rafmacalaba/datause-encoder-data huggingface dataset:
**Table 7.   Average MFN ad valorem equivalent of the specific tariff rates by industry /** a

||||||**_Ye_**|**_ar_**|||||
|---|---|---|---|---|---|---|---|---|---|---|
||**_20_**|**_01_**|**_20_**|**_02_**|**_20_**|**_03_**|**_20_**|**_04_**|**_20_**|**_05_**|
|**_Industry_**|_Un-_<br>_weighted_|_Trade_<br>_weighted_|_Un-_<br>_weighted_|_Trade_<br>_weighted_|_Un-_<br>_weighted_|_Trade_<br>_weighted_|_Un-_<br>_weighted_|_Trade_<br>_weighted_|_Un-_<br>_weighted_|_Trade_<br>_weighted_|
||..|..|8.8|8.8|16.0|15.8|5.7|16.1|4.6|15.3|
|_Non-ferrous metallurgy_|_.._|_.._|_.._|_(0.0)_|_(8.7)_|_(6.2)_|_(5.9)_|_(4.2)_|_(6.7)_|_(7.3)_|
|_Chemicals and_<br>|21.9|19.4|35.7|27.5|37.9|27.2|32.8|21.7|30.0|23.0|
|_petrochemicals_|_(25.9)_|_(14.5)_|_(38.7)_|_(29.8)_|_(39.0)_|_(23.6)_|_(43.3)_|_(16.6)_|_(38.6)_|_(11.1)_|
|_Mechanical engineering_|25.6|21.9|44.4|28.3|55.8|25.7|43.8|27.0|40.0|28.7|
|_and metal-working_|_(30.4)_|_(17.9)_|_(70.8)_|_(40.8)_|_(136.5)_|_(25.5)_|_(64.0)_|_(15.8)_|_(58.4)_|_(17.2)_|
|_Timber, wood, pulp and_|23.7|23.2|28.2|28.0|34.1|34.3|32.8|32.4|29.8|30.2|
|_paper_|_(11.6)_|_(6.0)_|_(14.2)_|_(5.5)_|_(14.4)_|_(8.8)_|_(15.0)_|_(9.4)_|_(12.5)_|_(9.6)_|
|_Construction materials_|16.5|16.5|20.4|19.0|19.1|23.1|16.4|22.2|17.8|23.3|
||_(9.3)_|_(3.7)_|_(16.9)_|_(3.4)_|_(13.3)_|_(5.6)_|_(6.7)_|_(3.9)_|_(6.6)_|_(3.7)_|
|_Light industry_|17.5|14.5|21.0|20.8|25.0|24.6|18.1|19.3|15.5|16.8|
||_(32.5)_|_(15.1)_|_(24.4)_|_(16.3)_|_(26.4)_|_(13.8)_|_(14.5)_|_(9.0)_|_(9.1)_|_(7.2)_|
|_Food industry_|21.4|20.9|21.8|27.6|26.0|39.6|26.1|35.2|25.4|33.7|
||_(19.2)_|_(12.4)_|_(29.7)_|_(18.2)_|_(28.2)_|_(36.4)_|_(29.6)_|_(40.7)_|_(32.6)_|_(32.8)_|
|_Other industries_|32.2|34.9|46.4|41.5|44.5|46.5|32.5|34.1|34.1|32.9|
||_(10.1)_|_(6.6)_|_(16.0)_|_(10.4)_|_(8.6)_|_(7.3)_|_(20.7)_|_(7.5)_|_(23.2)_|_(10.4)_|
|_Agriculture and forestry_|38.4|54.9|23.0|19.0|27.8|23.2|25.3|21.1|22.4|18.2|
||_(30.4)_|_(12.2)_|_(14.2)_|_(14.9)_|_(17.4)_|_(17.8)_|_(14.5)_|_(15.1)_|_(14.5)_|_(13.6)_|
|_Other goods-producing_|24.4|109.3|23.5|78.9|23.6|65.1|20.8|50.7|15.5|29.8|
|_sectors_|_(48.8)_|_(39.3)_|_(37.3)_|_(37.8)_|_(42.1)_|_(51.4)_|_(27.9)_|_(32.6)_|_(19.1)_|_(23.1)_|
|_Note:_<br>_a Ad valorem equivalent of_<br>_Summary statistics is calc_|_the specific_<br>_ulated for n_|_tariff is_**_(sp_**<br>_on-zero sp_|**_ecific taiff_**<br>_ecific tariff_|**_rate*100)/_**<br>_s only._|**_(unit value_**|**_)_**_._|||||

_<mark>a Ad valorem equivalent of the specific tariff is</mark>_ **_<mark>(specific taiff rate*100)/(unit value)</mark>_** _<mark>. Summary statistics is calculated for non-zero specific tariffs only. b Standard deviations in parentheses</mark>_ 22
Another fact emerging from the correlations in Table 6 is that entry and exit rates are strongly negatively correlated with the average number of firms, the average number of products per firm and the average number of destination markets served per firm. This indicates that across countries, less churning takes place in more sophisticated export markets, where there are a lot of firms that sell a wider range of goods to more markets.

**Table 6: Correlation among Selected Measures in the Database at the Country Level**

||**Entry Rate**|**Exit Rate**|**Survival Rate**|**Ln Mean**<br>**Number of**<br>**Products per**<br>**Exporter**|**Ln Mean**<br>**Number of**<br>**Destinations**<br>**per Exporter**|**Ln Number of**<br>**Exporters**|**Ln Mean**<br>**Exports per**<br>**Exporter**|**Share of Top**<br>**5% of**<br>**Exporters**|**Ln Total**<br>**Exports**|
|---|---|---|---|---|---|---|---|---|---|
|**Entry Rate**|1|||||||||
|**Exit Rate**|0.92|1||||||||
||(0.00)|||||||||
|**Survival Rate**|-0.65|-0.73|1|||||||
||(0.00)|(0.00)||||||||
|**Ln Mean Number of**||||||||||
|**Products per Exporter**|-0.59|-0.38|0.14|1||||||
||(0.00)|(0.01)|(0.42)|||||||
|**Ln Mean Number of**||||||||||
|**Destinations per Exporter**|-0.55|-0.52|0.21|0.47|1|||||
||(0.00)|(0.00)|(0.21)|(0.00)||||||
|**Ln Number of Exporters**|-0.57|-0.43|0.1522|0.5202|0.53|1||||
||(0.00)|(0.00)|(0.36)|(0.00)|(0.00)|||||
|**Ln Mean Exports per**||||||||||
|**Exporter**|-0.44|-0.45|0.10|0.30|0.54|0.35|1|||
||(0.00)|(0.00)|(0.53)|(0.05)|(0.00)|(0.02)||||
|**Share of Top 5% of**||||||||||
|**Exporters**|0.11|0.15|-0.40|0.00|-0.21|0.14|0.22|1||
||(0.50)|(0.35)|(0.01)|(0.99)|(0.17)|(0.34)|(0.16)|||
|**Ln Total Exports**|-0.62|-0.51|0.16|0.54|0.63|0.93|0.66|0.20|1|
||(0.00)|(0.00)|(0.32)|(0.00)|(0.00)|(0.00)|(0.00)|(0.20)||

Notes: P-values in parentheses. The correlations shown in the table are based on measures in the country-year level dataset (file CY.dta) averaged across the 2006-2008 period for each country except Kuwait for which averages are across the 2009-2010 period and Portugal for which averages are across the 2003-2005 period.

**_Stylized Fact 4: The typical exporter size, total exports, and exporter survival rates at the country-sector level are not explained by country or sector characteristics while the share of_**

29
THE IMPACT OF INFRASTRUCTURE SPENDING IN SUB-SAHARAN AFRICA

**Table 8. Macroeconomic results for nonproductive investments, funded by import duties**

||||Nonprodu<br>|ctive investm<br>|ent (import d<br>|uties funded)<br>|<br>|
|---|---|---|---|---|---|---|---|
|Variables|Definition|Benin|Mali|Senegal|Tanzania|Uganda|Cameroon|
|Ym|Aggregate household income|0.10|0.08|0.29|0.04|0.02|0.35|
|EV|Equivalent variation|0.09|0.08|0.26|0.04|0.02|0.32|
|s|Wage|4.81|0.72|1.27|0.10|0.11|0.78|
|yg|Government income|14.70|3.19|5.90|5.44|3.14|6.36|
|ye|Firm income|–0.77|–0.26|–0.44|–0.13|–0.14|–0.89|
|g|Total government expenditure|11.96|1.73|5.38|3.52|2.49|7.27|
|It|Total private investment|–0.18|–0.25|–0.65|–0.52|–0.85|–1.16|
|e|nominal exchange rate|–1.20|–0.70|–1.38|–1.00|–2.39|–1.92|
|GDP|GDP|0.01|0.00|0.01|0.00|0.00|0.00|

At the macro level, the results largely resemble the VAT-funded scenario. As seen in the previous section, the government income effect is by definition the same. The negative effect is stronger for all countries on private investment. The largest difference between the two funding options for this variable is observed for Uganda, which falls from –0.16 percent to –0.85 percent.

**Table 9. Sectoral results for nonproductive investments, funded by import duties**

||||Non<br>|productive inve<br>|stment (import d<br>|uties)<br>||
|---|---|---|---|---|---|---|---|
|Variable|Sector|Benin|Mali|Senegal|Tanzania|Uganda|Cameroon|
||Crop agriculture|–0.14|–0.08|–0.03|–0.14|–0.08|–0.87|
||Export agriculture|–0.19|–0.07|–0.95|–0.38|–0.27|–0.60|
||Mining and gas||–0.25||–0.04|0.06|–0.10|
|Va (value added<br>or output)|Industries|–0.65|–0.15|–1.08|–0.25|–0.64|–0.60|
||Construction||0.47|1.88|0.17|1.05|5.24|
||Private services|–1.10|–0.14|–0.35|–0.09|–0.12|–0.52|
||Public services|7.60|1.09|4.24|2.32|2.27|4.19|
||Crop agriculture|0.25|0.18|–0.08|0.14|0.07|0.74|
||Export agriculture|–1.08|0.00|0.38|0.47|–0.04|1.67|
||Mining and gas||–0.31||0.23|1.71|0.71|
|pq (market<br>prices)|Industries|3.38|0.65|1.69|0.54|0.02|2.65|
||Construction||0.65|6.07|0.60|0.49|2.24|
||Private services|0.80|0.35|0.28|0.10|0.15|0.73|
||Public services|4.05|0.61|1.09|0.18|0.21|1.13|
||Crop agriculture|–0.49|0.01|–0.13|–0.09|–0.03|–0.34|
||Export agriculture|–2.42|–0.44|–1.19|–0.49|–0.45|–0.42|
|r<br>|Mining and gas||–1.83||–0.51|0.40|–2.84|
|Borrowing rate<br>of capital|Industries|0.86|0.32|–3.17|–0.37|–1.26|–0.73|
||Construction||1.39|12.88|0.58|1.91|7.12|
||Private services|–1.85|0.09|–0.72|–0.14|–0.10|–0.27|

12
Policy Research Working Paper 9991

# **Abstract**

This paper analyzes the initial impact and recovery of globally engaged firms from the COVID-19 crisis. It uses rich survey data of nearly 65,000 firm-year observations in 45 countries spanning three waves of data collection. The findings are organized in a series of stylized facts, which suggest that although the pandemic had an immediate adverse impact on most firms, the globally engaged ones are recovering faster, possibly due to their higher capabilities. Among globally engaged firms, those directly involved with international markets show better recovery than the ones that were indirectly involved. These results mask wide variation by firm traits, sectoral attributes, and country characteristics.

At the core of the recovery of globally engaged firms is their heightened response to the crisis by finding novel ways to adapt supply chains even in the presence of lockdowns and uncertainty. These firms swiftly digitalized, introduced new products and changed their markets and sources of inputs. Over and above their capabilities, global engagement cushions firms against shocks. Policymakers could therefore facilitate global linkages by providing information on potential markets and products, by making production flexible in terms of facilitating remote work, reducing the rigidity of contracts; and incentivizing financial institutions to issue instruments that reduce uncertainty risk.

This paper is a product of the Finance, Competitiveness and Innovation Global Practice and the Development Research Group, Development Economics. 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 authors may be contacted at at ineagu@worldbank.org, afernandes@worldbank.org, agrover1@worldbank.org, s.poupakis@gmail.com, and sreyesortega@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
same marginal impact is calculated once under the LMDI method and once under the GMM method. We do this for the whole sample but also for the different income groups (Table 4). From this table, we can see that the level of the marginal impacts of various factors on CO2 growth obtained by the LMDI method are significantly bigger than that of the GMM. This may be because, as addressed previously (i.e., section 2), the system GMM estimator takes into account each country’s characteristics when calculating the marginal impact whereas the LMDI method ignores the individual characteristics. The difference in the results between LMDI and GMM are important to understand how CO2 growth responds to change in a given driver. Since the LMDI results are biased towards the country size (i.e., they are skewed due to China and the United States, the larger countries’ results), the response indicators (or marginal impact here) based on LMDI may be biased. The response indicators estimated based on GMM analysis are more credible.

**Table 4.** A comparison in the marginal impacts of various factors on CO2 growth between system GMM technique and LMDI method for the period 1980-2015.

|Panel|_EC_|_ECS_|_EI_|_PCG_|_PCG_<sup>_2_</sup>|_P_|
|---|---|---|---|---|---|---|
|**GMM m**|**ethod**||||||
|Global|0.057|0.331|0.445|0.290|-0.045|0.270|
|HI|0.594|0.052|0.619|1.504|-0.201|0.534|
|UMI|0.059|0.475|0.509|0.768|-0.148|0.838|
|LMI|0.027|0.507|0.471|0.334|0.049|0.886|
|LI|0.050|0.976|0.308|0.267|-0.158|0.865|
|**LMDI m**|**ethod**||||||
|Global|1.654|0.080|4.042|2.325|NA|0.724|
|HI|0.024|0.646|1.464|0.760|NA|1.086|
|UMI|0.661|3.978|8.904|2.087|NA|1.724|
|LMI|0.362|2.877|4.089|1.983|NA|1.846|
|LI|2.573|4.413|0.658|1.638|NA|0.911|

23
8

# **III   Constraints in Enterprise Surveys**

# - <u>3.1  Country Profiles and Country Level Differences in Firm Perceptions</u>

Many Enterprise Surveys ask firms the following question, with answers ranging from No Obstacle to Very Severe: Do you think that the following present any obstacle to the current operations of your <u>establishment?</u>

|1|Telecommunications|||
|---|---|---|---|
|2|Electricity|||
|3|Transportation|||
|4|Access to Land|||
|5|Tax Rates|||
|6|Tax Administration|**Obstacle**||
|7|Customs and Trade Regulations|No Obstacle|1|
|8|Functioningof the Courts|Minor Obstacle|2|
|9|Labor Regulations|Moderate|3|
|10|InadequatelyEducated Workforce|Major Obstacle|4|
|11|Business Licensingand Permits|VerySevere|5|
|12|Access to Finance(availabilityand cost)|||
|13|Political Instability|||
|14|Macroeconomic Instability|||
|15|Corruption|||
|16|Crime,Theft and Disorder|||
|17|Practices of competitors informal sector|||
Table 5: MDB Mobilization Effects: Including Sector-Level Trends

||**Panel A.**N<br>|umber of loans<br>||**Panel B.**Siz<br>|e (% GDP)<br>||
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|MDBcs,t|0.3846**|0.4931***|0.5126***|0.0637***|0.0693***|0.0665***|
||(0.161)|(0.149)|(0.154)|(0.020)|(0.025)|(0.024)|
|MDBcs,t-1|0.1235|0.2825**|0.3205**|0.0125|0.0171|0.0145|
||(0.158)|(0.137)|(0.127)|(0.024)|(0.027)|(0.026)|
|MDBcs,t-2|-0.0508|0.1246|0.1656|0.0492***|0.0537***|0.0518***|
||(0.159)|(0.129)|(0.113)|(0.018)|(0.019)|(0.019)|
|Dep. Var.cs,t-1|0.8685***|0.7006***|0.6519***|0.2155***|0.1334*|0.1366*|
||(0.078)|(0.068)|(0.062)|(0.071)|(0.071)|(0.070)|
|2<br>∑<br><br>_MDB_cs,t-k|0.457|0.900***|0.999***|0.125***|0.140**|0.133**|
|_k_=0<br>_Wald test_ : _p value_|0.214|0.002|0.000|0.005|0.016|0.015|
|Observations|24,219|24,219|24,219|24,219|24,219|24,219|
|R-squared|0.835|0.858|0.865|0.410|0.453|0.452|
|Average MDB|0.0483|0.0483|0.0483|0.0483|0.0483|0.0483|
|Average Dep. Var.|0.760|0.760|0.760|0.105|0.105|0.105|
||**Panel C.**Ba|nks||**Panel D.**M|aturity||
||(1)|(2)|(3)|(4)|(5)|(6)|
|MDBcs,t|0.4634**|0.5331***|0.4522**|0.6371***|0.5609***|0.5386***|
||(0.200)|(0.203)|(0.205)|(0.125)|(0.127)|(0.127)|
|MDBcs,t-1|0.6026***|0.7400***|0.6596***|0.1345|0.0966|0.0693|
||(0.180)|(0.208)|(0.200)|(0.125)|(0.131)|(0.131)|
|MDBcs,t-2|0.5157**|0.7416***|0.6660***|0.3096***|0.2784***|0.2605***|
||(0.226)|(0.199)|(0.198)|(0.092)|(0.090)|(0.090)|
|Dep. Var.cs,t-1|0.2221***|0.1193***|0.1244***|0.0751***|-0.0097|-0.0117|
||(0.031)|(0.035)|(0.034)|(0.017)|(0.018)|(0.018)|
|2<br>∑<br>_k_0<br>_MDB_cs,t-k|1.582***|2.015***|1.778***|1.081***|0.936***|0.868***|
|=<br>_Wald test_ : _p value_|0.001|0.000|0.000|0.000|0.000|0.000|
|Observations|24,219|24,219|24,219|23,439|23,439|23,439|
|R-squared|0.676|0.708|0.706|0.531|0.570|0.571|
|Sector-country FE|Yes|Yes|Yes|Yes|Yes|Yes|
|<br>Country-year FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Sector-year FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Country-sector Trends|No|Yes|Quadratic|No|Yes|Quadratic|
|Average MDB|0.0483|0.0483|0.0483|0.0483|0.0483|0.0483|
|<br>Average Dep. Var.|1.526|1.526|1.526|0.752|0.752|0.752|

_Notes_ : The table presents the estimates of equation 1 controlling for linear and quadratic country-sector trends. The dependent variables are: (i) the number of loans (Panel A, columns 1-3), (ii) the size of syndicated loans (% of GDP, Panel B, columns 4-6), (iii) the average number of banks per loan (Panel C, columns 1-3), and (iv) the average maturity of syndicated loans (in years) (Panel D, columns 4-6). MDBcs,t is a dummy equal to 1 if there is at least one MDB providing a syndicated loan in country-sector _cs_ at time _t_ . For each panel, the table reports, in the bottom rows, the cumulative effect of MDBcs,t between year _t −_ 2 and _t_ , with the associated p-value of a Wald test, and the sample averages of the MDBcs,t and outcome variables. Standard errors clustered at country level are in parentheses: * p _<_ 0.1, ** p _<_ 0.05, *** p _<_ 0.01.

29
# **4.5 Discussion**

Two distinct panel data strategies have yielded broadly consistent findings: Irrigation reform in Punjab increased tail shortage initially (i.e., in the first FO tenure) by around 50% of the pre-reform baseline. Since, as we have argued, the efficient allocation of canal water involves _zero_ tail shortage, decentralization had a social cost. In other words, the takeover by FOs could not both accentuate head-tail inequality in canal water _and_ (through side-payments) lead to a Pareto improvement of welfare. While we cannot compute the social cost directly, outlet-level data on land values from Punjab (see Jacoby and Mansuri 2018) allow us to infer that the wealth redistribution was substantial; in particular, the reform increased the value of head-end land by about 9% _relative_ to the value of tail-end land.<sup>20</sup> Evidence on the second FO tenure, which is far less frequent in the data than the first tenure, is not as clear-cut. Using spatial matching, the estimated second tenure treatment effect is significantly different from zero and not significantly different from the first tenure — effect, which is entirely plausible there is no theoretical reason to suggest that these effects _should_ differ. The pipeline strategy, by contrast, yields a more precise yet insignificant second tenure treatment effect. These divergent findings may indicate that the identifying assumption of the pipeline strategy is violated in the data; that water availability shocks _are_ correlated with the timing of (second tenure) FO operationalization. Be that as it may, when we drop observations in second FO tenures, both the pipeline and spatial matching strategies yield virtually identical results. In the analysis to follow, therefore, we will rely on this restricted sample and, because it is more robust, the spatial matching strategy.

> 20The data come from 3,922 outlets along 448 non-FO channels in Punjab. Within the same channel, land at a head outlet is valued at a 11.2% premium over land at a tail outlet (Jacoby and Mansuri 2018, Table 1). Moreover, average head _DPR_ on these same channels is 0.052 higher than average tail _DPR_ . Assuming, plausibly, that the entire head-tail land value differential is attributable to variation in canal water availability, a treatment effect of 0.043 is tantamount to a 11 _._ 2 _×_ 0 _._ 043 _/_ 0 _._ 052 = 9 _._ 3% increase in relative land values.

27
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20

# **Annex** A. **The** Data Sets

The national and urban data sets used in the current paper are essentially the same as those used in Ingram and Liu (1997). The primary data sources are explained in the Appendix of Ingram and Liu (1997, p. 35). The national data are from 50 countries with full data for 1970, 1980, and 1990. The urban data include 35 cities with data in two years (mostly in 1960 and 1980). The summary statistics for developing and high-income countries at both national and urban levels for the year of 1980 are shown in Table A-I below. Data for 1980 are used for the comparison because it is the only common year for the national and urban data sets.

When we carried out the empirical analyses presented in the current paper, a database of world infrastructure stocks for 1950-1995 compiled by Canning (1998) became available to us. Canning (1998) identified inconsistencies in the definition of paved road among countries and adjusted the official paved road data to fit a more consistent definition. Based on the Canning data, we made a few corrections to our national paved road length data. We also used the corrected data to re-estimate the paved road regressions presented in Ingram and Liu (1997) and found that the minor data corrections did not alter the empirical findings summarized there. The national data used in the current analyses are presented in Appendix Tables 1O, 1 1, and 12.

**Table A-1. Sample Summary Averages, 1980**

||Developing|economies|High income|economiesa|
|---|---|---|---|---|
||National|Urban|National|Urban|
|Item|(31 countries)|(9 cities)|(19 countries)|(26 cities)|
|PercapitaGNPin<br>1987US$b|546|1,589|14,247|15,037|
|Population per sq. km.|86|14.982|125|3,989|
|Motor vehicles/1000 persons|41|60|379|482|
|Auto share of fleet|60%|61%|86%|82%|
|Kilometers of road per sq. km.|0.2|5.9|1.2|10.9|
|Percent of roads paved|34%|n.a.|77%|n.a.|
|Meters of road per capita|3.7|0.5|16.5|4.5|
|Meters of paved road per capita|1.0|n.a.|11.2|n.a.|
|Motor vehicles per km. of road|12|142|31|152|
|Motor vehicles per paved km.|41|n.a.|41|n.a.|

n.a. Not available.

a. In 1980 high-income countries had a GNP per capita over $4,800; see World Bank (1992).

b. Average weighted by population.