rafmacalaba/datause-encoder-data huggingface dataset:
Table D5: Difference-in-Difference Regressions by Urban/Rural [Dependent Variable: Food Insecurity] <!-- Start of picture text --> Fractional Index 3 Fractional Index 4<br>Ethiopia Malawi Ethiopia Malawi<br>-1 -.8 -.6 -.4 -.2 0 .2 .4 .6 .8 1 -1 -.8 -.6 -.4 -.2 0 .2 .4 .6 .8 1 -1 -.8 -.6 -.4 -.2 0 .2 .4 .6 .8 1 -1 -.8 -.6 -.4 -.2 0 .2 .4 .6 .8 1<br>Effect on Food Insecurity Effect on Food Insecurity Effect on Food Insecurity Effect on Food Insecurity<br>Nigeria Nigeria<br>-1 -.8 -.6 -.4 -.2 0 .2 .4 .6 .8 1 -1 -.8 -.6 -.4 -.2 0 .2 .4 .6 .8 1<br>Effect on Food Insecurity Effect on Food Insecurity<br>Mild Moderate Severe Index Mild Moderate Severe Index<br>HHI 1 HHI 2<br>Ethiopia Malawi Ethiopia Malawi<br>-1 -.8 -.6 -.4 -.2 0 .2 .4 .6 .8 1 -1 -.8 -.6 -.4 -.2 0 .2 .4 .6 .8 1 -1 -.8 -.6 -.4 -.2 0 .2 .4 .6 .8 1 -1 -.8 -.6 -.4 -.2 0 .2 .4 .6 .8 1<br>Effect on Food Insecurity Effect on Food Insecurity Effect on Food Insecurity Effect on Food Insecurity<br>Nigeria Nigeria<br>-1 -.8 -.6 -.4 -.2 0 .2 .4 .6 .8 1 -1 -.8 -.6 -.4 -.2 0 .2 .4 .6 .8 1<br>Effect on Food Insecurity Effect on Food Insecurity<br>Mild Moderate Severe Index Mild Moderate Severe Index<br>56<br><!-- End of picture text --> _Note:_ The figure plots difference-in-difference regression results with region and round controls and standard errors clustered at the household level (see Equation C.2). We display coefficients for the interaction of lagged income diversity indices (Indices 3-6) and a head-of-household gender indicator. Male-headed households serve as the comparison group. Horizontal lines represent 95 percent confidence intervals.
<!-- Start of picture text --> &<br>3<br>Y“<br>ae“<br>©=° “ ’ ’ ’<br>’ ’ ’ oo ’ ’<br>3B28aeu5 6 aoa “7 7 - ae - “7<br>FIoFx2axo3S 8 -----77 eo<br>xé°<br>Ss<br>0.0 0.1 0.2 0.3 04 0.5 0.6 07 0.8 09 1.0<br>Share of diary modules<br><!-- End of picture text --> <!-- Start of picture text --> 8<br>©x° weer Uee--- se<br>2 -<br>§<br>28<br>gs<br>Pp<br>a3<br>©zcS) True value<br>So8<br>0.0 0.1 0.2 0.3 0.4 0.5 0.6 07 0.8 0.9 1.0<br>Share of diary modules<br><!-- End of picture text --> <!-- Start of picture text --> 2g<br>,S<br>’’<br>S§<br>2 eo or -- --c<br>F 33°©8s Ea U7 “7 “7 ae u- u-7 - uc _- ae _ “7<br>z=ofo) 8<br>zg8<br>ro)<br>True value<br>28a<br>3<br>1°aao<br>&S<br>0.0 0.1 0.2 0.3 04 0.5 0.6 07 0.8 09 1.0<br>Share of diary modules<br><!-- End of picture text --> <!-- Start of picture text --> 3<br>sécS)<br>True value<br>©5s °2c<br>%<br>E aoa<br>E ee<br>Zz Task<br>8 gS WT ss----2>><br>Ss<br>Ss<br>)Ss<br>0.0 0.1 0.2 0.3 0.4 0.5 0.6 07 0.8 0.9 1.0<br>Share of diary modules<br><!-- End of picture text -->
|**Average per capita DAU allocation**|1,576,243|948,519|765,176|840,495|
|---|---|---|---|---|
|**Maximum per capita DAU**<br>**allocation (IDR)**|21,675,276|3,966,180|5,555,236|3,184,663|
|**Minimum per capita DAU**<br>**allocation (IDR)**|273,026|353,957|15,913|461,592|
|**Max to min ratio of per capita**<br>**DAU allocation **|79.39|11.21|349.10|6.90|
|**Maximum per capita revenue after**<br>**transfer (IDR)**|32,266,992|14,557,896|16,146,952|11,791,707|
|**Minimum per capita revenue after**<br>**transfer (IDR)**|520,858|606,292|136,799|744,153|
|**Max to min ratio of per capita**<br>**revenue after transfer**|61.95|24.01|118.03|15.85|
|**Coef. of variation**|1.477|0.962|1.695|0.848|
|**Weighted coef. of variation**|0.871|0.594|1.395|0.522|
|**Relative mean deviation**|0.315|0.254|0.433|0.228|
|**Gini Index**|0.445|0.355|0.574|0.322|
|**Weighted Gini Index**|0.312|0.218|0.418|0.192|
|**No. of Observed D3 Group**|99|99|99|99|
|**No. of Receiver**|99|99|31|99|
|**Average DAU allocation (million)**|331,227|292,833|55,243|306,006|
|**Average per capita DAU allocation**|2,051,840|1,185,130|1,205,038|1,061,774|
|**Maximum per capita DAU**<br>**allocation (IDR)**|16,397,758|5,053,894|7,083,893|2,240,744|
|<br>**Minimum per capita DAU**<br>**allocation (IDR)**|340,037|49,799|22,213|440,941|
|**Max to min ratio of per capita**<br>**DAU allocation **|48.22|101.49|318.91|5.08|
|**Maximum per capita revenue after**<br>**transfer (IDR)**|36,078,212|24,734,346|26,764,344|21,921,194|
|**Minimum per capita revenue after**<br>**transfer (IDR)**|547,832|797,903|169,158|648,736|
|**Max to min ratio of per capita**<br>**revenue after transfer**|65.86|31,00|158.22|33.79|
|**Coef. of variation**|1.298|1.142|1.886|1.061|
|**Weighted coef. of variation**|1.013|0.686|1.666|0.608|
|**Relative mean deviation**|0.339|0.285|0.466|0.255|
|**Gini Index**|0.462|0.385|0.609|0.351|
|**Weighted Gini Index**|0.388|0.253|0.510|0.232|
|**No. of Observed D4 Group**|100|100|100|100|
|**No. of Receiver**|95|100|24|100|
|**Average DAU allocation (million)**|335,726|408,791|202,092|438,260|
|**Average per capita DAU allocation**|2,850,914|2,166,975|3,346,229|1,925,755|
|**Maximum per capita DAU**<br>**allocation (IDR)**|19,862,532|21,037,218|34,552,332|5,095,879|
|**Minimum per capita DAU**<br>**allocation (IDR)**|136,836|32,246|4,320|891,093|

20
and education—played a major role, but equally important was the fact that they were jumping onto a moving train, to wit, that of globalization and new technology.

We can see the great divergence in growth among emerging economies in the most recent numbers. Table 2 provides growth data for the BRICS countries. These countries have never seen such disparities in performance before. Again, there are, no doubt, proximate drivers, such as the fall in oil and commodity prices adversely impacting Russia and Brazil for instance, but the story does not end with that. China and, more recently India, which are growing well in this cluster, have been able to take advantage of the globalization, albeit in different ways. I shall briefly describe the India story in the next section.

**Table 2. GDP growth in the “BRICS”**

||Average<br>2001‐2008|2009|Average<br>2010‐2013|2014|2015 (est.)|
|---|---|---|---|---|---|
|_Brazil_|3.7|‐0.2|4|0.1|‐3.7|
|_China_|10.7|9.2|8.9|7.3|6.4|
|_India_|7.1|8.5|7.2|7.3|7.3|
|_Russian Federation_|6.6|‐7.8|3.4|0.6|‐3.8|
|_South Africa_|4.2|‐1.5|2.7|1.5|1.3|

_Source: World Development Indicators, and Global Economic Prospects (for 2015 estimates)._ A similar growth divergence in the Sub‐Saharan African experience is captured well by recent data. Contrary to the more‐uniform stagnation experienced before, there are now countries in that region that are growing at a pace earlier associated with East Asian nations and among the highest‐growing nations in the world currently. Table 3 provides a cross‐country view of Sub‐Saharan Africa.

**Table 3. GDP Growth in Sub‐Saharan Africa**

||Average 2001<br>‐2008|2009|Average 2010<br>‐2013|2014|2015<br>(est.)|
|---|---|---|---|---|---|
|_Top 5 higher expected_|_growth in 2015_|||||
|Ethiopia|8.3|8.8|10.7|10.3|10.2|
|Côte d'Ivoire|0.7|3.3|4.4|8.5|8.4|
|Congo,Dem. Rep.|4.6|2.9|7.4|9.0|8|
|Rwanda|8.2|6.3|7.2|7.0|7.4|
|Tanzania|6.8|5.4|6.7|7.0|7.2|

_Top 5 lower expected growth in 2015_ 10
CAMEROON’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 29. Patterns of capital investment in infrastructure, benchmarked against comparator countries**

Investment in infrastructure sectors as percentage of GDP, by source <!-- Start of picture text --> 2.0 Cameroon Resource Rich<br>1.5<br>1.0<br>0.5<br>0.0<br>ICT Power Transport WSS ICT Power Transport WSS<br>Public ODA Non-OECD Private<br>Percentage of GDP<br><!-- End of picture text --> _Source:_ Derived from Briceño-Garmendia, Smits, and Foster (2009). _Note:_ Private investment includes self-financing by households. Non-OECD financiers include China, India, and the Arab countries. ODA = official development assistance; OECD = Organisation for Economic Co-operation and Development; ICT = information and communication technology; GDP = gross domestic product; WSS = water supply and sanitation; LIC = low-income countries.

# How much more can be done within the existing resource envelope?

Approximately $586 million in additional resources could be recovered each year by improving efficiency (table 17). The largest potential source of efficiency gains is improving cost. Raising tariffs to cost-recovery levels could generate annual savings of $266 million in the power sector and $22 million in the water sector annually. Also, reducing operational and financial inefficiencies (distributional losses and undercollection of bills) in the power and water sectors could generate annual savings of around $254 million. Looking across sectors, power-related efficiencies offer the greatest potential for savings—up to $487 million per year (table 17).

**Table 17. Cameroon’s potential gains from greater operational efficiency**

<u>$ million</u>

||ICT|Irrigation|Power<br>Transport|WSS|Total|
|---|---|---|---|---|---|
|Underpricing|-|n.a.|266<br>0|22|288|
|Overstaffing|n.a.|—|30<br>—|n.a.|30|
|Distributional losses|—|—|168<br>—|14|181|
|Undercollection|—|n.a.|21<br>33|19|73|
|Low budget execution|0|0|3<br>9|2|15|
|Total|0|0|487<br>43|57|586|

_Source_ : Derived from Foster and Briceño-Garmendia (2009). _Note:_ WSS = water supply and sanitation; ICT = information and communication technology. — = Not available; n.a. = Not applicable.

## **Raising tariffs to cost-recovery**

Setting tariff at a cost-recovery level in the power and water sector could generate the highest savings in Cameroon. About $288 million could be saved if underpricing of power and water services is tackled (table 17). The high subsidies in the power and water prices are highly inequitable as the richest are the ones hook-up to the networks.

42
# **1. Introduction**

Small and Medium Size Enterprises (SME) are the most common employers across the world. In 48 out of 76 nations covered in Ayyagari, Beck and Demirgüç-Kunt (2007), SMEs employed more than 50% of the formal workforce. In addition, Ayyagari, Demirgüç-Kunt and Vojislav (2011) find that small firms and mature firms have the highest levels of total employment and small firms and young firms have the highest rates of job creation. SMEs are a fundamental part of a dynamic and healthy economy.

Consequently, it is important to understand the different factors that can help or hinder SME creation and development. Recent research around the developing world provides evidence that SMEs face greater financing obstacles than large firms (Beck, Demirgüç-Kunt & Maksimovic 2005; Beck & Demirgüç-Kunt 2006; and Beck, Demirgüç-Kunt, Laeven Maksimovic 2006). Ayyagari, Demirgüç-Kunt and Vojislav (2006) show that finance, crime, and political instability directly affect the rate of growth of firms, with finance being the most robust variable affecting firms’ growth rate. Furthermore, Beck, Demirgüç-Kunt and Maksimovic (2008) find that small firms use less external finance, especially bank finance. This result, coupled with the evidence found by Kumar, Rajan and Zingales (1999) that financial constraints limit the average firm size, explains the paramount importance of investigating the usage of finance by SMEs.

With this motivation, this paper tries to answer the following questions using a unique data set covering 116 countries across the developing world (Enterprise Surveys): What type of credit do SMEs use to finance their working capital and their investments? Moreover, which SMEs are satisfied with the credit they have and which ones are credit-constrained? The paper illustrates how firm-level survey data collected by the World Bank under a standard 2
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# Acknowledgements

Financial support for this study was provided by the Economics of Adaptation to Climate Change study administered by the Environment Department of the World Bank. Funding for the Economics of Adaptation to Climate Change study has been provided by the governments of the United Kingdom, the Netherlands, and Switzerland.

We would like to extend our special thanks to Ms. Maria Sarraf, Dr. Ainun Nishat and Mr. Khawaja Minnatullah for their valuable guidance. We are thankful to the participants of the Economics of Adaptation to Climate Change country consultations for their useful comments and suggestions. We are also grateful to Ms. Roufa Khanam for her valuable help with the GIS and Brian Blankespoor for editorial help.

2
poverty and welfare statistics, we also propose to deviate slightly from the sampling design and estimate standard errors using the 16 strata originally defined for the HIES 2010. Using this approach to estimate standard errors of poverty rates and welfare statistics will allow for full consistency in the way these statistics are produced across time.

# **8. Conclusion**

This paper describes the different steps involved in the design of the sample for the Bangladesh HIES 2016/17, whose main objective is to produce reliable poverty and welfare statistics at different levels. Designing a sample to achieve multiple objectives is not a simple exercise as objectives might not necessarily be mutually consistent. How to prioritize the objectives of a survey and address any potential inconsistencies that might arise between multiple objectives is important to ensure that the final selected sample does not compromise statistical precision for all levels of interest to policy makers.

The HIES 2016/17 sample was designed to provide reliable poverty and welfare statistics at three different levels: (i) annual poverty estimates at the district level; (ii) annual poverty estimates at the division level for urban and rural areas; and (iii) quarterly poverty estimates at the national level. The sampling follows a two-stage clustered design. In the first stage, we allocate an equal number of PSUs to each of the 64 districts in Bangladesh to ensure that the sample estimates district-level poverty and welfare statistics with similar levels of reliability across districts. In the second stage, we allocate the sample across three sampling strata (urban, rural, and City Corporation) for each district independently and select households from each of the selected PSUs. To allocate the sample across strata in each district, we modify the standard Neyman allocation rule that allows the achievement of an optimal allocation for clustered stratified samples (the standard Neyman allocation rule only allows an optimal allocation for stratified samples). In the process of selecting the final sample, we also discussed how field data collection protocols are taken into consideration and illustrate their importance in fine tuning the final allocation of the sample.

37
are particularly pronounced after 2011, when further increasing private protection would only encourage pirates to shift toward smaller vessels while leaving the total number of attacks unaffected. At these levels of private security, introducing naval patrol is a necessary condition to effectively deter pirates.

To further highlight the displacement effect, we split ships by size in 2012 and construct the iso-crime curves for small (below median) and large (above median) ships separately (Figure 14). The vertical curves for large vessels imply that private protection is the only effective policy to protect large vessels. Unsurprisingly, the optimal policy for protecting large vessels is to rely exclusively on private maritime security contractors. However, for smaller vessels, private protection leads to more attacks due to displacement, therefore the optimal policy to protect smaller ships is to _reduce_ armed guards and at the same time, deploy more navy patrols. The role of the navy then becomes instrumental in keeping the number of attacks on small ships low.

These findings highlight the distributional implications of various policy instruments, which might matter when determining what the optimal level and composition of policing should be.

# **6 Conclusion**

We provide quantitative evidence on the relative contribution that navy patrols and private maritime security contractors had in explaining the collapse of Somali piracy in 2012. The critical role of private security was highlighted, with emphasis made on the externalities – both positive and negative – generated by private measures. The analysis brings new mechanisms to consider when exploring the tension between private and public provision of security.

# **References**

**Admati, Anat R. and Motty Perry** , “Strategic Delay in Bargaining,” _The Review of Economic Studies_ , July 1987, _54_ (3), 345–364.

43
Table 1

# Growth Rates in Developing Regions

(average annual % growth)

||2000-2009|2010|2011|2012|
|---|---|---|---|---|
|East Asia and the Pacific|9.4|9.7|8.2|7.8|
|China|10.9|8.5|9.1|8.4|
|Europe and Central Asia|5.8|5.2|5.3|4.0|
|Turkey|4.9|9.0|8.2|2.9|
|Latin America and the Caribbean|3.8|6.0|4.2|3.6|
|Brazil|3.6|7.5|2.9|3.4|
|Colombia|4.5|4.3|5.6|4.4|
|Mexico|2.2|5.5|4.0|3.2|
|Argentina|5.4|9 .2|7.5|3.4|
|Middle East and N. Africa|4.7|3.6|1.7|2.3|
|South Asia|7.3|9.1|6.6|5.8|
|India|7.9|8.7|6.5|6.5|
|Sub-SaharaAfrica|5.1|4.8|4.9|5.3|

Source: World Bank (2012) and World Bank World Development Indicators 2011 Finally, the entire region underperforms in competiveness. The region’s participation in world exports of manufactures has grown little and its participation in services has fallen (ECLAC 2011). Meanwhile, only Chile and Barbados are in the top 50 of the more than 140 countries in the World Economic Forum’s Global Competitiveness Index; and even so they do not perform well in dynamic sub-indexes like education and innovation<sup>2</sup> . This situation is concerning since the region risks being squeezed by China moving up into higher tech areas where Latin America has excelled (e.g., aeronautics, autos) and by low wage countries emerging as new laborintensive exporters (e.g., in Africa and South Asia).

These outlined handicaps facing Latin America have been overcome by other countries that actively deployed industrial policies. Countries such as Singapore; Taiwan, China; and Ireland began their respective productive transformations with labor intensive assembly operations for export, much like Central America and the Dominican Republic today, primarily geared to creating employment. However, with ambition, forward looking strategies and proactive horizontal, as well as selective, public promotional policies, they diversified and upgraded into

> 2 The OECD Program for International Student Assessment (PISA) scores for Latin American participants reveals poor student performances.www.oecd.org/edu/pisa/2009

5
qualifiers: it stands on its own and is clearly defined.<sup>3</sup> The WDR 2018 illustrated this approach using: (1) the Grade 8 TIMSS learning assessment results for mathematics in 2015 to derive L�; (2) mean years of schooling completed by the cohort of 25- to 29-year-olds, as calculated by Barro and Lee (2013) to measure years of schooling S�; and (3) the learning achievement of Grade 8 students in Singapore (the top performer on this assessment) to derive L�.<sup>4</sup> The resulting chart, which appeared in the WDR 2018, is reproduced here as Figure 2.1. Based on this calculation, for example, 25- to 29-yearolds in Chile have on average 11.7 years of schooling; the learning adjustment reduces that to 8.1 “adjusted” years. The same cohort in Jordan has 11.1 years of schooling on average; adjusting for learning brings that down to “adjusted” 6.9 years.

**Figure 2.1: Average years of schooling of the cohort of 25‐ to 29‐year‐olds, unadjusted and adjusted for learning (using the** **_LAYS_ adjustment)**

<!-- Start of picture text --> Years of schooling LAYS<br>15<br>10<br>Years<br>5<br>0<br>Australia Bahrain Botswana Canada Chile Egypt, Arab Rep. England Hong Kong SAR, China Hungary Iran, Islamic Rep. Ireland Israel Italy Japan Jordan Kazakhstan Korea, Rep. Kuwait Lithuania Malaysia Malta Morocco New Zealand Norway Qatar Russian Federation Saudi Arabia Singapore Slovenia South Africa Sweden Thailand Turkey United Arab Emirates United States<br><!-- End of picture text --> Source: WDR 2018 (World Bank 2018), based on analysis of TIMSS 2015 and Barro and Lee (2013) data.

> 3 Of course, as a “mash-up” index, many other possible approaches to scaling and combing average years of schooling and learning outcomes are possible, e.g. using relative years of schooling, or adding rather than multiplying the two indicators.  As will become clear in the next section, we do aim to provide a more substantive meaning to the index.

> 4 The illustration included the additional assumption that learning starts at Grade 0, a point we come back to below.

8
8

- in the United States.1<sup>0</sup> Since illegal migrants are likely to be<sup>less educated than legal</sup> migrants, this may in turn lead to an overestimate of the<sup>averagelevelof migrant</sup> education. This source of bias, however, should not seriously<sup>distort our estimates of the</sup> migration rate of individuals with tertiary education, since<sup>those with higherlevels of</sup> education are more likely to be in the United States on<sup>a legal basis.</sup>

The second step is to estimate from the 2000 U.S. Census data<sup>the number of</sup> "foreign born" from each country who are 25 years of age or<sup>older.This is necessaryto</sup> maximize the comparability of the immigrant population<sup>from eachcountry with the</sup> Barro and Lee (2000) data set, which measures the educational<sup>attainmentof the</sup> population over the age of 25 in each country.

The third step is to calculate for each population from each labor-exporting country the number of migrants in three specific educational<sup>categories:primary or less</sup> (0-8 years of schooling), secondary (9-12 years of schooling)<sup>and tertiary(13 or more</sup> years of schooling). For the United States, this is done by<sup>using data froma special</sup> tabulation from the 2000 U.S. Census done by the U.S. Census<sup>Bureau. This special</sup> tabulation shows by county of origin the educational attainment<sup>level of the foreign born</sup> population living in the United States in the year 2000.

The final step is to use the Barro and Lee educational attainment<sup>data set to</sup> compute migration rates for each labor-exporting country for<sup>the same three educational</sup> categories mentioned above. These calculations enable us<sup>toshow whatfraction of each</sup> country's educational group has migrated to the United States.

> 10 In 1992 the stock of illegal immigrants in the United States was<sup>estimatedat 3.4 million,or about16</sup> percent of the stock of the "foreign-born" population (Borjas,<sup>1995).</sup>