rafmacalaba/datause-encoder-data huggingface dataset:
Despite their intuitive appeal, transfer schemes that target poor communities remain difficult to design. One of the difficulties concerns information on the spatial distribution of poverty. In many developing countries there is now a good deal of experience with methods to conceptualize, measure, and analyze poverty. Yet most of the empirical evidence that has been brought to bear on this topic tends to be at the national level. This is primarily due to the fact that data on incomes or consumption expenditures, which serve as prime input into the quantitative analysis of poverty, tend to derive from sample surveys of households. The sample size of such surveys is often rather small. This implies that statements about the spatial distribution of poverty can generally refer only to very broad geographic breakdowns. It is not uncommon, for example, for surveys to be “representative” only for urban and rural areas of a country, in combination with perhaps a very crude geographical breakdown of the country into broad regions (north/south or hills/plains). While there is a general suspicion in many countries that poverty occurs in geographic “pockets” that are defined fairly precisely, sample surveys do not readily allow one to confirm or refute such notions.<sup>2</sup> Absent detailed information on poverty outcomes at the local level, policymakers interested in targeting transfers to the local level have often sought to use proxy indicators of local poverty outcomes. For example, the Microregions program that is being contemplated by SEDESOL (Ministry of Social Development) in Mexico is proposing to deliver social services and to promote productive investment projects in a set of municipalities that have been selected on the basis of a marginality index produced at the municipality level by CONAPO (de Janvry, Sadoulet, Calva and Solaga, 2003). This index is based on a set of simple variables taken from the population census, and is believed to be correlated with conventional notions of poverty.

> 2 However, recent research shows that beliefs regarding the existence of  such “pockets of homogeneous poverty” may not always be true.  Elbers et al. (2004) demonstrate that small communities in three developing countries are found to vary markedly from one

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Low-fertility countries, to the contrary, manifest quite different experiences in their demographic transition and growth pattern. The turnaround of their economies since the mid-1990s is not as drastic as that of high-fertility countries, but the growth rates mildly recovered, reversing the steady downward trend from the 1970s. The recovery benefits from the slowdown in population growth and accelerated growth in the share of working age population. In addition, the progress in TFP contributes to growth. However, unlike in other countries, capital accumulation in low-fertility countries has slowed down.

Mid-fertility countries still experience population growth at a steady pace like the high-fertility countries. Among the three types, mid-fertility countries experience the slowest growth in GDP per capita and output per worker during the post-1995 period, although the share of working age population was on the rise similar to that of low-fertility countries. Growth in TFP and capital accumulation improved but turned positive only in the recent 2005-2010 period. For these countries, policies to promote continued fertility decline and positive TFP growth are particularly critical for growth.

# **5.2 Where Does Capital Come From?**

As seen in the previous section, capital accumulation (investment) plays an important role in accounting for growth. Figure 5 presents the level of investment as a percent of GDP and confirms an upward trend since the mid-1990s and particularly in the late 2000s.<sup>30</sup> By 2010, investment as a percent of GDP is up to 23 percent. The same statistics were 38, 35, 27, and 21 percent in the Republic of Korea; Singapore; Hong Kong; and Taiwan, respectively, in 1990 when their economic growth peaked. Rapidly growing countries like China and India recorded 48 and 37 percent of GDP as investment in 2010. These examples suggest that investment needs to be further promoted in SSA to ensure sustainable growth.

A strong association is believed to exist between public investment and economic growth as long as debt sustainability is managed. Thus improving

> 30 In this section, statistics on investment, savings, FDI, ODA, and remittances are based on three-year moving averages.

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<!-- Start of picture text --> Char school selection (April 2013)<br><!-- End of picture text --> Detailed information of each identified Chars school to find eligibility. Our eligibility criterion was "Most of the school students do not have access to electricity or solar light" (May- 2013) <!-- Start of picture text --> Eligible students selection, based on the criterion a) those who have no access<br>to electricity and b) those who are regular students with 80% attendence<br>record (June 2013)<br>Baseline survey of 882 students' households (July-August 2013)<br><!-- End of picture text --> Public Randomization at the school premises (September-October 2013) <!-- Start of picture text --> Household level Follow-up Survey (July-August 2014)<br><!-- End of picture text --> <!-- Start of picture text --> Final academic result collection from each sample school<br><!-- End of picture text --> <!-- Start of picture text --> (January 2015)<br><!-- End of picture text --> Source: Prepared by the authors. The blue panels show events regarding interventions, red panels show events regarding surveys and the green panels show events regarding sample selection.
# **TABLE OF CONTENTS**

Summary ............................................................................................. **6**

1. Estimating the Impact of Climate Change on Smallholders: A Case Study on the Agricultural Sector in Sri Lanka

Pradeep Kurukulasuriya and Mohamed Ihsan Ajwad ........................................... **9** 2. Climate Analysis with Satellite Versus Weather Station Data Robert Mendelsohn, Alan Basist, Claude Williams, Felix Kogan, and Pradeep Kurukulasuriya .................................................................................... **33** 3. What Explains Agricultural Performance: Climate Normals or Climate Variance?

Robert Mendelsohn, Alan Basist, Ariel Dinar, Claude Williams, and Pradeep Kurukulasuriya ...................................................................................... **59**

## 4. Climate and Rural Income

Robert Mendelsohn, Alan Basist, Pradeep Kurukulasuriya, and Ariel Dinar ......................................................................................................... **79** 5
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With respect to export delays, only one of the eight African countries performs better than the Philippines and only three perform better than China. Even this is likely to underestimate the relatively poor performance of customs administration in the African countries since the point of exit for most exports from China and the Philippines will be a port and, therefore, the delays will include port delays.<sup>9</sup> In contrast, several of the African countries (including Zambia and Uganda the two best performers) are landlocked—meaning all exports will clear customs at a land border.<sup>10</sup> Further, as noted above, for most of the African countries neighboring countries are the most important export partners for the firms in the Investment Climate Surveys.

Although customs delays reflect one aspect of the burden associated with trade and customs regulations, they are not the only aspect of trade regulation that is burdensome and costly for enterprises involved in exporting. Further, as noted above, trade regulations that make importing more difficult might also discourage exporting. If trade regulations make it more difficult for exporters to get imported capital and intermediate inputs—and hence to meet international standards in these respects—restrictions on imports will also discourage exporting.

Most firms that use imported goods use clearing agents to help arrange for customs clearance. Over 85 percent of enterprises that used imported inputs reported that they used clearing agents to help with these procedures (see Table 6). The median cost of hiring a clearing agent in Zambia—the only country where the survey asked about the cost—was about 1 percent of the shipment value.

In addition, many companies reported that they had had to obtain (at least one) import license within the two years prior to the survey (see Table 6). In most countries, the average wait to get a license was about a week and in some countries it was close to two weeks. About 10

> 9 In contrast to the African countries, the most important markets for Chinese goods are overseas (i.e., China does not share a land border with its most important markets).  Although the most popular destination for ‘exports’ from China was Hong Kong, the three next most important destinations were the United States (32% of exporters rated it among their 3 most important destinations), Japan (31%) and Germany (10%). Since the Philippines has no land borders with any other country, all Philippine exports will ultimately be shipped overseas.

> 10 The survey asks about clearing customs in their own country not at the outgoing port if they are later shipped from a neighboring country (e.g., Ugandan exports from Mombasa or Dar es Salaam).

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advanced and 20 emerging. Appendix Table 1 reports the list of economies, the number of debt issuances, and the number of firms per economy. The main results of this paper are robust to the exclusion of the largest advanced and emerging economies (the United States and China).

To study domestic banking crises, we merge our data on corporate debt issuances with data from the Reinhart and Rogoff’s financial crises <u>database, which covers the 1991-2014 period. In their</u> database, domestic banking crisis years are marked by two types of events: (i) bank runs that lead to the closure, merging, or takeover by the public sector of one or more financial institutions; and (ii) no runs, but the closure, merging, takeover, or large-scale government assistance of an important financial institution that marks the start of a string of similar outcomes for other financial institutions.<sup>7</sup> After merging the data sets, we obtain a sample that comprises 170,947 debt issuances conducted by 51,989 firms from 36 economies. We do not split the sample across groups of economies when analyzing domestic banking crises for three main reasons: (i) domestic banking crises consist of relatively similar events across economies; (ii) our sample of economies is smaller when merging the domestic crises data; and (iii) most of the domestic banking crises in emerging economies occurred during the 1990s, when the corporate debt issuance activity by this group of economies was scarcer.

# **3. Changes in Debt Issuance Composition during Crises**

The primary debt markets we analyze in this paper have rapidly expanded worldwide since the early 1990s (Figure 1). Between 1991 and 2014, the total amount of corporate bonds and syndicated loans issued increased more than 7-fold in advanced economies and almost 27-fold in emerging economies, reaching $4.6 trillion and $0.81 trillion in 2014, respectively. The growth in debt issuance activity

> 7 The authors of the database acknowledge the difficulty in accurately pinpointing the year in which a crisis ended. For robustness, we also analyzed the Leaven and Valencia (2012) database on banking crises. The results were qualitatively similar to the ones reported in this paper.

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# **The Economic Impact of Banking the Unbanked: Evidence from Mexico**<sup>**#**</sup>

Miriam Bruhn<sup>a</sup> _The World Bank_ Inessa Love<sup>b</sup> _The World Bank_ _Keywords: Finance and Development; Entrepreneurship; Banking. JEL codes: G12, O12_

> # We thank the Knowledge for Change Program for supporting this project with funding and Kiyomi Cadena for providing excellent research assistance. We are also grateful to David McKenzie and to James Vickery for their very useful comments. The views expressed in this paper do not necessarily represent those of the World Bank, its Executive Directors, or the countries they represent.

> a,bThe World Bank,  MSN MC 3-307, 1818 H Street, NW, Washington, DC 20433, USA. Corresponding author: Miriam Bruhn, Phone: 202-458-2732, Fax: 202-522-1155, <u>mbruhn@worldbank.org</u>
there are exceptions to this rule)<sup>17</sup> and personal and private imports also enter tariff free. We also provide estimates of the tariff rates where we adjust for zero tariff collections on CIS imports. That is, in the above formulas for calculating the tariff on a tariff line, we set ad valorem and specific rates on imports from the CIS countries equal to zero to take into account the special trade regime within the CIS _. We call these calculations our estimated collected tariff rates._ In appendix B we produce tables comparable to tables 1 and 3-8. The overall estimated collected tariff rates are lower than the MFN rates by about one percent.

# **Comparison of the collected rates based on our calculations with actual collected rates based on Ministry of Finance data**

According to our calculations, the overall collected rate was equal to 10.5% in 2001, 11.0% in 2002, 11.5% in 2003, 11.2% in 2004 and 11.0% in 2005. On the other hand, the actual collected rate was 9.5% in 2001, 9.7% in 2002, and 9.8% in 2003. The difference can be attributed to the fact that we did not take into account any exemptions other then the CIS free trade zone exemption.

To calculate actual collected rate, we used the Ministry of Finance data on collected import duties as a numerator. As a denominator, we used the overall import volume less imports from Belarus as reported by the Russian Customs Committee<sup>18</sup> .

# **Comparison of the collected rates based on our calculations with earlier estimates**

Prior to the acquisition of the dataset for trade data described above, World Bank staff used the official publication of the Russian Customs Committee, namely “Customs Statistics of the External Trade of the Russian Federation.” This required the manual entry of the data. But this publication does not provide tariff line data—only data aggregated at various levels, typically to six or four digits, with a total of about 1,700 lines, as opposed to about 11,000 in the electronic dataset.

We find that both approaches identify the food industry and light industry as the sectors with the highest tariff rates. The overall collected tariff rate in 2001, based on the Customs Committee publication, was estimated at 8%, as opposed to our estimate of 10.5% or 10.6% if trade weighted. We attribute the differences to the level of

> 17 Imports of sugar from Ukraine are a case in point.

> 18 The exclusion of the imports from Belarus is determined by the fact that the electronic dataset which we used in the calculations reported import volume without imports from Belarus.

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line corresponding to the international rural line. When explicit urban-rural lines were not reported in the PA, but a deflator was applied to adjust for the urban COL differential, we “backed out” the latter from the real and nominal consumption numbers given in the micro data (in some cases this was already done in the form of a price index in the data files). When urbanrural lines (either explicit or implicit) were not available, we applied the population-weighted regional average poverty-line differential to the country in question. We used the countryspecific CPI’s to adjust the urban and rural index over time. For most countries, we had little choice but to assume that the poverty line differential is constant over time; in only a few cases (though some of the largest countries, including China, India and Nigeria) did we have separate urban and rural CPIs, in order to calculate a date-specific urban-rural poverty line differential. Table 2 gives the numbers of countries in each data category at the regional level.

We were able to derive rural and urban income/consumption per capita distributions for 87 low- and middle-income countries from 208 household surveys representing 92% of the population of the developing world; Appendix 2 provides details on the country coverage and survey dates.<sup>15</sup> Of these, 157 are for consumption expenditure and 51 are for incomes. Within the 87 countries, 19 use only one distribution, 38 have two distributions while the rest (30) use at least three distributions over the period.<sup>16</sup> All the household surveys used here are national coverage except Argentina and Uruguay which only cover the urban population (though 90% or more of their populations live in urban areas).

The use of a “per capita” normalization in measuring poverty is standard in the literature on developing countries; for example, virtually all of the PA’s use household income or 15 It was not feasible to obtain separate rural and urban distributions for all the countries used in Chen and Ravallion (2004) since for some we only have grouped data or in a few cases there is no ruralurban identifier in the individual record data. So this is a subset of the data set we have compiled we have for 100 developing countries’ income or consumption distributions from 600 + household surveys spanning 1980 to 2004, which is an updated version of the data base described in Chen and Ravallion (2004); the data are available from the _PovcalNet_ site: <u>http://iresearch.worldbank.org/povcalnet.</u> 16 For some countries, we did not use all available surveys as some were not considered sufficiently comparable over time; there are examples for India, Mongolia, Cambodia, Malawi and Gambia.

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where _x_ represents the amount of exports of good _i_ and _n_ is the number of products. When a country exports a single good, the value of _HHIP_ is equal to one while _HHIP_ is equal to 0 when the country exports a large variety of products (Haddad et al. 2013; Cadot et al. 2011 2013). Lower values of _HHIP_ indicate a greater extent of product diversification. COMTRADE data on exports by product at a 4-digit level from the SITC Revision 1.0 are used to compute _HHIP_ . On the other hand, the Herfindahl-Hirschman index of export markets (HHIM) is the sum of squared weights of export by destination in total trade. The formula is similar than the one above: in the case of _HHIM_ , _x_ represents the amount of exports to country _i_ and _n_ is the number of destinations. Again, lower values of _HHIM_ indicate a greater extent of market diversification. Bilateral trade data from COMTRADE are used to compute _HHIM_ .

Table 1 shows the medians for _HHIP_ and _HHIM_ across groups in Sub-Saharan Africa for the periods 1990-94 and 2010-14. In terms of export product concentration, the (median) value of _HHIP_ for SubSaharan Africa declined from 0.31 in 1990-94 to 0.21 in 2010-14 (that is, a reduction of 0.10). The reduction of _HHIP_ was sharper among resource rich countries in the region —with the median declining from 0.8 in 1990-94 to 0.54 in 2010-14. Non-resource rich countries —with greater levels of export product diversification than resource rich countries— also experienced a decline in _HHIP_ from 0.27 to 0.16.

In the case of export market concentration, the (median) value of _HHIM_ remained almost invariant in Sub-Saharan Africa over the past two decades. Export market concentration slightly fell from 0.14 in 1990-94 to 0.13 in 2010-14. The _HHIM_ also remained invariant over time for the group of non-resource rich countries (0.12 in both 1990-94 and 2010-14). However, export market concentration fell from 0.29 in 1990-94 to 0.21 in 2010-14 across resource rich countries. In sum, export market diversification increased among resource rich countries; however, non-resource rich countries still export their goods to a relatively greater number of destinations.

_Natural resource dependence._ There is no consensus in the empirical literature about the effects of natural resources on growth. A recent meta-analysis of the impact of natural resources on long-term economic growth is not conclusive: about 80% of the papers reviewed find a negative effect or no effect (Havranek et al. 2016). However, the long-term effects of natural resources might depend upon: (a) distinguishing between the different types of natural resources, and (b) differentiating between resource dependence and resource abundance.

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