rafmacalaba/datause-encoder-data huggingface dataset:
tries to be picked up in the θ estimates. On the other hand, the technology transfer story would suggest Anglo-Saxon patenting would precede export growth abroad by India. These types of timing arguments are not perfect, which is why they are relegated to an appendix, but are useful for thinking through the technology trend argument.

In the end, the paper is able to make substantial progress towards causal identification in a Ricardian model, beginning with the panel estimation approach and extending through the many IV approaches. This effort remains incomplete, however, and it is hoped that future work both identifies natural experiment settings to test these arguments and also identifies other forms/impetuses for heterogeneous technology transfer that can provide identification in a setting that focuses on large country-industry samples like this one.

# Additional Supplemental References

Baldwin, Richard, and James Harrigan. 2011. “Zeros, Quality, and Space: Trade Theory and Trade Evidence.” American Economic Journal: Microeconomics 3 (1): 60-88.

Branstetter, Lee, and Yoshiaki Ogura. 2005. “Is Academic Science Driving a Surge in Industrial Innovation? Evidence from Patent Citations.” NBER Working Paper 11561. Cambridge, Mass.: National Bureau of Economic Research.

Burstein, Ariel, and Jonathan Vogel. 2012. “Factor Prices and International Trade: A Unifying Perspective.” Working Paper.

Coe, David, and Elhanan Helpman. 1995. “International R&D Spillovers.” European Economic Review 39 (5): 859-887.

Coe, David, Elhanan Helpman, and Alexander Hoffmaister. 1997. “North-South R&D Spillovers.” Economic Journal 107 (440): 134-149.

Cohen, Wesley. 2011. “Fifty Years of Empirical Studies of Innovative Activity and Performance.” In B. Hall and N. Rosenberg, eds., Handbook of the Economics of Innovation. Amsterdam: Elsevier.

Dornbusch, Rudiger, Stanley Fischer, and Paul Samuelson. 1980. “Heckscher-Ohlin Trade Theory with a Continuum of Goods.” Quarterly Journal of Economics 95 (2): 203-224.

Feenstra, Robert. 1994. “New Product Varieties and the Measurement of International Prices.” American Economic Review 84 (1): 157-177.

Frankel, Jeffrey. 1997. Regional Trading Blocs in the World Economic System. Washington, DC: Institute for International Economics.

Griliches, Zvi. “Patent Statistics as Economic Indicators: A Survey.” Journal of Economic Literature 28 (4): 1661-1707.

Griliches, Zvi. “Productivity, R&D, and the Data Constraint.” American Economic Review 84 (1): 1-23.

Grossman, Gene, and Elhanan Helpman. 1991. Innovation and Growth in the Global Economy. Cambridge, Mass.: MIT Press.

Hall, Bronwyn. 2005. “Exploring the Patent Explosion.” Journal of Technology Transfer 30 (2.2): 35-48.

16
- 17 -

subject to cuts were some developmental investments such as those in natural resources and transport and allocations for the Office of the Presidency. The modifications clearly do not bring out any clear picture; they are likely t.o lead to difficulties in the implementation of the budget.

**Table** 11: Budget*d Expenditures by Sector. 1988

(X of Totol)

||Percont|
|---|---|
|Administrative<br>Expenditures|15.60|
|Soclal<br>Sector.|82.0|
|e/w Education|(19.0)|
|*/W HoaI<br>th|(11.6)|
|Infrastructure|17.6|
|_o/w_Transport,<br>Communications|(11.8)|
|o/w Natural<br>Resources|(5.8)|
|**Defense**|7.0|
|Debt Servic-'/|28.4|
|Total|100.0|
|In millons of t|2,168.6|
|Memorandum<br>Item||
|Total<br>Budgeted<br>Expenditures<br>**as**X of GDP|_2.60_|

Source: Ministry of Planning.

**_!/_** Includes amortization payments.

# The Budget Process

57. Before 1988, the budget for the Central Government was prepared by the Budget Office at the Ministry of Finance. In 1988, the budget was prepared by the Ministry of Planning in order to ensure consistency between current and capital expenditures of the Central Government.
Table 7: Marginal Climate Effects on Probability of Crop Choice by AEZs (%)

|Fruits/Vegetables|Africa<br>|AEZ1<br>|AEZ2<br>|AEZ3<br>|AEZ4<br>|AEZ5<br>|
|---|---|---|---|---|---|---|
|and Maize|0.1805|0.0729|0.0894|0.4491|0.2369|0.1505|
|T (<sup>◦</sup>C)|0.0172|0.0268|0.0219|0.0443|0.0255|0.0299|
|P (mm/mo)|0.0010|-0.0003|0.0005|0.0032|0.0019|0.0005|
|Fruits/Vegetables|0.0793|0.1366|0.0706|0.0812|0.0598|0.0802|
|T (<sup>◦</sup>C)|0.0115|0.0112|0.0223|0.0097|0.0098|0.0165|
|P (mm/mo)|0.0002|-0.0008|0.0003|0.0002|0.0002|0.0003|
|Ground nuts and<br>millet|0.1136|0.0001|0.0241|0.0001|0.0149|0.0001|
|T (<sup>◦</sup>C)|-0.0066|0.0000|0.0174|0.0000|0.0043|0.0002|
|P (mm/mo)|-0.0020|0.0000|-0.0014|0.0000|-0.0007|0.0000|
|Maize|0.3376|0.1015|0.3145|0.3470|0.4046|0.4878|
|T (<sup>◦</sup>C)|-0.0280|0.0164|0.0098|-0.0391|-0.0176|-0.0078|
|P (mm/mo)|-0.0028|-0.0007|-0.0019|-0.0032|-0.0030|-0.0016|
|Maize and ground<br>nuts|0.1412|0.0058|0.0137|0.0270|0.0412|0.0108|
|T (<sup>◦</sup>C)|0.0153|-0.0005|0.0053|-0.0009|0.0074|0.0017|
|P (mm/mo)|0.0025|0.0000|0.0002|0.0005|0.0008|0.0001|
|Millet|0.0414|0.0083|0.0756|0.0043|0.0357|0.0136|
|T (<sup>◦</sup>C)|0.0004|0.0010|0.0173|0.0006|0.0059|0.0066|
|P (mm/mo)|0.0011|0.0003|0.0019|0.0003|0.0011|0.0006|
|Millet and|||||||
|Sorghum|0.0447|0.0642|0.0794|0.0418|0.0687|0.1459|
|T (<sup>◦</sup>C)|-0.0005|0.0026|-0.0019|-0.0031|0.0058|-0.0083|
|P (mm/mo)|0.0000|-0.0004|-0.0001|-0.0001|0.0000|0.0000|
|Wheat|0.0616|0.6106|0.3328|0.0496|0.1382|0.1111|
|T (<sup>◦</sup>C)|-0.0093|-0.0576|-0.0920|-0.0114|-0.0410|-0.0387|
|P (mm/mo)|-0.0001|0.0019|0.0005|-0.0008|-0.0003|0.0001|

26
making crossing times longer for everyone else. The same phenomenon can also happen on return. The non-cluster traders therefore can face high costs (Poulton et al., 2010). However, when trading in clusters, SSCBTs claim to have more time to spend at the market, which improves the profitability of their activities. To test this, we regress our benchmark model on the perceived transactions time. As the variable is captured on a scale of one to five, we use both OLS and logit regressions (where the variable is dummy transformed) to check how it is affected by the CTA adoption. The result reported in Table 11 Panel 2 show a negative and significant effect for both OLS (-0.151) and logit (-0.102) regressions. In line with Gelo et al. (2019), our result implies that CTA adopters are more likely to spend few transactions time, which increase the time allocated to selling on the market.

## **d. Acess to credit**

Given the law level of their income, the lack of guarantees and the informal status of their activities, SSCBTs are excluded from access to credit with classic financial institutions. With CTA, SSCBTs are able to save money on a regular basis. The savings are used to provide members with credit within the clusters. According to SSCBTs, the credit enables them to increase their purchases and to mitigate the effects of unexpected events such as illness or similar shocks. To test this, we regress the CTA adoption on the dummy variable, access to credit, which is equals to one when SSCBTs has access to credit and zero otherwise. The results in Table 11, Panel 4 show that CTA adoption increases significantly the probability to get a credit by 0.061 percentage point. CTA adopters have more access to credit, which increases their purchasing power and in turn their turnover and profit. Acting collectively correct some of the market imperfections such as missing credit markets (Markelova et al., 2009).

# **4.4 The impact on household income**

As seen from Section 4.2, CTA adoption shapes the daily profit of traders which represents the share of trade activities in the SSCBT’s total income.<sup>14</sup> In fact, changes in household income could be result of a spillover effect of the change in trade outcomes. In this section we check whether CTA adoption affects household income. To do so, we restrict our sample to SSCBTs with a dependency ratio greater than zero.<sup>15</sup> We then analyze this subsample on the one side and on the other side a sub-sub-sample of SSCBTs household 14Data on income were collected using different items that reflect possible sources of current household income. These items are listed in Table 9 in the appendices.

> 15Dependency ratio is the ratio between dependent individuals and the household size. For this purpose, single SSCBTs with dependency ratio equal to zero are removed from the sample.

18
reported instructional practices, but not teacher theory of intelligence. _Journal of Educational Psychology_ , _108_ (3), 300.

<mark>Parker, J. G., & Asher, S. R. (1993). Friendship and friendship quality in middle childhood: Links with peer group acceptance and feelings of loneliness and social dissatisfaction.</mark> _<mark>Developmental Psychology</mark>_ <mark>,</mark> _<mark>29</mark>_ <mark>(4), 611-621.</mark>

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57
# **The Impact of Hurricane Strikes on Short-Term Local Economic Activity: Evidence from Nightlight Images in the Dominican Republic**<sup>**1**</sup>

Oscar A. Ishizawa ( _World Bank_ )<sup>2</sup> Juan José Miranda ( _World Bank_ )<sup>3</sup> Eric Strobl ( _University of Bern_ )<sup>4</sup> JEL Classifications: O11, O44, Q51, Q54, R11 Keywords: Hurricanes, Natural Disasters, Dominican Republic, Climate Resilient Development, Economic Shocks and Vulnerability, Economic Shocks and Climate Change, Climate Change and Disaster Risk, Natural Disaster Shocks

> 1 This work was done under the preparation of the World Bank Disaster Risk Management Development Policy Loan with a Catastrophe Deferred. Drawdown Option (Cat DDO). The work was funded by The World Global Facility for Disaster Reduction and Recovery - GFDRR (Trust Fund # TF0A2512).

> 2 Social, Urban, Rural and Resilience Global Practice (GSURR): oishizawa@worldbank.org.

> 3 Environment and Natural Resources Global Practice (GENDR): jjmiranda@worldbank.org.

> 4 Department of Economics, University of Bern: eric.strobl@vwi.unibe.ch
_S_ maturity), and that the reduction of country risk was relatively small compared to the reduction of foreign exchunge rate risk.

In order to assess the domestic macroeconomic impact of the Brady deal, first, we consider the impact on physical capital accumulation and, second, we assess the impact on interest rates. We then separate out foreign exchange rate risk from more general country risk by analyzing the response to the Brady deal of both local currency and dollar-denominated Mexican debt instruments.

# 111.1 Debt Relief and Private Investment

Equ. (1) links private investment7 to the measures of net transfer and variance of the stream of anticipated transfers using Ordinary Least Squares (OLS): (1) log(Ip) - 2.276 + 0.020*Zog(NT) - 0.146*log(SNT) + 0.476*1og(Ip-l) (3.30) (0.56) (-3.03) (2.99)

- 0.69 H-Statistic - -1.409 F _ 32.62 Sample: 1988.04-1990.12

The variance of net transfers enters with the right sign and is highly significant. The debt overhang proxy (NT) is insignificant and even gets the "wrong' sign. The H-statistic indicates absence of serial correlation. The results suggest that NT, the proxy for debt overhang, is not significant, and that the variance of the transfer variable is.

# 111.2 Interest rates and debt relief I: exchange rate risk

Consider next a similar analysis, but now for peso-denominated domestic government debt (CETES)- This asset allows us to focus the analysis directly on exchaage rate expectations by first looking at the rate differential between CETES and PAGAPE (dollar-indexed domestic debt). The difference in 7 The monthly index of privete fixed investment published in 'lndicadores Econanicos" of the Bank of Mexico.
8 the expectation that it will ultimately be used with a source of geocoded data (we will return to this issue in the next section).

The most recent release of the HCPM also contains a clustering module that explicitly incorporates optimization routines as part of the clustering formation process (see below), and allows the user to choose from three different clustering algorithms. The HCPM currently consists of two independent modules: a customer location module and a loop design module.

_Customer location module._ The local exchange telephone network must connect every customer to a local central office switch. A critical component in the design of such a network is the definition of a 'serving area', which consists of a group of customers served from a common remote terminal. Then feeder plant connects every serving area to the central office and distribution plant connects every customer in a serving area to a 'serving area interface'. The customer location module first groups individual geographic locations of customers into clusters, to form serving areas based on engineering considerations. These considerations include a distance constraint, so that no customer is farther from a potential terminal location than is allowed by the maximum copper distance, and the maximum number of customers in a serving area, which depends on the capacity of the largest terminal.

A divisive clustering algorithm successively splits new clusters from a main cluster that initially contains every customer location. Clusters are evaluated on the basis of the relative distance of customers from the line-weighted centroid of the new and old clusters. After an initial clustering process, two different optimization algorithms look for ways to re-assign customers to clusters, so as to reduce the total distance from the cluster centroids, while satisfying the maximum distance constraints.

After geocoded customer locations have been grouped into clusters, it is necessary to further process the location data, so that they can be used by the loop design module. HCPM defines a grid on top of every cluster and then subdivides each grid into a large 10 To geocode a customer or a wire center location (or any location), a coordinate (latitude-longitude) must be attached to that particular location (e.g., the customer's address).
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# **Table A2: Variables for the panel local projection models (quarterly data)**

|**Variable**|**Transformation**|**Source**||
|---|---|---|---|
|**Real GDP in local currency, seasonally**<br>**adjusted**|Log first difference|Haver Analytics||
|**Real private consumption expenditure,**<br>**seasonally adjusted**|Log first difference|Haver Analytics||
|**Real gross fixed capital formation, seasonally**<br>**adjusted**|Log first difference|Haver Analytics||
|**Real exports, seasonally adjusted**|Log first difference|Haver Analytics||
|**Headline consumer price index, seasonally**<br>**adjusted**|Log first difference|Haver Analytics||
|**Real effective exchange rate based on 120**<br>**trading partners deflated using consumer**<br>**inflation, not seasonally adjusted**|Log first difference|Darvas<br>(2021);<br>Analytics|Haver|
|**Portfolio (and other) investment liabilities to**<br>**GDP**|NA|International<br>M<br>Fund|onetary|
|**Stock market index**|Log first difference|Haver Analytics||
|**10-year local-currency government bond yield**<br>**(or nearest maturity)**|First difference|Haver Analytics||
|**3-month interest rate (or nearest equivalent)**|First difference|Haver Analytics||
|**EMBI+ spread**|First difference|J.P. Morgan||
|**Primary balance (general government or**<br>**central government) to GDP**|NA|Haver Analytics||
|**Revenue (general government or central**<br>**government) to GDP**|NA|Haver Analytics||
|**Expenditure (general government or central**<br>**government) to GDP**|NA|Haver Analytics||
|**Gross debt (general government or central**<br>**government) to GDP**|First difference|Haver<br>An<br>Quarterly Public<br>Debt Database,<br>Bank|alytics;<br>Sector<br>World|
|**Foreign-currency government debt as a ratio**<br>**to total debt**|NA|Quarterly Public<br>Debt Database,<br>Bank|Sector<br>World|
|**Short-term (less than one year maturity) debt**<br>**as a ratio to total debt**|NA|Quarterly Public<br>Debt Database,<br>Bank|Sector<br>World|
|**Debt held by external creditors as a ratio to**<br>**total debt**|NA|Quarterly Public<br>Debt Database,<br>Bank|Sector<br>World|

44
same background or training. Notwithstanding the explanation for the lack of teacher effects, our key finding is that peer effects are robust to the inclusion of variables that control for teacher influence.

An alternative mechanism that might be related to our findings is that the nature of instruction in the course might differ depending on the background of the students who are enrolled. For instance, even with a rigid syllabus and random assignment, in a class where some students have positive returns, the teacher could be more affirming of the virtues of active trading. If investors with trading background dominate the class discussion, inexperienced students might get less instruction that is tailored to their needs. In turn, there would more participation after the course, but less success among new investors. Under such conditions, we should expect to find complementarities between the teacher and the students’ trading outcomes. For example, an instructor with high portfolio returns teaching in a class where experienced students also have positive returns could be more encouraging of stock trading. In column 4 of Table D.1, we include interaction terms between teacher returns and peer returns, but we do not find any evidence of complementarities between teachers’ and students’ outcomes. Our evidence, including our results using the survey sample, is most consistent with the idea that direct communication between classmates are driving the market participation decision.

# **6.4. EXTERNAL VALIDITY**

A final important concern regards the external validity of the findings. There are several important qualifications to the generality of the impact from selective communication on financial decision making. First, the type of social learning on which we focus differs from classic models such as Banerjee (1992) and Bikhchandani, Hirshleifer, & Welch (1992), in that peer choices and outcomes are revealed imperfectly. Many individual choices are accurately observed by members of a social group; for example, peer effects have been documented in automobile purchases (Grinblatt, et al., 2008); choice of workplace (Bayer, et al., 2008); and health plan (Sorensen, 2006). But several other choices are only transmitted via informal channels and could suffer from self-enhancement and selectivity. In social media, people take time when posting information about themselves, carefully choosing the aspects they want to emphasize. This might 35