rafmacalaba/datause-encoder-data huggingface dataset:
|Share of land areas with clay soil|-1,934|-1,591|
|---|---|---|
||(1.29)|(1.02)|
|Share of land areas with silt soil|4,141|3,746|
||(3.58)***|(3.61)***|
|Plain (1=Yes; 0=No)|-463|1,095|
||(0.56)|(1.75)*|
|Road (1=Yes; 0=No)|564|4,660|
||(0.42)|(4.84)***|
|Distance to township government|72.0|-50.7|
||(0.98)|(1.26)|
|If participate production association<br>1Y 0N|3,138|-2,586|
|(=es; =o)|(2.70)***|(1.46)|
|Share of labor without receiving education|32.9|-9.87|
||(2.21)**|(0.92)|
|Cultivated land area per household|-2,720|-1,189|
||(5.78)***|(5.95)***|
|Constant|-66240|65,090|
||(2.10)**|(4.47)***|
|Observations|2750|2119|
|Adjusted R-squared|0.12|0.20|
|F-test|14.94|20.25|

39
Table 2: Exporter participation rate and export intensity by size class ________________________________________________________________________________________________________________________________

|Country<br>_____________|Year<br>_______|Variable<br>All<br>firms<br>____________________________|________|20 – 49<br>employ<br>_______|<br>ees<br>______|50 – 24<br>employ<br>_______________|9<br>ees<br>_______|250 – 499<br>emplo<br>______________|yees<br>_______|500 and more<br>emplo<br>______________|yees<br>__|
|---|---|---|---|---|---|---|---|---|---|---|---|
|Austria|1999|Participation rate 69.2||53.1||82.2||95.0||95.9||
|||Export intensity|40.2||28.2||42.9||57.5||68.0|
|||Number of firms 3,868||1,922||1,515||260||171||
||2005|Participation rate 71.4||56.1||84.6||95.0||97.0||
|||Export intensity|44.1||32.0||47.4||63.3||71.3|
|||Number of firms 3,840||1,949||1,465||261||165||
|Belgium 1996||Participation rate 83.6|74.3|||89.4||96.4||98.1||
|||Export intensity|39.8||33.0||43.0||45.3||53.1|
|||Number of firms 3,110||1,387||1,368||195||160||
||2005|Participation rate 80.3||68.1||87.5||92.9||97.2||
|||Export intensity|44.3||37.8||47.8||45.1||51.8|
|||Number of firms 2,403||1,014||1,076||168||145||
|Chile|1990|Participation rate 22.4||7.6||32.1||54.6||66.7||
|||Export intensity|30.2||25.9||32.1||28.6||28.8|
|||Number of firms 3,230||1,610||1,304||220||96||
||1999|Participation rate 30.9||15.3||42.4||68.8||85.7||
|||Export intensity|27.4||25.0||27.2||29.9||33.0|
|||Number of firms 2,709||1,428||1,041||170||70||
|China|1998|Participation rate 24.4||10.8||20.4||32.1||45.2||
|||<br>Export intensity|59.9||67.3||65.4||62.3||46.5|
|||<br>Number of firms 131,49|9 19,376|||71,615||21,997||18,511||
||2005|Participation rate 30.4||15.5||28.8||44.9||56.9||
|||Export intensity|60.3||54.5||61.1||65.0||56.0|
|||Number of firms 241,32|6 48,089|||144,034|29,110||20,093|||

29
**Table 3. Impact of hurricanes on nightlight**

||**(1)**<br>Ln (nightlight)|**(2)**<br>Ln (nightlight)|**(3)**<br>Ln (nightlight)|
|---|---|---|---|
|f<br>|-0.0177**<br>(0.00715)|-0.0244***<br>(0.00733)||
|f(t−1)<br>||0.0991***<br>(0.00731)||
|fin non-storm surge-prone areas<br>|||0.0211**<br>(0.00831)|
|<br>fin storm surge-prone areas<br> Authors’ calculations.|||-0.103***<br>(0.0117)|

_Source:_ Authors’ calculations.

_Notes:_ Dependent variable: nightlights. Estimates from OLS regression, cell and year fixed effects included but not reported. ***p<0.01, **p<0.05, *p<0.1. Robust standard errors in parentheses. The number of observations is 1,159,340 for column 1 and 3; and 1,070,160 for column 2). Average nightlight in 2000 is 12.1.

# **4.2.Mangroves reduce hurricane impact**

Mangrove forest, an essential component of many Caribbean coastlands, can act as a natural defense against hurricanes. For example, Zhang et al. (2012) find that along South Florida’s Gulf Coast, each kilometer of mangrove width reduces storm surge by 50 centimeters, while Del Valle et al. (2020) find a similar result in their study of how mangrove forest mitigates the impact of hurricanes on economic activity in Central America. Based on these initial findings, it is possible to interpret as hurricanes having an adverse effect on local economic activity, we explore whether mangrove forests can mitigate this effect in the Caribbean region.

To measure the level of protection that mangroves offer, we use two data sets with different calculations of mangrove width and test for potential nonlinear relationships. Our interest is in studying the effectiveness of this potential mitigation property as the mangrove width increases (see Section 2.3 for a description of how the two databases used for the econometric results calculate mangrove width).

Our results indicate that mangrove forests act as natural defense and mitigate the impact of hurricanes in local economic activity. We estimate three models, as in Del Valle et al. (2020), and discretize mangrove width data into various bins or compartments that correspond to its q-quantiles<sup>23</sup> in low-elevation coastal zones (LECZ). The first model uses _q=2_ , and we create a dummy variable for each bin and interact these variables with the damage index. We then take the resulting variables and include them in our two-way fixed effect econometric equation in place of the damage index. We repeat this approach for the second model using ff _q=3_ ; and the third model, _q=4_ . ff Excluding Cuba from the sample, figure 5 plots the results for 29 countries and territories, in three panels (a, b and c) for three models. Panel a shows point estimates for each of the models regarding the impact

> 23 A quantile is a set of values which divide a frequency distribution into equal groups, each one containing the same fraction of the total sample data.

17
disproportionately relevant to the country’s exports over the ensuing two decades. Given the importer-industry-year fixed effects, this selection must also be disproportionate for the country compared to other nations. Important tests against this concern include the exclusions of candidate industries in Table S8a and the robustness checks on spatial distributions in Table S7a.

With respect to the technology trends of US cities being endogenous, the robustness checks consider a variety of techniques, including the stringent restrictions around technology trends built upon Anglo-Saxon inventors and the work they cite. Another test utilizes the ethnic patenting growth in the United States to formulate an additional control against reverse causality. This control is calculated as the patenting growth within each industry in the United States by members of the focal ethnic community. Thus, estimations consider the technology transfer instrument Mit<sup>koutlinedinthemaintextanditsimpactforproductivitygrowthandtrade</sup> after controlling for the direct growth of patenting by industry of ethnic communities. The control is calculated across the ethnic groups discussed for ethnic patenting data, with the same control applied to countries within an ethnicity (e.g., the growth in US patenting by ethnic Hispanic inventors for computers is used as a control with both Mexico’s and Chile’s computer productivity and exports). As some countries do not map to an identifiable ethnic group with the name matching approach, the sample is reduced to 73,545 observations. For this sample, the weighted and unweighted instrumented elasticities before the ethnic patenting control is introduced are 1.32 (0.54) and 2.21 (0.92), respectively. With the control, these instrumented elasticities are very similar at 1.40 (0.64) and 2.32 (1.37), respectively. These too suggest the technology transfer is happening over and above direct immigrant contributions. Earlier versions of this paper consider direct ethnic patenting and exports more extensively. The early work also evaluates the role of sector reallocation from agriculture, the empirical contrast of the technology states in the exporter and the importer, and the potential for vertical integration through parts trade (e.g., Ng and Yeats 2001, Schott 2004). These results are available upon request.

The timing of effects is important as shown in Table S5, where the five-year lagged instruments outperform contemporaneous variables. There is not a simple story that identifies why a productivity boom in an Indian industry, for example, would become first visible in spillover patenting to US-based Anglo-Saxon inventors versus first increasing directly Indian exports in the industry. This timing could be argued– if, for example, local institutions or complementary factors in India are not developed suffi ciently to realize exports without a significant delay– but the inverted timing structure is far from obvious and would need to be systematic across coun15
# **Table A-1 (continued)**

# **<u>(ii) Brazil</u>**

|Variable label|White Men|Afro- Men|White Women|Afro- Women|
|---|---|---|---|---|
|1 year of schooling|0.10**|0.00|0.07|0.08|
||(0.03)|(0.03)|(0.06)|(0.05)|
|2 years of schooling|0.05*|0.04|-0.03|0.08|
||(0.03)|(0.12)|(0.05)|(0.04)|
|3 years of schooling|0.10**|0.05|-0.01|0.04|
||(0.03)|(0.03)|(0.04)|(0.04)|
|4 years of schooling|0.20**|0.08**|0.07|0.05|
||(0.03)|(0.03)|(0.04)|(0.04)|
|5 years of schooling|0.18**|0.06|0.03|0.06|
||(0.03)|(0.03)|(0.05)|(0.05)|
|6 years of schooling|0.20**|0.08*|0.11|0.13**|
||(0.03)|(0.04)|(0.06)|(0.05)|
|7 years of schooling|0.20**|0.06|0.10|0.06|
||(0.04)|(0.04)|(0.06)|(0.06)|
|8 years of schooling|0.27**|0.07|0.17**|0.14*|
||(0.04)|(0.04)|(0.06)|(0.06)|
|9 years of schooling|0.25**|0.05|0.18**|0.13*|
||(0.04)|(0.05)|(0.07)|(0.07)|
|10 years of schooling|0.22**|0.06|0.20**|0.16*|
||(0.05)|(0.05)|(0.07)|(0.07)|
|11 years of schooling|0.42**|0.17**|0.39**|0.28**|
||(0.04)|(0.05)|(0.07)|(0.07)|
|12 years of schooling|0.52**|0.39**|0.61**|0.47**|
||(0.06)|(0.09)|(0.08)|(0.11)|
|13 years of schooling|0.53**|0.30**|0.59**|0.57**|
||(0.06)|(0.10)|(0.08)|(0.11)|
|14 years of schooling|0.53**|0.34**|0.72**|0.66**|
||(0.07)|(0.09)|(0.09)|(0.11)|
|15 years of schooling|0.75**|0.45**|0.82**|0.78**|
||(0.06)|(0.08)|(0.09)|(0.10)|
|16 years of schooling|0.78**|0.62**|1.02**|0.86**|
||(0.07)|(0.10)|(0.10)|(0.12)|
|17 years of schooling|0.97**|0.64**|1.11**|1.07**|
||(0.08)|(0.12)|(0.11)|(0.15)|
|Mother’s years of schooling|0.08**|0.06**|0.09**|0.07**|
||(0.01)|(0.01)|(0.01)|(0.01)|
|Mother’s years of schooling^2|-0.005**|-0.003*|-0.006**|-0.003|
||(0.00)|(0.00)|(0.00)|(0.002)|
|Age|0.08**|0.06**|0.05**|0.03**|
||(0.00)|(0.00)|(0.00)|(0.00)|
|Age^2|-0.001**|-0.001**|-0.001**|-0.0004**|
||(0.00)|(0.00)|(0.00)|(0.00)|
|Age*Schooling|0.001**|0.002**|0.001*|0.001**|
||(0.00)|(0.00)|(0.00)|(0.00)|
|With labor card|0.04**|0.11**|0.19**|0.32**|

33
the Survey of Consumer Finance and finds evidence consistent with both informational problems.<sup>4</sup> The paper is also related to a framed experiment conducted by Giné et al. (2010) in Peru that shows that dynamic incentives can be important. In addition, there is a theoretical and empirical literature on the impact of credit bureaus that are also related to this paper. The exchange of information about borrowers should theoretically reduce adverse selection (Pagano and Jappelli, 1993) and moral hazard (Padilla and Pagano, 2000). Empirically, de Janvry, McIntosh and Sadoulet (forthcoming) study the introduction of a credit bureau in Guatemala and find that it did contribute to efficiency in the credit market. Finally, the paper is related to the literature motivated by the rise in personal bankruptcies in the US in the last decades (Livshits et al. 2010).

The remainder of this paper is organized as follows. Section 2 describes the experimental design and survey data and Section 3 presents the intuition of a simple model of loan repayment. Section 4 describes the regression specifications, and Section 5 presents the empirical results. Section 6 provides additional discussion and robustness checks. Section 7 presents the benefitcost analysis of introducing biometric technology, and Section 8 concludes.

# **2. Experimental Design and Survey Data**

The experiment was carried out as part of the Biometric and Financial Innovations in Rural Malawi (BFIRM) project, a cooperative effort among Cheetah Paprika Limited (CP), the Malawi Rural Finance Corporation (MRFC), the University of Michigan, and the World Bank. CP is a privately owned agri-business company established in 1995 that offers extension services and high-quality inputs to smallholder farmers via an out-grower paprika scheme. MRFC is a government-owned microfinance institution and provided financing for the in-kind loan package for 1/2 to 1 acre of paprika. Loaned funds were not disbursed in cash, but rather took the form of

> 4 Ligon, Thomas and Worrall (1999) write down competing models of risk-sharing that are taken to the data and find evidence of limited commitment. In a paper similar in spirit, Paulson, Townsend, and Karaivanov (2006) estimate structurally competing models of credit markets in Thailand and find moral hazard to be important.

5
the focus of the Mexican antitrust agency on absolute monopolistic practice cases, though does not indicate it should give up on relative monopolistic practices entirely. Though some have advocated for greater enforcement against relative monopolistic practices (Khan, 2017) actions in the United States have been rare since the 1970s (Areeda, Kaplow, Edlin and Hemphill, 2021). As a result, economics research on price discrimination and vertical control (e.g., Mortimer, 2007, 2008) does not observe them in the context of an enforcement action, as could become more common. To the best of our knowledge, this is the first paper to systematically compare the quantitative effects of antitrust enforcement actions across these different types of conduct.

Finally, our paper contributes to the literature focused on competition authorities themselves and their decision-making. This literature has focused primarily on merger control, and so we complement it by providing evidence on enforcement related to illegal conduct. Kwoka (2014) conducts a meta-analysis of all available merger retrospectives in the United States, concluding that enforcement has been too lenient and that more mergers should be blocked on the margin. Our result that the Commission has made Type II errors is similar in spirit, suggesting that there are gains from more sanctions on the margin. Affeldt, Duso and Sz ̈ucs (2021) use a comprehensive database of European merger reviews to document how the approach to merger control in Europe has changed over time. Similarly, our analysis of closed cases describes the characteristics of industries that are targeted for investigation by the Commission.

The paper unfolds as follows. Section 2 provides background on the Commission and the data. Section 3 describes our approach to estimating difference-in-differences outcome changes after each case, the triple difference approach to identifying the effects of sanctions, and three case examples. Section 4 reports the results, including the triple difference analysis of the effects of sanctions on sales, wages, employment, and profit margins, as well as the heterogeneity by cases of absolute and relative monopolistic practices. Section 5 reports the effects on total factor productivity and measured misallocation and discusses implications for aggregate outcomes. Section 6 concludes with policy implications.

# **2 Institutional Background and Data**

This section provides background on Mexican competition law and the Federal Competition Commission, before describing the data used in our study.

8
WPS4231

# **WORK-RELATED MIGRATION AND POVERTY REDUCTION IN NEPAL**

Michael Lokshin, Mikhail Bontch-Osmolovski, Elena Glinskaya<sup>**1**</sup> _The World Bank, 1818 H Street NW, Washington DC, 20433 USA_

**Abstract:** Using two rounds of nationally representative household survey data, in this study, we measure the impact on poverty in Nepal of local and international migration for work. We apply an instrumental variable approach to deal with nonrandom selection of migrants and simulate various scenarios for the different levels of work-related migration, comparing observed and counterfactual household expenditure distribution. Our results indicate that one-fifth of the poverty reduction in Nepal occurring between 1995 and 2004 can be attributed to higher levels of work-related migration and remittances sent home. We also show that while the increase in international work-related migration was the leading cause of this poverty reduction, domestic migration also played an important role. Our findings demonstrate that strategies for economic growth and poverty reduction in Nepal should consider aspects of the dynamics of domestic and international migration.

**Keywords** : migration, remittances, poverty measurement, Nepal **JEL** :  I31, O15, F22

## World Bank Policy Research Working Paper 4231, May 2007

_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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

> 1 Corresponding author: _Michael Lokshin_ , mlokshin@worldbank.org, DECRG; _Mikhail BontchOsmolovski,_ <u>misha.bonch@gmail.com, DECRG and University of North Carolina at Chapel Hill</u> _, Elena Glinskaya,_ <u>eglinskaya@worldbank.org, SASPR. All at the World Bank, 1818 H Street,</u> NW, Washington DC, 20433, MSN MC3-306

For their comments, the authors are grateful to Martin Ravallion, David McKenzie, Jed Friedman, Jerro Carletto, Alan de Brauw, Karen Macours, and Toan Do. Special thanks to Pedro Carneiro for his help with the simulation methodology. We also thank Dilip Parajuli for his help with the 2001 Nepal Census data.
**Figure 13: EXPRELY of World GVC Final and Intermediate Exports, 1988‐2014**

<!-- Start of picture text --> 60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>Intermediate Goods Final Goods<br>1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014<br><!-- End of picture text --> <u>Notes: Values in ratios from original variables expressed in thousand current US$ and calculated on the total number of 181</u> countries/geographies with available data on the variables necessary for the construction of both indexes and non‐missing normalized GDP, in at least some years of the period 1988‐2014, even though some have missing data in some years over the GVC mirror data series over the period 1988‐2014 (HS 1988/92 nomenclature, six‐digits). Normalized GDPs are calculated for each country and year as a ratio relative to the USA GDP in the fixed year 2001, with both countries’ GDPs measured in constant 2005 USD (normalized GDP for the USA equals 1). All trade data is drawn from Comtrade on WITS, and countries’ GDP data comes from WB WDI.

<u>Sources: Trade: World Bank MC‐GVC database (http://wits.worldbank.org/WITS/WITS/AdvanceQuery/GVC/GVCQueryDefination.aspx?Page=GVCIndicator).</u> World GDP: World Bank WDI (http://databank.worldbank.org/data/reports.aspx?source=world‐development‐indicators).

**Figure 14: EXPRELY of Regions’ GVC Exports, 1988‐2014**

<!-- Start of picture text --> 0.7<br>East Asia & Pacific (all income<br>0.6 levels)<br>Europe & Central Asia (all income<br>0.5 levels)<br>Latin America & Caribbean (all<br>0.4 income levels)<br>Middle East & North Africa (all<br>0.3 income levels)<br>North America<br>0.2<br>South Asia<br>0.1<br>0 Sub‐Saharan Africa (all income<br>1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 levels)<br><!-- End of picture text --> <u>Notes: Values in ratios from original variables expressed in thousand current US$ and calculated on the total number of 181</u> countries/geographies with available data on the variables necessary for the construction of both indexes and non‐missing normalized GDP, in at least some years of the period 1988‐2014, even though some have missing data in some years over the GVC mirror data series over the period 1988‐2014 (HS 1988/92 nomenclature, six‐digits). Normalized GDPs are calculated for each country and year as a ratio relative to the USA GDP in the fixed year 2001, with both countries’ GDPs measured in constant 2005 USD (normalized GDP for the USA equals 1). All trade data is drawn from Comtrade on WITS, and countries’ GDP data comes from WB WDI.

<u>Sources: Trade: World Bank MC‐GVC database. (http://wits.worldbank.org/WITS/WITS/AdvanceQuery/GVC/GVCQueryDefination.aspx?Page=GVCIndicator).</u> World GDP: World Bank WDI (http://databank.worldbank.org/data/reports.aspx?source=world‐development‐indicators 30
Ghani, E., Goswami, A.G. and Kerr, W.R., 2016. “Highway to success: The impact of the Golden Quadrilateral project for the location and performance of Indian manufacturing.” The Economic Journal, 126 (591): 317-357.

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Government of China. 2017. Vision for Maritime Cooperation under the Belt and Road Initiative <http://www.xinhuanet.com/english/2017-06/20/c_136380414.htm> Jones, L. and Hameiri, S. 2020. “Debunking the Myth of Debt-Trap Diplomacy” Chatham House Research Paper 19 August 2020 https://www.chathamhouse.org/2020/08/debunking-myth-debt-trap-diplomacy Jedwab, R. and Moradi, A., 2016. ``The permanent effects of transportation revolutions in poor countries: evidence from Africa.” Review of Economics and Statistics. 98 (2): 268-284.

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36