rafmacalaba/datause-encoder-data huggingface dataset:
differentiate between these explanations. We can say that we find no evidence that returns are lower among firms with suppliers or customers who account for a large share of trade. In Columns 2 and 3 of Table 3, we interact the treatment amount with a variable indicating the enterprise has a single customer accounting for at least 25 percent of sales (Column 2) or one supplier accounting for at least 25 percent of input purchases (Column 3). Neither interaction is significant, suggesting that friction in trading relationships is not a cause of slow recovery. However, we take even this limited evidence with a grain of salt. The questions on which these variables are based were asked at the time of the baseline survey. As such, they reflect the situation after the tsunami rather than before.

# _Other Dimensions of Heterogeneity_

Appendix 1 compares the characteristics of manufacturing and retail firms in our sample. The manufacturing firms are more likely to be run by females, have lower profits before the tsunami, and are more reliant on a single customer and a single supplier of inputs. They sell to far fewer customers per day, on average. The three largest subindustries within our manufacturing sample among directly affected firms are sewing clothes, spinning lace, and making food such as string hoppers (Sri Lankan rice noodles which are a dietary staple). These are all industries dominated by female owners. With the exception of food preparation, all other products made by the manufacturing firms are semi-durables, for which demand is likely to recover more slowly. Furthermore, a greater reliance on a major customer and/or a major supplier increases the likelihood that a disruption in this relationship as a result of the tsunami will have a large effect on the firm.

In de Mel, McKenzie, and Woodruff (2008, 2009) we find that returns are significantly lower in enterprises owned by females than in enterprises owned by males. Indeed, we cannot rule out the possibility that the overall mean effect of the treatment is zero for female owners. In the tsunami-affected zone, however, we find no significant 23
standards and heavy regulation (Chen and Findlay 2008).Chinese exports in agricultural and food products are increasing. Between 1992 and 2008, total agricultural exports rose from 9.7 to 30.1 billion USD. Exports in the seven product categories represent about 40 percent of Chinese agricultural exports, and went up from 5.1 billion USD in 1992 to 12.1 billion USD in 2008.

The evolution of Chinese exports in the seven product categories is shown in Figure 1. Exports seem to increasingly follow China‟s comparative advantage in labor intensive product (Fang, Beghin 2000). Labor intensive products –such as vegetables and tea– register large growth rates especially after China‟s accession to the WTO in 2001. In contrast, exports of land-intensive products –such as sugar, cereal and meat– have increased to a lesser extent. In 2008, China‟s agriculture exports accounted for about 10 percent of all agriculture exports from low and middle income countries.

# [Insert Figure 1 here]

Data on mandatory and voluntary standards are compiled from the Standards Administration of the Peoples Republic of China (SAC), the Chinese official national standards body.<sup>7</sup> Chinese standards developed in SAC are accessible through an online database: the SAC National Standards Query (SAC 2011b). The database has recently been used in Mangelsdorf (2011). To ensure reliability and completeness, the standards have been cross checked with the “Chinese Bulletin of Standards” (SAC 2011a) and the German-Chinese Standards Portal (DIN and SAC 2011). The Chinese National Standards Query provides information on the date the standards came into effect and the date the standards have been withdrawn or replaced by newer versions. Each standard is classified according to the International Classification of Standards (ICS) nomenclature, which allows for matching standards to trade data. The database allows differentiation between Chinese standards harmonized to international norms such as Codex Alimentarius and standards that are unique to China.<sup>8</sup> . International standards in our paper are defined as standards developed by international standards setting organizations (e.g. Codex Alimentarius Commision) which are adopted as Chinese national standards. A concordance table between the seven-digit ICS categories and the trade data in HS codes can be found in Table A.1 in the Appendix. The database allows us to construct four different standards variables according to two criteria: (i) mandatory vs. voluntary standards, and (ii) domestic vs. international harmonized standards.

Table 1 provides some examples to illustrate the different types of standards. The first example, the standard GB/T 19838-2005 for fish and fishery products is a voluntary domestic Chinese standard. It makes companies accountable for analyzing potential hazards

> 7 SAC obtained legal recognition by the Chinese government through the Standardization Law of the People’s Republic of China from 1988 (source: ISO).

> 8 SAC develops mandatory and voluntary standards. The prefix “GB” indicates mandatory standards and voluntary standards are prefixed “GB/T.

7
There is also a gap in the development of online retail between urban and rural areas **.** Up to December 2016, the rural Internet users in China accounted for 27.4 percent of the national total (731 million), while urban Internet users took up 72.6 percent of the total, much higher than the share of urban population (57 percent). The Internet penetration in urban areas was 69.1 percent, while in rural areas it was only 33.1 percent. Nearly three-quarters of online stores and Internet users were concentrated in urban areas.<sup>10</sup> From a positive perspective, the wide gaps across regions and between rural and urban areas imply large growth potential in the less developed areas. In fact, total online retail transactions have grown faster in rural areas than in urban areas in the past several years. Rural online retail transactions increased from RMB 353 billion in 2015 to RMB 895 billion in 2016, representing 17 percent of the total online retail transactions, an increase from 9 percent in 2015.<sup>11</sup>

**Figure 1. Share of online retail sales to total retail sales of consumer goods in provinces (2015)**

<!-- Start of picture text --> 50%<br>45%<br>40%<br>35%<br>30%<br>25%<br>20%<br>15%<br>10%<br>5%<br>0%<br> to Total<br>Sales<br>Share of Online Retail Retail Sales of Consumer Goods (2015) Beijing Shanghai Zhejiang Guangdong Fujian Jiangsu Shaanxi Tianjin Sichuan Anhui Hainan Hubei Chongqing Hebei Jiangxi Hunan Shandong Henan Yunnan Guangxi Liaoning Guizhou Gansu Xinjiang Shanxi Ningxia Heilongjiang Jilin Qinghai Inner Mongolia Tibet<br><!-- End of picture text --> Source: Staff calculations based on _China Statistical Yearbook 2016_

# **III Theoretical motivation: Extension of Starts (2018)**

In the model developed in Startz (2018), consumers care about both quantities and style of the goods they consume. Traders choose whether to order remotely from suppliers or travel to the suppliers. They also need to decide the optimal stocking period (frequency) to maximize average profit over a period _T_ , taking travel cost and the cost of search and contracting associated

> 10 Ministry of Commerce of the People’s Republic of China 2016. “E-commerce in China, 2016” (中国电子商务报告). 11 Calculated based on data from the National Bureau of Statistics of China and the Ministry of Commerce of the People’s Republic of China, “E-Commerce in China.” <u>Total rural online retail transactions refers to the sum of online retail</u> transactions from e-commerce enterprises (including individuals) operated in the administrative regions at the county level or below (excluding city districts) (http://images.mofcom.gov.cn/dzsws/201706/2017061110205702.pdf).

·· 7
Table C11: Access to basic amenities and location

|||Water|||Sanitation|||Electricity||
|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|**Panel A: DOU**||||||||||
|Urban center<br>Urban cluster|0.219***<br>(0.004)<br>0.159***<br>(0.004)|0.200***<br>(0.004)<br>0.152***<br>(0.004)|0.167***<br>(0.004)<br>0.136***<br>(0.004)|0.236***<br>(0.005)<br>0.144***<br>(0.004)|0.161***<br>(0.005)<br>0.118***<br>(0.004)|0.110***<br>(0.005)<br>0.097***<br>(0.004)|0.369***<br>(0.004)<br>0.149***<br>(0.004)|0.293***<br>(0.004)<br>0.122***<br>(0.004)|0.216***<br>(0.004)<br>0.087***<br>(0.004)|
|Log of real exp.||0.035***<br>(0.002)|0.021***<br>(0.003)||0.138***<br>(0.002)|0.125***<br>(0.003)||0.137***<br>(0.002)|0.113***<br>(0.003)|
|**Panel B: DB**||||||||||
|Core|0.156***<br>(0.004)|0.134***<br>(0.005)|0.106***<br>(0.005)|0.165***<br>(0.005)|0.099***<br>(0.005)|0.062***<br>(0.005)|0.261***<br>(0.004)|0.187***<br>(0.004)|0.124***<br>(0.005)|
|Suburb|0.100***|0.087***|0.067***|0.148***|0.110***|0.089***|0.157***|0.116***|0.071***|
||(0.004)|(0.004)|(0.004)|(0.006)|(0.006)|(0.006)|(0.005)|(0.005)|(0.005)|
|Town|0.047***|0.041***|0.036***|0.064***|0.046***|0.037***|0.058***|0.037***|0.024***|
||(0.004)|(0.004)|(0.004)|(0.004)|(0.004)|(0.004)|(0.004)|(0.004)|(0.004)|
|Log of real exp.||0.050***|0.027***||0.149***|0.126***||0.166***|0.126***|
|||(0.002)|(0.003)||(0.002)|(0.003)||(0.002)|(0.002)|
|Country FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Demographic|No|No|Yes|No|No|Yes|No|No|Yes|
|Education|No|No|Yes|No|No|Yes|No|No|Yes|
|Employment|No|No|Yes|No|No|Yes|No|No|Yes|
|Panel A Adj.R<sup>2</sup>|0.194|0.197|0.203|0.295|0.325|0.362|0.326|0.357|0.388|
|Panel B Adj.R<sup>2</sup>|0.170|0.177|0.189|0.282|0.318|0.359|0.280|0.330|0.374|
|Nr. of countries|19|19|19|19|19|19|19|19|19|
|Nr. of hh|418622|418622|418622|418619|418619|418619|415296|415296|415296|

Source: International Urban Poverty Database. Note: *** p < 0.01, ** p < 0.05, *** p < 0.1. Robust standard errors are in parentheses. WorldPop 250m is used for the DOU and DB method. The dependent variable is a dummy variable taking the value 1 if a household has access to a respective amenity. The baseline category is “Urban center” in all specifications in Panel A, and “Cores” in all specifications in Panel B. Demographic control variables include household size and household head’s age, sex, and marital status. Education is a categorical control variable that summarizes the education of the household head in seven categories. Employment is a categorical control variable that summarizes the household head’s labor status and employment sector.

93
6 probably sending most of its migrants to the OECD and the Arab Gulf.<sup>Becauseof its</sup> proximity to the United States, Mexico is sending most of its migrants<sup>to its northern</sup> neighbor. Turkey, the third largest remittance-receiver, is sending most of<sup>its migrants</sup> to the OECD (especially Germany).

Table 2 provides another way of looking at the flow of official<sup>remittances.Here</sup> the data on worker remittances for the 24 countries are disaggregated by<sup>region of the</sup> world. Because of the importance of the United States as a labor-receiving<sup>region,Latin</sup> America and the Caribbean is the largest recipient of official remittances.<sup>This region of</sup> the world also recorded the highest percentage rate of increase<sup>in official worker</sup> remittances over the last twenty years.

For the 24 study countries as a whole, Table 2 reveals that official<sup>worker</sup> remittances have increased at a strong 3.86 percent per year in real<sup>terms. To put this</sup> figure into perspective, Table 3 compares the annual rate of growth in official remittances for the 24 study countries with that of gross domestic<sup>product (GDP)for</sup> various regions of the developing world. For the twenty-year<sup>period,official remittances</sup> grew at a faster annual rate than did GDP in 119 low and lower middle-income developing countries (3.86 versus 1.61 percent per year). In two of the<sup>six regionsof the</sup> developing world (Latin America and the Caribbean, Sub-Saharan<sup>Africa), official</sup> remittances also grew at a faster annual rate than did GDP.
Survey respondents were asked to report their individual and household monthly income, but these values are missing for almost 40% of the sample (i.e., 40% of respondents refused to answer). In our sample, mean personal monthly income is US $1,528, while median income is US $2,345. This compares with official 2005 statistics for mean gross income of US $3,010 and suggests that our survey might under-represent high-income individuals (Russian Statistics Office, 2008) or that high-income individuals were less likely to report their income. Therefore, for our main regressions in the next section we first impute missing income observations (using other individual characteristics) and, second, create income quartiles that we include as dummy variables. The survey also included a self-reported categorical measure of buying capacity and all results are robust to the substitution of imputed income brackets with a dummy variable equal to one if individuals reported that they cannot afford to purchase even food or clothes. We also include a variable labeled ̳Income shock‘ if the individual responded ̳Yes‘ to the question, ̳ _Did your family experience an unexpected significant reduction of your income over the past 12 months’_ (35.8% of the sample).

The variable of primary interest to this study is that related to retirement planning, stemming from a question that asked respondents: ̳ _What funds will you live on after you reach retirement age?’_ A set of nine response options was offered, allowing for multiple answers. We distinguish between three primary retirement planning strategies, based on the responses provided: (a) _Planners: private pension funds_ are defined as those who chose at least one of the following responses: ̳Pension that you will receive from a privately owned retirement fund‘, ̳Income from leasing and selling property‘, ̳Additional pension or financial aid from an enterprise where you have been working‘, or ̳Your own savings‘. The total number of respondents in this group is 259 (19%).

(b) _Planners: public pension funds_ are identified as those who responded ̳Pension that you will receive from a publicly owned retirement fund‘. A remarkable 82.4% of respondents replied that they will rely on public funds, which is indicative of the coverage of the public pension system in Russia, and its post-socialist attribute. A portion 5
**Table 2. Discrete time hazard analysis of first‐migration for men and women in Albania, baseline models for men and women separately and combined baseline for both sexes,  Albania LSMS 2005**

|||**Male**|||**Female**|||**Both Sexe**|**s**|
|---|---|---|---|---|---|---|---|---|---|
|**First Migration**|**OR**|**SE(b)**|**P>|z|**|**OR**|**SE(b)**|**P>|z|**|**OR**|**SE(b)**|**P>|z|**|
|**Female**|‐|‐|‐|‐|‐|‐|0.285|0.013|0.000|
|**1991**|0.987|0.149|0.929|1.177|0.377|0.610|1.019|0.138|0.890|
|**1992**|0.882|0.137|0.420|1.280|0.399|0.429|0.951|0.131|0.715|
|**1993**|0.745|0.120|0.069|1.429|0.432|0.238|0.868|0.122|0.313|
|**1994**|0.904|0.139|0.510|2.237|0.627|0.004|1.143|0.150|0.307|
|**1995**|0.889|0.137|0.444|1.208|0.373|0.541|0.942|0.129|0.664|
|**1996**|1.261|0.180|0.104|3.245|0.865|0.000|1.629|0.198|0.000|
|**1997**|1.903|0.253|0.000|3.701|0.975|0.000|2.229|0.259|0.000|
|**1998**|1.737|0.235|0.000|3.022|0.809|0.000|1.960|0.232|0.000|
|**1999**|2.129|0.281|0.000|3.890|1.017|0.000|2.433|0.280|0.000|
|**2000**|1.892|0.256|0.000|3.836|1.005|0.000|2.239|0.262|0.000|
|**2001**|1.803|0.247|0.000|3.949|1.030|0.000|2.190|0.257|0.000|
|**2002**|1.909|0.259|0.000|2.593|0.701|0.000|1.960|0.233|0.000|
|**2003**|1.647|0.231|0.000|2.205|0.604|0.004|1.677|0.205|0.000|
|**Age 20‐25**|1.235|0.079|0.001|1.520|0.147|0.000|1.313|0.070|0.000|
|**Age 25‐30**|1.069|0.077|0.357|1.241|0.134|0.046|1.118|0.067|0.063|
|**Age 30‐35**|0.641|0.058|0.000|0.822|0.109|0.139|0.693|0.052|0.000|
|**Age 35‐40**|0.497|0.056|0.000|0.658|0.110|0.012|0.539|0.050|0.000|
|**Age 40‐45**|0.314|0.053|0.000|0.740|0.147|0.128|0.416|0.053|0.000|
|**Age 45+**|0.088|0.029|0.000|0.330|0.115|0.001|0.136|0.033|0.000|
|**Wealth Index**|1.039|0.016|0.012|1.069|0.020|0.000|1.051|0.013|0.000|
|**At least some HS**|1.028|0.056|0.611|1.707|0.152|0.000|1.203|0.055|0.000|
|**At least some Univ**|0.627|0.066|0.000|1.254|0.148|0.055|0.830|0.064|0.016|
|**Coastal Urban**|1.892|0.197|0.000|1.471|0.173|0.001|1.783|0.145|0.000|
|**Coastal Rural**|1.786|0.173|0.000|0.813|0.101|0.095|1.358|0.104|0.000|
|**Central Urban**|1.391|0.152|0.003|1.177|0.148|0.196|1.331|0.113|0.001|
|**Central Rural**|1.301|0.132|0.009|0.780|0.110|0.078|1.082|0.089|0.337|
|**Mountain Urban**|0.924|0.106|0.492|0.568|0.093|0.001|0.784|0.074|0.010|
|**Mountain Rural**|0.928|0.091|0.445|0.207|0.041|0.000|0.667|0.054|0.000|
|**Number of Cases**||27028|||36134|||63162||
|**Log Pseudo likelihood**||‐6188.9|||‐3487.1|||‐9780.2||

Note: Reference categories are 1990, Age 15‐20, 0‐8 years schooling, and Tirana 24
Deposit dollarization 1985-2004 Unbalanced panel (133 countries) <!-- Start of picture text --> 35<br>30<br>25<br>20<br>Mean<br>15<br>Median<br>10<br>5<br>0<br>%<br>1980 1985 1990 1992 1994 1996 1998 2000 2002 2004<br><!-- End of picture text --> Figure 1: _Trends in deposit dollarization 1980-2004_ . Data for each based on all available countries for that year. Source: Based on data in De Nicoló, Honohan and Ize, (2005); Levy-Yeyati (2006) However, the growth in 2002 is an optical illusion. The number of countries represented in each year changes significantly over time, and it is important to check that there is no sample selection bias in these averages. That becomes evident when we restrict attention to the 76 countries for which data are available for all of the years 1995-2004. For this large sample, both median and mean peak in 2001 and there has been quite a decline since then (Figure 2). The same is true of upper quartile and top decile (Figure 3). Indeed, a prior expectation that the smaller samples of the earlier years would disproportionately represent high dollarization countries does not appear to be confirmed by Figure 2. (Inspection of the countries that drop out of the larger sample because of incomplete data, reveals that the jump in the median of the unbalanced panel after 2001 is partly attributable to the loss from the panel at 2002 of several euro-zone countries which had low dollarization reported, but which drop out of the sample because of the introduction of the euro.) 3
In this paper, we have endeavored to restrict the number of variables that could well depict bonding and bridging attitudes by means of a factor analysis conducted in four steps. As a result of this analysis, we have found that bonding and bridging attitudes are better described as two sides of the same latent variable, rather than as two different latent variables. We have called this new latent variable the _locus of socializing_ to distinguish it from the broader concept of social capital. In fact, we have argued that the _locus of socializing_ can be seen as a specific dimension of social capital and that different individuals or population groups can be more bonding or more bridging when they socialize.

Based on these findings, we have then studied the association between bonding and bridging and the _locus of socializing_ with individual life satisfaction. We find that happier people are those who tend to have more balanced attitudes towards family and friends and towards work and leisure, compared with those who focus on only one type of socializing (bonding or bridging). We also find that more religious people are invariably happier than people more dedicated to politics; that people with a greater appreciation of child obedience are happier than people with a greater appreciation of child independence; and that people who do not justify homosexuality are happier. These are all indications that bonding attitudes may deliver more happiness than bridging attitudes. Instead, more trust in people, as opposed to more trust in institutions, yields more happiness, at least in some world regions. This we have interpreted as a sign that more bridging attitudes may deliver more happiness.

If we compare our results with those on social capital and economic growth reviewed in section 2 of this paper, we find that the recipe for happiness does not necessarily coincide with the recipe for economic growth. In particular, while bridging attitudes seem more beneficial for economic growth than bonding attitudes, a more balanced approach towards bonding and bridging may be conducive to more life satisfaction and, in some cases, bonding may deliver more happiness than bridging. Our findings are very preliminary, but they are likely to be crucial for better understanding of such longstanding issues as the “Easterlin paradox”, i.e. the argument that economic growth is not necessarily associated with growth in happiness.

# **References**

Ainsworth, M.D.S., Blehar, M.C., Waters, E., Wall, S. (1978) Patterns of Attachments: a Psychological Study of the Strange Situation. Hillsdale (NJ): Erlbaum 20
**_Core Indicators for Determinants and Performance of Electricity Sector Reform in Developing Countries_**

# **5.4**

# **VI. Firm-level indicators**

|**Focus area**|**Indicator**|**Data Source**<br>**(e.g. UK)**|**Source/reference**<br>**(example)**|
|---|---|---|---|
|? Electric energy|**? Total units of energy sold (MWh)**<br>? Domestic/industrial/commercial sale (MWh)<br>**? Maximum demand (MW)**<br>? Service area (Sq. km)|Company annual<br>reports and<br>regulatory accounts.<br>Regulator’s<br>publications.|Pardina and Rossi<br>(2000)|
|_? Customers_|**? Total no. of customers**<br>? No. residential/non-residential customers||Jamasb and Pollitt<br>(2003)|
|_? Networks_|**? Total network length (km)**<br>? Low/medium/high-voltage (km)<br>? T otal transformer capacity (MVa)<br>? Low/high-voltage transf. cap. (MVa)<br>? T otal no. of transformers<br>? No. of low/high -voltage transformers<br>**? Total energy loss at T/D networks (MWh)**<br>? T echnical and non-technical energy losses at<br>networks (MWh, % of total energy)||Jamasb and Pollitt<br>(2003)|
|_? Quality of service_|**? Security of service (no. of minutes lost)**<br>**? Reliability of service (no. of interruptions)**||Giannakis et al.<br>(2003)|
|_? Costs and revenues_|**? Total costs (value)**<br>**? Controllable O&M costs (value)**<br>? Labour costs (value)<br>**? Annual capital expenditures (value)**<br>? Electricity capital stocks (value)<br>? Revenues from electricity sales (value)<br>? Net margin (net earnings/revenue %)<br>? Corporate capital employed (value)||Jamasb and Pollitt<br>(2003)<br>Delmas et al. (2003)|
|_? Power generation_|**? Generation capacity – by fuel type (MW)**<br>**? Energy production – by fuel type (MWh)**<br>**? Load factor (avg. load MW/ capacity MW)**<br>? Electricity sold to other utilities (MWh)<br>? Purchased power from other utilities (MWh)|Company annual<br>reports<br>Department of Trade<br>and Industry|Arocena et al. (1999)<br>Hattori (1999)|

24