rafmacalaba/datause-encoder-data huggingface dataset:
# **3 Data and Motivating Findings**

## **3.1 Data**

My primary unit of observation is the urban census tract (Area Geoestadística Básica in the Mexican micro-data). I use a sample of approximately 3,500 census tracts from 116 different neighborhoods and 24 different municipalities, 16 municipalities of which are in Mexico City and 8 of which are adjacent municipalities from the State of Mexico.

The first source of information is standard GIS data on the location of the transportation network and the new transit subway lines. I also use data on roads and highways in Mexico City to calculate commuting times for different transportation modes using the network analysis toolkit from ArcMap. By merging these datasets, I can estimate commuting/trade costs before and after the transit shock using a weighted average of travel times across the different transportation modes.

The second source of data is the Mexican Economic Censuses collected by INEGI. This is an establishment level data set that provides standard information such as sales, value added, number of workers, salaried workers, social security, and other outcomes. This census is carried out every five years starting in 1994. I am able to define the informal sector at the firm level using social security payments as discussed in Section 2. I categorize firms and workers in four different groups based on labor market regulations. I also calibrate wedges for each location and sector using wage bill, sales, and social security payments.<sup>17</sup> The third source of information is the Mexican Population Census. This census is carried out every ten years, and INEGI provided me with the data since 2000. With this information, I am able to calculate the number of informal, formal, and total residents in each location. In 2000, the Population Census also reported other variables such as household income and job characteristics the week before the census interview. Moreover, I use the 2015 Intercensal Survey that provides information on the workplace, residence, and transportation mode of formal and informal workers at the municipality/locality level. This data allows me to observe commuting flows in Mexico City for each sector. I also use the 2017 Origin-Destination Survey collected in the commuting zone area of Mexico City. I use this data for two purposes. First, I infer trade flows across the city using trips to restaurants and other types of shops at different hours of the day. Second, I discuss some motivational findings on commuting patterns.

Finally, I complement my results with standard household survey data from the Encuesta Nacional de Ocupación y Empleo (ENOE). I calibrate some of the parameters of the model using this data.

> 17For the Economic Census, I observe data for periods before and after the transit shock, which allows me to test for parallel trends in my main specification.

9
Therefore, our analysis implies that there was an average effect of approximately 76 additional votes for the incumbent party in an electoral section close to a weather station that actually registered rainfall below the pre-established threshold. Given the nature of the government transfer, it is plausible that more than one vote is gained by beneficiary household. Nonetheless, our estimates are consistent with the aggregate sum of indemnities paid even if only one individual per household changes her vote.

# **_Mechanisms_**

In principle, there is no theoretical reason to expect an effect of disaster spending on electoral turnout. In the Political Science literature, a consistent finding is that bad weather conditions at the time of an election significantly reduce participation. However, here we analyze weather conditions six to nine months before the day of the Presidential elections. The relationship between economic conditions and participation is more complex and evidence goes in both senses. For example, Pop Eleches and Pop Eleches (2009) show that individuals located just below the income cutoff (end thus eligible for the transfer program they analyze) were significantly more likely to declare an intention to vote in the next election than survey respondents just above the cutoff. Similarly, De la O (2013) finds that cash transfers in Mexico increased turnout among voters that benefited from the program for a long period, but finds no effect among beneficiaries enrolled six months before the election. Moreover, Chen (2013) finds that hurricane aid awards in the US increased turnout among the incumbent party’s supporters but decreased turnout among the opposition party’s voters.

In order to test for this, we estimate the basic regression model outlined in Equation (1) but this time using the total number of votes cast in the 2006 in electoral section _i_ as the dependent variable. Table 6 shows no evidence of an effect for the units that are geographically close to weather stations that received the government monetary transfer. This analysis indicates that higher voter support for the incumbent party in those sections close to weather stations that received drought compensation cannot be explained statistically by recipients of disaster aid voting relatively more or by non-recipients voting relatively less. Even though the coefficient is not statistically significant, its magnitude is not small and provides additional information to help constructing boundary conditions for the interpretation of our main effect.

Page **14** of **29**
# **III. Data**

This paper uses harmonized cross-section microdata for 15 countries in Latin America and the Caribbean (i.e., Argentina, Bolivia, Brazil, Colombia, Costa Rica, Chile, Dominican Republic, El Salvador, Ecuador, Honduras, Mexico, Panama, Paraguay, Peru, and Uruguay) for the series starting in 2010 and ending in 2019.<sup>11</sup> These data are from the Socio-Economic Database for Latin America and the Caribbean (SEDLAC), a joint effort of the World Bank and the Center for Distributive, Labor, and Social Studies (CEDLAS) at the National University of La Plata in Argentina.<sup>12</sup> The selection of these countries is based on the availability of at least two comparable data points between 2010 and 2019 within a two-year interval and on the accessibility of the necessary variables to conduct the estimation. More than 85 synthetic panels were constructed for the selected countries with a two-year length following Balcazar et al (2018). Table 1 summarizes the countries, initial and end years, household-survey names in SEDLAC, and the total number of synthetic panels. This study considers information for small and large economies in the region; lower-middle, upper-middle, and high-income countries, and represents 94 percent of the LAC population. The countries excluded from the exercise are those whose data is non-existent or not available circa the end year of the interval.

Table 1: Available cross-section surveys

|Country|Years|Survey|Total #<br>SP|Classification|
|---|---|---|---|---|
|Argentina|2010,2011,<br>2012,<br>2013,<br>2014,2016, 2017, 2018, 2019|Encuesta Permanente de<br>Hogares- Continua|6|H|
|Bolivia|2011, 2012, 2013, 2014, 2015,<br>2016, 2017, 2018, 2019|Encuesta Continua de<br>Hogares|7|LM|
|Brazil|2012,2013, 2014, 2015, 2016,<br>2017, 2018,2019|Pesquisa Nacional por<br>Amostra de Domicilios|6|UM|
|Colombia|2010,2011, 2012, 2013, 2014,<br>2015, 2016, 2017, 2018, 2019|Gran Encuesta Integrada de<br>Hogares|8|UM|
|Costa Rica|2010,2011, 2012, 2013, 2014,<br>2015, 2016, 2017, 2018, 2019|Encuesta Nacional de<br>Hogares|8|UM|
|Chile|2006, 2009, 2011, 2013, 2015,<br>2017|Encuesta de Caracterización<br>Socioeconómica Nacional|3|H|
|Dominican<br>Republic|2017,2018, 2019|Encuesta de Fuerza de<br>Trabajo|1|UM|
|Ecuador|2010,2011, 2012, 2013, 2014,<br>2015, 2016, 2017, 2018, 2019|Encuesta de Empleo,<br>Desempleo y Subempleo|8|UM|
|El Salvador|2010,2011, 2012, 2013, 2014,<br>2015, 2016, 2017, 2018, 2019|Encuesta de Hogares de<br>Propósitos Múltiples|7|LM|

> 11 Guatemala, Nicaragua, and Haiti were not included in the analysis due to the lack of microdata circa 2019.

> 12 The SEDLAC project consists of more than 400 household surveys in more than 25 LAC countries to provide statistics on poverty and other distributional and social variables. See Bourguignon (2015) for a detailed description of the SEDLAC project.

10
Policy Research Working Paper 8102

# **Abstract**

Achieving universal access to electricity is one of the most important energy policy goals set by governments in the developing world. The recent empirical literature, however, paints a mixed picture about the economic viability of rural electrification. Although many studies find substantial socioeconomic benefits from rural electrification, others propose that these benefits are overstated. This paper examines the hypothesis that the magnitude and the nature of benefits associated with electrification are highly context dependent. Using a panel data of 7,018 rural households in Bangladesh for 2005 and 2010, the paper explores two underlying determinants of the heterogeneity: the quality of electricity supply and the number of years of being connected to the grid. The analysis uses an instrumental variable and propensity-score-weighed fixed-effects model to address potential endogeneity of electricity adoption. The analysis finds that power outages have a negative impact on almost all development outcomes considered, while some benefits of electrification accrue only over the long run. The overall gain from expanding access to and improving reliability of electricity supply in Bangladesh is estimated to be US$2.3 billion a year.

This paper is a product of the Office of the Chief Economist, South Asia Region. 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://econ.worldbank.org. The authors may be contacted at fzhang1@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
subject to the following constraints with _u_ ( _s_ ), a dummy variable for unskilled (skilled) individuals _i_ , _lu_ ( _ls_ ), the initial unskilled (skilled) labor force, and _gu_ ( _gs_ ), the cumulative growth rate (between 2001 and 2015) of the unskilled (skilled) labor force. _lu_<sup>_*_</sup> ( _ls_<sup>_*_</sup> ) hence denotes the target value for the number of unskilled (skilled) labor. Equation (33) hence states that the new weights have to reflect the new skill composition (the ratio of unskilled to skilled workers) of the workforce. Equation (34) is the adding-up normalization constraint.

Note that this procedure gives new individual weights for just the employed population. Yet, for our purposes we need household weights for entire population. So we averaged over the new weights in those households where one or more individuals were employed. Households without any employed members were assigned the old weight. Note that the resulting unskilled-skilled ratio under these “final” new weights is of course not exactly equal to the ratio imposed in the cross-entropy reweighting.<sup>24</sup> Since the workforce in agriculture is almost entirely unskilled (more than 95 percent), the share of the unskilled labor force in agriculture is only slightly lower under the new weights. We use these new weights throughout the following parts of the microsimulation.

The estimation of the sectoral choice logit model and the two wage equations provide the basis for the following steps in the simulation. In the second step, we apply the changes in the sectoral composition of the workforce from the CGE (from agricultural into nonagricultural sectors) to the microlevel. In the simulation, those individuals from agriculture with the highest propensity to move to non-agricultural sectors are chosen to

> 23 For details on maximum entropy econometrics see Golan, Judge and Miller (1996). Robilliard and Robinson (2001) apply these methods to reweight household survey weights. They also provide a GAMS code for solving this type of problems.

> 24 We acknowledge that this is an ad-hoc procedure. In principle, it is possible to reweight all individuals respecting all necessary constraints. Yet, we consider the value added of this computationally quite expensive exercise too low.

22
# What Drives the High Price of Road Freight Transport in Central America?

Theresa Osborne, Maria Claudia Pachón, and Gonzalo Enrique Araya World Bank KEYWORDS: Imperfect Competition, Road Freight, Cabotage, Central America, Oligopoly Pricing, Transportation Services, International Trade JEL Codes: D4, L1, L11, L13, L41, N76, O18, R40

## **Acknowledgements**

Funding for the survey and for subsequent analysis was provided by The Trade Facilitation for Regional Integration in Central America Trust Fund and the Latin America Region of the World Bank. In addition, the surveys for Panama where financed by the Spanish Fund for Latin America and the Caribbean (SFLAC). Tomás Serebrisky, Daniel Saslavsky, Maria Claudia Pachon, and Jordan Schwartz led the design and implementation of the survey. Jasmin Chakeri and Magaly Clavijo provided valuable support to the implementation of the survey. The survey data collection was led by Fares Khoury from EECCanada. Cecilia Briceno-Garmendia, Stefano Pagiola, Adam Stern, Maria Florencia Millan Placci, Ayat Soliman, Oscar Avalle, and Carlos Felipe Jaramillo, and peer reviewers Gael Raballand, Oscar CalvoGonzalez, and Anca Dumitrescu provided valuable comments on earlier drafts of this paper.
services and the types of policies that can overcome market failures that prevent firms from using these markets.

# **References**

Berge, Lars Ivar Oppedal , Kjetil Bjorvatn, Kartika Sari Juniwaty, and Bertil Tungodden (2012) “Business training in Tanzania: From research driven experiment to local implementation”, _Journal of African Economics_ , forthcoming.

Berge, Lars Ivar Oppedal , Kjetil Bjorvatn, Bertil Tungodden (2011) “Human and financial capital for microenterprise development: Evidence from a field and lab experiment”, _NHH Discussion Paper Sam 1, 2011._ Bloom, Nicholas, Benn Eifert, Aprajit Mahajan, David McKenzie, and John Roberts (2012) “Does management matter? Evidence from India”, _Quarterly Journal of Economics_ , forthcoming.

Bloom, Nicholas and John van Reenen (2010) “Why do management practices differ across firms and countries?”, _Journal of Economic Perspectives_ 24(1): 203-24.

Bjorvatn, Kjetil and Bertil Tungodden (2010) “Teaching business in Tanzania: Evaluating participating and performance”, _Journal of the European Economic Association_ 8(2-3): 561-70.

Bruhn, Miriam, Dean Karlan and Antoinette Schoar (2012) “The Impact of Consulting Services on Small and Medium Enterprises: Evidence from a Randomized Trial in Mexico”, Yale Economics Department Working Paper no. 100.

Bruhn, Miriam, Dean Karlan and Antoinette Schoar (2010) “What capital is missing in developing countries?”, _American Economic Review Papers and Proceedings_ 100(2): 629–633.

Bruhn, Miriam and David McKenzie (2009) “In Pursuit of Balance: Randomization in Practice in Development Field Experiments “, _American Economic Journal: Applied Economics_ , 1(4): 200-232 Bruhn, Miriam and Bilal Zia (2012) “Stimulating Managerial Capital in Emerging Markets: The Impact of Business and Financial Literacy for Young Entrepreneurs”, Mimeo. World Bank Calderon, Gabriela, Jesse Cunha and Giacomo de Giorgi (2012) “Business literacy and development: Evidence from a Randomized Trial in Rural Mexico”, Mimeo. Stanford University.

De Mel, Suresh, David McKenzie and Christopher Woodruff (2012) “Business Training and Female Enterprise Start-up, Growth, and Dynamics: Experimental evidence from Sri Lanka”, Mimeo. World Bank.

24
# B. Injuries Associated with Disasters

Tables 3 and 4 present estimates of injuries associated with natural disasters from the EM-DAT database (CRED, 2007). Injuries are defined as those “requiring medical attention.” It seems likely that injuries are under-reported, even if only severe injuries are counted: the number of injuries is actually less than the number of deaths between 1960 and 2007, although it is approximately twice the number of deaths over the past 20 years. In the United States, the number of persons hospitalized for injuries is approximately 10 times the number of fatal injuries that occur each year (Segui-Gomez and MacKenzie, 2003). Although the ratio of hospitalized to fatally injured might be lower in developing countries, it is unlikely that the ratio falls below one. Armenian et al. (1997) for example report 831 deaths and 1454 persons hospitalized for injuries in their study population in Armenia.

**-** **<u>Table 3 Injuries associated with natural disasters (1960 2007)</u>** _<u>(thousand)</u>_

|<br>**Injured 1960-2007**|<br>**Climatological**|<br>**Geophysical**|<br>**Hydrological**<br>**Mete**|**orological**|
|---|---|---|---|---|
|**Europe**|**3**|**48**|**20**|**2**|
|**Asia**|**7**|**744**|**1,030**|**1,092**|
|**Oceania**|**1**|**1**|**1**|**3**|
|**Rest of the World**|**2**|**411**|**63**|**49**|
|Africa|1|48|23|8|
|Northern-America|0|12|0|6|
|Latin America and the Caribbean|1|350|40|35|
|**Total**|**13**|**1,204**|**1,114**|**1,145**|

Source: EM-DAT

**-** **<u>Table 4 Injuries associated with natural disasters (1988 2007)</u>** _<u>(thousand)</u>_

|<br>**Injured 1988-2007**|<br>**Climatological**|<br>**Geophysical**|<br>**Hydrological**|**Meteorological**|
|---|---|---|---|---|
|**Asia**|**7**|**481**|**851**|**390**|
|**Rest of the World**|**4**|**68**|**35**|**36**|
|Europe|2|17|3|1|
|Oceania|1|1|1|1|
|Africa|1|11|21|6|
|Northern-America|0|12|0|4|
|Latin America and the Caribbean|0|27|10|23|
|**Total**|**11**|**549**|**886**|**426**|

Source: EM-DAT Epidemiological studies provide information about the nature of injuries suffered in disasters, as well as about the incidence of illness that may accompany the disruption of water and sanitation services following a disaster. The most serious injuries frequently suffered in earthquakes include crush injuries, fractures (including skull fractures) and internal hemorrhaging. In the 1988 Armenia earthquake, Armenian et al. (1997) report that 37% of injured persons experienced fractures and 27% crush injuries. Noji (1997) reports that the most common injuries among those hospitalized in the 1968 Iran 6
<!-- Start of picture text --> Figure 10: China - trade liberalization<br>China: Reduction in weighted tariffs (%)<br>90<br>80<br>Transport equipment<br>70<br>60<br>Wearing Apparel<br>50 Non-metallic mineral<br>products Textiles<br>40<br>30<br>20Non-machinery ele ctric a l Iron and steel Food Products<br>10<br>Non-ferrous metals<br>0<br>1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007<br>Food Products Textiles Wearing Apparel<br>Industrial chemicals Petroleum refineries Non-metallic mineral<br>Iron and steel Non-ferrous metals Fabricated metal pro<br>Non-electrical machinery Electrical machinery Transport equipmen<br>Source: TRAINS database<br><!-- End of picture text --> The Chinese brand of “distance shortening” cum industrial policy interventions seem to explain its success in moving closer to the frontier, significantly ahead of Brazil and India (figure 4). China transformed from high tech original equipment manufacturing (OEM) contractor to a global competitor in high tech exports by leveraging foreign technology through FDI which became the means to access capital and introduce competition in the domestic economy. FDI was also used as a distance-shortening mechanism. Instead of waiting for its technological capabilities to mature, China used its ‘open door’ policy to let the 'market for technology' model coordinate FDI, foreign trade and technology transfer to modernize its economy (OECD, 2008). Until the 1990s, China relied heavily on imported technology especially from Japan and help with the training of engineers in Toshiba, Changhong and Sanyo (Mathews, 2009). In recent years, its own technological capabilities have improved and "indigenous innovation" is being given priority (OECD, 2008). This is distinct from Korea’s experience in which the country put the main onus of developing a pool of technological capabilities on itself.

In the past few years, the Chinese government has initiated a variety of measures to speed up convergence with OECD policies. These include enhanced public R&D and tax incentives, government facilitated absorption of imported technology, active use of IPRs and other international standards. Its GERD is 50 percent larger than Brazil’s and its business enterprise expenditure on R&D (BERD) almost 100 percent larger. China is almost at par with Korea in terms of the strength of the industry-university linkages proxied by the share of R&D financed by industry. National Guidelines for Science and Technology Development offer tax incentives for business R&D, public procurement, industrial research alliances, and human resource development in S&T to ensure that its industry is not constrained by the paucity of skilled and technical workers (OECD S&T Outlook, 2008). In 2005, as a percent of all new degrees, China had a larger proportion of science and engineering degrees than Korea and nearly four times as 22
Policy Research Working Paper 8160

# **Abstract**

The development of the Internet is often seen as a source of demand for skilled workers and therefore a potential driver of the wage gap between skilled and unskilled workers. This paper focuses on the impact that international trade in online platforms has on the wage gap. Because online trade allows smaller firms with relatively more unskilled workers to access world markets it can be expected a priori that an expansion of online exports reduces the wage gap. After correcting for potential endogeneity bias in a sample of 22 developing countries for which online trade and wage gap data can be matched, the study finds that a 1 percent increase in the share of online exports over GDP leads to a 0.01 percent decline in the wage gap.

This paper is a product of the Trade and Competitiveness Global Practice Group. 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://econ.worldbank.org. The authors may be contacted at marciocruz@worldbank.org, emmanuel.milet@unige.ch, and marcelo.olarreaga@unige.ch.

_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