few large trading companies), and sugar (too many policy interventions reduce the significance of the world price indicator).</mark>

<mark>The prices were grouped into six broad categories, each of which contained at least three but no more than six series in order to maintain a balanced representation. The rest of this Appendix lists the commodities used in the analysis,
best way to identify it is at the industry level.<sup>37</sup> Table 1 presents the tariff levels and their changes for our sample. Although our analysis is conducted at the product level we provide some statistics here aggregated by industry. The highest tariff rates before and after the UR appear in the tobacco sector (SIC-314): an average of 42 and 25 percent respectively. The lowest pre-UR tariffs are in the miscellaneous petroleum and coal products sector (SIC-354) with 3.9%, whereas the iron and steel industry (SIC-371) became the least protected in terms of tariffs after the UR, 0.4%. The footwear sector (SIC-324) experienced the least liberalization, 0.8 percentage points, and tobacco the highest, 17. Note also that there is a considerable amount of variation in tariff changes both within industries, with coefficients of variation between 0.28 and 1.5, and across industries, with a coefficient of variation of 0.44.

# 4.3 Estimation Results

The unconditional mean reduction in MFN tariffs by the EU was 4.4 percentage points for non-PTA products but only 2.9 for PTA products during the UR. A simple t-test con fi rms that the difference of 1.5 percentage points, with a standard error of 0.1, is statistically signi fi cant. This difference may be due to other factors that are correlated with the PTA variable. Therefore in table 2 we present the estimates of the parameters in (23). In order to address the endogeneity issues discussed above we employ an instrumental variables technique. More speci fi cally, we use the two-step efficient generalized method of moments estimator, which is robust to heteroskedasticity with an undetermined form. Furthermore, we allow for the errors to be correlated through clustering within each industry for which the production data is used to ensure the estimation is also robust to arbitrary within-industry correlation.<sup>38</sup> The variable I<sup>any0
intra-household level information on employment and assets,”<sup>2</sup> the LSMS+ program has coordinated with country national statistical offices to include self-reported, individual-level modules on asset ownership and rights, as well as labor, as part of ongoing, multi-topic, nationally-representative surveys. The LSMS+ modules follow 2019 United Nations guidelines for producing statistics on asset ownership from a gender perspective, prepared under the Evidence and Data for Gender Equality (EDGE) project.<sup>3</sup> With an emphasis on self-reported data to fully capture intra-household gender inequalities in asset ownership, LSMS+ aims to address widespread data gaps on individual wealth and improve the accuracy in estimates of wealth. This is in contrast to more common survey approaches that — when data on wealth are collected — tend to either collect this information at the household level, or by one respondent per household who answers for others (see Kilic, Moylan and Koolwal, 2021 for a discussion).

In this paper, we use publicly available data from LSMS+ supported national surveys that were conducted in Malawi (2016 Integrated Household Panel Survey), Tanzania (2019/20 National Panel Survey), Ethiopia (2018/19 Ethiopia Socioeconomic Survey), and Cambodia (2019 LSMS+ Survey).<sup>4</sup> Table 1 presents the main features of these surveys. Individual wealth is constructed in our analysis using data on individuals’ selfreported ownership and valuation of different assets in the LSMS+ surveys. The aforementioned UN guidelines recommend that countries collect information, at a minimum, on the principal dwelling, 2 World Bank, 2016.

3 See UN (2019). EDGE was launched in 2013 as part of a collaboration between the US Government, the United Nations Statistics Division and UN Women, along with key regional and international agencies including the OECD and the World Bank. The UNEDGE guidelines were also informed by country pilots including the Methodological Experiment on Measuring Asset Ownership from a Gender Perspective (MEXA) in Uganda (
Akbar, Couture, Duranton, and Storeygard (2021) on urban mobility has shown that traffic in developing economies is generally much slower than in developed countries,<sup>4</sup> particularly because of infrastructure deficiencies in the metropolitan areas of the developing economies. However, apart from being a direct agglomeration cost, the lack of urban mobility can also indirectly affect pure agglomeration externalities because it makes sharing, matching, and learning in cities more difficult. Mobility is key to the generation of agglomeration economies, which tend to decay strongly with travel time (Graham, 2007; Rosenthal and Strange, 2008; Graham & Melo, 2011). At the same time, the lack of mobility is a consequence of the concentration of people and economic activities and a by-product of agglomeration benefits (Duranton and Puga, 2020). Ultimately, it is unclear whether mobility issues harm some firms more than others and whether these issues are more pronounced in some parts of the developing world. Building on the agglomeration economies literature that has examined the role of transport networks and travel times (e.g., Graham, 2007; Melo et al., 2017) and using a new database of mobility indexes for 1,228 cities constructed by Akbar et al. (2021), based on more than 530 million simulated trips on Google Maps for the period between June and October 2019, we examine how firm-specific agglomeration benefits are dependent on urban “uncongested” travel speeds and congestion in the developing countries. Low uncongested travel speed or the speed in the absence of traffic originates from the lack of good infrastructure in a metropolitan area and is characterized by slow traffic during the whole day, while congestion delay is typically caused by traffic during peak travel times. Thus, both low travel speeds and congestion limit mobility within metropolitan areas and, therefore, obstruct the generation of external economies of scale (see also Collier & Venables, 2016; Venables, 2017b; Gerritse & Arribas-Bel, 2018), but congestion does so during specific
levels after a v-shape recovery early in the crisis. We include the 30-day mobility average in our estimations to correct for these cross-country differences in the potential severity of the pandemic measures adopted by public authorities at the time of the COV-BPS. Notice the mobility indicator resembles a country fixed effect in our linear regressions because most of the variation occurs across countries, with smaller differences across firms within a country when businesses were interviewed at different dates.

One of the salient features of the COVID-19 pandemic has been the heterogeneous impact across industries. We construct a second indicator of potential exposure to the shock for narrowly defined sectors using publicly available aggregate data from the Small Business Pulse Survey in the U.S. (SBPS-US; ( _20_ )). We first compute the share of firms in each 3-digit NAICS sector reporting in the SBPS-US that they have experienced a “large overall negative effect from the pandemic” averaging the weekly averages published by the U.S. Census Bureau for the period April-June 2020.<sup>8</sup> We sort sectors into two categories for potential exposure, low and high, depending on whether the average fraction of firms reporting a large overall negative effect is below or above the U.S. national average (which we obtain for the period April-June 2020 also averaging the weekly averages publicly available). The list of less/more exposed sectors is available in appendix A (see tables A2 and A3).<sup>9</sup> In our specifications we include the interaction of this binary variable with fixed effects for broad sector and country.

# **2.4 Descriptive statistics**

Table 1 shows the characteristics of our sample across quintiles of the distribution of technology sophistication pre-pandemic (Q1-Q5). Our sample covers around 1,000 businesses. As documented in ( _17_ ), the right tail of the distribution of technology sophistication in our data is characterized by relatively larger firms; a higher management z-score; and managers compara-

> 8The exact wording of the question
from education, schooling is interacted with a dummy for schooling in the bottom twothirds across the sample. A dummy is included for commodity exporters during the period 2003-07. This dummy is intended to capture the impact of the exceptionally large commodity price boom that temporarily lifted commodity exporters’ growth during this period. Potential TFP is thus: + _α_ 5 Dcebi,t + _α_ 6 Δ invi,t + _ε_ i,t , where Δ tfpi,t is the logarithmic first difference of trend TFP, GDP per capitai,t is GDP per capita in percent of advanced-economy per capita GDP, wapi,t is the working-age share of the population, educationi,t is the percent share of the population who completed secondary school, Δ invi,t is the five-year moving average of real investment growth, Dedu is a dummy variable taking the value of 1 if the secondary completion rate is in the bottom two-thirds of the distribution, and Dcebi,t is a dummy variable for the period 2003-07 taking the value 1 if the country is a commodity exporter.<sup>18</sup> The data were compiled using a wide range of sources: UN Population Statistics (for population growth, the working-age share of the population); Barro and Lee (2013) (for secondary school completion); the World Development Indicators (for secondary school completion and GDP per capita relative to the advanced economies); and Haver Analytics (for investment).

The regression results are broadly in line with the previous literature (table 5). TFP growth slows as per capita incomes converge toward advanced-economy levels (Barro and Sala-i-Martin 1997). A better-educated population and accelerated investment growth are associated with higher TFP growth. However, the impact of education diminishes as education levels rise toward advanced-economy levels (Benhabib and Spiegel 1994, 2005; Coe, Helpman, and Hoffmaister 1997; Kato 2016). As a result, the coefficient on secondary school completion rates is only significant for countries with completion rates below the top third.

The results
.28|5.7|4.33|8.2|
|Formal capital real rent (index)|1.00|-0.36|-2.2|-0.35|-2.6|
|Informal capital real rent (index)|1.00|0.85|-3.8|0.87|-5.8|
|Canal capital real rent(index)|1.00|-0.11|-1.1|-0.20|0.3|
|Total labor supply|1,178|1.6|1.3|1.6|1.3|
|Unskilled labor|713|1.6|1.3|1.6|1.3|
|Unskilled farm labor|236|-0.4|-0.4|-0.4|-0.5|
|Unskilled non-farm labor|477|2.5|1.9|2.5|2.0|
|Unskilled formal non-farm labor|217|2.2|3.2|2.3|3.5|
|Unskilled informal non-farm labor|260|2.7|0.8|2.6|0.6|
|Unskilled migration|5.2|-2.0|-1.6|-2.0|-0.8|
|Skilled labor|465|1.6|1.3|1.6|1.3|
|Skilled formal non-farm labor|365|1.7|1.8|1.7|1.8|
|Skilled informal non-farm labor|84|1.3|-0.9|1.2|-1.0|

The structural shift out of agriculture is also driven by falling farm employment, which declines at an average rate of 0.4 percentage points per year
# **_Individual Financing Obstacles_**

Our results indicate that Financing is one of the most important obstacles that directly constrain firm growth. We would like to get a better understanding of exactly what type of obstacles related to financing are constraining firm growth. Fortunately, our survey data also includes more detailed questions regarding the Financing obstacles.

To get at specific issues, the entrepreneurs were asked to rate the extent to which the following financing factors represent an obstacle to their growth: (i) collateral requirements of banks and financial institutions, (ii) bank paperwork and bureaucracy, (iii) high interest rates, (iv) need for special connections with banks and financial institutions, (v) banks lacking money to lend, (vi) access to foreign banks, (vii) access to non-bank equity, (viii) access to export finance, (ix) access to financing for leasing equipment, (x) inadequate credit and financial information on customers, and (xi) access to long-term loans. The ratings are again on a scale of one to four, increasing in the severity of obstacles.

# **Insert Table 6 here**

Table 6 reports the regressions that parallel those in Table 2, but this time focusing on specific financing obstacles. In addition to the individual financing obstacles, we also include a residual, the component of the general financing obstacle not explained by the individual obstacles. The results indicate that not all financing obstacles reported by firms are constraining. Only the coefficients of collateral, paperwork, high interest rates, special connections, banks’ lack of money to lend, lease finance and the residual are significant when entered individually. In terms of economic impact high interest rates has the highest impact – a one standard deviation increase in the obstacle 25
) and Haver<br>Analytics.|Haver Analytics, OECD,<br>World Bank.|
|Trade|Exports plus imports of goods and services, in constant 2010 U.S. dollars, taken from the World Bank.<br>Prior to 1960, the series of exports and imports are extended using data from PWT 9.1. Trade for<br>aggregatedgroups is the sum of exports and imports of individual countries.|Feenstra, Inklaar, and<br>Timmer (2015); World<br>Bank.|
|Unemployment<br>rate|Unemployment, in percent of labor force, taken from the International Monetary Fund (IMF). Data<br>for aggregated groups are computed as the sum of those unemployed in individual countries divided by<br>the sum of labor force.|IMF.|
|Oil consumption|Oil consumption, in thousand barrels per day. Oil consumption in advanced economies and EMDEs is<br>computed as the sum of consumption in individual countries. If there are differences between the<br>world total and the sum of advanced economies and EMDEs, the residuals are added to the EMDE<br>aggregate. The number of world oil consumption for 2019 is based on data over the first nine months<br>of theyear.|British Petroleum, U.S.<br>Energy Information<br>Administration.|

62
gaps in local social infrastructure as a component in the allocation of funds to municipalities, instead of the index of poverty and social deprivation gaps. Separating FAIS allocations from the results the program aims to achieve (multidimensional poverty) is key to minimizing potential perverse incentives, such as incentives encouraging municipalities to maintain higher rates of deprivation so they may receive more resources through the program. Resource distribution in an initial period could be determined on the basis of a component that reflects the gaps in investment in social infrastructure. In subsequent periods, 20
(0.012)|(0.013)|(0.017)|
|Ln(GDP per capita) –mean(Ln(GDP per capita))|0.033|0.041|-0.014|
||(0.038)|(0.038)|(0.060)|
|Observations|167,034|144,914|22,120|

Note: ***p < 0.01, **p < 0.05, *p < 0.1. Standard errors in parentheses. Standardized weights are used that give each country equal weight. The standard errors are clustered at a sector × country level. Measures for GVCB share and GVCF share are standardized. All regressions include controls for technology (computer use, computer use squared), FDI, skills, education, age, gender, sector FE, and sector FE interacted with GDP per capita.

Source: Authors’ calculations based on PIAAC, STEP, CULS (tasks), World Bank (GDP), EORA data and Borin and Mancini (2015, 2019) (GVC participation measures).

# c. Sector-specific regression results

Next, we study differences between broad sectors by estimating regressions for specific subsamples. We distinguish three broad sectors – industry (B-F ISIC rev. 4 sections), business services (G-N ISIC rev. 4 sections), and other services (O-S ISIC rev. 4 sections). For details, see Table A2 in Appendix A.

For the industrial sector, we find no correlation between backward GVC participation and RTI. In contrast, a higher forward GVC participation is linked to more routine tasks, although decreasing with development level (Table 2, Panel A, column 1). We also find no correlation between backward and forward GVC participation and the RTI among non-offshorable occupations (Table 2, Panel A, column 2). Both these patterns are consistent with the overall results for all sectors (Table 1
Policy Research Working Paper 8613

# **Abstract**

Foreign direct investment may play an important role in transferring technologies from high-income to emerging economies, which can lead to uneven effects on the wages of skilled and unskilled workers. This paper combines project-level data on greenfield foreign direct investment with household surveys to estimate the effects of foreign direct investment on the wage skill premium across sectors and regions in seven emerging economies (Brazil, Colombia, Ethiopia, Mexico, the Philippines, South Africa, and Vietnam). The results suggest that foreign direct investment is associated with a higher probability of employment and higher wages for unskilled workers, relative to skilled workers, in six of the seven countries analyzed in this paper. Moreover, the effects of foreign direct investment on wages are relatively larger for unskilled women.

This paper is a product of the Finance, Competitiveness and Innovation Global Practice. 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/research. The authors may be contacted at marciocruz@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Policy Research Working Paper 5650

# **Abstract**

This paper quantifies the magnitude of transitions across occupational categories in Colombia, a country with high unemployment and informality but quickly increasing its social security coverage for health. The analysis makes use of a panel of households between 2008 and 2009, representative of the main metropolitan areas in the country. Results confirm previous evidence found in Colombia and elsewhere in the region that transitions between occupations are large and asymmetric: they are disproportionally more likely to happen from formal to informal occupations than _vice versa._ The paper finds for the first time that such transitions are also different for salaried workers compared with the selfemployed, as well as by poverty status of the worker. Salaried workers are more likely to transition first into other salaried jobs, while self-employed are more likely to transition into unemployment or out of the labor force. There are marked differences in the profiles of transitioning and non-transitioning workers, both in terms of socioeconomic characteristics and social security coverage. Causal analysis shows that affiliation to social security on health deters occupational transitions, while pension insurance does not. Hence, high-volume transitions may not be crisis-specific phenomena, but rather associated with contributive and non-contributive social security mechanisms that incentivize informality, and workers’ preferences for informal jobs. The debate on labor market and social security reforms needs to take these features of transitions into account.

This paper is a product of the Poverty Reduction and Equity Unit, Poverty Reduction and Economic Management Network; and the Latin America and Caribbean Social Protection Sector, Human Development Network. 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://econ.worldbank.org. The author may be contacted at jcuesta@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is