rafmacalaba/datause-encoder-data huggingface dataset:
urban and 36% in rural). However, an exception to this is computer manufacturing where a gap of 93% versus 76% exists along urban-rural dimensions.

Table 3d presents some results on the value of financial loans relative to industry output and employment in the organized sector. “Office, accounting and computing machinery” and “Textiles” are two examples of industries where the average value of the loan-to-output ratio during the 1989-2010 period is over 0.5. Computer manufacturers also have a high loan-peremployee ratio during 1989-2010. At the other end of the spectrum is the tobacco industry where the average loan-to-output intensity during 1989-2010 is barely 0.1, and loans per employee is similarly low.

In sum, Tables 3b and 3d suggest that a larger proportion of plants in advanced industries access financial loans, and the relative value of these loans is also higher. An exception to this rule is the non-metallic mineral products industry where only 79% of plants access external credit but the extent of their loan-to-output ratio is 0.48. Given the high dependence of this industry on financial loans for plants that are able secure them, it seems that other plants in this industry may also be in need of external finance but are unable to obtain access.

Table 4a presents the share of organized plants having financial loans by their age cohort and two-digit manufacturing industry. We define young plants as those that are less than three years old, while incumbents are those that have been in operation for three years or longer. The table suggests that incumbents have a marginal advantage in accessing loans, with the national average share over the 1989-2010 period being 88% for incumbents and 86% for young plants. National differences are mainly driven by a few industries. For instance, in the tobacco industry, where the share of plants with access to external finance is the lowest, there is a stark difference in participation by incumbents and young plants. An average of 46% of incumbents in the tobacco industry successfully secured loans, while only 34% of young plants borrow from the external market. At the other end, young firms have greater loan shares in the publishing and printing industries, and to a lesser extent the paper industry. For most industries, however, the differences between incumbents and young plants are minimal with respect to financial loans.

Next, Table 4b shows financial loans among organized plants by the share of the value of land and buildings that are rented. Using the imputed values for land and buildings from Duranton et 25
14

**Figure 7.  Latin America and East Asia: Gross Domestic Savings (1970-2008)**

(Percent of GDP) <!-- Start of picture text --> 40<br>35<br>30<br>25<br>20<br>15<br>1970 1975 1980 1985 1990 1995 2000 2005<br>LAC East Asia<br><!-- End of picture text --> _Note: East Asia is the un-weighted average of Hong Kong, Singapore, South Korea, Taiwan, Indonesia, Thailand and Malaysia, while LAC covers the whole Latin American and Caribbean region._ _Source: World Development Indicators (2010)._

**Figure 8.  Latin American Capital Markets Reform Implementation (1990-2002)**

<!-- Start of picture text --> 120%<br>100%<br>100% 94% 91% 92%<br>88%<br>80%<br>63% 64% 62%<br>56%<br>60%<br>40% 31% 33%<br>25% 27%<br>15%<br>20%<br>0%<br>0%<br>Supervisory Agency Establishment of Custody Trading Systems Clearing and<br>Creation Insider Trading Arrengements Settlement<br>Laws Processes<br>Before 1990 By 1995 By 2002<br><!-- End of picture text --> _Note: Percentage of Latin American countries having implemented reforms. Source: De la Torre and Schmukler (2007)._
Table 3: Relationship between management and energy intensity (quantity).

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||All Firms|Manufacturing|Services|All Firms|Manufacturing|Services|
|General|0.162<sup>_∗_</sup>|0.294<sup>_∗∗∗_</sup>|-0.082|0.353<sup>_∗∗∗_</sup>|0.518<sup>_∗∗∗_</sup>|0.087|
||(0.085)|(0.112)|(0.130)|(0.087)|(0.132)|(0.134)|
|No. Employees||||0.496<sup>_∗∗∗_</sup>|0.312<sup>_∗∗∗_</sup>|0.459<sup>_∗∗∗_</sup>|
|||||(0.061)|(0.089)|(0.102)|
|Log(Sales)||||-0.561<sup>_∗∗∗_</sup>|-0.619<sup>_∗∗∗_</sup>|-0.567<sup>_∗∗∗_</sup>|
|||||(0.041)|(0.061)|(0.069)|
|Capital Stock|||||0.217<sup>_∗∗∗_</sup><br>(0.037)||
|3-digit sector|✓|✓|✓|✓|✓|✓|
|Age|✓|✓|✓|✓|✓|✓|
|Country|✓|✓|✓|✓|✓|✓|
|Observations|3587|2338|1155|3582|1837|1153|
|Adjusted _R_<sup>2</sup>|0.262|0.266|0.261|0.312|0.331|0.316|
|df<br>~~m~~|110|79|58|112|81|60|

Standard errors in parentheses Capital, labor, and output enter in logs. _∗ ∗∗ ∗∗∗ p <_ 0 _._ 1, _p <_ 0 _._ 05, _p <_ 0 _._ 01 ensure that the increased energy requirement does not result in a higher share of energy expenditure.<sup>13</sup> The positive effect is concentrated among firms in high and upper middle income countries (see Appendix Table A9). As shown in Appendix Table A8, moderate and low energy-intensive manufacturing as well as high knowledge-intensive services all show a positive effect. To our knowledge, we are among the first to examine this relationship. More study is needed, especially to understand this effect in services firms<sup>14</sup> Which management practices matter for energy use? Examining the correlation between

> 13Although the samples used for estimation in Tables 2 and 3 are different, our results are robust to considering the set of firms that consistently report both energy value and quantity. These additional results are available upon request.

> 14(Karplus and Zhang, 2020) find a negative relationship between management score and energy intensity in quantity terms in a relatively small sample of firms in the metal machining sector in Shandong Province, but smaller in magnitude compared to the effect on energy intensity in value terms.

20
areas with less than 100,000 population in column 3, and for robustness, show results for less than 15,000 population in column 4. Across all population sizes the OLS results show a significant positive coefficient on the linear term in education, and a significant negative coefficient on the quadratic term. Migration is more likely for individuals who are married, household heads, and in communities with larger networks. The interaction between education and network size is negative, as theory predicts, and significant at the 5% level in communities with less than 100,000 population. These results continue to hold after instrumenting for community migration networks in columns 5 through 7, with some changes in the magnitudes of the coefficient. The first-stage F-statistics are all above 30, see Appendix table A1 for the full first-stage results for column 6.

Columns 8 and 9 examine the robustness of our key parameter of interest, the interaction between education and community networks, to the inclusion of municipality fixed effects. Including municipality fixed effects will control for any differences across communities which might affect the level of migration, such as differences in infrastructure and attitudes towards education and migration. We see that the interaction coefficients remain significant and negative, with minor changes in the size of the coefficient. For example, for communities under 100,000, the interaction changes from -0.55 in column 6 to -0.43 in column 9, whereas for all communities the interaction only changes from -0.46 to -0.47.

Comparing the OLS and 2SLS results, we see that the interaction between education and the migrant network becomes more negative after instrumentation. This suggests that communities with higher migration have unobservable factors such as better schooling infrastructure or an education culture that leads, ceteris paribus, to higher education. This accords with the evidence in Feliciano (2001), who found migrants in 1910 (a time when migrant networks were first forming) to have higher literacy rates than non-migrants.

Interpreting the fitted relationship between education, network size and the probability of migration is most easily done graphically. For illustration purposes, and because networks are found to be more powerful outside of large cities, we show this for the regressions which consider individuals in areas of less than 100,000 population. Figure 2 plots the predicted probability of migration from OLS (left panel) and 2SLS (right panel) at different percentiles of the community migration 14
**Table 3 CO2 emissions by** **<u>production activity and energy input (million metric tons)</u>**

||||**Total CO2**|**Percent of**<br>**total CO2**|
|---|---|---|---|---|
|**Sector **|**Coal **|**Petroleum **|**Emissions**|**Emissions**|
|Electricity|198.83|0.20|199.04|47.6|
|Basic Iron and Steel|70.87|0.55|71.41|17.1|
|HH total|11.37|25.42|36.79|8.8|
|Transportation and Storage|0.72|17.76|18.49|4.4|
|Basic Non-ferrous Metals|9.90|1.64|11.54|2.8|
|Metal Products Excluding Machinery|9.34|0.33|9.67|2.3|
|Other Mining|7.03|1.08|8.11|1.9|
|Food|6.44|0.95|7.38|1.8|
|Other Chemicals and Man-Made Fibers|5.28|1.67|6.95|1.7|
|Water Supply|5.43|0.06|5.50|1.3|
|Basic Chemicals|0.94|4.10|5.03|1.2|
|Government Services|2.74|2.17|4.91|1.2|
|Agriculture, Forestry, and Fisheries|0.00|3.65|3.65|0.9|
|Health, Community, Social, and Personal Services|2.25|1.11|3.35|0.8|
|Rubber Products|2.54|0.46|3.01|0.7|
|Business Services|0.00|2.94|2.94|0.7|
|Wholesale and Retail Trade|0.00|2.71|2.71|0.6|
|Construction and Civil Engineering|0.00|2.45|2.45|0.6|
|Machinery and Equipment|1.65|0.67|2.32|0.6<br>|
|Communication|0.00|2.31|2.31|0.6|
|Textiles|1.53|0.13|1.67|0.4|
|Non-metallic Minerals|1.09|0.19|1.28|0.3|
|Wearing Apparel|1.20|0.07|1.27|0.3|
|<br>Beverages and Tobacco|0.63|0.13|0.77|0.2|
|Catering and Accommodation|0.50|0.22|0.73|0.2|
|Gold and Uranium Ore Mining|0.53|0.18|0.70|0.2|
|Paper and Paper Products|0.42|0.12|0.54|0.1|
|Coal Mining|0.00|0.45|0.45|0.1|
|Motor Vehicles Parts and Accessories|0.00|0.44|0.44|0.1|
|Leather and Leather Products|0.41|0.01|0.42|0.1|
|Other Producers|0.00|0.41|0.41|0.1|
|Electrical Machinery|0.00|0.38|0.38|0.1|
|Plastic Products|0.00|0.37|0.37|0.1|
|Wood and Wood Products|0.28|0.06|0.33|0.1|
|Other Industries|0.00|0.32|0.32|0.1|
|Financial Services|0.00|0.31|0.31|0.1|
|Professional and Scientific Equip|0.00|0.06|0.06|0.0|
|<br>Printing, Publishing, and Recorded Media|0.00|0.04|0.04|0.0|
|<br>TV, Radio, and Communication Equip|0.00|0.04|0.04|0.0|
|<br>Other Transport Equipment|0.00|0.04|0.04|0.0|
|<br>Furniture|0.00|0.04|0.04|0.0|
|Glass and Glass Products|0.00|0.03|0.03|0.0|
|Footwear|0.00|0.01|0.01|0.0|
|Coke, Refined Petroleum & Gas|0.00|0.00|0.00|0.0|
|Total|341.93|76.30|418.23|100.0|

_Source:_ CO2 coefficients from Table 2 and intermediate energy input by production sector from SAF SAM 2003.

9
37

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20
-8-

**Table 2: Bulgarig : ; Como of Average Housebold iwme Per Capita by Sowres of lwome, 1992**

|Income Source|Income (Leva)|Percent|
|---|---|---|
|1. Income Earned|7098|42.2|
|1.1 Wage and salary from primary|6334|37.7|
|1.2 Salary from secondary employment|64|0.4|
|1.3 Other income, besides salary|363|2.2|
|1.4 Self-employment income|338|2.0|
|2. Property Income|42|0.2|
|2.1 All types of rents, copyright|36|0.2|
|2.2 Interest|2|0.0|
|2.3 Dividends|4|0.0|
|3. Social Insurance|4014|23.9|
|3.1 Unemployment compensation|74|0.4|
|3.2 Pensions|3492|20.8|
|3.3 Children's allowance|295|1.8|
|3.4 Compen. for tempor. incapacity for work|79|_0.5_|
|3.5 Compen. for pregnancy, maternity leave|74|0.4|
|4. Social Benefits|56|0.3|
|4.1 Lump-sum family benefits|3|0.0|
|4.2 Disabilities and illness|11|0.1|
|4.3 Other lump-sum benefits|18|0.1|
|4.4 Scholarships|24|0.1|
|5. Income from Sales|605|3.6|
|5.1 Farm produce|489|2.9|
|5.2 Other products|18|0.1|
|5.3 Property|98|0.6|
|6. Other Sources of Income|4988|29.7|
|6.1 Insurance|25|0.1|
|6.2 Gifts|438|2.6|
|6.3 Lottery, raffle, etc|3|0.0|
|6.4 Income home-made goods|9|0.1|
|6.5 Others|36|0.2|
|6.6 In-kind income|4477|26.6|
||16803|100.0|

Source: World Bank estimates based on 1992 Individual Budgets of Households Survey, NSI.
advantageous as it allows us to get computationally simple estimators (Papke and Wooldridge, 2008). As will be seen latter, our explanatory variables of interest are endogenous, which makes it relevant to choose a Probit link function in the same line as Papke and Wooldridge (2008).

This regression model is estimated using the generalized estimating equation (GEE) method discussed in Liang and Zeger (1986) and Zeger and Liang (1986). Table 7 reports the results of the regression, which, as is shown by the level of the Wald-statistic, is significant overall. We see that while Protection has a positive and significant effect on Participation in both MENA and OECD, Provision has a negative and significant effect in MENA and is not significant in OECD.<sup>37</sup> This negative relationship between Provision and Participation in MENA is in line with the descriptive evidence provided by the data in the previous section and the discussion in the overview of the MENA SC in section 2. We will thus further investigate this hypothesis.

While this hypothesis is verified when using a simple fractional Probit regression, it is necessary to account for potential endogeneity of Provision and Protection. This is all the more necessary as in the conceptual framework on which our empirical methodology is based, the three dimensions of an SC are determined simultaneously. For this purpose, we rely on IV techniques (Papke and Wooldridge, 2008). More specifically, in the same line as Papke and Wooldridge (2008), the fractional Probit model is estimated using the pooled quasi-maximum likelihood estimator (QMLE) and the endogeneity of Provision and Protection is handled through a control function approach.<sup>38</sup> Hence, the standard errors of the parameter estimates are obtained using bootstrapping.<sup>39</sup> The results are reported in Table 8. This table also gives the parameter estimates of the variables 21 and 22 that are used to perform a Hausman (1978) test of endogeneity of Provision and Protection.<sup>40</sup> The hypotheses that the variables and pp� pp� are not endogenous in the MENA and OECD datasets are both rejected, which justifies the need PP1 PP2 to instrument them as we did.

The results reported in Table 8 confirm those from the simple fractional Probit as to our main hypothesis, i.e., Provision ( ) and Participation ( ) are negatively and significantly related in MENA, implying that governments in the region seem to bargain more Provision PP2 PP3

> 37  As to the controls, we see that rurality is associated negatively to Participation in OECD countries and that human development is associated positively to it in MENA countries. As we will see later, because we have not yet accounted for potential endogeneity problems, these results should not be taken as final.

> 38 For an overview of the control function approach, see Wooldridge (2015).

> 39 Following Papke and Wooldridge (2008), we consider 500 bootstrap replications.

> 40 For a contribution on the application of the Hausman approach to testing for exogeneity in the Probit modeling framework, see Rivers and Vuong (1988).

23
**_<u>________________________________________________________</u>_**

# **Annex G.   Trade Logistics Indicators: Selected Examples**

||**Core Measure**|**Unit**|**Definition of Term**|**Sources of data**|
|---|---|---|---|---|
|1.1|Clearance Time|Days|For imports, average time taken<br>from when goods arrive at the<br>port of entry until the time they<br>are claimed from customs|World Business<br>Environment Survey<br>(WBES)|
|1.2|Longest day to clear<br>customs for imports|Days|For imports, longest time taken<br>from when goods arrive at the<br>port of entry until the time they<br>are claimed from customs|Investment Climate<br>Assessments (ICAs)|
|1.3|Clearance Time:<br>TemporaryEntry||||
|1.3.1|Clearance Time for<br>sea cargo: Temporary<br>Entry|<br>Days|Clearance time for temporary<br>entry sea cargo (LCL and FCL)|International Exhibition<br>Logistics Associates<br>(IELA)|
|1.3.2|Clearance Time for<br>air cargo: Temporary<br>Entry|Days|Clearance time for temporary<br>entry air cargo|International Exhibition<br>Logistics Associates<br>(IELA)|
|1.4|Release Time of<br>goods|Days|Normally from the time of<br>arrival of goods at the<br>port/airport/land border until<br>their release to the importer or a<br>third party on his behalf|World Customs<br>Organization : Time<br>Release Methodology,<br>and (simplified) TTFSE<br>methodology|
|1.5|Revenue<br>Collected/Customs<br>Employee|USD/employee|Total revenue collected/ Total<br>number of customs employees|TTFSE methodology|
|1.5.1|Revenue Collected|USD|Revenue collected by customs|TTFSE methodology|
|1.5.2|Customs Employees|Number of<br>people|Total number of customs<br>employees|TTFSE methodology|
|1.6|Customs<br>Environment /<br>Efficiency|number 0-1<br>(The value of 1<br>stands for the<br>best<br>performer)|<br>Average of two indexed inputs:<br>hidden import barriers other than<br>published tariffs and quotas,<br>irregular extra payment or bribes<br>connected with export and<br>import payments|<br> <br>GEP 2004 database<br>Chapter Five, (2003)|

57 ____________________________________________________________________________________________________________