rafmacalaba/datause-encoder-data huggingface dataset:
they are in overall sales. Analogously, the exit of poorly performing firms should raise productivity. And reallocating resources in the sector toward more productive firms will also raise sector productivity growth.

Over 1998–2013, most of the increase in productivity—two-thirds—came from the entry of new firms (Figure 9).<sup>11</sup> To help put this in perspective, the rate of entry of new firms averaged 10 percent a year. The other primary source of growth was improving productivity of incumbents, which contributed 40 percent.

**Figure 9 Decomposition of annual TFP growth in manufacturing, 1998–2013**

(percent a year) <!-- Start of picture text --> 2.00<br>1.63<br>1.28<br>1.22<br>1.11<br>0.95<br>0.78<br>0.52<br>0.37<br>-0.15 0.05 - 0.10 0.04 -0.16 -0.04<br>1998~2013 1998~2007 2007~2013<br>Overall Within<br>Between Entry<br>Exit<br>2<br>1.5<br>1<br>.5<br>0<br><!-- End of picture text --> Source: Authors’ estimates based on NBS’s Industrial Enterprise Survey.

Note: Calculations exclude firms that switched between sectors in the sample period. The number of such firms was small.

Over the entire period, firm exit contributed negligibly to manufacturing productivity growth, reflecting one of several possibilities. Poorly performing firms either did not exit or exited but accounted for only a small percentage of aggregate output. Or the productivity of some firms that exited was average or better. Similarly, there were no gains from reallocating resources (labor, capital, and intermediate inputs) to more productive firms, which would have increased aggregate productivity, all else equal. In fact, the contribution was slightly negative. In advanced countries, this is the most important source of productivity growth,<sup>12</sup> and thus stands out as a possible major source of future productivity growth in China.

After 2007, average manufacturing productivity growth in China decreased almost by half. The most important reason for the decline was that the contribution of better entrants disappeared. In some sectors, the contribution of new entrants was actually negative, implying that these firms entered the productivity distribution lower than the sector average. One

> 11 New firms are those that were established in either the current year or previous year (the date is given in the NBS survey).

> 12 See, for example, Bartelsman and Dhrymes (1998).

10
66

# TABLE 20 RELATIVE IMPORTANCE OF DIFFERENT FACTORS IN THE TECHNOLOGICAL CAPABILITY OF GARMENT FIRMS IN THE INITIAL PERIOD OF DIRECT ENTRY INTO EXPORT MARKETS AND OVER THE LIFE OF THE FIRM

|||Ini|tal Export E|ntry||Uletime of Flr|m|
|---|---|---|---|---|---|---|---|
|Factor||Average<br>Score|Percenta<br>Assigning|ge of Firms<br>a Score of|Average<br>Score<br>|Pefcentag<br>Assigning a|e of Firms<br>Score of|
|.<br>___|-||||1-1|4|____4<br>5|
|Support trom Other<br>Provides External to<br>the Firm|T<br>P<br>NP|3.7<br>3.7<br>3.6|30.4<br>0.0<br>38.9|30.4<br>66.7<br>16.7|3.7<br>3.5<br>3.7|39.4<br>33.3<br>40.7|26.1<br>18.7<br>20.8|
|EntrsproenurlOwner's<br>Prior Sklgls|<br>T<br>P<br>NP|3.2<br>2.3<br>32|13.1<br>0.0<br>11.1|34.8<br>33.3<br>38.9|3.0<br>2.7<br>3.5|18.2<br>16.7<br>20.8|182<br>16.7<br>20.8|
|Domestic Employes|T|3.5|21.7|30.4|2.7|242|3.0<br>|
|Prior Skdlls|P|3.7|33.3|33.3<br>|2.7<br>|33.3<br>|0.0<br>|
||NP|3.5|16.7|33.3|2.7|16.7|42|
|OnthaeJob Leaning|T|3.0|8.7|30.4<br>|4.1<br>|12.9<br>|58.1<br>|
|by|P|2.6|0.0|33.3|4.3 -16|.7|66.7<br>|
|Employees and<br>Ent<br>reprneur|NP|3.3<br>_<br>_<br>_<br>_|11.1<br>_<br>_<br>_<br>_|33.3|4.2<br>_<br>_<br>_|13.0<br>_<br>_<br>_<br>_|60.9<br>_<br>_<br>_|
# **Farm Size and Productivity:**

# **A “Direct-Inverse-Direct” Relationship**

Sara Savastano<sup>1,2</sup> and Pasquale L. Scandizzo<sup>2</sup> 1World Bank, 2University of Rome Tor Vergata -DEF

## **JEL codes** : O12, Q12, Q15

_Keywords: Agricultural Policies, Agriculture Productivity, Agriculture_ and Food Security, Ethiopia Sara Savastano is Senior Economist at the Global Indicators Group, Development Economics and Assistant Professor of Economics at the University of Rome Tor Vergata (currently on leave). Pasquale L. Scandizzo is professor at the University of Rome Tor Vergata. For their comments the authors thank participants of the conference on “Farm Size and Productivity: A Global Look “organized by USDA – ERS & Farm Foundation on February 2-3, 2017, and participants of the 21th ICABR Conference on “Bioeconomy in Transition: New Players and New Tools”, Berkeley May 30 – June 2, 2017. The authors acknowledge the FAO & CEIS-Tor Vergata Letter of Agreement on “Smallholder agriculture in transition: Behavior, constraints and policies in Ethiopia” for the use of the data.
along the shoreline, the greater Pirambú area has become one of the biggest shantytowns in South America with over 300,000 dwellers. In 2000, Pirambú had a population of 18,453 people. As shown in Table 4, the public services offered to the population by the State and Municipal governments are not enough to cover all the needs of Pirambú’s residents.

# **Autran Nunes**

Autran Nunes is located in the eastern part of Fortaleza. This neighborhood is densely populated in a rather small geographical area. In 2000, Autran Nunes had 21,323 people and is the largest neighborhood in terms of the size of the population amongst the three surveyed neighborhoods. This neighborhood also has the lowest HDI (0.380) and is the least equipped in terms of public infrastructure and services (see Table 4). As in Pirambú, infrastructure and basic living conditions in Autran Nunes are quite precarious.

|**Table 4: Public Services in Autr**<br>|**an Nunes, Edson Queiroz, and Pirambú**<br>|
|---|---|
|**Neighborhood**|**Infrastructure**|
|**Pirambú**|•2 Health Centers<br>•1 Community Center<br>•1 Kindergarten<br>•3 Schools<br>• 1 Citizen Center|
|**Autran Nunes**|•2 Health Centers<br>•6 Kindergartens<br>• 5 Schools|
|**Edson Queiroz**|•1 Center of Professional Initiation<br>•1 Health Center opened to the local community,<br>some services in this center are freely offered by<br>the_Universidade de Fortaleza_(UNIFOR)**_._**<br>•10 Schools|
|**Source:** _Secretaria de Planejamento e Or_|_çamento/ Prefeitura de Fortaleza_|

11
<!-- Start of picture text --> @=m Motor Gasoline |= Gas/diesel oil =m Aviation fuels @mm Biofuels mm other<br>x<br>0.200 @<br>)<br>i<br>0.175<br>0.150 Other MICs q 8<br>a5 EEme x<br>© 0.125 ee ee =fg 2<br>g | —_2<br>8 [—o<br>| oa<br>5 0.100 =<br>a sss” Other HiCs<br>&5£ 0.075 POO—_—EE>E>:a Chinai<br>——— EU<br>~0.000 | | | zaPS|<br>Share of global GDP (%)<br><!-- End of picture text -->

<!-- Start of picture text --> 6000 USA<br>|_|<br>5 2<br>q@ 4000 e)<br>FS =<br>°LS 3000 oO.2P=<br>ac =<br>© g<br>©<br>eve5 =I38<br>2—_@<br>1000 | | im =<br>a India<br>; iea—es |S<br>Share of global population (%)<br><!-- End of picture text -->
<!-- Start of picture text --> 0.064<br>0.039 0.037<br>0.011<br>Pre-GFC Post-GFC Pre-GFC Post-GFC<br>(n = 669) (n = 1,176) (n = 669) (n = 1,140)<br>(p-value = 0.09) (p-value = 0.70) (p-value = 0.32) (p-value = 0.21)<br>Original Commitments Disbursements<br><!-- End of picture text -->

# **4.2 Regional Comparison**

The observed correlations between IFC investment and GDP cycles were consistent across regional groups at both stages of the investment cycle (Figure 2). IFC investment activities in Europe and Central Asia (ECA), South Asia (SAR), and Sub-Saharan Africa (SSA) regions were procyclical in terms of both disbursements and original commitments. While commitments were most procyclical in ECA, disbursements were most procyclical in SAR. In SSA, commitments and disbursements were both procyclical in the last two decades, but the former turned countercyclical following the GFC. Conversely, IFC investment activities in East Asia and the Pacific (EAP) and Latin America and the Caribbean (LAC) and Middle East and North Africa (MENA) were countercyclical over the full period.

**Figure 2: Region-Level Correlation Results**

<!-- Start of picture text --> Correlation of Orig. Commitments w/ Correlation of Disbursements w/ Quarterly<br>Quarterly Real GDP (2000-2019) Real GDP (2000-2019)<br>0.2 0.11<br>0.09 0.02 0.03<br>0.01<br>-0.01<br>-0.03<br>-0.02 -0.01<br>-0.04<br>-0.13<br>EAP ECA LAC MENA SAR SSA EAP ECA LAC MENA SAR SSA<br>EAP ECA LAC MENA SAR SSA EAP ECA LAC MENA SAR SSA<br>Countries 6 5 15 3 2 4 Countries 6 5 15 3 2 4<br>p-value 0.51 0.00** 0.45 0.92 0.49 0.92 p-value 0.64 0.72 0.66 0.26 0.4 0.66<br><!-- End of picture text --> 10
Table 4 shows the errors of inclusion (the proportion of beneficiary households that are in the top six deciles, or relatively rich) and the error of exclusion (the proportion of poor households that do not receive a water consumption subsidy). These errors are not conditional on location in a service area or connection, so they differ from those in table 3. The estimates show that in most countries covered here, the errors of exclusion range from 78 percent to nearly 100 percent, except for Jamaica, Panama, and Vietnam (column 2). This is consistent with the very low levels of Ω presented in table 3. The countries on the lower end of this spectrum exhibit errors of exclusion around 50 percent, suggesting that their level of outreach to the poor could still be improved.

Inclusion errors (column 1 of table 4 ) are extremely high in all countries, suggesting a wide margin for the improvement of targeting, consistent with the analysis presented so far. The inclusion errors are particularly high in African countries, with figures close to 100 percent in all countries except Nigeria. Bangladesh also stands out with figures close to 90 percent.

**Table 4. Water consumption subsidies, and errors of inclusion and exclusion**

||Error of inclusion (%)|Error of exclusion (%)|
|---|---|---|
||(1)|(2)|
|Ethiopia|96.01|97.57|
|Mali|97.82|99.15|
|Niger|99.73|99.93|
|Nigeria|74.35|89.59|
|Uganda|98.28|99.60|
|El Salvador|82.01|78.27|
|Jamaica|51.78|51.78|
|Panama|71.14|38.56|
|Bangladesh|87.67|95.45|
|Vietnam|66.51|50.28|

_Source:_ Original calculations using data from household surveys, IBNET, and national utilities and programs. See the data and methodology section, and the appendix, for more detail. _Note:_ Poor households are defined as belonging to the first four deciles of the expenditure (or income) distribution in each country. Error of inclusion is measured by the percentage of all beneficiary households that are rich; error of exclusion is measured by the percentage of poor households that do not get a subsidy. All figures are calculated using sample weights.

# **4 Conclusion**

This paper offers a methodology to both estimate the amount of subsidy resulting from piped water consumption tariffs and assess the resulting subsidy’s efficacy in improving the accessibility and affordability of service for the poor. In an analysis of 10 low- and middle-income countries, we find that piped water is substantially subsidized, as existing tariffs do not fully recover the costs of service provision. Moreover, these consumption subsidies fail to efficiently address service gaps among the poor due to ineffective targeting. Our analysis reveals remarkably consistent findings across countries, despite their great diversity in terms of economic development, national income levels, piped water coverage, and the costs of producing and distributing piped water.

20
**Table 3: Regression of firm level exports on country dummies and access variables, all industries**

||**Dependent**<br>|**variable : Lo**<br>|**g (exports)**<br>||
|---|---|---|---|---|
||OLS||Tobit by<br>likeliho|maximum<br>od|
||(1)|(2)|(3)|(4)|
|National supplier access|2.268|||19.135|
||(13.99)**<br>|||(18.99)**<br>|
|Local supplier access|1.111|||6.171|
||(16.40)**|||(15.27)**|
|Foreign market access|-0.050|||0.265|
||(2.32)*|||(3.01)**|
|Log (employment)|1.671<br>|||8.466<br>|
||(66.06)**|||(53.41)**|
|Log (age)|0.092|||0.065|
||(1.96)|||(0.37)|
|Foreign owned|4.032|||11.100|
||(27.08)**|||(25.68)**|
|Control of corruption|3.470<br>**|||18.651<br>**|
||(9.48)|||(14.84)|
|Bangladesh|3.025|5.519|20.122|8.090|
||(12.15)**|(29.12)**|(26.48)**|(9.73)**|
|Malaysia|1.577|9.358|29.937|-13.564|
||(3.29)**|(47.30)**|(40.26)**|(8.59)**|
|China|-9.068|1.917|7.913|-70.896|
||(17.64)**|(12.11)**|(9.30)**|(20.76)**|
|India|-5.491|0.719|3.671|-46.832|
||(10.87)**|(5.40)**|(4.91)**|(17.92)**|
|Brazil|-9.595|-1.353|-8.752|-65.918|
||(17.77)**|(8.07)**|(8.53)**|(23.66)**|
|Cameroon|2.861|0.181|1.592|36.707|
||(5.00)**|(0.41)|(0.62)|(11.51)**|
|Cote D'ivoire|-1.533|0.733|4.878|9.655|
||(3.81)**|(1.76)|(1.87)|(3.81)**|
|Ghana|2.910|-0.432|-2.376|30.585|
||(6.64)**|(1.31)|(1.43)|(12.57)**|
|Mozambique|-3.450|-1.740|-11.922|4.915|
||(9.73)**|(4.01)**|(5.00)**|(1.75)|
|Nigeria|-6.540<br>|-3.430<br>|-30.642<br>|-34.619<br>|
||(21.73)**|(9.74)**|(8.25)**|(11.35)**|
|Kenya|8.170|1.829|10.256|56.032|
||(14.74)**|(6.86)**|(8.23)**|(19.44)**|
|Tanzania|4.075|-0.261|-1.592|37.430|
||(9.32)**|(0.93)|(1.06)|(14.57)**|
|Uganda|6.856|0.189|2.014|52.284|
||(15.21)**|(0.90)|(2.22)*|(20.14)**|
|Zimbabwe|6.996|7.362|32.620|44.846|
||(13.51)**|(16.85)**|(19.71)**|(18.84)**|
|Industry sector dummies|Yes|Yes|Yes|Yes|
|<br>Year dummies|Yes|Yes|Yes|Yes|
|Constant|-94.267|-2.565|-42.272|-735.014|
||(18.56)**|(10.77)**|(44.08)**|(23.04)**|
|Estimated sigma [standard error]|||29.90 [0.12|]<br>25.32 [0.15]|
|Observations|39002|39002|39002|39002|
|R-squared|0.222|0.09|||
|Pseudo R-squared|||0.03|0.09|
|Loglikelihood|||-57992|-54541|

z statistics in parentheses based on bootsrapped standard errors (with 50 replications per estimate) * significant at 5%; ** significant at 1% 29
6 international PPP exchange rates).<sup>11</sup> This is a deliberately conservative definition of “poverty,” being anchored to the poverty lines typical of low-income countries. It is also one that has acquired considerable currency in international policy discussions: The first Millennium Development Goal (MDG1), for example, is to halve the 1990s “$1 a day” poverty rate by 2015. To gauge sensitivity, we also use a line set at twice this value, $65.48 per person per month. Following common practice we refer to these as the “$1 a day” and “$2 a day” lines ($1.08 and $2.15 would be more precise). The higher line is more representative of what “poverty” means in middle-income developing countries.

These international lines are converted to local currencies using the Bank’s 1993 PPP exchange rates for consumption, and each country’s consumer price index (CPI). PPP exchange rates adjust for the fact that non-traded goods tend to be cheaper in poorer countries. There is more than one way to calculate PPP exchange rates. The Geary-Khamis (GK) method used by the Penn World Tables (PWT) uses quantity weights to compute the international price indices. For our purposes, this method gives too high a weight to consumption patterns in richer countries when measuring poverty globally. The Elteto-Kones-Sculc (EKS) method — a multilateral extension of the usual bilateral Fisher index — attempts to correct for this bias. Since 2000, the World Bank’s global poverty and inequality measures have been based on the Bank’s PPP rates, which use the EKS method.<sup>12</sup> At the time of writing, new PPP rates, based on 2005 prices, are about to become available. While existing poverty and inequality measures have not yet been revised accordingly, we comment later on some of the likely implications.

Once the international poverty lines have been appropriately converted into local currency, and local CPI has been used to inflate the line to the nominal currency of the survey year, poverty measures are calculated for each survey year. Naturally, different countries do not all field their household surveys (which are rarely annual) in the same year. In Table 1, we report the year(s) in which the latest surveys available to us were conducted in each country, and report poverty measures for those years. In Tables 2 and 3, where we seek to describe regional and global poverty aggregates, the poverty measures are lined up in time for each of a set of “reference years” using the interpolation method described in Chen and Ravallion (2004).

> 11 See Chen and Ravallion (2001) for a detailed description of how this line was constructed.

> 12 For further discussion of the difference between these two methods and the bearing on poverty measurement see Ackland et al. (2006).
global findings with novel descriptive statistics from the Sahel, demonstrating just how rapid urban growth over the last three decades has been.

Our results point to a number of implications for domestic and international policy makers. First and foremost, there is a need for better sharing of information between international, national, and local actors. Despite remarkable progress on the effects of climate change within expert literature, some of the most vulnerable communities on the frontlines of the changing climate are poorly informed about both looming disasters and slow-onset changes that will nevertheless impact everyday life. Early warning systems that alert local communities about impending climate events remain in short supply. Second, policy responses to climate change in many developing countries remain overly fragmented at a time when a coordinated approach that works across space and time is needed. For instance, the arrival of climate migrants in urban areas puts pressure on local land markets, many of which continue to be characterized by unregistered property rights. In this context, local attempts to facilitate registration of plots sometimes clash with national-level initiatives, clearly evidencing the need for better coordination.

While global in its scale, our work leaves important related questions to future research. For one, we do not know with sufficient precision how exactly drought-induced urban growth looks. Our data from the Sahel that we discuss in section 2 suggests that climate migrants first settle at the outskirts of growing cities, building informal settlements that likely become formalized over subsequent decades. Whether this speculation is correct and whether it generalizes beyond the Sahel remains a task for future research. Second, although extant literature makes it hard to gainsay the fact that slow-onset events cause population movement, the data limitations inherent in the global scope of our study do not allow us to show it directly. In addition, we still know far too little about how drought-induced migration differentially affects individual households and their members. In the same vein, wide gaps remain in our understanding of how climate migrants adjust to their new conditions once they arrive in rapidly growing cities, what coping mechanisms they rely on, and what challenges and opportunities they represent for the host municipalities in which they settle. These questions must remain at the forefront of future research because if big droughts are indeed followed by big cities, it 27
For the sub-samples of firms that did not report a product innovation, the averages are 2.232 versus 2.229 and the standard deviations are 0.714 versus 0.855. In both cases, the differences in the averages are not statistically significant, but the addition of thousands of observations from nine countries allows us to study the cross-country determinants of product innovation rates with more precision.

The trade-policy variables also turned out to limit the sample of countries. Hence we imputed the missing values of the NTB and tariff index with industry dummies and the other country variables, but not with the firm-level variables. Again, Table 1 shows that the within group averages and standard deviations of the original data and the imputed values are strikingly similar. We return to this issue in Section 4.

# Summary statistics

Table 1 presents descriptive statistics for firms from 68 countries, the sample used in the econometric analysis discussed below. The number of firms by country appears in the Data Appendix. The data show diverse international product innovation rates, ranging from about 6.8 percent in Nepal to 77.8 percent in Peru. It is noteworthy that the percentages for countries with a Gross Domestic Product per capita above the World Bank’s high-income country threshold of $14,000 (purchasing-power adjusted as of 2000, which are identified with an asterisk in Table A1) are not above the cross-country average of 48.8 percent or the average for the overall sample of firms, which is 47.5 percent, as reported in Table 1.

# [Table 1 here]

Table 1 also shows that a large share of firms that reported new products also report R&D expenditures. In the total sample, 60 percent of firms with product innovation also report R&D, whereas only 15 percent of non-innovative firms report some R&D expenditures. This pattern holds for most countries individually for R&D, but also for licensing, export status, and foreign ownership. China is the exception. In this country, the percentage of non-innovative firms that report R&D expenditures, licensing payments, exporting, and foreign ownership is higher than among innovative firms. The data for China is consistent with product-level export data that suggests that mainland China introduced comparatively few new export products during 1994-2003 (Klinger and Lederman, 2006).

There is no clear relationship between trade policies and the share of innovative firms across countries, however. For example, Argentina appears with 75 percent of firms being innovative, but it also utilizes numerous non-tariff barriers (NTBs) covering, on average across the 8 manufacturing sectors, slightly over 29 percent of its tariff lines. In contrast, Bolivia has a low NTB coverage rate of about 3 9
28 Table 5. Arnial Population Increase and Growth Rate by World Bank Region and Country Department, 1985-2100

||Anual p|opulatio|n<br>incr|ease (m|iltions)|Anrua|l growt|h rate|(percen|t)|
|---|---|---|---|---|---|---|---|---|---|---|
|Regionand<br>|||||||||||
|country<br>department|1985-90|2000|2025|2050|2100|1985-90|2000|2025|2050|2100|
|World total|87.6|90.6|74.4|40.5|6.9|1.73|1.46|0.90|0.42|0.07|
|Borrowers and non-borrowers|||||||||||
|Sub-Saharan Africa|15.7|20.8|22.5|16.9|2.2|3.17|2.87|1.75|0.95|0.10|
|Asia and Oceania|46.0|44.2|31.4|13.2|3.0|1.71|1.35|0.75|0.28|0.06|
|Europe,<br>iddleEast,<br>and North Africa|15.3|15.8|13.0|8.0|1.3|1.29|1.14|0.74|0.40|0.06|
|America|10.8|10.0|6.9|2.6|0.7|1.55|1.21|0.66|0.23|0.06|
|Borrowers only|||||||||||
|Africa Region|14.5|19.5|21.5|16.1|2.0|3.22|2.94|1.80|0.97|0.10|
|I Occidental and Central|1.4|1.9|1.9|1.5|0.2|3.31|2.91|1.69|0.94|0.10|
|2Eastern|3.7|4.9|5.5|4.1|0.5|3.19|2.92|1.80|0.98|0.10|
|3 South-Central<br>and|||||||||||
|Indian ocean|1.8|2.4|2.7|2.0|0.2|3.12|2.88|1.79|0.97|0.09|
|4Western|4.4|5.8|6.3|4.7|0.6|3.36|3.01|1.80|0.97|0.10|
|5Sahelian|1.1|1.5|1.8|1.4|0.2|2.75|2.75|1.83|1.00|0.12|
|6 Southern|2.2|3.0|3.3|2.5|0.3|3.32|2.99|1.82|0.98|0.09|
|Asia Reoion|44.4|42.5|31.2|13.4|2.9|1.78|1.39|0.79|0.30|0.06|
|1Other South<br>|3.4|4.1|3.6|2.4|0.4|2.38|2.14|1.25|0.66|0.09|
|2 Southeast|6.0|6.1|4.6|2.1|0.6|2.08|1.67|0.92|0.36|0.09|
|3 China|15.4|14.1|8.6|2.0|0.0|1.43|1.10|0.56|0.12|0.00|
|4India|16.2|14.9|11.6|5.9|1.5|2.01|1.49|0.88|0.38|0.09|
|5 Indonesia and Pacific|3.2|3.4|2.5|1.1|0.3|1.83|1.54|0.87|0.32|0.09|
|EMN Region|11.9|13.5|12.1|7.7|1.3|2.36|2.03|1.21|0.62|0.09|
|1 Pakistan and Turkey|4.5|4.8|4.9|3.4|0.4|2.84|2.23|1.42|0.76|0.08|
|2 North Africa|1.6|1.9|1.5|0.9|0.2|2.66|2.29|1.22|0.57|0.10|
|3 Niddle East|5.4|6.4|<br>5.5|3.7|0.6|2.94|2.47|1.34|0.70|0.10|
|4 Europe|0.5|0.5|0.2|0.0|0.0|0.49|0.42|0.15|0.04V|0.03|
|LAC Regifn|8.5|8.6|6.6|2.8|0.7|2.07|1.66|0.93|0.34|0.08|
|I Brazil|2.9|2.9|<br>2.1|0.9|0.3|2.05|1.61|0.87|0.33|0.12|
|2 Central<br>America|2.5|2.8|<br>2.2|<br>1.0|<br>0.2|2.26|1.93|1.06|0.39|0.08|
|3 Caribbean<br>and Other|1.6|1.6|<br>1.2|<br>0.5|0.1|2.14|1.65|0.94|0.33|0.04|
|4 Temperate ard Tropical|<br>1.5|1.4|<br>1.0|<br>0.5|0.1|1.79|1.38|0.79|0.32|0.06|

Note: World Bank regions and country departments are defined in Table 14.