rafmacalaba/datause-encoder-data huggingface dataset:
including the US GDP (or GNI) deflator, which is the most obvious candidate if the thresholds and GNI per capita estimates are to use US dollars as their common numeraire. Other candidates reviewed include average measures based on G20 countries, average global measures, and measures which represent the countries close to the thresholds—as suggested in a previous review (World Bank, 1983b). Different options for producing average measures have been used, include commonly used simple medians, unweighted and weighted arithmetic means, and the geometric mean. Weights can be based on relevant macroeconomic variables, such as the size of currency reserves, the size of exports, GDP, or population size. In all, for this review, 12 different candidate deflators have been examined.

Detailed results are presented in Annex 1. Overall, World unweighted-mean and population-weighted deflators are more sensitive to changes in lower income economies; inflation in many lower income economies has been higher than in higher income economies in the past few years, so these measures tend to provide a much higher measure of inflation for the period tested than the SDR deflator. Similar results hold when using the average and median deflators based on countries in bands close to each threshold for the low/middle and lower middle/upper thresholds. The middle/high threshold deflator follows a similar pattern to the SDR, US, G20 and GDP-weighted World deflators. Only very small differences are observed in the World deflator when using weights derived from the SDR weighting method (the value of reserves held by other governments, plus exports, in US$) or straightforward exchange-rate based GDP, partly because reserves and exports are converted with market exchange rates.

The deflators with the lowest overall tendency for volatility (i.e., relatively large numbers of countries changing classification each year) are the SDR, US, G20 and World (GDP weighted) deflators, reflecting the relative stability over the period reviewed. Deflators that are based on medians rather than weighted means show the lowest annual variability; the deflators that use the SDR averaging method, the World unweighted average, or the countries in the threshold “bands” appear to show the highest volatility. In some cases, more than 10 countries changed category compared with the previous year.

An alternative approach to adjusting thresholds over time and using current price series would be to use constant price estimates, with some specified base year. In this case, thresholds would be set at a constant level, eliminating the need for estimating “international” inflation.

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of construction was cited as the reason (and was also slightly more popular when high prices and a lack of rental housing were cited).

# **Table 6: Experiment Results**

||Do N|othing|Restrict|Migration|Change|Propiska|Sub<br>Ho|sidized<br>using|
|---|---|---|---|---|---|---|---|---|
||(1)<br>Mean|(2)<br>Marginal<br>Change|(3)<br>Mean|(4)<br>Marginal<br>Change|(5)<br>Mean|(6)<br>Marginal<br>Change|(7)<br>Mean|(8)<br>Marginal<br>Change|
|Control|0.06***||0.22***||0.38***||0.35***||
||(0.008)||(0.014)||(0.016)||(0.016)||
|High Prices|0.06***|0.00|0.21***|-0.01|0.34***|-0.03|0.39***|0.04*|
||(0.008)|(0.011)|(0.014)|(0.019)|(0.016)|(0.022)|(0.017)|(0.023)|
|Pace Construction|0.06***|0.00|0.17***|-0.04**|0.35***|-0.02|0.41***|0.06***|
||(0.008)|(0.011)|(0.013)|(0.019)|(0.016)|(0.022)|(0.017)|(0.023)|
|Propiska|0.05***|-0.00|0.18***|-0.04**|0.44***|0.06***|0.33***|-0.02|
||(0.008)|(0.011)|(0.013)|(0.018)|(0.017)|(0.022)|(0.016)|(0.022)|
|Rent Scarcity|0.06***|0.01|0.20***|-0.01|0.34***|-0.04*|0.39***|0.04*|
||(0.009)|(0.012)|(0.014)|(0.019)|(0.017)|(0.022)|(0.017)|(0.023)|
|Observations|4,568|4,568|4,568|4,568|4,568|4,568|4,568|4,568|

Notes: Estimates from logistic regression. Standard errors in parenthesis, clustered at the household level. Constant term included. In columns 1 and 2, the dependent variable is equal to one if the respondent believes that the government should do nothing, and zero otherwise. In columns 3 and 4, the dependent variable is equal to one if the respondent believes that the government should respond by restricting migration, and zero otherwise. In columns 5 and 6, the dependent variable is equal to one if the respondent believes that the government should loosen propiska requirements, and zero otherwise. In columns 7 and 8, the dependent variable is equal to one if the respondent believes that government should provide subsidized housing, and zero otherwise. Estimates in columns 2, 4, 6 and 8 should be interpreted as marginal changes relative to the base category. *** p<0.01, ** p<0.05, * p<0.1 The results can also be broken down by population subgroups. For instance, respondents are much less likely to support restrictions on migration if they think that the reason for current problems is due to propiska restrictions, and households with fewer workers are much less supportive of restrictions. If forced to choose between them, households with unemployed people are slightly less likely to support changing registration rules, and slightly more supportive of government subsidies for affordable housing. Households with more unemployed people are much less likely to support the view that the government should “do nothing” about the plight of the vignette’s fictional 2,000 workers looking for work in Tashkent.

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about 11,500 households, took about two years to complete, and it cost about US$2 million. By contrast, the CDR analysis performed for this report cost about US$100,000, and the majority of the costs went to the development of the computer algorithm, which is a fixed cost. Thus, if the CDR analysis is conducted again with new data, it would be significantly cheaper.

While this was the first analysis of its kind conducted in Guatemala and designed primarily to test the validity of various methodologies, a more thorough exercise would only require a small fraction of the time, funds, and human resources required relative to a traditional census or survey. Moreover, these costs would likely be even lower in later iterations of the CDR analysis, as methodological innovation and testing are replaced by the routine implementation of established techniques. While CDR analysis cannot fully supplant conventional research methods, it can greatly enhance their value by providing high-frequency updates and complementary information. Moreover, if CDR analysis can be shown to provide sufficiently accurate inferences to allow countries to slightly extend the time between traditional surveys, it could potentially generate a net savings for the national research budget.

# **Background**

## **_Call Detail Records (CDRs)_**

MNOs record and store data about their customers’ phone use, primarily for billing purposes. In addition to recording cellular data consumption, MNOs collect information on each call and text message. Stored data do not generally reflect the content of a call or message. Instead, they record circumstantial details, such as the time and duration of a call, the size of a message, the identities of the parties involved and their network information. These data are referred to in the telecommunications industry as CDRs.

**Figure 1: Sample Call Detail Records**

|**Interaction**|**Direction**|**Correspondent ID**|**Date and Time**|**Call Duration**|**Antenna ID**|
|---|---|---|---|---|---|
|Call|In|8f8ad28de134|2012-05-20 20:30:37|137|13084|
|Call|Out|fe01d67aeccd|2012-05-20 20:31:42|542|13084|
|Text|In|c8f538f1ccb2|2012-05-20 21:10:31||13087|

Source: http://bandicoot.mit.edu/docs/quickstart.html In addition to CDRs, MNOs often store certain personal details about their customers, including their name and home address, and in some cases their gender, age or other characteristics. For prepaid customers, which are very common in low- and middle-income countries, MNOs typically keep a record of credit recharges or “top ups.”

**4**
section 4.2 for _μ1_ , albeit at a lower scale because the effects on these economic variables are counteracted by the impacts of _μ2_ .

**Figure 10. Composite Shock & Saving Rate Figure 11. Composite Shock & Fertility**

<!-- Start of picture text --> n<br>s<br>μ1,2 μ1,2<br>Source: Author Source: Author<br><!-- End of picture text --> The sum of effects on fertility is a clear decline (Figure 11), due to increased savings, which leads to more capital per worker, more second period income, and less need for children as a mechanism for old age consumption. In addition, children become relatively cheaper when _μ2_ increases (see the price of children: _κwt-zt+1wt+1μ2/Rt+1_ , so fertility falls even further (hence, altruistic gifts also fall; Figure 12). Regarding productivity growth, the increased knowledge spillovers on the wage rate will yield a higher rate of productivity growth (Figure 13).

**Figure 12. Composite Shock & Transfers Figure 13. Composite Shock & Productivity**

<!-- Start of picture text --> z g<br>μ1,2 μ1,2<br>Source: Author Source: Author<br><!-- End of picture text --> The results above rest upon the assumption that _μ1_ and _μ2_ increase in equal proportions such that all effects on the savings rate net out. In a case where the increase in _μ1_ is larger (smaller) than the increase in _μ2_ , the increases in fertility and productivity growth will also be larger (smaller).

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25 _Icafé, mill marketing margins and producer prices_ In its supervision of the coffee market in Costa Rica, Icafé recognizes that Costa Rican processors/exporters are perfectly competitive in the output market (that is, they are price takers in the international coffee market) but embodies a concern that input prices for cherries can be paid below their value marginal product at a monopsonistic price. Icafé therefore regulates the marketing margin (the difference between the price received by mills/exporters and that received by farmers) of processed green coffee to ensure an input price for farmers exactly equal to their value of marginal product, ensuring that mills earn only a normal economic profit. Figure 6 heuristically illustrates the Icafé approach.

**Figure 6 Mill Processing Cost Curves and Margins**

<!-- Start of picture text --> p*-pp<br>MPC<br>APC<br>CC<br>c<br>b = APCIcafé<br>BB<br>Q<br>q2 q1<br><!-- End of picture text --> APCIcafé represents the total marginal processing (i.e. non-cherry) cost at the mill’s cost-minimizing point of its long run average processing cost curve. Icafé explicitly assumes that all mills have had the opportunity to adjust all inputs, including their level of fixed capital (the mill itself), and calculates the costs of different sized mills<sup>23</sup> at the minimum point<sup>24</sup> of their (long run) average processing cost curve. The variable p* is the perfectly competitive international benchmark price for green coffee from Costa Rica. The variable pp is the producer price paid by the mill to the farmer for coffee cherry. The expository framework of Figure 6 closely follows the approach of Stiegert et. al. in their NEIO study of the US beef packing industry (Stiegert, Azzam, and Brorsen 1993). In that study, the authors assume a fixed proportional relation between the agricultural input and processed output and illustrate their

> 23 In calculating processing costs, Icafé does not assume that all mills have the same long run average cost curves, but that each mill is at MES of its respective cost curve.

> 24 Icafé is explicit about calculating APCIcafé at the MES point, in order for the cost study to serve as an efficiency guide for mills (Torres 1978;Torres 1982;Torres 1983) and to ensure that mills are operating in such a way as to guarantee that farmers are receiving their value marginal product (VMP). This is why APCIcafé  is often referred to as ‘minimum costs’ in their literature (Icafe 1991;Icafé 1976;Icafé 1977;Icafé 1978;Icafé 1980;Icafé 1983;Icafé 1984;Icafé 1985;Icafé 1986;Icafé 1987;Icafé 1988;Icafé 1990).
Figure 24. Distribution of internal migrant and non-migrant women and men by degree of urbanization <!-- Start of picture text --> Less than 2,500 people From 2,500 to 14,999 people From 15,000 to 49,999 people<br>From 50,000 to 99,999 people 100,000 and more people<br>100<br>80<br>48.4 48.8<br>58.2 58.0<br>60<br>4.8 4.9<br>40 4.2 10.8 4.0 10.9<br>12.8 15.0 12.8 15.0<br>20 13.4 13.5<br>21.0 20.4<br>11.4 11.7<br>0<br>Men who migrated Men who did not Women who migrated Women who did not<br>migrate migrate<br><!-- End of picture text --> Source: Own estimates based on Population and Housing Census 2020.

# 5. Methodology

**This section describes the methodologies used to analyze the determinants of internal mobility in Mexico and the earning gains from the migration decision for both women and men.** The study focuses on identifying both macroeconomic push and pull factors influencing internal mobility flows and individual factors affecting the propensity to migrate. Push and pull factors at the aggregate level are estimated using a gravity-type model, while the propensity to migrate is estimated using a discrete choice model that accounts for different migration reasons. Earning gains from internal migration are measured using a double selection model that simultaneously accounts for the migration and employment decisions.

## 5.1. Gravity model

**Gravity models have been widely used in the internal migration literature.** Gravity models allow understanding the characteristics of origin and destination places that influence (encourage or discourage) migration flows at the aggregate level. Next, the conceptual framework and the empirical strategy used to model internal migration flows in Mexico using a gravity-type model are presented.

### 5.1.1. Conceptual framework

**The gravity model is a popular mathematical model used to predict the interaction between two or more places.** It is based on Newton’s law of universal gravitation, which establishes that the attractive force between two bodies is directly proportional to the size of the masses and inversely proportional to the square of the distance that separates them. Ravenstein (1885) later applied these principles and the gravity model concept to the social sciences with a study of migration patterns during the 19th century in Ireland.

**Of course, migration flows do not necessarily operate exactly as the gravity law of physics would dictate.** Indeed, Zipf (1946) established that for US intercity movement of people, the flows were inversely related to distance and not to distance squared.

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population study<sup>2</sup> (Brown et al, 2019) revealed that 78 percent of the population of registered refugees lived below the Jordanian poverty line, and between 2017 and 2019, there was only a 2- percentage point reduction of highly vulnerable cases.<sup>3</sup> UNHCR (2018a) also reports that close to 37% of the registered Syrian refugees in Jordan are separated from a family member. Household structures have often been fluid. Many Syrian families sent members ahead to Jordan to settle and sometimes, after the reunification of the rest of the family in Jordan, male family members traveled on to <mark>Türkiye</mark> or to Europe (UNHCR, 2018a). These separations likely affected families emotionally and economically. However, little is known about the changes in economic well-being over time or how gender inequality has shaped poverty and expenditure outcomes.

We use two waves of data from a unique household survey, the United Nations High Commissioner for Refugees (UNHCR) Home-Visits survey for 2013-14 and 2017-18. First, we create comparable measures of expenditure per capita over time. Then, we assess whether certain types of households are more likely to be below the median of the per capita expenditure distribution in each time period. The UNHCR registers households as having a male or female principal applicant (PA) and we use this classification as equivalent to male or female headship used in the poverty literature. We find that there is no significant difference in the share of female versus male- PA households below the median in either 2013-14 or 2017-18.

However, there are important differences between male and female PA households that impacts poverty risk. As is evidenced in other countries (see for example Brown et al.’s study of 43 African countries (2020)), we find that Syrian refugee female PA households are smaller in size than malePA households. Once economies of scale in consumption are considered we find female-PA households are more likely to be poor than male PA households and that this gap has widened over time. Compared to other household heads, single caregivers (the vast majority of whom are women) exhibit some of the highest risks of falling in poverty and this risk has increased over time.

We also find that the distribution of expenditure changed over time and negatively affected femaleheaded households. The gap between male and female-headed households grew over time causing a

> 2 The Vulnerability Assessment Framework for 2019 is based on analysis of data collected from a random, representative sample of registered refugees in October and November 2018. The sample consisted of 2,248 Syrian refugee households, which comprised 3,712 cases and over 10,400 individuals and was fielded in October and November 2018.

> 3 A case is roughly equivalent to a household unit.

3
Policy ReseaRch WoRking PaPeR 4497

# **Abstract**

This paper estimates the elasticity of substitution of U.S. imports using detailed trade data over the 1990-2003 period. The authors use a two-stage least squares framework in order to identify the elasticity parameter of interest. The authors use the elasticity estimates to assess the extent to which Latin American and Chinese goods compete in the U.S. market by providing forecasts of how alternative policy scenarios may affect exports to the United States. The analysis considers the following scenarios: (i) currency revaluation in China; (ii) elimination of U.S. tariffs on Latin American exports under a hemispheric free trade agreement; and (iii) the elimination of quotas on apparel and textile exports under the Multi-Fiber Agreement. The findings show that a 20-percent appreciation of the renminbi reduces Chinese exports to the United States by a fifth, although since other regions increase sales to that market (0.5 percent for Latin America), U.S. imports decline by only 1.7 percent. Hemispheric free trade would increase Latin America's exports to the United States by around 3 percent. The removal of the quotas would lead to a sharp increase in Chinese sales to the United States (40 percent), but Latin America would see its share of the U.S. market decline by around 2 percent (2.5 percentage points). China's gains would come mainly at the expense of other regions of the world.

This paper—a product of the Enterprise Analysis Unit, Financial and Private Sector Development Vice Presidency—is part of a larger effort to study and promote reforms in the business environment. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The corresponding author may be contacted at jlopezcordova@ifc.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
traditional brick-and-mortar stores can be particularly large. E-tailing is not just a replacement of purchases that would otherwise take place but could spur incremental consumption particularly in small cities and towns where there is pent-up demand for goods that local physical stores cannot deliver (McKinsey, 2013). Couture et al. (2017) show that 62 percent of goods bought through Alibaba’s Rural Taobao online terminal were not available in the village, which rises to 84 percent for durable goods. Therefore, they likely benefit more from e-commerce development than their counterparts in more populous and developed regions.

Third, the consumption of durable goods and in-style goods exhibits stronger growth than the consumption of local services. Startz (2018) examines the search and contracting frictions in trade and their role in goods for which sourcing costs and style evolution are of different importance in traditional international trade. Under this framework, by enabling traders to locate nearby producers and sell products directly to consumers, e-commerce can lower travel costs and contracting costs associated with remote ordering. Thanks to the lower costs of travel and contracting, sellers can source more frequently and provide more _a la mode_ products and greater variety to consumers. If the cost saving is passed through, consumers will also get lower prices. As the importance of stylishness and degree of cost savings from search and contracting frictions vary by goods and services, the impact of e-commerce on consumer welfare is likely to differ by types of goods and services. Our finding is accordance with the prediction of Startz (2018). Following this introduction, the paper consists of the following parts: Section II describes e- commerce development in China; Section III discusses the theoretical motivation; Section IV presents the empirical model and data; Section V discusses the empirical results; Section VI conducts robustness analysis; and Section VII concludes.

# **II E-commerce development in China**

China has quickly become the largest e-commerce market in the world. The number of Internet users in China reached 772 million in 2017,<sup>3</sup> of which 533 million made purchases online.<sup>4</sup> The annual total e-commerce trade volume in China increased thirtyfold from RMB 930

> 3 China Internet Network Information Center (CNNIC) (2018). 41st Statistical Report on the Internet Development in China.  http://www.cac.gov.cn/files/pdf/cnnic/CNNIC41.pdf

> 4 Ministry of Commerce (2017). E-commerce in China 2017.

> http://cif.mofcom.gov.cn/cif/html/upload/20181101112235744_2017%E5%B9%B4%E4%B8%AD%E5%9B%BD%E7 %94%B5%E5%AD%90%E5%95%86%E5%8A%A1%E6%8A%A5%E5%91%8A.pdf

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