rafmacalaba/data-use-mentions-extended huggingface dataset:
Policy Research Working Paper 5989

# **Abstract**

This paper estimates how changes in China’s exchange rates would affect exports from competitor countries in third-country markets—in other words, the “spillover effect.” The authors use recent theory to develop an identification strategy, with a key role for the competition between China and its developing country competitors in specific products and export destinations. Using disaggregated trade data, they estimate the spillover effect by exploiting the variation across different exporters, importers, products, and time periods. They find a spillover effect that is statistically and quantitatively significant. Their estimates suggest that a 10-percent appreciation of China’s real exchange rate boosts a developing country’s exports of a typical four-digit Harmonized System product category to third markets by about 1.5 to 2 percent on average. The magnitude of the spillover effect varies systematically with the characteristics of products, such as the extent to which they are differentiated.

This paper is a product of the Trade and Integration Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at amattoo@worldbank.org, pmishra@imf.org, and asubramanian@piie.com.

_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
least beaten tracks by making use of Multiple Correspondence Analysis for the construction of the composite indicator. This technique has been recently rediscovered for the study of multidimensional poverty (Asselin, 2009, Njong, and Dschang, 2008) as it provides a method for assigning weights to different dimensions while it responds to some essential welfare axioms.

Our application is on Morocco using 2001 and 2007 consumption data. During the last decade, Morocco has experienced consistent growth generated by economic reforms and investments in infrastructures. These developments have resulted in improved living conditions as shown by official statistics and reports (Douidich and Ezzrari, 2009). However, it is typically heard from local and foreign commentators that these gains have been unequally shared and/or that living conditions have been improved in certain areas but not in others. Thus, a multidimensional approach to the study of poverty can provide a better sense of whether the gains in Morocco have been robust and comprehensive.

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in the 2000s (largely driven by countries in Latin America). By contrast, the _average person_ in the developing world (i.e. using population weights) lived in a country with steadily increasing inequality between the mid-1980s and 2010 (driven by the surge in China and more recently India). Despite the recent fall, inequality remains at a higher level than during the 1980s, and is substantially greater in the developing than the developed world. The overwhelming majority of developed countries experienced increasing inequality since the 1970s.

The decline in average inequality among developing countries in the 2000s was largely driven by the encouraging developments in Latin America, which have been widely documented (e.g. LopezCalva and Lustig, 2010; Lustig et al., 2013). This can be explained by a large number of factors, which certainly include more stable macroeconomics, the growth of low-skilled wages (Cord et

> 13 In the US, Luttmer (2005) finds that an increase in neighbors’ earnings reduces reported happiness after controlling for individual income, where the spatial unit has 150,000 residents on average. By contrast, in Malawi such relative concerns are not the dominant concern, except for the better off (Ravallion and Lokshin, 2010).

14 There are notable differences between these three sources, so they need to be compared carefully. World Bank (2016c) is also the data source used by Alvaredo and Gasparini (2015). However, Alvaredo and Gasparini cover only developing countries and adjust for differences between income and consumption surveys. Morelli et al. (2015) cover only rich and (some) middle-income countries, tend to use equivalence scales, and a mix of primary data and secondary sources. My updated calculations based on World Bank (2016c) (see Figure 4) cover all income groups and mix per capita income or consumption without any adjustment.

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et al., 2015), and on economic development (Okoye et al., 2019). These papers have focused on transport investments only.

Very few papers have looked at _complementarities_ in infrastructure, especially in Africa. Analyzing data from Ethiopia, Moneke (2020) examines the impact on employment and welfare that stems from combined investments in road and electrification. Using data from 27 Sub-Saharan African countries, Abbasi et al. (2022) explore the heterogeneous effects of joint investments in road and electrification on employment, finding strong complementarities between these infrastructures. Gebresilasse (2023) documents complementarities between road construction and extension services in agricultural productivity in rural Ethiopia. Our paper adds to this new literature by examining the complementarities of internet and road investments.

Our paper also makes a methodological contribution. It adds to the empirical literature on infrastructure by using two original instrumental variables to address the possible endogeneity of infrastructure locations. We exploit the least-costpath procedure to construct theoretical road networks connecting the centroids of different ethnic territories in Sub-Saharan Africa. We interact the resulting exogenous distance to the nearest main road with the accessibility of submarine internet.

The remainder of this paper is organized as follows: Section 2 presents the conceptual framework. Section 3 describes the context, data, and our empirical strategy. Sections 4 and 5 present the main findings. Section 6 concludes.

# **2 The Gains from Internet Connectivity and Roads**

Following Hjort and Tian (2021), we present an overview of potential mechanisms driving the job and economic impacts of the combined provision of internet connectivity and road networks. Internet and road access can drive economic development through both supply- and demand-side impacts.

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toxicity. When asked about environmental effects, nearly 70% of the IPM farmers in our survey report improvements in soil, water and air quality after adoption of IPM (Figure 3), as well as increased numbers birds, fish and soil organisms such as earthworms.

**Figure 3. Observed change in soil, water and air quality after IPM adoption**

# **8.  Summary and Conclusions**

In this paper, we have used new survey data on rice production to assess the net economic, health and environmental benefits of switching to Integrated Pest Management in Bangladesh. We have assessed the net economic benefits of IPM adoption in three productivity comparisons, using input ratios, standard production functions and stochastically-estimated production frontiers. In all three cases, we have found no significant difference in productivity for IPM and non-IPM rice farming. Our results hold when we control for hypothesized farm-efficiency factors that also affect the probability of IPM adoption. Our evidence suggests that IPM adoption increases profits for rice farmers, since pesticide costs are reduced with no countervailing reduction in output. The reported incidence of sickness is lower for IPM farmers, although the difference is not statistically significant in our sample. Most IPM farmers also report that environmental conditions improved after adoption of the new technique.

To summarize, our evidence suggests that further promotion of Integrated Pest Management for Bangladeshi rice farmers will yield economic, health and environmental benefits for rural communities. As we have noted, local adoption of IPM is a collective decision because farmers’ pesticide applications affect their neighbors’ fields as well.

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# 2. Some methodological issues

We start with the simplest question of all: what is “income” in the calculations of global inequality? As we have seen, most of the early work used national accounts data, that is GDI per capita expressed in the same currency (international or PPP dollars). This is because household survey data for many countries of the world were simply unavailable (and even when they existed, researchers could not get them because the statistical agencies refused to release them).<sup>11</sup> There are currently three main sources of world wide data on GDI per capita across time and across countries. They are World Bank data available in World Bank World Development Indicators (WDI), Penn World Tables, and Angus Maddison’s data. The advantage of using GDI per capita as “income” is that these numbers, even if the three sources do not always agree among themselves, are relatively uncontroversial. We know what is meant by GDI per capita and we know that these values do give some generally accepted mean incomes of all nations.<sup>12</sup> The main drawback of this approach is that GDI per capita is not “income” in any recognizable sense to any individual or household. Gross domestic income includes components such as corporate investment from retained profits, build-up of stocks, government spending on defense etc., which are not part of an even broadly defined household disposable income. In addition, publicly-financed health and education are part of GDI per capita but not included in household per capita disposable income unless one is able to impute—which is quasi impossible in a multi-country context—these values back to individual households based on survey data on their school attendance and use of medical services.

11 This is still the case with many countries that refuse to release micro data to any institution or individual. Such countries are quite diverse, ranging from Japan to Algeria. There is thus a paradoxical situation that a number of expensive instruments like
ventional point estimate and conventional standard error, the bias-corrected point estimate and conventional standard error, and the bias-corrected point estimate and the robust biascorrected standard error. Unless otherwise noted, we report ‘Conventional RD’ estimates in the text because they remain the default estimators in applied microeconomics.

In addition to displaying point estimates and standard errors in table format, we visually reproduce the primary regression results using binned sample means and local linear conditional mean functions to allow the reader to assess the variation of the dependent variable around the gross tonnage threshold. We specify three non-default options so that the difference in the intercepts between the two local linear conditional mean functions corresponds exactly to the conventional regression discontinuity point estimate. Those non-default options are: triangular kernel, local linear specification for the running variable, and the same optimal bandwidth from the corresponding regression table and column. Unless otherwise noted, we use default options in all other instances. The most important of these default options calculates the number of bins and the bin endpoints with the mimicking variance evenly-spaced method using spacings estimators (Calonico et al., 2015).

If the regulator chose subsidy thresholds to award more subsidy to the vessels they expected would fish most, then our estimates of the effect of fuel subsidies on fishing are upward biased. We explore the likelihood of this scenario with pre-period fishing data. Between 2006 and 2014, fuel subsidies for distant water vessels were linear in engine power and independent of gross tonnage (Department of Industrial Policy and Regulation, Ministry of Agriculture, 2021). GFW fishing data begins in 2012. As a placebo test, we estimate Equation 2 on log monthly fishing hours between 2012 and 2014 by “soon-to-be binding” vessels—those with gear, gross tonnage, and engine power such that their engine power became binding under the 2016-2020 fuel subsidy policy. We exclude 2015 data from this test because some firms may have been aware of the fuel subsidies policy that was applied to calculate 2015 subsidies, even though the policy was not officially announced until 2016 (Ministry
agencies or administrative business register. For Brazil, the sampling frame was based on the 2017 _Rela ̧c ̃ao Anual de Informa ̧c ̃oes Sociais_ (RAIS). RAIS is an employer-employee administrative registry database managed by the Ministry of Labour (MTE), which covers all Brazilian formal firms. For Senegal, the sampling frame was based on the 2016 _Recensement G ́en ́eral des Entreprises_ (RGE) from the _Agence Nationale de la Statistique et de la D ́emographie_ (ANSD). The RGE covers all establishments operating in Senegal. For Vietnam, the sampling is based on the 2018 Establishment Census from the General Statistical Office (GSO), which covers all registered establishments operating in Vietnam.

The universe of study, which defined the population of firms included in the FAT survey, covers firms with 5 or more employees in agriculture, manufacturing, and services. The sector classification is based on the International Standard Industrial Classification of All Economic Activities (ISIC), Rev. 4. More specifically, our sample includes firms from the following ISIC rev 4 sectors: Agriculture (ISIC 01, from Group A); All manufacturing sectors (Group C); Construction (Group F), Wholesale and retail trade (Group G), Transportation and storage (Group G), Accommodation and food service activities (Group I), Information and communication (Group J), Financial and insurance activities (Group K), Financial services (ISIC, 64), Travel agency (ISIC 79, from group N), Health services (ISIC 86, from group Q), and Repair services (ISIC 95, from Group S).

We exclude micro-firms with fewer than 5 employees. Micro firms, particularly in developing countries, are less likely to be captured in the sampling frame, due to informality, and they would require further adjustment in the survey instrument and sampling design.<sup>52</sup> This decision is aligned with other firm-level standardized surveys with comparability across countries.
,<br>24|.<br> <br>24|.<br> <br>19|
|percent in 1995. By||||||

the end of fiscal 1995/96. required savings stood at Rp. 2.1 billion (about US$900,000), and equaled 19 percent of outstanding loans (Table 25).

_Outreach Depth._ This program's objective is to deepen rural financial markets. The program defines its target group as households that have not previously received a commercial bank credit. According to the program' s 1993 Impact Study carried out in Yogyakarta and Central Java, 93 percent of beneficiaries in those provinces met this criteria.

Although the program does not target a specific income group, it is nevertheless interesting to obtain some indication of the income of program beneficiaries. The program's monitoring system does not track beneficiaries' incomes. The 1993 Impact Study found that among surveyed participants in two provinces, less than 20 percent had monthly household expenditures below the poverty line. More than 40 percent of respondents had household expenditures more than twice as high as the poverty line. Furthermore, about I I percent of households were in the highest 13 percent of the income distribution. If these findings are representative of borrowers under the program today, then this program cannot be considered to be primarily reaching the low-income population, although some low-income households are being served. It is likely that virtually all of P4K's loan recipients have incomes as low or lower than the poorest one-third of PHBK participants.

The program does not keep track of the number of borrowers per group, and estimates for this figure are based on old data that no longer accurately reflect the makeup of participating groups. Thus, it is difficult to estimate the average loan size per individual borrower. One program manager's estimate of the average loan size per group member results in loans per person equal to 13 percent of GDP per capita. However, management estimates of the
Acquisition Trust, another multilateral initiative, made its first purchase commitment in April 2021.

Table 1: When low- and middle-income countries entered bilateral contracts for vaccines, they did so later than high-income countries

|World Bank country<br>income classification|Number of<br>advance<br>purchase<br>agreements|Average month contract<br>signed|Earliest month contract<br>signed|
|---|---|---|---|
|High income|211|December 2020|May 2020|
|Low income|8|May 2021|February 2021|
|Lower-middle income|105|March 2021|August 2020|
|Upper-middle income|135|February 2021|August 2020|

Source: IMF-WHO Vaccine Supply Tracker To quantify the role of these later orders in explaining late deliveries, Table 2 reports the results of a regression of the month of first delivery (measured in months since January 2020, the month the WHO declared COVID-19 a Public Health Emergency of International Concern) on the month the contract is signed (also measured in months since January 2020), fixed effects for the country’s income classification, fixed effects for each vaccine candidate, and a constant. In this regression, the effect of the contract signature date can have a causal interpretation, as vaccine developers reported publicly that they allocated vaccines through delivery queues.<sup>6</sup> The vaccine fixed effects control for developer-specific delays, providing reassurance that the effect of the contract signature date is not driven by countries that ordered later also selecting specific vaccines that ended up facing long delays due to production difficulties or regulatory delays. The vaccine fixed effects also control for vaccine price, to account for the fact that vaccines with faster delivery times may have been more expensive. Overall, the regression summarizes the average delivery

> 6 For instance, Pfizer CEO, Dr. Bourla, said, “I had asked heads of state by letters that I sent them to [
link between remittances and the domestic financial system. Aggarwal, Demirgüç-Kunt, and Martínez Pería (2011) study the impact of remittances on the financial sector using balance of payments data on remittances and aggregate data on bank credit and deposit amounts for 109 developing countries over the period 1975-2007. They find strong evidence indicating that

> 2 Other studies that find that microcredit increases consumption or income include Pitt and Khandker (1998) and Khandker (2005). However, Roodman and Morduch (2009) are unable to reproduce those results.

> 3 Ashraf et al. (2010) show that giving migrants from El Salvador access to bank accounts that allow them to have control over the funds they remit results in an increase in savings.

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<!-- Start of picture text --> % of children under-2<br>9 any 3 protein<br>100% 95 96 96 96 95 96 95 96 97 96 sources, at least 1<br>a a a in past week<br>all 3 protein<br>80 7 sources, at least 1<br>time in past week<br>60<br>all 3 protein<br>sources, at least 3<br>times in past week<br>40<br>1<br>20 1<br>1 13 13<br>7<br>0<br>1 2 3 4 5 6 7 8 9 10<br>Income decile<br>Source: Author's calculations using 2015 NNS data (Household Dietary Diversity Module).<br><!-- End of picture text -->
(2006) and François, Hoekman and Manchin (2006)). To give an example using data from this study, between 90% and 97% of qualifying African exports of apparel enter the US and EU under their respective preferential regimes, the AGOA for the US and Everything But Arms or Cotonou preferences for the EU (see table 1). Yet, as shown here, export patterns of textiles and apparel to the two destinations has been drastically different in recent years (see figure 1). When, the origin requirement was drastically simplified under AGOA, in spite of remarkably similar average preferential margins in the US and in the EU (US MFN tariff of 11.5% in 2004 and EU preferential margin of 11.0%), growth of apparel exports to the US took off while apparel exported to the EU remained flat. Thus, assessing the restrictiveness of RoO only by inspecting utilization rates would suggest low costs, while ignoring that export growth rates to the two destinations have diverged around the time when meeting origin requirements to one market, the US, were relaxed.

This is why it is desirable to go beyond inspection of utilization rates and indices of restrictiveness to isolate the effects of meeting origin requirements. This paper isolates the costs of meeting these origin requirements by exploiting a relaxation of the ‘yarn forward’ rule for Textiles and Apparel applied by the US in its preferential trade policies. Early on under AGOA, the US introduced a ‘special regime’--the ‘third-country provision’--which consists in allowing the use of fabric from any origin in the making of apparel (rather than requiring US fabric originating domestically or from the US), relative to the no-change environment of EU preferential regimes still requiring European or locally-produced fabric. To our knowledge, this is the first such estimate.

Controlling for other factors, we estimate that relaxing RoO by allowing the use of fabric from any origin increased apparel exports to the US by about 168 percent for the top 7 (out
using their exchange rate data. In a second step, we compute yearly average returns using equal weights for the 16 countries since 1870.

To compute excess returns, we use two approaches. First, we benchmark against monthly UK or US Treasury _bills_ , depending on the currency denomination of the respective bond or asset. Using bills facilitates the comparison across asset classes, including to long-term US/UK government bonds. Moreover, most of the earlier work on long-run returns uses bills as the benchmark, for example, Dimson, Marsh, and Staunton (2001) or Jordá et al. (2017). However, as an alternative, we also report excess returns above long-term UK or US _bonds_ , which has been our approach so far but is less standard in the literature comparing asset classes. In what follows, the resulting excess returns series are also used to compute commonly used metrics in this literature, such as standard deviations and Sharpe ratios.

# 6.2. Results on asset comparisons

Table 7 shows the results for the full sample, as well as for the modern period subsample (1995–2016). Figure 14 visualizes these findings, by focusing on average returns and their respective Sharpe ratios 38