rafmacalaba/datause-encoder-data huggingface dataset:
more competitive banking systems are more stable than monopolistic systems because of a lower likelihood of bank failure and a longer time to crisis. However, their proxy of market structure, the Panzar and Rosse H-statistic, is derived from another study by Claessens and Laeven (2004) that was estimated using another data set.

These findings are tested by Jiménez, Lopez, and Saurina (2007) in the context of Spanish banks. The authors construct a Lerner index based on bank-specific interest rates as a measure of the degree of market power in the commercial loan market and find a negative relationship between loan market power and portfolio risk. They show that nonperforming loans decrease with a rise in the degree of power in the loan market, thus promoting financial stability. Their findings support the franchise value paradigm and do not provide evidence for the “riskshifting” paradigm identified by Boyd and De Nicolo (2005). However, the Jimenez, Lopez and Saurina (2007) study only considers loan portfolio risk and does not examine the risk of the bank; as a result, it does not provide evidence on overall bank risk or financial fragility.

Other recent empirical cross-country evidence on the relationship between bank concentration, bank competition, and banking system fragility is ambiguous. For instance, Beck, Demirguc-Kunt, and Levine (2006) find that the likelihood of financial crises is lower in more concentrated banking systems, yet higher in less competitive systems (characterized by fewer entry and activity restrictions) and countries with less developed legal systems.

# **III. Outline of the Econometric Methodology**

We test the implication of market structure on the risk potential of banks using firm-level data from 23 industrialized countries. We allow for a nonlinear relationship between financial stability and market structure in banking, following Martinez-Miera and Repullo (2008). Our basic GMM regression model is based on a cross-section of banks, and has the general form: 2 Financial Stability _i_ = f(Market Structure _i_ , Market Structure _i_ , Business Environment _k_ ) (1) Table 1 shows variable names and definitions of our dependent, explanatory and instrumental variables. Subscripts _i_ and _k_ refer to bank and country, respectively.

7
Pi = Population of locale i Ai = Area of locale i tij = Travel time from locale i to local j

# **4. Data**

Selection of our three country cases was guided by the availability of publicly-available Covid-19 infection data and the desire for geographic diversity. In the United States, daily county-level data on Covid-19 cases are available from multiple sources, including the New York Times national Covid-19 reporting project (NYT 2020) and a website operated by Johns Hopkins University. The data draw on the same reporting sources, and we chose the New York Times database because it provides better information about the early period of the pandemic. For the Philippines, we construct a daily database for municipalities from a public file of individual cases by date and location maintained by the Covid-19 Tracker website of the Philippines Department of Health (PDH, 2020). For South Africa’s Western Cape province, we construct a daily sub-district database from online daily reports by the Premier (WCP 2020).

We compute US county population densities from data files maintained by the US Census Bureau for county populations (USCB 2020a) and areas (USCB 2020b). For the Philippines and Western Cape province, we compute mean population densities for municipalities and sub-districts, respectively, from raster files at 100 m resolution maintained by the WorldPop project of the University of Southhampton (WorldPop 2020).

We compute travel-time-weighted infections (Wit in model (10)) in a multi-step process. We base the computation on the centroids of the relevant locales (counties in the United States, municipalities in the Philippines, sub-districts in Western Cape province).<sup>3</sup> For the centroid of each locale, we select all neighboring locales whose centroids are within 200 km. We compute all centroid-to-centroid travel times using the Open Source Routing Machine (OSRM 2020), which uses road information from OpenStreetMaps (OSM 2020) to compute the most time-efficient route between two points. Figures 2(a) and 2(b) display representative gradient maps for travel times within 200 km for five US counties and two Philippines municipalities.

> 3 We use geographic centroids for US counties and Philippines municipalities.  Some irregularly-shaped Western Cape sub-districts have geographic centroids lying outside the sub-districts, so we use centroids of the most denselypopulated census wards within the sub-districts.

8
designation. Though agreements of this type only began to proliferate from around the mid2000s onward, the number of PTAs possessing these standards continues to increase steadily. Notably (and as seen explicitly in Appendix Table A1), these BTRIPS agreements have increasingly been undertaken by countries besides the United States and EU/EFTA, such as Japan, Korea, and Australia, a fact that points to the increasing centrality of IPRs in these countries’ policy emphases.

A second important point is that IPAs, while increasing the scope of protection over time, do not treat all forms of intellectual property equally. They also vary in depth because they incorporate different numbers of specific IPRs provisions and their enforceability. Countries entering PTAs may have different socioeconomic interests in intellectual property protection and related regulatory policies (Maskus, 2012a). For example, the United States attaches considerable importance to assuring copyright protection for its own nationals’ rights held in foreign markets and negotiates its international agreements accordingly. The EU and EFTA do so also but prioritize strong systems of registering and enforcing GIs especially. GIs protect the rights to use place names in wines, spirits, and other consumer products, where production locality matters for the quality and character of such goods. Japan and Korea have focused on extending patent rights, while Australia traditionally has preferred weaker copyright rules. Developing countries often seek to sustain access to international technologies and information, perhaps through more limited patent standards or weaker trade secrets protection. Indeed, it may seem surprising that emerging and developing economies increasingly have agreed to strong IPRs chapters in PTAs. Here, we simply note that different countries seek to emphasize specific aspects of IPRs in negotiating and joining IPAs.

Rather than describe fully the many differences in strategies and provisions that can be found in the World Bank database, we summarize them in Table 1. The database identifies 130 detailed IPRs provisions, broken into 16 broad categories. These provisions are simply listed as either 0 (absent in the PTA) or 1 (present). In Panel A we list eight categories that seem relevant for studying potential impacts on both trade and innovation.<sup>4</sup> For example, there are 15 international treaties or conventions covering intellectual property that members of IPAs might

> 4 The unlisted categories focus on procedural questions (such as transparency and cooperation mechanisms), do not strengthen existing IPRs (such as incorporation of existing IPRs treaties or recognition of the importance of biodiversity and traditional knowledge), or focus on non-trade issues.

6
# TABLE OF CONTENTS

|Summ|ary and Conclusions||i|
|---|---|---|---|
|I.|Introduction||1|
|II.|Spatial Pattern of Production and Marketing in the|Export Sector|2|
|mI.|Pan-Territorial Pricing and its Alternatives||5|
|IV.|Timeliness and Quality of Transport||11|
|V.|The Density of Depots and Backhauling||14|
|Refere|nces||16|
|Figure<br>|s and Tables<br> <br> <br>|||
|Figu<br>Tabl<br>Tabl<br>|re1:<br>TheOntimalityof.Su1bsidizing<br>Tran1po7tion<br>e 1:<br>Deliveries and Distances at the Zone Level<br>e 2:<br>Price Variables<br> <br>|17<br>18<br>20<br>||
|Tabl<br>|e 3:<br>Alternative Pricing Policies<br> <br>|21<br>||
|Tabl<br>Tabl<br>|e 4:<br>Revenue Maximizing Pricing Policies<br>e 5:<br>Plantine and Deliverv LagsanrdQulity**at**the<br>Zone Level<br> <br>|22<br>23||
|Tab|le 6:<br>The Spatial Timing of Transportation and<br>Aggregate Post-Harvest Losses|24||

This research was supported by the World Bank under RPO 674-75 and by a grant from the Lynde and Harry Bradley Foundation to the Research Program in Development Studies at Princeton University. I thank Esra Bennathan, Alice Galenson, Jeffrey Gutman and Christina H. Paxson **for** their comments on earlier drafts.
41

# Appendix Table 1. Calculation of rate of subsidy on subsidized baladi bread (82 percent extraction) in Egypt, 1996/97

||Cell references|1996/97|
|---|---|---|
|**Pro**<br>**nto-+**-th:heat**_rr.1n)_**<br>I.......<br>**L**<br>**I**<br>**"'tf**<br>**ILgrI**<br>**aL**<br>**4**<br>**inI***|||
|Border CIF price (Egypt LE/ton)|(1)|694|
|Import charges (port charges, handling, etc.,)|(2)|20|
|import procurement cost-price (Egypt LEi/ton)|(3) = (1) + (2)|714|
|Domestic procurement price (Egypt LE/ton)|(4)|600|
|Collection. storage and transport (wheat grain)|||
|Import procurement price of grain (per ton)|(5) = (3)|714|
|Collection, storage and transport margin|(6)|20|
|Explicit subsidy at CS&T level|(7)|0|
|Grain sales price (per ton)|(8) = (5) + (6)|734|
|Milling (conversion from wheat grain to flour)|||
|Mill purchase price of grain (per ton of grain)|(9) = (8)|734|
|Mill purchase price in flour equivalent (per ton of flour)|(10) = (9)/CF|895|
|IVmlgll<br>in<br>Illdgylll(Per**IUI Ulo**IIoUfU|**I**I)|40|
|Explicit subsidy at milling level|(12)|0|
|**F..**<br>Ex-mi,l salesprice (pertLon**ofl**<br>l'our')|_IA11=IAf%_<br>**_IA_**<br>_A_**_I_**<br>**(I)**<br>=**lU)**+**1**1)|**935**|
|Retail-bakeries (flour to bread)|||
|Subsidized sales price to bakeries (per ton of flour)|(14)|290|
|Retail margin at bakeries (including transport)|(15)|160|
|Subsidized sales price to consumers|(16) = (15) + (14)|450|
|<br>Explicit subsidy at bakery (per ton of flour)|(17) = [(13) + (15)] - (16)|645|
|||(Continued)|
trained on historical data may be vulnerable. Yet, we do not find it to be the case that using GDP growth to project forward is problematic for some growth rates, suggesting, possibly, that even during recessions such as the one caused due to the pandemic, predicting changes from the last survey might be preferable to predicting poverty rates directly. In sum, we do not find overwhelming evidence that predicting poverty rates directly could be more attractive when compared to simply shifting the mean with adjusted GDP growth rates.

# **4.3 Predictors of poverty**

Though the findings so far suggest that growth in real GDP per capita is the overof we can look at this more ana- whelming predictor poverty, systematically by lyzing the features that are important for the predictions. For the lasso, this can be analyzed by looking at the order in which variables enter. For forests and gradient boosting, it can be analyzed through feature importance measures. Feature importance measures indicate the importance of a particular feature for the accuracy of the predictions.

In Figure 7, we use the feature importance measures from the conditional inference random forests, as this method generally performed best across the machine learning algorithms (judged by its average rank in Figure 4). For a particular feature, the feature importance is calculated by permuting the feature in the dataset and calculating how much the prediction error increases. The importance measure is standardized such that the variable with the highest value gets a value of 1. The equivalent plots for the other methods look similar.

The only six features that are substantively predictive of growth in the mean are all national accounts variables. They are all identical to, or highly correlated with, growth in GDP. The most predictive variable is growth in final consumption expenditure, which is the sum of two components of GDP, government expenditure and HFCE. The second most predictive variables is growth in HFCE, followed by growth in GDP, GNI, and gross domestic income (GDP measured from the income side). The variable most informative not from national accounting is growth in the employment rate. Yet, it is clear from this figure that just using growth in GDP per capita sums up the information well. Though growth in final consumption expenditure could be used instead to obtain a slightly lower error, final consumption expenditure is often not nowcasted widely, making it only feasible to use for nearcasting.

21
taken into account, this gaps drops to -3.9 pp and is no longer statistically significant. This finding is supported by the fact that Top Tier firms have significantly lower paid-in capital, which can not be completely explained by their size. Similarly, Top Tier firms are much less reliant on short term debt because they are larger.

# **Exhibit 2.19 – Statistical differences between the median Top Tier firm and the median Second Tier firm**

_<u>Quantile regressions for period 2003-2007</u>_

||**Median**|**difference, (pp)**|**Median diff**<br>**firm size i**|**erence also taking**<br>**nto account, (pp)**|
|---|---|---|---|---|
||_Difference_|_Statistically_<br>_different?_|_Difference_|_Statistically_<br>_different?_|
|**Total debt to Equity, %**|8.7|Yes|-3.9|No|
|**Total liabilities to Equity, %**|24.0|Yes|6.6|No|
|**Current to Total Liabilities, %**|-3.9|No|5.3|Yes|
|**ST debt to Total Liabilities, %**|-5.7|Yes|0.2|No|
|**EBIT / Total Interest Expense Ratio**|0.3|No|0.5|No|
|**Paid-in capital to total assets, %**|-11.5|Yes|-7.8|Yes|

_Source: Coface Egypt; World Bank staff analysis_ _Note: Differences are expressed in percentage points. Statistically different refers to a p-value less than 0.1._

# _2.2.4 Financing patterns: Reliance on trade credit, short-term credit, and retained earnings_

Exhibit 2.20 suggests that top-tier firms rely much more on other creditors (i.e. trade credits), short-term bank borrowing, and retained earnings as sources of finance. Median growth of 9.6 percent in other creditors and 15.2 percent in retained earnings are high compared to similar firms in the peer groups. Top-tier firms also experienced a smaller contraction in short-term debt and a larger contraction in long-term debt during the period. Issuance of equity (i.e., paid-in capital) by Egypt’s top-tier firms did not keep pace with inflation. Reliance on other creditors and short-term borrowing, may have led to higher financing costs.

Exhibit 2.20 shows that second-tier firms seem generally more constrained in terms of financing growth. Compared with the typical top-tier firm, the typical second-tier firm exhibited decreases in trade credits and lower growth in retained earnings during 2003-07. The relatively poor ability of raising external finance for second-tier firms will likely persist and impede future investment, and performance.

17
diminished sample to only 3 very new technologies: machine learning, autonomous cars and extremely high frequency. The estimates based on this restricted sample are presented in Table 16. Power is lost in this sample and so coefficients on the linkages variables are weaker and only the buyer coefficient remains statistically significant. However, the results remain qualitatively similar to those in the baseline analysis, with pioneer locations seeing higher technology postings and this advantage diminishing over time. This suggests that the early years after emergence are similar to the later years in terms of the extent to which technologies diffuse from pioneer firms.

Table 16. Keeping technologies that emerged after 2014

|||Dep|variable: N|ormalized sh|are||
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Pioneer|7.949***|7.949***|7.990***|7.974***|8.445***|8.442***|
||(0.860)|(0.861)|(0.860)|(0.861)|(1.927)|(1.927)|
|Pioneer_×_since emergence|-0.732***|-0.755***|-0.744***|-0.753***|-0.939***|-0.945***|
||(0.145)|(0.145)|(0.145)|(0.145)|(0.320)|(0.320)|
|**Linkages of pioneer frms**|||||||
|Buyer||0.942||||1.106*|
|||(0.593)||||(0.648)|
|Supplier|||-0.434|||-0.487|
||||(0.342)|||(0.326)|
|Innovation partner||||0.068||0.146|
|||||(0.561)||(0.595)|
|Affliate|||||-0.556|-0.535|
||||||(1.823)|(1.823)|
|**Linkages of pioneer frms over time**|||||||
|Buyer_×_since emergence||0.228**||||0.160*|
|||(0.089)||||(0.095)|
|Supplier_×_since emergence|||0.130**|||0.035|
||||(0.053)|||(0.044)|
|Innovation partner_×_since emergence||||0.240**||0.137|
|||||(0.096)||(0.098)|
|Affliate_×_since emergence|||||0.236|0.207|
||||||(0.304)|(0.304)|
|Technology FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Year FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Firms FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Adjusted R-squared|0.143|0.143|0.143|0.143|0.143|0.143|
|Observations|1,963,325|1,963,325|1,963,325|1,963,325|1,963,325|1,963,325|

Notes: Standard errors clustered at the firm level in parentheses.The normalized share is capped at the 99<sup>_th_</sup> percentile of non-zero observations. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1 34
travel, personal care, entertainment or hobbies (this includes total time spent watching television at home, listening to music, playing an instrument, or going to movies or similar events), or socializing (including relaxing with friends or family, talking over the phone, or visiting or hosting a social event).

Finally, we combine the remaining activities that do not fall under the three broad categorizations defined above as other activities. This includes, among other things, the total time spent in job search, child and elder care, non-market work, and home production, as well as time spent in education, tending to medical needs, and other civic and religious duties.<sup>11</sup>

## **3.4 Other Data**

We use GS1 Data Hub to link products with their manufacturing firms. Manufacturers purchase the right to use UPC codes from GS1. The first 6-9 digits of a UPC code corresponds to the company prefix. We follow the matching procedure of Argente et al. (2018) and are able to match almost all the products in the IRI data to their manufacturer. Other papers that use GS1 database for this purpose are Fracassi et al. (2020) and Baker et al. (2020). GS1 also provides the headquarter location of the company. In our analysis, we use this information to address the concern that inflation may be affected by the production cost of the manufacturer by omitting the upcs sold in headquarter locations where a MDP enters.

# **4 Empirical Analysis**

Causally identifying the changes in local inflation, income and shopping times for incumbent residents from entry of a new industrial plant requires a valid counterfactual that captures

> 11Job search includes all the activities related to time spent looking for new work, such as time spent in posting resumes, searching for vacancies in different job sites, and commuting to and appearing for interviews. Child care activities include the total time spent by an individual tending to, educating, and playing with their children, along with spending time engaging in activities related to children like researching or visiting daycare, school, or summer camps, and talking to teachers, counselors and tutors. The broadest category which falls under other work in our definition pertains to the time spent in home-production and non-market work. Home production includes any time spent in meal preparation and cleaning, doing laundry, indoor and outdoor household work (including maintenance), other activities related to household management, and daily organization. Non-market work includes activities provided, often in exchange for money, which do not fall under formal employment categorization. This includes babysitting, time spent in preparing food or crafting items for sale at the local flea market, repair work, or home-improvement activities.

21
main houses have more windows and those that are circular in shape are less likely to be in main houses (p<0.05). Houses that are made of sticks and mud (>90%) in Model 4 are less likely to have a kitchen in the main house (p<0.05). Ethnic (Oromo, Amhara) and religious variables (e.g. Muslim) are correlated with cooking/baking in main houses.

It is also of interest that in several models those who have higher levels of variables representing more assets report cooking/baking in separate areas rather than in their main living quarters. Those who have metal rather than thatched roofs (p<0.01), more agricultural land (p<0.10) and livestock (p<0.10), and who have used a bank in the past year (p<0.05) are more likely to cook in separate cooking/baking areas. Households that live in more remote areas – farther away from all-weather roads – are more likely to cook and bake in their main houses (p<0.01). Larger households with more members are also less likely to cook in main houses in two of three models (p<0.01). Households with more members have more labor and offer a number of other benefits, including old-age insurance, which could suggest they are more resilient than smaller households.

With regard to cooking and eating behaviors, those who bake injera more frequently (a possible sign of well-being, because we also adjust for household size) are more likely to cook in separate cooking/baking areas (p<0.10). Finally, households who use less-preferred fuels like dung and agricultural wastes, which also tend to be smokier than fuelwood, are more likely to cook in their main living areas. This result especially seems to hold for agricultural wastes in Model 4 (p<0.01).

**Table 4. Marginal Effects of Probit Regressions of the Decision to Locate Cooking/Baking areas in Main Houses**

||Data<br>Year|Model 1|Model 2|Model 3|Model 4|
|---|---|---|---|---|---|
|Kitchen floor area<br>anyshape(m<sup>2</sup>)|2016|**0.014**<sup>*******</sup><br>**(0.009)**|**0.010**<sup>*******</sup><br>**(0.011)**|**0.010**<sup>*******</sup><br>**(0.011)**|**0.002**<sup>******</sup><br>**(0.022)**|
|Flat<br>Ceiling<br>in<br>Kitchen(0/1)|2016|-0.12<br>(0.585)|-0.089<br>(0.562)|-0.108<br>(0.576)|-0.024<br>(0.574)|
|Number<br>of<br>windows in kitchen|2016|0.011<br>(0.258)|0.117<br>(0.314)|**0.146**<sup>*****</sup><br>**(0.332)**|**0.062**<sup>*******</sup><br>**(0.382)**|
|Kitchen is a circle<br>rather<br>than<br>rectangular shape|2016|**-0.23**<sup>******</sup><br>**(0.338)**|**-0.188**<sup>******</sup><br>**(0.305)**|**-0.215**<sup>*******</sup><br>**(0.308)**|**-0.029**<sup>*******</sup><br>**(0.675)**|
|Can<br>see<br>light<br>through the walls<br>of<br>baking<br>area<br>(0/1)|2016|0.082<br>(0.220)|0.042<br>(0.268)|0.041<br>(0.307)|0.007<br>(0.655)|

15
# Table 1

Gross CaDital Formation in Cguntries with and without _-Debt-Servicing Problems (fercent of GDP' 1980 1981 1982 1983 1984 1985 1986 1987 Countries with 25.5 24.7 22.6 19.0 18.4 18.6 19.0 18.4 Debt-Servicing Problems Countries without 27.9 27.9 26.9 26.5 26.5 27.8 27.4 27.2 Debt-Servicing Problems Source: IMF World Economic Outloo_k, October 1988, Table A7, p.66 14