rafmacalaba/datause-encoder-data huggingface dataset:
Data on the bilateral intensity of China’s processing trade provide further evidence of this triangular trade structure. As it is shown in Figure 5, East Asian economies more intensively supply China with processing inputs than countries outside of East Asia. Except for Indonesia and Vietnam, more than 35% of China’s imports from its major East Asian trading partners were processing imports in 2007 (see Figure 5). Almost 40% of its imports from Japan and between 40% and 60% of its imports from the Newly Industrialized Economies were aimed at supplying inputs for processing industries. This is a significantly higher share than for Western economies. The share of processing imports in China’s total imports from the EU-19, Canada and the United States amounted to 15.4%, 17.6% and 25.0%, respectively.

## **[Figure 5 about here]**

At the same time, China more intensively supplies processed goods to developed economies than to its East Asian neighbors. As it is shown in Figure 6, more than 50% of the exports that China sends to the United States, the EU-19 and Japan are processing exports. For most developing East Asian economies the number is significantly lower.

## **[Figure 6 about here]**

The triangular trade pattern suggests that China is primarily used as an export platform by East Asian firms that sell their goods to Western markets. However, in a cross-section of 29 Chinese provinces, the weighted average distance traveled by processing imports (import distance) has been negatively correlated to the weighted average distance travelled by processing exports (export distance) for most years between 1995 to 2008 (Ma, Van Assche and Hong, 2009). In other words, locations in China that import their processing inputs from nearby tend to export their processed goods far away and vice versa.

# **4. Trade Costs and Intra-GPN Trade**

To understand the role of trade costs on China’s processing trade, we in subsection 4.1 develop a theoretical model that builds on a recent literature about export platform FDI 11
Models 1, 2, 8 and 9, which require predictors related to household demographics, employment attributes, housing characteristics, assets, utility expenditures, and (2) A shorter version of the TA 1 food consumption module - with an identical set-up/set of questions but with a reduced list of food items – aligned with the earlier Survey of Household Welfare and Labour in Tanzania (SHWALITA) and specifically the “short list” treatment arm in that study.<sup>4</sup> The TA 2 food consumption module is slotted immediately after the TA 2 core modules, covering 26 items out of the 71 items included in TA 1.<sup>5</sup> These selected items account for 69 percent of the monetary value of food consumption in TA 1, indicating that the reduced list of food consumption items under TA 2 misses out on a considerable share of the food expenditure compared to the full TA 1 food consumption module. As discussed later, TA 2 data on food consumption are used to estimate an additional imputation model, namely Model 3 as presented in Dang _et al._ (forthcoming), which includes household food consumption expenditures as a predictor. The TA 2 sample consists of 701 households. Table A.2 in Appendix A presents expenditures on these food categories for TA 2 and TA 3 in comparison with those from TA 1.

Finally, Treatment Arm 3 (TA 3) administered an alternative light questionnaire variant that includes:

- (1) The same TA2 core modules that allow for the estimation of Models 1, 2, 8, and 9 as presented in Dang _et al._ (forthcoming),

> 4 For more information regarding SHWALITA, please see Beegle et al. (2012) and visit <u>https://www.uantwerpen.be/en/staff/joachim-deweerdt/public-data-sets/shwalita/#introduction.</u> 5 TA 2 covers 13 individual food items and 4 item categories corresponding to 13 items on TA1. The 13 individual items include: rice (husked); maize (grain); maize (flour); millet and sorghum (flour); cassava fresh; cassava dry/flour; sweet potatoes; cooking bananas and plantains; sugar; beef including minced sausage; dried/salted/canned fish and seafood; fresh milk; cooking oil. The 4 grouped item categories (covering 13 items in TA 1) include: peas, beans, lentils, and other pulses; Onions, tomatoes, carrots, and green peppers; Spinach, cabbage, and other green vegetables; and Fresh fish and seafood.

8
_The Creative Wealth of Nations_ For instance, when cable companies first began to provide Internet service that eliminated the need for telephone lines, few customers signed up. As Edwin Mansfield and Gary Yohe explain, “One issue was, and continues to be, cost.” Broadband access was more expensive than phone connections, and even at the same price, demand for broadband was diluted by difficulties such as installation and congestion. Also, “few consumers saw a fundamental need for more speed for the services that they were then demanding.”<sup>109</sup> But things have changed: Major companies have become convinced that movies on demand, instant music downloads, and the next generation of broadband software and interactive television will begin to increase consumer demand for nanosecond communication. Indeed, Comcast, Inc., of Pennsylvania used backing from Microsoft to win a bidding war with AOL Time Warner for AT&T Broadband in December 2001. The combined bid of $72 billion speaks volumes about what these companies see as the future—a future that must be driven by consumer demand.<sup>110</sup> This example is of particular interest here because it supports the hypothesis that consumer demand in cultural products such as music can help drive major investments in other areas.* The WBG’s strategy to utilize the performing arts via this concept could fall under the following categories, for example:

# _<u>Nation Branding</u>_

Branding is a modern coinage that came from advertising and marketing in the twentieth century.<sup>111</sup> So, such a nomenclature as ‘nation branding’ (‘or country branding’) may sound rather counterintuitive in development practice. Nevertheless, today, a country’s brand is closely tied to the full spectrum of its political, cultural and commercial history.<sup>112</sup> Why does this matter? According to Futurebrand, Country branding is a vital element in both domestic and international affairs. The difference between a successful, defined and understood brand and a weaker, less differentiated one can have a significant impact on a nation’s attractiveness for investment as well as tourism, and can compromise domestic confidence and social unity. [Consequently, country] brands are more than just tourism.<sup>113</sup>

> 109 Edwin Mansfield and Gary Yohe, _Microeconomics: Theory and Applications_ , 11th ed. (New York; London: Norton, 2004), 31.

> 110 Ibid.

> * Recent arts-related purchases include Apple’s acquisition of Beats, a headphone maker that also offers a music-streaming service. See: Hannah Karp and Alistair Barr, “Apple Buys Beats for $3 Billion, Tapping Tastemakers to Regain Music Mojo,” _Wall Street Journal_ , May 28, 2014, accessed July 14, 2014, <u>http://online.wsj.com/articles/apple-to-buy-beats-1401308971.</u>

> 111 FutureBrand, _2011-2012 Country Brand Index_ , accessed February 8, 2013, - <u>http://www.futurebrand.com/images/uploads/studies/cbi/2011 2012_FutureBrand_CBI_ENG.pdf.</u> 112 Ibid. 113 Ibid.

<mark>31</mark>
# **1. Introduction**

A growing body of evidence points to the role of risks, shocks and vulnerability in perpetuating poverty (Morduch, 1990; Skoufias and Quisumbing, 2003; Dercon, 2004; Dercon, Hoddinott and Woldehanna, 2005). While all households are negatively affected by uninsured shocks, relatively poorer households are likely to lack the necessary human and physical capital to recover from them (Del Ninno and Marini, 2005). Some shocks can have long-lasting effects leading to chronic poverty (Dercon, 2004; Dercon, Hoddinott and Woldehanna, 2005) and adverse human development outcomes (Foster, 1995; Jacoby and Skoufias, 1997; Jensen, 2000). Thus, understanding the nature of shocks, identifying those vulnerable to them, and analyzing households‟ ability to cope with shocks is important to help design programs and interventions to prevent households from falling into poverty and/or compromise their human capital.

While Bangladesh has witnessed a remarkable 28 percentage point drop in poverty over the last two decades (from 59% in 1990 to 31.5% in 2010 using Household Income and Expenditure Survey data), a sizeable number of Bangladeshis remain clustered around the poverty line. This implies that even a small shock can cause large movements in poverty rates as many non-poor remain highly vulnerable in an environment with limited insurance markets. But what type of shocks really matter to Bangladeshi households and how do they cope?

Despite the pervasive nature of shocks in Bangladesh, particularly weather related covariate shocks, there is limited knowledge of their incidence and the coping mechanisms adopted by households to deal with them. Quisumbing (2007) in a longitudinal study in rural Bangladesh finds more than half of all sampled households to have been affected by shocks in a ten year period (1997-2006/07). The three most frequent shocks reported include expenses and foregone income due to illness, dowry and wedding related expenses, and floods. The data suggests households with lower endowments in terms of education, and land/asset ownership, are more vulnerable to shocks. The study, however, is unable to shed any light on how households cope with these shocks.

This paper uses household survey data collected in September-October 2009 on a nationally representative sample of 2000 households in Bangladesh to examine the nature of 2
lower value of _Ac_ in low-income economies.

To explore whether the estimates in columns 1-4 reflect omitted variable bias or correctly reflect that _Ac_ is not lower in low-income economies, we split the sample of establishments along potential proxies for the omitted variable. We then examine if there is a differential association between _Sj_ and productivity within the subsamples for high- and low-income economies. If no differential association is observed, the case for omitted variable bias is weakened.

We consider two proxies for the omitted variables: the establishments’ human capital and size, respectively measured by the fraction of college-educated workers and the number of employees. We split establishments between those above and below the median fraction of college-educated workers (columns 5-6 of Table 13) and those above and below the (unweighted) median number of employees (columns 7-8 of Table 13). In all four subsamples, we find that the coefficient of _Sj_ is not higher in high-income economies than in low-income economies. This finding suggests that the failure to find a stronger association between technology sophistication and productivity in high-income countries is unlikely to be due to the omission of variables that affect productivity more in low-income economies. This leads us to conclude that our findings suggest that more sophisticated technologies are generally appropriate for use in all countries, regardless of their development level.

# **7 Conclusions**

This paper presents a new approach to comprehensively characterize the technologies used in an establishment. Introduces a tool, the grid, that describes the key business functions involved in production and the possible technologies to perform the main tasks in each function. We have implemented this methodology and assembled a dataset covering over 21,000 establishments in 15 countries at all stages of development. An exploration of the FAT dataset has uncovered three main findings. First, the most widely used technology in a business function (MOST) typically is not the most sophisticated technology available (MAX). This gap between MAX and MOST is not transitory. It reflects the different nature and dynamics of the two upgrading processes: adoption versus extension of the use of an adopted technology. Second, there are large differences in technology sophistication across establishments. Factors associated with greater technology sophistication include the establishment size, the human capital of its employees, the quality of the managerial practices, being an exporter, and being part of a multinational or a multi-establishment firm. Third, there is a strong and robust cross-establishment association between technology sophistication and productivity both within and between countries. This relationship (i) is linear, (ii) is much 33
Table 6: Coffee price shock, borrowing, and assistance

||Received Credit|Total Credit|Received Assist|Total Assist|
|---|---|---|---|---|
|Land share of cofee|-0.240|-982.719**|0.068|-162.086|
|* LogPrice|(0.179)|(459.695)|(0.079)|(242.118)|
|_N_|9968|9968|9968|9933|
|_R_<sup>2</sup>|0.530|0.369|0.617|0.512|

Notes: This table is based on three rounds (2011, 2013, and 2015) of ESS survey. Standard errors are clustered at zone level. All regressions include household and zone-year fixed effects, and log rainfall. _Received Credit_ and _Received Assistance_ are dummy variables indicating whether the household has received loan or transfers from formal or informal sources. _Land share of coffee_ is the fraction of household land allocated to coffee in 2011. _LogPrice_ is the log of average coffee price over the four quarters before the survey month. * _p <_ 0 _._ 10, ** _p <_ 0 _._ 05, *** _p <_ 0 _._ 01 Table 7: Credit, assistance and consumption smoothing

||Cre|dit|Assis|tance|
|---|---|---|---|---|
||Total AEC|Food AEC|Total AEC|Food AEC|
|Land share of cofee * LogPrice|0.817***<br>(0.197)|0.827***<br>(0.202)|0.821***<br>(0.212)|0.822***<br>(0.215)|
|Land share of cofee * LogPrice * Credit|-0.042*<br>(0.023)|-0.056*<br>(0.033)|||
|Land share of cofee * LogPrice * Assistance|||0.003<br>(0.029)|0.024<br>(0.030)|
|_N_|9525|9525|9525|9525|
|_R_<sup>2</sup>|0.651|0.609|0.650|0.608|

Notes: This table is based on three rounds (2011, 2013, and 2015) of ESS survey. Standard errors are clustered at zone level. All regressions include household and zone-year fixed effects, and log rainfall. _Land share of coffee_ is the fraction of household land allocated to coffee in 2011. _LogPrice_ is the log of average coffee price over the four quarters before the survey month.* _p <_ 0 _._ 10, ** _p <_ 0 _._ 05, *** _p <_ 0 _._ 01 28
conducted on total portfolio flows. Subsequently, the tests are repeated for debt portfolio flows and equity portfolio flows in order to assess whether these portfolio flows are functionally related across regions. Given that equity portfolio flows have greater risk sharing characteristics between investor and recipient than debt portfolio investment (through bonds, certificates of deposit and Commercial paper), knowing which flows are related across regions is crucial **in** narrowing down the type of policy prescriptions that would be relevant in each case.

The first step is to estimate the cross correlation matrix for the eight countries in the sample to get a preliminary idea about any possible relationships between portfolio flows across countries over time. Then the autocorrelation plots are examined. In addition, _Box-Pierce Q-tests_ and _Ljung-Pierce 9-tests_ are subsequently conducted in order to test for autocorrelation in each time series, for each country and each series (i.e. total quarterly portfolio flows, dcbt portfolio flows, and quity portfolio flows, respectively) for the sample period. This allows us to get a preliminary idea about whether the time series for an individual country are serially correlated (i.e. whether the autocorrelation coefficient is significantly different from zero) or not. In order to construct a panel for each region (i.e. Asia and Latin America) in our sample that is stationary, it is crucial that each individual series that makes up the panel to be stationary itself. If this is not found to be so, the particular series has to be differenced first to achieve stationarity before combining them to get the regional panel. Next the _Augmented Dickey-Fuller (ADF) Unit Root tests_ are conducted _for_ each country's time series of portfolio flows. For a AR(1) process, this test consists of running a "levels" regression of the form: _Y, =c+pY(I-n)+eI_

- where c and p are parameters and et is a random error term et LD (0, ca). In this case,

16
Table 4: Robustness Checks

||(1)|(2)|(3)|
|---|---|---|---|
||IV:_Growtht−_1|IV:_Growtht_+1|IV: All instruments|
|**Panel A: Weekly profts(**_l_|_og_(_x_+ 1)**transfor**|**mation)**||
|Opportunity|12.482<br>[29.381]|1.218<br>[0.985]|1.368<sup>_∗_</sup><br>[0.806]|
|**Panel B: Composite Busin**|**ess Practice score**|||
|Opportunity|7.584<br>[17.986]|1.785<sup>_∗_</sup><br>[1.030]|2.138<sup>_∗∗_</sup><br>[0.881]|
|**Panel C: First stage**||||
|_Growth_|-|-|1.183<sup>_∗∗_</sup><br>[0.575]|
|_Growtht−_1|0.200|-|-0.286|
||[0.467]||[0.494]|
|_Growtht_+1|-|1.402<sup>_∗∗∗_</sup>|1.097<sup>_∗∗_</sup>|
|||[0.513]|[0.553]|
|F-statistic|0.184|7.476|3.856|
|_Prob > F_|0.668|0.006|0.009|
|Observations_†_|3531|3531|3531|
|Year of openingdummies|Yes|Yes|Yes|

Robust standard errors in brackets;<sup>_∗_</sup> _p <_ 0 _._ 1,<sup>_∗∗_</sup> _p <_ 0 _._ 05,<sup>_∗∗∗_</sup> _p <_ 0 _._ 01 _†_ Observations in top 1% of weekly profits ( _log_ ( _x_ + 1) transformation) were not included.

14
variable ends up being zero for a large proportion of the sample.'<sup>7</sup> In order to overcome this problem we use a stock indicator-household wealth as captured by the real value of household durables. In addition we also use the real value of agricultural assets for rural households and the real value of urban property for urban households.

# III. Data and Summary Statistics

The data used in this paper are the LSMS panel of Peru. The 1994 and 1997 subsets were used to construct the panel.'<sup>8</sup> This yielded a total of 898 households and 2095 individuals.

The data set contains information on three components of time use-housework, selfemployment and wage work. Housework is not disaggregated further into its various components such as child care, cooking, energy or water collection etc. This restricts the manner in which hypotheses can be tested. For instance, the sickness of a child may result in the mother reducing the time she spends on cooking and increasing care for the sick child. If this happens, then the effect of the child's sickness on the mother's housework would be zero. Of course someone else in the household would have to substitute for her time in preparing food. The lack of disaggregation of the housework variable also prevents us from explicitly testing hypotheses regarding energy and water infrastructure on time use (these are discussed in detail below). Note however that this limitation does not prevent us from testing the effect of these variables on time use in income-generating activities.

Reliable community-level data are not available in the Peru LSMS. These variables are constructed from the data itself, aggregating up to the segment level. Note that in aggregating up to the cluster level we do not just use the panel households but the larger sample in each of the

**"** What is left is typically pensions, rents and other small sources of income which are typically zero for a large proportion of the sample.

**I'** Earlier years of the Peru LSMS can also be hooked up together in a panel. However, the 1994 and 1991 have not been set up as a panel at the individua **l** evel. Regardless, going to an additional year (1991) would either substantially reduce the sample size for a balanced panel, or it would yield an unbalanced

12
measures of district-level infrastructure such as the number of post offices, schools, banks and health posts. Nepal has very few natural resources which can be easily expropriated (such as oil or diamonds); the only natural resource which the Maoists have expressed an interest in controlling is land. To capture geographic characteristics which make insurgency easier, we use the altitude (elevation) of the district to indicate the presence of hilly territory, as well as the proportion of district area which is forested. We construct a measure of the presence of roads as the total kilometers of road length, normalized by the area of the district. We also construct measures of government investment by looking at the change in the provision of roads and post offices in the 1990s.

To measure the extent of social divisions, we use information on the caste and language divisions in Nepalese society. We collected data on the number of people in different caste groups, as well as speaking different languages from the census of 2001.<sup>10</sup> We consider all castes which constitute more than 1% of the population. This gives us 76 categories, which we use to construct a diversity index. The index we use is the measure of polarization, proposed by Esteban and Ray (1994). Montalvo and Reynal-Querol (2005) find that ethnic polarization is highly correlated with the incidence of civil war in cross-country data.<sup>11</sup> This index attains a maximum value of 1, when the society is divided into two equal-sized groups, thus maximizing the chances of conflict. The index attains a minimum value of zero, either when the society is completely homogenous, or when every single person belongs to a different group. We also compute a measure of caste fractionalization, similar to the ethno-linguistic fractionalization measures used in several cross-country studies. This fractionalization measure captures the probability that two randomly selected persons would belong to different caste groups. We also use two alternative measures for the dominance of upper castes (Brahmins, Chhetris, Thakuris and Newars) in the district: one is simply the proportion of these castes in the population, and the second is a dummy which equals one if the proportion of these castes is between 45% and 90%.

> 10 We were unable to get this at the district level from the pre-conflict 1991 census; however, the nationwide proportions have changed very little between 1991 and 2001.

> 11See the Data Appendix for the details of the construction of the index.

10
|**Table 1: Base reg**|**ression resul**|**ts for the lev**|**el of laborp**|**roductivity**||
|---|---|---|---|---|---|
|Dependent variable: Labor<br>Productivity (logs)|**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|
|Ethnic Fractionalization|-2.127***|-1.107***|-1.045***|-0.969***|-0.911***|
||(0.395)|(0.322)|(0.324)|(0.318)|(0.315)|
|GDP per capita (logs)||0.594***|0.573***|0.570***|0.514***|
|||(0.090)|(0.088)|(0.088)|(0.113)|
|Log of Number of workers||0.247***|0.192***|0.169***|0.163***|
|(lagged)||(0.036)|(0.034)|(0.034)|(0.035)|
|Age of firm (logs)|||0.115*|0.134**|0.135**|
||||(0.059)|(0.060)|(0.060)|
|Manager experience (years, logs)|||0.041|0.021|0.012|
||||(0.060)|(0.059)|(0.056)|
|Exports (proportion of sales)|||0.232*|0.209*|0.244**|
||||(0.119)|(0.113)|(0.113)|
|Foreign Ownership Y:1 N:0|||0.188*|0.186*|0.182*|
||||(0.101)|(0.101)|(0.100)|
|Firm is Financially Constrained|||-0.288***|-0.261***|-0.261***|
|<br>Y:1 N:0|||(0.075)|(0.076)|(0.072)|
|Female Top Manager Y:1 N:0||||-0.240***<br>(0.070)|-0.216***<br>(0.068)|
|Firm Provides Training Y:1 N:0||||0.001*|0.001*|
|||||(0.001)|(0.001)|
|Courts Obstacle Y:1 N:0||||-0.023|-0.009|
|||||(0.064)|(0.059)|
|Total hours of power outages||||-0.001**|-0.001**|
|||||(0.000)|(0.000)|
|Firm Purchased Fixed Assets Y:1||||0.143**|0.157**|
|N:0||||(0.063)|(0.065)|
|Time Tax||||-0.001|-0.001|
|||||(0.002)|(0.002)|
|Population (logs)|||||0.028|
||||||(0.054)|
|Primary Education|||||-0.008|
||||||(0.005)|
|Freedom from Business|||||0.021|
|Regulations|||||(0.098)|
|<br>Merchandize Trade to GDP ratio|||||-0.002|
||||||(0.003)|
|Growth rate of GDP per capita|||||0.022|
|<br>(%, annual)|||||(0.021)|
|Inflation (GDP deflator)|||||0.015|
||||||(0.013)|
|Rule of Law|||||<br>-0.063|

44