rafmacalaba/datause-encoder-data huggingface dataset:
measure of trade facilitation (including indicators of port efficiency, customs environment, regulatory environment, and service sector) in a gravity model, Wilson, Mann, and Otsuki (2004) observe a $377 billion increase in global trade of manufacturing goods arising from improvements in trade facilitation. Furthermore they find that the response of exports to unilateral improvements in trade facilitation exceeds that of imports.

All the above studies use partial equilibrium techniques and are somewhat limited in providing estimates of the welfare impacts of trade facilitation. Feeding estimates from Wilson et al. study into a CGE (GTAP) model, Hertel and Keeney (2005) observe that the combined global benefits from the liberalization of merchandise and services trade of $150 billion in welfare gains is boosted by $110 billion p.a. with the addition of trade facilitation. Fox, François and Londono-Kent (2003) and OECD (2003) extended the analysis of trade facilitation within the GTAP framework by explicitly accounting for both direct and indirect trade facilitation costs. The former study observes that trade facilitation improvements between the U.S Mexican border would yield $1.8 billion and $1.4 billion per year in welfare gains to the Mexican and U.S. economy respectively. The latter study estimates that a reduction in trade transactions costs of about 1% of the value of world trade would yield aggregate welfare gains of some $40 billion.

# **MENA and Trade Facilitation**

Though work on the quantitative impact of trade facilitation is gaining importance, very limited quantitative work has been undertaken for the MENA region. Anecdotal evidence (and individual country analyses) on trade facilitation situation in MENA countries does however point to the need for improvements. Customs procedures in many MENA countries are noted to be burdensome, due to: several documentary requirements; multiple agencies with duplicative roles involved in the clearance process; and subjective application of procedures, especially in customs valuation. This causes uncertainty amongst traders and leads to significant costs and delays in customs clearance. The 2001 Investment Climate Assessment for Algeria found that it takes some 11.7 days to clear goods through customs, and in some cases up to 44 days. In Syria, this average is around 15 days, with the typical firm having waited over 30 days for an imported shipment 7
calculated as where _r_ ̄ _f_ is the rank of the last technology that conforms the pure ladder. In words, if the most sophisticated technology that the company adopts in the function is below _r_ ̄ _f_ , then the EXT is computed as a pure quality ladder. If the company has adopted some technology that is in the tree top (i.e. with greater sophistication than _r_ ̄ _f_ ), then the index counts how many of those technologies the company uses, and adds them as in a horizontal relationship. We use formulas (B.1) to (B.4) to compute EXT indices at the function, company-level depending on the structure of the relationship between the technologies in each function.

All the function-company-level, the EXT indices defined so far take values in the closed interval [0,1]. This property is inherited by indices at greater levels of aggregation, since those are simple means of the function-company level indices. For some of the graphical presentations of our results, it is more appealing to re-scale the technology indices so that the lower bound is greater than zero. We therefore re-scale the technology indices in the following way where _T_<sup> ̄</sup> _j,a_<sup>_X_is the EXT (</sup><sup>_X_equal to the empty set) index for unit</sup><sup>_j_at the level of aggregation</sup> _a_ . Note that, the range of our EXT and MOST technology indices will be [1,5].

# **B.4 Regional-level analysis**

In this section, we explain how we examine the relationship between technology and regional productivity. A cross-country analysis of the relationship between technology and productivity using FAT data would face two limitations. First, we only three countries: Brazil, Vietnam, and Senegal. In addition, the data for Brazil covers only one region, whereas the data from Vietnam and Senegal cover the entire country. To overcome these limitations, we focus on the region level to conduct aggregate cross-sectional analysis. Because all three surveys are stratified by region, regional aggregates of our technology measures are representative. Using the sampling weights, we compute the average level, cross-firm variance, and within-firm variance at the regional level for all technology measures (e.g., EXT, MOST, and GAP of ABF, GBF, and SSBF).

Another issue we must confront is that the regional-level data on GDP per capita or labor productivity is unavailable for both Senegal and Vietnam. We overcome this challenge by taking advantage of the fact that our sample of companies is representative at the regional level, and use firm-level information on value added per worker to estimate regional labor 68
**Table 3. 1  Channels of receiving remittances**

|||Domestic r<br>|emittances<br>||R<br>|emittances f<br>|rom abroad<br>||
|---|---|---|---|---|---|---|---|---|
|Channels|Banked|%|Unbanked|%|Banked|%|Unbanked|%|
|Cash|20,175|28%|32,740|56%|7,067|11%|17,716|43%|
|Remittance Agency|7,337|10%|5,798|10%|28,232|45%|19,199|47%|
|Deposit in Account|25,186|35%|9,244|16%|2,909|5%|0|0%|
|Transfer in Account|18,054|25%|10,821|19%|15,747|25.%|0|0%|
|Other|1,271|2%|0|0%|4,237|7%|3,954|10%|
|Total|72,023|100%|58,603|100%|62,707|100%|40,869|100%|

_Source: DANE household study of Bogotá , 2003_ 17. **The cost of sending and receiving remittances is high.** Receiving hand carried cash remittances is not secure and takes time for the unbanked. In order to have access to more secure alternatives such as deposit and account transfer, the unbanked frequently have to pay an obligation or cost for using someone else’s account<sup>45</sup> . Remittance agencies regularly charge up to 25% of the received amount in fees. International remittance agencies charge substantial fees both at the sending and receiving end of remittances. For example, to send US$100 from New York to Bogotá, Western Union charges 12% to the sender<sup>46</sup> and another 7% to the receiver for changing dollars to Colombian pesos. In addition, the remittance sender is responsible for advising the receiver, either by phone, mail or personal message and transmit the order number, since the remittance company does not offer this service. The cost of an international phone call thus needs to be added to the explicit charges. In contrast, fees charged by the formal banking system can be substantially lower, illustrating the high cost of being unbanked. Fees for transferring money from a US bank to a Colombian bank can be as low as $9 (fixed fee) payable by the sender with no additional charges to the recipient. Given the importance of remittances to the Colombian economy<sup>47</sup> , reducing their costs by providing the unbanked with access to the formal banking system, can thus yield significant economic benefits.

# **3.3 SAVINGS SERVICES FOR THE UNBANKED**

18. **The savings capacity of the unbanked in Bogotá is low.** In the survey, only 9% of Bogotá’s unbanked or 5.5% of the total population reported having any form of financial savings. This percentage reflects

> 45 Further work is necessary to understand better the exact costs in time and money for the use of third persons’ accounts.

> 46 Many banks charges a flat US$60- $100 for sending any amount up to $1,000 – a costly rate for small amounts. Further study should advise how other institutions scale remittance charges and whether they are reduced or increased as the amount sent increases.

> 47 Remittances represent about 90% of foreign direct investment in Colombia and are the second largest source of foreign currency entering the country after petroleum exports (Colombian Foreign Relations Ministry).

52
which included the place of purchase variables. The countries which ultimately satisfied the criteria for inclusion span four regions of the world, concentrated in Sub-Saharan Africa and Latin America and the Caribbean, as detailed in Table B1. Unfortunately we were not able to include countries in Asia, since the question on the place of purchase was almost always missing from their budget surveys.

Table B1: Regional Survey Representation

|**Region**|**# Countries **|**Pop. of Surveyed Countries **<br>(Millions)|**Total Pop. **<br>(Millions)|**Proportion of pop.**|
|---|---|---|---|---|
|Sub-Saharan Africa|16|379|1078|35%|
|Middle East & North Africa|2|48|449|11%|
|Europe & Central Asia|2|9|918|1%|
|Latin America & Carribean|10|489|641|76%|
|East Asia & Pacifc|1|9|2328|0.4%|

While some surveys appeared from their questionnaire to satisfy our criteria, we ultimately could not include them, either because of issues with data access, or because when we looked more closely at the data one of our criteria was violated. For completeness, Table B2 further details countries that were considered, but could not be included/ Table B2: Discarded Household Expenditure Surveys

|**Country**|**Survey**|**Year **|**Reason not Included**|
|---|---|---|---|
|**Armenia**|Integrated Living Conditions Survey|2016|PoP often missing|
|**Bosnia & Herzegovina**|Household Budget Survey|2007|PoP asked as a purchasing habit|
|**El Salvador**|Encuesta de Hogares de Propositos Multiples|2010|PoP often missing; limited product categories|
|**The Gambia**|Integrated Household Survey|2003|No Data access to PoP|
|**Gerogia**|Integrated Household Survey|2018|Limited product categories|
|**Ghana**|Living Standards Survey|2006|No Data access to PoP|
|**Guatemala**|Encuesta Nacional sobre Condiciones de Vida|2000|PoP often missing; limited product categories|
|**Mauritius**|Household Budget Survey|2012|PoP asked as a purchasing habit|
|**Namibia**|Namibia Household Income and Expenditure Survey|2015|limited product categories|
|**Nicaragua**|Encuesta Nacional de Hogares sobre Medicion de Nivel|2014|PoP asked as a purchasing habit; limited product categories|
|**Tajikistan**|Household Budget Survey|2016|limited product categories|
|**Turkey**|Household Income and Consumption Survey|2009|No Data access to PoP|

Table B3 lists the 31 countries which we could include, with summary statistics and the structure of each survey. Any slight deviation from our inclusion criteria is outlined in the last column.

74
international markets. Further, our results confirm the importance of accounting for firms export status on the TFP estimation when testing for learning-by-exporting. Not accounting for firm export status leads to underestimate the extra productivity growth of export starters vs. non-exporters (from 7.1% when accounting for firms export status to 1.6% when not accounting for it). However, our found learning-by-exporting effect that only appears in the first year after the start of exporting is somehow poor. One would expect learning to have more dynamics than a one period hike in productivity in the initial year of exporting. This may be pointing out to a short term nature of learning from foreign markets for Brazilian firms and/or to the nature of exports and product markets where they operate (and the possibilities of learning from these markets).

The remaining of the paper is organized as follows. Section 2 presents the data and provides empirical evidence on the differences between exporters and non-exporters in critical variables. Section 3 is devoted to the production function estimation method. Sections 4, 5 and 6 empirically analyze the relationship between firms’ productivity and their export status (a noncausal relationship in Section 4, self-selection in Section 5 and learning-by-exporting in Section 6). Section 7 concludes.

# **2. Data and descriptive analysis of exporters** **_vs._ non-exporters**

In order to analyze firms’ productivity and trade exposure we use a data set that links firm characteristics, production and export data for Brazilian firms for the period 2000 to 2008. For production and firm characteristics, we use the survey PIA _empresa_ (Pesquisa Industrial Anual). PIA is a firm level survey for manufacturing and mining sectors conducted annually by the Brazilian Statistical Institute, IBGE (Instituto Brasileiro de Geografia e Estatistica). Firms with 30 or more employees are included in the sample, while smaller firms of up to 29 workers are included randomly in the sample. Important efforts have been made to minimize attrition and to annually incorporate new firms so that the sample of firms remains representative over time. In total PIA 7
# **I. Introduction**

**Explosive growth.** One of the most interesting financial phenomena of the 1990s was the explosive growth of mutual funds. This was particularly true in the United<sup>States</sup> where total net assets of mutual funds grew from USD 1.6 trillion in 1992 to 5.5 trillion in 1998, equivalent to an average annual rate of growth of 22.4 percent. But, with<sup>the</sup> exception of some East Asian countries (including Japan), it was also<sup>trueof mostother</sup> countries around the world.

The 15 countries that are members of the European Union witnessed an increase in their total mutual fund assets from USD I trillion in 1992 to 2.6 trillion<sup>in1998</sup> (average annual growth rate of 17.7 percent). Among EU member countries,<sup>Greece</sup> recorded the highest growth rate at 78 percent, followed by Italy at 48 percent<sup>and</sup> Belgium, Denmark, Finland and Ireland, all with growth rates of around<sup>35percent.</sup> Some developing countries, such as for example Morocco, registered even<sup>highergrowth</sup> rates, but from much smaller starting points.

In the United States, not only did mutual fund assets grow explosively over this period, but household ownership of mutual funds also experienced rapid<sup>growth.Survey</sup> estimates reported by the Investment Company Institute (the trade association of<sup>US</sup> mutual funds) show that the proportion of US households owning mutual funds<sup>grew</sup> from 6 percent in 1980 to 27 percent in 1992 and 44 percent in 1998 (ICI 2002).' The global growth of mutual funds was fuelled by the increasing globalization of finance and expanding presence of large multinational financial groups in a large<sup>number</sup> of countries and by the strong performance of equity and bond markets<sup>throughoutmost</sup> of the 1990s. A third factor was probably the demographic aging that characterizes the populations of most high and middle-income countries and the search of financial instruments that are safe and liquid but also promise high long-term returns by growing numbers of investors.

**Mutual fund attributes.** Mutual funds offer investors the advantages of portfolio diversification and professional management at low cost. These advantages are

> ' The proportion of US households owning mutual funds continued to increase after 1998 and reached 52 percent in 2001, before falling back slightly to 49.6 percent in 2002 (ICI<sup>2002).</sup>

2
island of Tongatapu). Table 1 compares these characteristics for the ballot winner and ballot loser samples. We see our samples are balanced on gender and island, but not completely balanced on age. Nevertheless, a joint test of orthogonality cannot reject that these three characteristics are jointly balanced. This suggests that our treatment (ballot winner) and control (ballot loser) samples are comparable and we can use the random assignment provided by the lottery to assess causal impacts. Nevertheless, we condition on age, gender, and island of birth to improve power and control for any effect of this slight difference in age, and also consider robustness to alternative weighting schemes that allow for different response rates among subgroups.

Table 1 shows that the average applicant is 41 years old at the time of the follow-up survey. A 10-90 percentile range for age is 32 to 51. Two-thirds of the principal applicants are male.

# _2.3 Survey Variables for Measuring Impacts_

In both the long and short surveys, the PAC principal applicant reported pre-tax earnings in the previous week, and also total weekly earnings of all household members. These earnings, and other monetary values, are converted to New Zealand dollars (NZD) at the average market exchange rate for the month of the interview. The New Zealand CPI rose just 0.4 percent from December 2013 to March 2015, covering the span of the survey, and so nominal values can also be treated as real values.<sup>8</sup> For robustness we will also show impacts using PPP exchange rates to convert the income gains, using prices we collected in both countries.<sup>9</sup>

> 8 Over the same period the Pa’anga exchange rate averaged 0.65 NZD, and had a range of just three NZ cents. To allow comparison with results from other countries, the NZD--USD exchange rate averaged 1.33 over this period. 9 The PPP exchange rates for Tonga from sources such as the International Price Comparison Project and the World Bank are extrapolations from other countries rather than being based on directly collected prices and these extrapolations do not appear to be reliable. See McKenzie et al. (2010).

11
community di¤erences (spatial disparity=0). We adopted a mixed e¤ects model to estimate the spatial disparity index. Our empirical approach has at least two advantages. First, the spatial disparity index can be estimated from di¤erent representative household surveys to ascertain the evolution of spatial disparity over time. The magnitudes of estimated spatial disparity indices for di¤erent years can be compared to check out for spatial convergence or divergence in welfare. Second, the mixed e¤ects model can be utilized to determine the community attributes that are important in explaining spatial di¤erences in welfare.

The factor and non-traded goods prices in a spatial equilibrium model internalize the amenity and productivity di¤erences among communities and household welfare –appropriately adjusted for sorting –are equated across areas. With free factor mobility, welfare di¤erences should thus be explained mostly by household attributes. The role of opportunity becomes more prominent when labor mobility is restricted. There is now credible evidence that even when there are no formal o¢ cial restrictions on labor mobility, migration is costly due to uncertainty about job search as well as travel and other personal costs (Shilpi (2011), Fafchamps and Shilpi (forthcoming), Ravallion and Wodon (1999)). Moreover, people may lack necessary human capital to switch easily between jobs (Puga (1999)). For instance, parents with relatively low human capital may end up living in a village where school access is limited and their children may not have the education and skill to avail themselves better jobs in urban areas. This inequality of opportunity will perpetuate spatial inequality overtime.

The e¤ects of a change in opportunity on spatial disparity in the equilibrium –for instance due to investment in infrastructure and services in some locations –depend on the relative strength of "amenity" and "productivity" e¤ects of investment. Improved access to services such as electricity not only contributes to productivity of ...rms/farms ("productivity" e¤ect) but also enables households to carry out chores more e¢ ciently ("amenity" e¤ect). When productivity (amenity) e¤ects predominate, spatial disparity tends to increase (decrease) in the short to medium term. In 3
Figure 3.2: TURKEY, Kernel density estimates of PF variables (without M.V in PF variables and with M.V in any PF variable) <!-- Start of picture text --> A. Sales B. Materials<br>5 10 15 20 5 10 15 20<br>Log-sales Log-materials<br>Reported sales and the rest of PF vars Reported materials and the rest of PF vars<br>Reported sales with m.v in at least one PF var Reported materials with m.v in at least one PF var<br>C. Labor D. Capital<br>8 10 12 14 16 0 5 10 15 20<br>Employment (hours per year) Log-capital stock<br>Reported employment and the rest of P F vars Reported empl. with m.v in at least one PF va Reported capital and the rest of PF vars<br>Reported capital with m.v in at least one PF var<br>.2<br>.2<br>.15 .15<br>.1 .1<br>Density Density<br>.05 .05<br>0 0<br>.3<br>.2<br>.2 .15<br>Density Density .1<br>.1<br>.05<br>0 0<br><!-- End of picture text --> Notes: Reported X and the rest of PF variables is the distribution of those establishments reporting all PF variables Reported X with m.v in at least one of the rest of P.F is the distribution of those establishments reporting the corresponding PF variable and also reporting at least one missing value in the remaining PF variables Epanechnikov kernel. Each point estimated within a range of 300 values.

Source: Authors’ estimations with ICSs data.

42
divergent estimates of poverty. After the change in recall periods, reported food expenditures increased by about a third and total expenditure increased by about 17 percent. This increase in total expenditure reduced the estimated number of poor by close to 200 million (United Nations Statistical Division, 2005).

The World Bank global poverty estimates for 2012 in contrast continue to use the uniform 30-day recall period in the headline estimates for India, but also report on the difference in the poverty count when using a modified mixed recall period (MMRP).<sup>21</sup> The MMRP contains a shorter, 7-day recall period for a subset of food items, which leads to much higher estimates of consumption and therefore lower poverty estimates. For 2011, the estimates based on the URP suggest a poverty estimate of 21.2%, while the MMRP suggest significantly lower poverty rate at 12.4%, which is a difference of more than 100 million people being moved in our out of the current poverty count depending on the recall period in one country’s survey.

In the case of India, the change in the recall period creates a challenge for making comparisons over time. But the challenge is not limited to India alone, nor just to changing questionnaires over time. Smith, Dupriez and Troubat (2014) assess recent questionnaires for national household surveys from 100 countries, and focus on how data on food consumption is collected. They document large variation in all dimensions of survey implementation from mode of collection, duration of fieldwork, to coverage of food items. They report, for example, that 30 percent of the countries have a recall period for food consumed that is greater than two weeks, while 41 percent have recall periods of less than a week. In comparing extreme poverty based on the $1.90 line across countries, one is implicitly assuming that despite the variation in questionnaire design, consumption is measured comparably.

In order to shed light on this assumption, Beegle et al. (2012) carried out a careful experiment in Tanzania to examine how changing several different aspects of the questionnaire affects measured consumption and, thereby, the estimates of poverty. They administered eight different questionnaires, designed to elicit the same information on consumption, to random subsamples from Tanzania. They tested a variety of relatively common ways of collecting information about consumption, contrasting diary with recall, shorter recall periods with longer

> 21 From 2009, a “modified mixed reference period” (MMRP) was introduced, in which a 30-day recall period was changed to a 7-day recall for key food items, and a one-year recall period for low-frequency non-food consumption items.

14