rafmacalaba/datause-encoder-data huggingface dataset:
The dependent variable is the binary business environment indicator (B ) for firm _i_ . A dummy variable is included that takes a value of 1 if the firm belongs to the group with the standard ES questionnaire, and takes a value of 0 if the firm belongs to the group that received the questionnaire with the business environment perception variables at the beginning ( E E ). Standard errors are robust and clustered at the sector, size, and location levels. The estimation also accounts for the response rate for every section of BBE the questionnaire R . The response rate captures two effects. First, the higher the response rate, the more likely context has had an influence on the respondent. Second, a higher response rate is likely to (RREER RREE) be correlated with survey exhaustion. Response rates are included for the 10 sections. There are certain firm characteristics that may influence the evaluation of the business environment. The estimation accounts for size that include two dummy variables capturing whether a firm is a small or medium-sized firm. The log of firm age (EES EE) (L L is also accounted for as a startup may face a different set of constraints than a long-established firm. Firms that are part of a larger firm may have ways to circumvent certain challenges, EE) and thus a dummy variable is included that takes a value of 1 if the establishment is part of a larger firm and 0 otherwise (M . Firms exporting status is also accounted for given that exporting firms may shoulder the burden of trade permits and face greater exposure to corruption via their need to interact MMRRSSBBR RRM SSRRh) with customs officials . (BBEERRE RR) The survey also includes questions answered by the enumerator. Enumerators do report whether they believe the respondent was truthful. We include two dummy variables capturing whether the enumerator believes the firm was truthful or somewhat truthful, with the omitted category being “not truthful” . Only 12 firms in the survey were identified as such. Financial figures may be obtained from (BBLLBBEERRMM)official books or estimated. We include two dummy variables, one capturing whether the figures are from books, and another capturing if they were estimated with precision ( . The omitted category is “Arbitrary and unreliable numbers,” which 33 firms were tagged under. The estimation also accounts for IILLIIE EEMMEEIIEE) whether the interview was completed in one sitting or through several visits. Two dummy variables are included – one capturing whether the interview was taken in one visit with one respondent, and the second 12
Alibaba’s Rural Taobao program was present, Couture et al. (2017) finds that e-commerce expansion reduces the cost of living for certain groups of the rural population who are induced to use it, though the average effect is muted.

Following the same spirit of Fan et al. (2016) and Couture et al. (2017), our paper examines the relationship between e-commerce development and consumption in China. We extend the model of Startz (2018) to investigate the differential impact of e-commerce on consumption growth by various goods and services. Compared with Fan et al. (2016) and Couture et al. (2017), our paper has a few new features. First, by matching a nationally representative China Family Panel Studies (CFPS) survey with county-level e-commerce information obtained from Alibaba, we can directly examine the impact of e-commerce development on consumption growth at the household level rather than at the aggregate city level as in Fan et al. (2016). Second, our findings are likely more representative because CFPS covers many more counties than the sample used in Couture et al. (2017). Third, using the rich consumption information of the CFPS survey, we can study the heterogenous associations between e-commerce development and various categories of consumption, which were not discussed in Fan et al. (2016) and Couture et al. (2017).

Our paper offers three major findings. First, e-commerce development is associated with higher consumption growth. Lower search cost is a key feature of e-commerce (Bajari and Hortaçsu 2003, Hong and Shum 2006, Brynjolfsson, Dick and Smith 2010, Lieber and Syverson 2011, Levin 2011). Lower search cost makes price discovery easier, bringing the law of one price closer to reality (Gorodnichenko and Talavera 2017). Lower transaction costs increase the level of specialization in the society and create more trade. More competitive prices tend to reduce the cost of living for residents. Couture et al. (2017) show that the expansion of e-commerce to the Chinese countryside is associated with lower cost of living, and for the goods that are available at both the Rural Taobao online terminal and in the village, the median price from the online terminal is cheaper by 15 percent. According to a McKinsey report (McKinsey, 2013), e-tailing may have lowered China’s average retail price by 0.2 to 0.4 percent in 2011 and 0.3 to 0.6 percent in 2012. Holding disposable income constant, lower cost of living means more discretionary spending power, which implies higher consumption.

Second, the impact on consumption is conspicuously larger for the rural residents, inland regions, and the poor households, suggesting that e-commerce development helps reduce spatial inequality in consumption. For people in remote areas with limited access to markets, the saving in search costs and the increase in variety of products accessible online compared with in ·· 3
education or on women’s labor market participation. In fact, women and girls renegotiate these norms of seclusion when opportunities present themselves (Kabeer, 2001; World Bank, 2007).

Finally, the demand for female education in Bangladesh and other South Asian cultures is considered to be low has been due to low opportunities and returns in the labor market. Several studies on India have argued that low returns to education for women, discourage families from educating their daughters (Kingdon and Unni, 1997; Dreze and Gazdar, 1996). Where female labor is valued only in the home and the labor force participation rate in India, Bangladesh or Pakistan does not exceed 37 percent, the returns to education in the form of entry into the labor market is perceived to be low. Thus, this discussion has shown that there are both cultural and economic reasons against educating girls at higher levels, which has to a large extent been responsible for low educational attainment of girls in South Asia.

Recent qualitative work shows however that there has been a widespread change in perception about girls’ education and about gender norms in general. Today, local populations take great pride in the expansion of girls’ education in their areas, and in the impact they see of this on the community, on children’s well-being and on women’s empowerment (Das and Hossain, forthcoming). How and why did this change in perceptions about education come about? At the macro level, we argue that a supply side push for education tapped the latent demand among families of girls, which has existed despite what seem to be conservative norms and values. Once the impact of education on girls and communities became apparent, this fueled further demand. The access to new job opportunities in the garment sector and with NGOs showed families that girls can 9
_5_ labor-exporting country.<sup>7</sup> No other organization<sup>-national or intemational -collects and</sup> publishes annual records on any other part of<sup>the international migration process.</sup> It should, however, be noted that the IMF<sup>only reports data on official worker</sup> remittance flows, that is, remittance monies which<sup>are transmitted through official</sup> banking channels. Since a large (and unknown)<sup>proportion of remittance monies is</sup> transmitted through informal, unofficial channels,<sup>the level of remittances recorded by</sup> the IMF underestimates the actual flow of<sup>remittancemonies retuming to labor-exporting</sup> countries. For instance, a recent IMF study<sup>(El-Qorchi, Maimboand Wilson, 2002)</sup> estimated that informal transfers of remittance<sup>monies could amount to $10 billion per</sup> annum.<sup>8</sup> Despite these problems, Table 1 shows the<sup>level of official worker remittances</sup> received by each of the 24 study countries<sup>over the twenty-year period,1981to2000.</sup> Total official remittances - measured in real<sup>terms-for the 24 labor-exportingcountries</sup> now amount to over $36 billion per annum.<sup>Among thestudy countries,the threelargest</sup> recipients of official worker remittances<sup>in 2000 are:India ($7,994 billion),Mexico</sup> ($5,816 billion) and Turkey ($4,035 billion).<sup>Mexicois also one of the countries</sup> recording the largest percentage rate of increase<sup>in remittancesover the twenty-year</sup> period.

It is interesting to note that that each of the three<sup>largest remittance-receiversis</sup> sending migrants to a different labor-receiving<sup>region. Although data arescanty, India is</sup> 7The IMF records amnual flows in international<sup>remittancesin its publication, Balance of Payments</sup> Statistics Yearbook.

> 8 Focusing on the hawala system of informal transfer, this IMF study of 15 developing countries (2002: 64) estimated that about $35 billion per annum of<sup>remittancemonieswas transmitted throughinformal</sup> channels in the early 1980s, falling to $10<sup>billion per annum in more recentyears.The declinein the level</sup> of informal transfers was attributed to the<sup>disappearanceof theblack marketexchangepremiums inmany</sup> developing countries during the 1990s.
# **Table 4: Dependent variable:** **_Planners: private pension funds_ (1/0)** (Marginal effects from probit models)

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|All 3 responses correct|0.223***|-|-|0.256**|-|-|
|<br>|[0.077]|||[0.107]|||
|<br>Number of correct responses|<br>-<br>|0.046***|<br>-|<br>-|0.054***|-|
|||[0.013]|||[0.018]||
|Interest correct|-|-|0.045**|-|-|0.059*|
||||[0.022]|||[0.031]|
|Inflation correct|-|-|0.006|-|-|-0.014|
||||[0.020]|||[0.029]|
|Risk correct|-|-<br>|0.155***|-|-|0.207***|
||||[0.036]|||[0.048]|
|Age|-0.001|-0.001|-0.001|-0.018|-0.017|-0.019*|
||[0.003]|[0.003]|[0.003]|[0.011]|[0.011]|[0.011]|
|Age squared/1,000|-0.026|-0.018|-0.015|0.192|0.188|0.214|
||[0.036]|[0.036]|[0.036]|[0.132]|[0.132]|[0.132]|
|Female|-0.01|-0.014|-0.014|-0.010|-0.017|-0.015|
||[0.021]|[0.021]|[0.021]|[0.030]|[0.029]|[0.030]|
|Single-person household|-0.04|-0.031|-0.035|-0.012|0.001|-0.004|
||[0.036]|[0.036]|[0.036]|[0.058]|[0.059]|[0.059]|
|Number of household members|0.007|0.007|0.009|-0.001|-0.002|0.001|
||[0.010]|[0.010]|[0.010]|[0.014]|[0.014]|[0.014]|
|Rural region|-0.083***|-0.077***|-0.076***|-0.060*|-0.056*|-0.053*|
||[0.024]|[0.024]|[0.024]|[0.032]|[0.032]|[0.032]|
|High-school|0.089|0.089|0.102|0.095|0.112|0.138|
||[0.061]|[0.061]|[0.062]|[0.107]|[0.107]|[0.113]|
|Technical|0.116*|0.113*|0.126**|0.131|0.143|0.169|
||[0.061]|[0.060]|[0.061]|[0.100]|[0.099]|[0.103]|
|Some college|0.113|0.109|0.127|0.145|0.148|0.189|
|<br>|[0.091]|[0.090]|[0.093]|[0.151]|[0.149]|[0.159]|
|College|0.128*|0.120*|0.139*|0.157|0.167|0.203*|
||[0.074]|[0.072]|[0.075]|[0.117]|[0.116]|[0.123]|
|2nd quartile|-0.008|-0.011|-0.008|-0.012|-0.012|-0.006|
||[0.033]|[0.033]|[0.033]|[0.048]|[0.048]|[0.048]|
|3rd quartile|0.014|0.008|0.001|0.023|0.021|0.007|
||[0.035]|[0.034]|[0.034]|[0.049]|[0.049]|[0.049]|
|4th quartile (highest)|0.112***|0.104***|0.095**|0.153***|0.146***|0.135**|
||[0.041]|[0.040]|[0.040]|[0.054]|[0.054]|[0.054]|
|Has experienced income shock|0.060***|0.059***|0.051**|0.111***|0.110***|0.102***|
|in the last year|[0.022]|[0.022]|[0.022]|[0.030]|[0.030]|[0.030]|
|Self-Employed|0.042|0.043|0.035|0.067|0.062|0.055|
||[0.064]|[0.063]|[0.062]|[0.078]|[0.077]|[0.075]|
|<br>Non-employed|<br>-0.032|<br>-0.036|<br>-0.034|<br>-0.084**|<br>-0.089***|<br>-0.086***|
|<br>|[0.026]|[0.025]|[0.026]|[0.033]|[0.032]|[0.033]|
|<br>Retired|<br>-0.001|<br>0.002|<br>0.004|<br>-|<br>-|<br>-|
||[0.036]|[0.036]|[0.036]||||
|No. of Observations<br>|<br>1,366|1,366|1,366|814|814|814|
|Pseudo R<sup>2</sup>|0.109|0.111|0.120|0.083|0.084|0.101|
|<br>Log-Likelihood<br>|<br> <br>-590.9<br>|<br>-589.9<br>|<br>-583.8<br>|<br>-393.3<br>|<br>-392.7<br>|<br>-385.5<br>|
|LRχ<sup>2</sup> <br>Notes<br>:* p<0.10, ** p<0.05, ***|134.11***<br>p<0.01.|133.77***|144.70***|66.31***|69.63***|81.21***|

<u>Notes: * p<0.10, ** p<0.05, *** p<0.01.</u> Our second set of estimates, presented in Table 5, allows for a more detailed distinction between the three retirement fund groups. The estimation method is a multinomial probit model, and marginal effects, along with robust standard errors, are 14
# **Figure 8. Selected areas for the IBLI pilot program.**

Source: Authors.

**Table 4. Aimag correlation of mortality rates**

|Aimag|Species|Cattle|Sheep|Horse|Goat|
|---|---|---|---|---|---|
|Bayankhongor|Cattle|1.00||||
||Sheep|0.87|1.00|||
||Horse|0.92|0.83|1.00||
||Goat|0.92|0.94|0.87|1.00|
|Khentii|Cattle|1.00||||
||Sheep|0.35|1.00|||
||Horse|0.51|0.62|1.00||
||Goat|0.91|0.94|0.87|1.00|
|Uvs|Cattle|1.00||||
||Sheep|0.62|1.00|||
||Horse|0.86|0.66|1.00||
||Goat|0.72|0.90|0.76|1.00|

Source: Authors from AFSC (2004).

The size of the animal population varies greatly between species within a _soum._ Limited data make mortality rates less credible, i.e., the mortality rates and their variability can be questioned. A potential consequence would be to provide over-payments or under-payments, thus increasing the potential dissatisfaction with the BIP program, either on the part of the insured herders or the private insurance providers. The limited credibility fluctuation approach is performed to identify the minimum number of animals under which the data are not credible and thus cannot be safely used to base payments on (see Box 5). A threshold of 3,000 animal heads was selected. A proxy is used for the _soums_ having less than 3,000 animals of a specific species. Given this criterion, cattle are excluded in 17% of _soums_ and horses are excluded in 19% of _soums_ . This represents 21
costs in 1999. Changes in employment are utilized as a proxy for changes in the wage bill, with mean employment increasing between 12 and 16%. The percentage by which investment in IT/telecoms hardware and software is higher or lower in 1999 than in 1997 is used as a proxy for changes in IT-related costs.

In addition to data on possible enterprise-based performance gains from cost efficiencies, data was collected on changes in sales revenues for the most important product line for each enterprise. Changes in sales for the median enterprise and mean values are substantially higher for Hungary than the Czech Republic and Poland, no doubt driven by the relative boom experienced in Hungary at the time: real GDP growth of 4.9% between 1997 and 1998, and 4.2% between 1998 and 1999, with real increases in industrial gross output of 12.4 and 10.4%, versus real increases in industrial gross output of 2.8 and -0.4% for Czech Republic and of 4.8 and 4.4% for Poland.

Two key characteristics of an ‘innovative’ logistics service is that it not be fully adopted by all users, that is, only adopted by a limited sub-set of enterprises, and that it be relatively recently introduced in the market by providers (or conversely recently adopted by users). Of the logistics-related variables collected in the survey, three variables that meet this requirement are reported in Table 2: use of Just-in-Time consolidation and sequencing services, use of bar coding and tracking services, and use of packaging and labeling services. In the case of JIT services, 43 enterprises or 9% of the full sample make use of these services, while for bar coding/tracking and packaging/labeling the respective figures are 33 enterprises (7% of the sample) and 53 enterprises (11% of the sample). Regarding the above-mentioned ‘recent introduction into the market’ criterion, 82% of JIT users first started using the services after 1995, while the comparable figures for bar coding/tracking and packaging/labeling are 70% and 67% respectively.

12
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23
12 reported above? To address this question, we ran another partial liberalization scenario in which we:

- remove all cotton export subsidies globally,

- remove tariffs on imports by all high-income countries (HICs) of cotton from pertinent UN-defined least-developed countries (LDCs, comprising South Asia excluding India, Pakistan and Sri Lanka plus Sub-Saharan Africa excluding Nigeria and the Southern African Customs Union in terms of our regions),<sup>7</sup> and

- reduce by one-third all applied cotton production subsidies in all high-income countries (not just in the US as in the previous partial reform scenario).<sup>8</sup>

# **_Comparison of the two partial-reform scenarios with the full-liberalization results_**

Impacts of this Doha partial reform simulation and the US-only partial reform simulation on regional welfare and on cotton net farm income are reported in Table 4 and in Figures 1 and 2, and the effects on cotton output and exports are shown in Table 5, from which several points are worth stressing.

First, the US-only reform would provide virtually all of the net benefits to the US economy that are generated by the Doha scenario, but only around three-fifths of the estimated net welfare and net cotton income effects, and two-fifths of the export effects,

> 7 For the list of LDCs, see <u>http://www.un.org/special-rep/ohrlls/ldc/list.htm. Due to regional</u> aggregations in the GTAP dataset we use, our Sub-Saharan African group has some non-LDCs (including Zimbabwe, although it – like the three poorest Central Asian nations – probably now qualifies as an LDC) while LDCs in other regions are not so classified because they are too small a part of ‘rest of region x’ categories.

> 8 There may be also some reduction in bound cotton tariffs as a result of the non-agricultural market access negotiations, but we ignore that by assuming applied tariffs are sufficiently below bound rates (‘binding overhang’) for the latter to remain unchanged, which is especially likely in developing countries as they are to be allowed to make lesser cuts than high-income countries (under so-called Special and Differential Treatment, SDT – see Anderson and Martin 2006). Similarly, because of binding overhang also in domestic subsidies, and SDT, we assume developing countries will not have to lower their cotton production subsidies.
drawing on the financial SAM, the IMMPA model could also provide the framework for investigating effects of alternative macroeconomic policies on income and poverty at the household and individual levels, exploiting the linkages between national accounts and household surveys. These will be considered in future research.

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