rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 4983

# **Abstract**

Despite the rising volume of remittances flowing to developing countries, their impact on banking sector breadth and depth in recipient countries has been largely unexplored. The authors examine this topic using _municipio_ -level data on the fraction of households that receive remittances and on measures of banking breadth and depth for Mexico. They find that remittances are strongly associated with greater banking breadth and depth, increasing the number of branches and accounts per capita and the ratio of deposits to gross domestic product. These effects are significant both statistically and economically, even after conducting robustness tests and addressing the potential endogeneity of remittances.

This paper—a product of the Finance and Private Sector Team, Development Research Group—is part of a larger effort in the department to understand the impact of remittances on financial development. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at mmartinezperia@worldbank. org and ademirguckunt@worldbank.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
_Cost of a Recommended Diet (CoRD) calculation_ The Cost of a Recommended Diet, or CoRD, is a method to add up the prices of the recommended amounts for each food group. CoRD estimates the lowest daily cost of achieving the recommended diet for an individual. First, each food in the CPI list, typically organized by COICOP categories (an international system of Classification of Individual Consumption Purpose), is categorized according to the food groups established under the FBDGs (see Table 2). Second, duplicate foods are eliminated; for example, it is not uncommon for a country to measure several different kinds of the same staple, for example rice (local), rice (imported), rice (local inferior). Only the lowestcost duplicate item is kept. Third, all item prices are converted into price per edible serving, which is calculated by multiplying the price of a food item by the following: ����� ���������� �������<sup>����� ��� �������</sup> ~~�~~ ������ ������� ���� �� ���� ���� where grams per serving is based on the regional FBDG (see Table 1 above) and unit of food item is given in the CPI data from the NSO.

**<u>Table 2: Guide for Grouping Food and Beverage Items into FBDG Categories</u>**

|<br>FBDG categories|<br>COICOP categories included|
|---|---|
|Starchy staples|01.1.1-Bread and cereals|
||01.1.7 – Vegetables: Only starchy root crops such as potatoes,<br>sweet potatoes, yam|
|Vegetables|01.1.7 – Vegetables: All vegetables except pulses and starchy<br>roots|
|Fruits|01.1.6–Fruit: All fruit except nuts and seeds|
|Protein foods<br>(Pulses/Fish/Meat/Eggs)|01.1.7 – Vegetables_:_Only pulses|
||01.1.2–Meat|
||01.1.3-Fish and seafood|
||01.1.4-Milk, cheese and eggs_:_Eggs only|
|Dairy|01.1.4-Milk, cheese and eggs_:_Milk and cheese only|
|Oils|01.1.5-Oils and fats_(_excluding margarines/vanaspati_)_|
|None|01.1.8 - Sugar, jam, honey, chocolate, and confectionery<br>01.1.9 - Food products n.e.c. (not elsewhere classified)<br>01.2.1 - Coffee, tea, and cocoa<br>01.2.2-Mineral waters, soft drinks, fruit, and vegetable juices|

_Note:_ Occasionally an item is misclassified in a country’s CPI lists (for example, baby formula or ice cream is classified as dairy); the detailed rules for COICOP classifications are used for categorizing foods into FBDG categories.

Fourth, for each food group we take the average of the two items with the lowest price per serving. We choose more than one low-cost item, because FBDGs are based on the concept of diversity among and within food groups. For the vegetables group, the three cheapest items are selected, 8
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## Hot Money Method.

This calculates private capital flows directly by taking the (negative of) errors ancd omissions and private short-term capital figtires from the balance-of-payments. The measure of private short-tenm capital varies (from country to country) in sorne of the studies employing this method and three different hot money measures has thus developed.

> Hot Money 1 = - (G + C1) Hot Money 2 = - (G + C) Hot Money 3 = - (G + C + D1 + D2)

## Where

- G Net errors and omissions (IMF BoPY line 112)

- C Other short-term capital of other sectors (IMF BoPY line 93 ..97) of which:

- C1. Other assets (IMF BoPY line 94)

- DI Portfolio investment: other bonds (IMF BoPY line 56.. 58)

- D2 Portfolio investment: corporate equities (IMF BoPY line _59._ .61)

## Trade Misinvoicing

Export underinvoicinig and import overinvoicing can hide capital flight, and differences in statisLics of the reporting country and its trading partners can help identify it. '° Here, we adopt the industrial countries' reported trade figures as the standarc6 of reference from which to calculate invoicing biases and use the IMF Direction of Trade (IMF-DOT) data. To put imports as reported by the country and imports as reported by the world (exports by the country) on a comparable basis, both are adjusted from a CIF (cost, insurance, freight) basis to a FOB (free-on-board) basis. This implies that reported imports, nonnally expressed on a CIF basis, are adjusted downward bv a couintry-specific CIF/FOB ratio so that exports and imports can be compared on a consistent FOB basis.

## Export misinvoicing = (Xw/CIFFOB factor) - Xc

# Import misinvoicing = (MC/CIFFOB factor) - Mw

> 10 Difference in trade statistics can he due not only to capital flight (under-invoicing exports, over-invoicing imports), but also tax evasion (under-invoicing for both exports and imports), inconsistent reporting methods, and bad reporting.
## **Figure 6 Exporter Transparency Index for APEC Economies**

<!-- Start of picture text --> 1<br>0.8<br>0.6<br>0.4<br>0.2<br>0<br><!-- End of picture text --> <!-- Start of picture text --> NZL SGP AUS HKG CAN JPN USA CHL KOR MYS IDN MEX PER CHN PHL THA VNM RUS<br>Exporter Transparency<br><!-- End of picture text --> Table 4 shows the ETI and ITI component weights obtained via factor analysis. It is import time, hidden trade barriers, and irregular payments (level and dispersion) that are weighted most strongly in the final ITI. For the ETI, irregular payments (level and dispersion) and export time again stand out as having particularly high weights. Our results therefore suggest that these variables are important determinants of transparency in the trade context. Consequently, if the trade gains from greater transparency are found in the next Section to be significant, then reform efforts might initially be focused in those areas in order to have maximum impact.

# **4 Transparency and Trade Flows: Estimating the Impacts**

In this section, we provide a first assessment of the quantitative impact of transparency on trade flows among APEC economies. To do this, we use the workhorse of empirical international trade work, namely the gravity model. Our approach takes full account of recent developments in the literature in this area, in particular as they relate to four aspects of the model and estimation procedure. First, we derive our empirical specification from the theory-consistent model of Anderson and Van Wincoop (2003, 2004). Second, we use the Poisson estimator to take account of the presence of zeros in the bilateral trade matrix (Santos Silva and Tenreyro, 2006). Another important point relates to our trade policy data: we use highly detailed applied tariff data that take full 20
# **2.2. Data and Sampling**

Data for variables in (1) come from different sources. _Budget Balance_ data are from the World Bank. Time series end in 2004 when change in fiscal accounting methods and reporting was completed at the International Monetary Fund. Data for _Elections_ are from the International Federation of Electoral Systems and from Block and Vaaler (2004). Election years are adjusted for government fiscal years consistent with Hanusch and Keefer (2011). _Ratings_ data are from _Bloomberg_ , and are measured annually as an average of up to three ratings published by Moody’s, S&P and or Fitch (or CRAs merged into Fitch in the 1990s) on January 1. These raters dominate the industry, including sovereign rating segments, with the broadest country coverage, the most analysts and the largest market shares of ratings for individual bond issuances (White, 2010). Their ratings tend to move together with financial market players following them as a group. Data for other controls include the World Bank’s World Development Indicators ( _Employment, Inflation, Reserves, Curr Crisis_ ) and Database of Political Institutions (DPI) (Beck et al _._ , 2004) ( _Checks_ ), and Freedom House ( _Rights_ ).

We sample from 18 prominent emerging democracies including: Argentina, Bolivia, Brazil, Bulgaria, Chile, Colombia, Ecuador, Indonesia, Mexico, Paraguay, Peru, Philippines, Poland, Russia, South Africa, South Korea, Uruguay, and Venezuela. They uniquely meet three sampling requirements. Starting in different years from 1989 and the fall of the Berlin Wall to 1998 and the fall of the Suharto regime in Indonesia, these countries have: 1) competitive presidential executive electoral systems that are new (e.g., Russia) or recently reformed (e.g., Brazil); 2) ratings from Moody’s, S&P and or Fitch; and 3) comparable budget balance data for years when rated. Sampling from these 18 countries yields 141 country-year observations from 1989-2004 with as few as three (Ecuador) and as many as 14 (Brazil) observations per country. The election year dummy, _Electionit_ , equals one for 32 of these 141 country-year observations.<sup>2</sup>

> 2 Classification as a presidential system is based on the DPI’s “SYSTEM” variable.  A country must score a 0 or 1, meaning that it has a non-ceremonial chief executive elected directly by voters or specialized electors (0), or have a non-ceremonial chief executives chosen by the legislature but with substantial powers to avoid recall by that same

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Table 4 Descriptive Statistics: Farming

||Non-Club<br>Members|Club Members|
|---|---|---|
|_TOBACCO_|||
|Output per acre - in kg|11,007|16,308|
|Sales per acre - in MK|10,199|16,090|
|Area dedicated to tobacco|29.79%|28.90%|
|Visits from agricultural feld assistants|0.49|1.39|
|Percentage of tobacco growers in the community|60.92%|52.03%|
|Tobacco experience (grew tobacco in any of the last 5 seasons)|49.34%|63.92%|
|Average month when tobacco is sold (Jan= 1, Dec= 12)|5.47|5.91|
|Did not bale the tobacco|24.44%|3.20%|
|Did not grade the tobacco|18.04%|3.21%|
|Temporary work in days (Ganyu)|16.17|11.62|
|Fertilizer 23 : 21 : 0 + 4_S_ (kg/acre)|48.06|41.14|
|Fertilizer DAP (kg/acre)|0.401|0.742|
|Fertilizer CAN (kg/acre)|42.41|69.33|
|Fertilizer UREA (kg/acre)|22.25|148.04|
|Fertilizer COMPOUND D(kg/acre)|387.63|43.59|
|_OTHER CROPS_|||
|Cash crops - output value per acre|31.4|35.3|
|Food Crops - output value per acre|33.0|38.1|
|Number of obs.|991|377|

Notes: Own calculations based on Malawi Integrated Household Survey (2004/05). Cash Crops: hybrid maize, groundnuts, cotton and sugar cane. Food Crops: maize, cassava, potato, ground beans, rice, finger millet, sorghum, pearl millet, beans, soybeans and pigeon peas.

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as reliable if a (connected) household receives electricity supply always and unreliable if otherwise. Using these data, I compute a measure of reliability in a community based on the share of electrified households in the primary sampling unit that have reliable access to electricity. Specifically, two main measures of reliability are computed: first, a dummy variable (outages in community) equal to 1 if more than 50 percent of connected households in the primary sampling unit (PSU) do not have access to reliable electricity; and second, the share of connected households without reliable access to electricity (outages in community % HH).

## **3.1.2 Living Standards and Measurement Surveys**

Household survey data from the Ghana Living Standards Survey (GLSS) and Nigeria General Household Survey (GHS) are used to supplement the analysis in the countrycase studies. The GLSS is a nationally representative repeated cross-sectional data. Five rounds of the GLSS data between 1998 and 2017 are used. The GHS on the other hand is a nationally representative household panel data from Nigeria. The analysis relies on three waves of the GHS panel data surveyed between 2011 and 2016. Employment status of respondents is measured in slightly different ways in the two surveys. In the case of the GLSS, individuals are asked about their employment activities over the last 12 months, while in the case of GHS, individuals are asked about their employment activities within the past 7 days preceding the survey.

# **3.2 Firm Data**

## **3.2.1 World Bank Enterprise Surveys**

The World Bank Enterprise Surveys (WBES) dataset is a global firm survey that undertakes face-to-face interviews with top managers and business owners in about 145 countries. The survey collects data on several issues relating to firm attributes, access to in11
Table D-2. City Size Growth Equation (continued)<sup>a,b</sup> (standard errors corrected for spatial dependence in parentheses)

||(5)|(6)|
|---|---|---|
||Spatial GMM|Spatial OLS|
|Δ Ln(ruralpop. supplymarket|5.815***|3.227***|
|potential)|(1.779)|(0.655)|
|Δ Ln(rural income opportunities:|-0.632|0.229|
|marketpotential)|(0.720)|(0.244)|
|Δ Ln(marketpotential)|1.257|2.127***|
||(0.890)|(0.480)|
|Average schooling (t-1)|0.066***|0.035***|
||(0.016)|(0.010)|
|Δ Average schooling|0.489***|0.093***|
||(0.092)|(0.024)|
|Δ Ln(inter-citytransport costs)|-0.107**|-0.059**|
||(0.047)|(0.025)|
|state capital dummy|0.183***|0.113***|
||(0.038)|(0.025)|
|Ln(population) (t-1)|-0.056***|-0.023***|
||(0.008)|(0.009)|
|Manu / service(t-1)|0.131***|0.066***|
||(0.031)|(0.022)|
|Ln(homicide /pop) (t-1)|-0.105***|-0.092***|
||(0.031)|(0.023)|
|Public industrycapital /|0.006|-0.780*|
|total industrycapital in 1980|(0.385)|(0.425)|
|time dummies|Yes|Yes|
|Observations|245|245|
|Hansen J statistic<br>(overidentificationtest)|3.945||

*** significant at 1% level; ** significant at 5% level; * significant at 10% level.

- a. Public industry capital / total industry capital (1980) is assumed to be exogenous by adding it to the IV list of (3).

- b. Coordinate variables are latitude and longitude. Cutoffs are 3/2 standard deviations of latitude and longitude (10.23, and 8.20), which correspond to about 900 miles.

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important feature helping to sustain the overall liberal trade bargain struck between countries in the GATT/WTO system (see again Bagwell and Staiger, 2002 and 2004; Sykes, 1991; Hoekman and Kostecki, 2001).

# **3 Economic Theory: When to Challenge a U.S. Trade Remedy at the WTO?**

## **3.1 The Sample of Challengeable U.S. Trade Remedies**

Our question of interest is the determinants of a WTO member’s decision of whether to formally challenge a U.S. trade remedy that was imposed in year _t_ , where the year of the remedy falls in the 1992-2003 period. All of the data on U.S. antidumping and countervailing duty actions have been compiled from the publicly available U.S. government publication, the _Federal Register_ . This publication documents the country of the firms under investigation, the 10-digit Harmonized Tariff System (HTS) product codes of the products under investigation, the dates of the phases of the investigation, the outcomes of various phases of the investigation, and the level of applied duties in affirmative cases. We include all trade remedy actions that either ended in the U.S. application of duties or through a suspension agreement, implicitly assuming that all such remedies were “challengeable” at the WTO.<sup>15</sup> Our hypothesis is that a country that is targeted by a U.S. trade remedy will initiate a formal WTO trade dispute and contest the measure if the expected benefit to a dispute are greater than its expected costs. We assume that the expected benefit depends on the size of the gains the foreign country receives from a successfully resolved case as well as the probability that the case will be resolved successfully. We allow for the expected costs of dispute initiation to be made up of two separate elements that include both the expected litigation costs and the expected political-economic costs to confronting the United States in a formal dispute. As we describe in more detail below, our hypothesis allows for economic interests to affect decisions, but we also include proxies for some of the institutional biases that WTO scholars have been concerned might also influence a country’s ability to stand up for its market access interests.

The primary _alternative_ hypothesis to the one we pursue here is that imposed trade remedies are not formally challenged under dispute settlement proceedings because of the _Agreement on Antidumping_ ’s Article 17.6 requirement that WTO Panels show deference to national authorities’ decision-making during antidumping

> 15 We will only be able to use cases involving manufacturing products due to our need to control for injury (requiring industry-level data, which is not comparably available for agricultural products.) This does not severely restrict the size of the sample, however.

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largest manufacturing industry in India, accounting for 22% of manufacturing employment. The fourth panel shows the management scores for the 232 textile firms in the BVR Indian sample, which look very similar to Indian manufacturing in general.

Within textiles, our experiment was carried out on 28 plants operated by 17 firms in the woven cotton fabric industry. These plants weave cotton yarn into cotton fabric for suits, shirts and home furnishing. They purchase yarn from upstream spinning firms and send their fabric to downstream dyeing and processing firms. As shown in the bottom panel of Figure 1, the 17 firms involved had an average BVR management score of 2.60, very similar to the rest of Indian manufacturing. Hence, our particular sample of 17 Indian firms also appears broadly similar in terms of management practices to manufacturing firms in developing countries.

# **_II.B. The selection of firms for the field experiment_**

The sample firms were randomly chosen from the population of all publicly and privately owned textile firms in Maharashtra, based on lists provided by the Ministry of Corporate Affairs.<sup>6</sup> We restricted attention to firms with between 100 to 1000 employees to focus on larger firms but avoided multinationals. Geographically we focused on firms in the towns of Tarapur and Umbergaon (the largest two textile towns in the area) since this reduced the travel time for the consultants. This yielded a sample of 66 potential subject firms.

All of these 66 firms were then contacted by telephone by our partnering international consulting firm. They offered free consulting, funded by Stanford University and the World Bank, as part of a management research project. We paid for the consulting services to ensure that we controlled the intervention and could provide a homogeneous management treatment to all firms. We were concerned that if the firms made any co-payments they might have tried to direct the consulting, for example asking for help on marketing or finance.

Of this group of firms, 34 expressed an interest in the project and were given a follow-up visit and sent a personally signed letter from Stanford. Of the 34 firms, 17 agreed to commit

> 6 The MCA list comes from the Registrar of Business, with whom all public and private firms are legally required to register annually. Of course many firms do not register in India, but this is generally a problem with smaller firms, not with 100+ employee manufacturing firms which are too large and permanent to avoid Government notice.

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(2008), Graham, 2010)).<sup>35</sup> Still, one potentially promising direction for further research is to employ subjective well-being data to provide better imputed household consumption.

# **VII. Conclusion**

We offer in this paper a review of poverty measurement methods in contexts where consumption data are missing or have inadequate quality. Some of the methods we reviewed are more established, but some are rather recent. While micro survey data are becoming available and more frequently collected in developing countries, we expect these methods to be useful at least in the immediate term, and particularly when a need arises for backcasting consumption from a more recent survey for better comparison with older surveys.

In addition, these imputation methods may also be appropriate in contexts where survey costs and/or survey implementation pose a challenge. For example, perhaps most national statistical agencies are keen on producing annual poverty statistics. But few, if any, developing countries can afford the associated expenses and demanding logistics of fielding a household consumption survey every year; rather, they are likely to implement the household consumption survey every few years. In such contexts, poverty rates can be imputed for the intervening years between the surveys at just a fraction of the cost of fielding a full-fledged consumption survey by, say using other non-consumption data or implementing a lighter (non-consumption) version of the survey in the spirit of the SWIFT approach.<sup>36</sup> Seen in this light, imputation techniques can offer a low-cost and arguably wieldy approach to poverty estimation. While we should be mindful of the various assumptions underlying imputation

> 35 There appears no consensus yet even on the use of subjective well-being data. While some researchers (see, for example, Bond and Lang (2014) and Gibson (2016)) remain skeptical, others are more optimistic (see, for example, Allin and Hand (2017) and Helliwell, Layard, and Sachs (2017)).

> 36 Kilic _et al._ (2017) estimate the average cost of implementing a recent household consumption survey (in 2014 or later) to range from approximately US$800,000 to US$5 million, depending on the context and sample sizes. On the other hand, applying poverty imputation methods would most likely require only researchers’ time costs.

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