rafmacalaba/datause-encoder-data huggingface dataset:
Figure: E.10.1

# BELIZE LABOR INTENSIVE EXPORTS

<!-- Start of picture text --> LABOR INTENSIVE TOTAL EXPORTS<br><!-- End of picture text --> <!-- Start of picture text --> 16000<br>p 14000<br>~o 12000<br>e c'1000014000 g |<br>o6 8000<br>6000<br>4000<br>2 6000 | |<br>1970 1980 1990<br><!-- End of picture text -->

<!-- Start of picture text --> LABOR INTENSIVE EXPORTS BY PARTNER<br>1970<br>ROW (32 00%)<br>CA (0 00%)<br>MEX (0 27%) NA (67 73%)<br><!-- End of picture text --> <!-- Start of picture text --> ILABOR INTENSIVE EXPORTS BY PARTNER<br>1980<br>ROW(22.10%)<br>CA (0.03%)I<br>MEX (0.00%) (<br>NA (77 87%)<br>___ ___ _<br><!-- End of picture text --> <!-- Start of picture text --> LABOR INTENSIVE EXPORTS BY PARTNER<br>1990<br>ROW (4.57%)<br>CA (0.00%)<br>MEX (0.11% 0/ --<br>L - NA (95.33%)<br><!-- End of picture text -->
Fort, St. Lucia. Jamaica, though an important hub in the Caribbean, is a stop on only had four of the known routes. However this may have underplayed the importance of the port of Kingston: There is an extensive flow between Trinidad and Jamaica, and eight of the known routes stopped in either one or both ports in Trinidad. One mainline route, for example, originated from Portsmouth, USA, with the first stop being transatlantic in Le Havre, France, then continuing on to another transatlantic stop in Guadeloupe in the Caribbean, before stopping by in both Trinidad and Barbados and them moving up the small island country chain.

Apart from the hub/transshipment ports, ports of Caribbean countries are small, and manage basically local demand in terms of imports and exports. For small island states, the country with the most movements in metric tons accounted for only 1.3 million tons a year in 2012, compared to 57 million tons a year moved in Panama or 22 million tons in Jamaica. Antigua and Barbuda moved about 1 million tons per year, declining after a peak in 2008. In St Vincent and the Grenadines, the movements were about 411 thousand metric tons in 2010, and in St Lucia 628 thousand for the same year. Interestingly, during the financial crises of 2008-09, Antigua and Barbuda increased its metric ton throughput, which then started to decrease in 2009. Saint Lucia and Saint Vincent, however, had downturns both years.

Kingston handles the largest volume of containerized traffic, accounting for 35 percent of total throughput in the sub-region, with almost 1.9 million TEUs handled in 2011. Following Kingston are Freeport, Caucedo, and Port of Spain, comprising 22 percent, 19 percent, and 7 percent (in 2010) of Caribbean throughput, respectively. The remaining ports in the Caribbean combined made up roughly 18 percent of the sub-region’s total throughput (Figure 34).

**Figure 34: Port container traffic in the Caribbean, 2011**

<!-- Start of picture text --> Thousands of TEU<br>0 250 500 750 1,000 1,250 1,500 1,750<br>Kingston<br>Freeport<br>Caucedo<br>Port of Spain<br>Haina<br>Puerto Plata<br>Point Lisas<br>Jarry<br>Bridgetown<br>Phillipsburg<br>Oranjestad<br>Georgetown<br>Santo Domingo<br>Vieux Fort, St Lucia<br>Castries, St Lucia<br>St John, Antigua<br>CPCP, St Vincent<br><!-- End of picture text --> _Source: UNECLAC (2012)._

# **b. Maritime Connectivity**

The ability to connect to global transport networks is now widely used by policy makers as a factor of competiveness to promote international trade or address the needs of remote regions in large countries.

81
Figure 2. Dropout rates in Guatemala <!-- Start of picture text --> 35%<br>31.9%<br>30%<br>24.0%<br>25%<br>18.4%<br>20%<br>15%<br>10%<br>5%<br>0%<br>Primary to lower Within lower Lower to upper<br>sec. transition secondary secondary transition<br>dropped out<br>Percentage of students who<br><!-- End of picture text --> _Source_ : Author calculations based on administrative data on enrollment and the 2013 National Evaluations of students _Notes_ : (1) Dropout rates in primary to lower secondary transition computed as the percentage of sixth grade students evaluated in 2013 who did not enroll in 2014, 2015 and 2016. (2) Dropout rates within lower secondary computed as the percentage of sixth grade students evaluated in 2013 who were enrolled in 2014 but did not enroll in 2015 or 2016, excluding students who dropped out in the transition from primary to lower secondary from the calculation. (3) Dropout rates in lower to upper secondary transition computed as the percentage of ninth grade students evaluated in 2013 who did not enroll in 2014, 2015 and 2016.

Figure 3. Dropout rates in Honduras <!-- Start of picture text --> 40% 37.4%<br>35% 33.2%<br>30%<br>25%<br>18.4%<br>20%<br>15%<br>10%<br>5%<br>0%<br>Primary to lower sec. Within lower secondary Lower to upper<br>transition secondary transition<br>Percentage of students who dropped out<br><!-- End of picture text --> _Source_ : Author calculations based on USINIEH administrative data from 2013-2015 _Notes_ : A dropout is a student who was enrolled in the previous year but did not enroll the following year.

22
is an omitted variable, positively correlated with GDP per capita. Thus, with the addition of this economic model, we have another alternative to the luxury good hypothesis:

**_Selection: The selective reporting hypothesis._** _Richer countries tend to have more complete SP spending data which imparts an upward bias to the estimated income slope of the SPEC._ We test this hypothesis.

Lastly, in the context of the pandemic, we consider a further hypothesis:

**_The COVID hypothesis:_** _Richer countries were impacted more by the pandemic and responded accordingly with their own SP spending._ By this view, rich countries had more of a shock to protect themselves from, reflecting higher levels of social and economic interaction that helped spread the infection (prior to vaccine availability). We can use COVID mortality data in measuring the severity of the pandemic shock.<sup>21</sup> These data do suggest a greater COVID impact in countries with higher GDP per capita; in our data set, the correlation coefficient between COVID deaths per capita and GDP per capita is 0.439. We test this as an extra covariate for SP spending during the pandemic.

# **3. Data and descriptive statistics**

The main dependent variable in our analysis is the share of national income devoted to public SP expenditure. In our first set of data, the coverage is across countries and over time up to (but not including) the pandemic, while the second is for the designated SP responses to the pandemic.

## **3.1 Pre-pandemic data on social protection**

Data on public spending at the country level were assembled for the purpose of this paper from the records kept by multiple agencies, notably Eurostat, OECD, ECLAC, IMF, and the World Bank. We have drawn on all these sources to provide as complete a data set as feasible. The Addendum provides details on the specific sources for every country and year, covering 142 countries over 1995-2020 (Table A1).

> 21 We acknowledge that these data may be subject to measurement error, especially in many low-income countries. Another indicator of the severity of the pandemic is the change in GDP during the pandemic, but this is endogenous to the SP efforts in response.

13
<!-- Start of picture text --> Panel C. Per capita<br>600<br>500<br>400<br>300<br>200<br>100<br>0<br>US$<br>Jamaica Barbados El Salvador Dom. Rep. Haiti Antigua Guatemala Mexico Honduras Trinidad Ecuador St. Kitts Nicaragua Costa Rica Colombia Belize Dominica Suriname Paraguay Peru St. Vincent Panama Argentina Bolivia Brazil St. Lucia Uruguay Chile<br><!-- End of picture text --> Source: Balance of Payments (macroeconomic) data.

**<u>Table 1. Household Surveys Used in the Paper</u>**

|**Country**|**Year**|**Survey**|
|---|---|---|
|Bolivia|2002|Encuesta de Hogares, MECOVI|
|Ecuador|2004|Sistema Integrado de Encuestas de Hogares, INEC|
|El Salvador|2000|Encuesta de Hogares de Propósitos Múltiples, MECOVI|
|Guatemala|2000|Encuesta Nacional sobre Condiciones de Vida (ENCOVI)|
|Haiti|2001|Les Conditions de Vie en Haiti, IHSI/Fafo/PNUD|
|Honduras|2002|Encuesta Permanente de Hogares de Propósitos Múltiples, MECOVI|
|Mexico|2002|Encuesta Nacional de Ingresos y Gastos de los Hogares, INEGI|
|Nicaragua|2001|Encuesta Nacional de Hogares sobre Medición de Nivel de Vida, INEC|
|Paraguay|2003|Encuesta Permanente de Hogares, Dirección General de Estadística|
|Peru|2002|Encuesta Nacional de Hogares, INEI|
|Rep. Dominicana|2004|Encuesta Nacional sobre Condiciones de Vida(ENCOVI)|

One natural question that may arise at this stage regards the degree of consistency between the BOP based data and the survey based data. This is important in this context because significant and non-systematic discrepancies between the two data sources would raise a number of questions regarding the reliability of the remittances data and therefore of the results. In practice, however, we find that while there are significant differences between BOP and household survey estimates of remittances flows, those differences are at least systematic – i.e. the ordering of countries on the basis of remittances flows relative to GDP is very similar regardless of the data source. Indeed, Acosta et al. (2006) find that there is a stable relationship across countries between the two sources of data, with BOP based estimates surpassing household survey data estimates by about 73 percent (with an R2 of 0.8 obtained for the corresponding regression line linking the two series). That is, household survey based estimates of remittances flows (as a percent of household income) are about 40 percent lower than BOP based estimates (as a percent of GDP). While in principle these differences are likely to be motivated by reasons similar to those behind the existing 8
_Table 1: Summary Statistics_

|||R|ural|||Ur|ban||
|---|---|---|---|---|---|---|---|---|
||Fe|male|M|ale|Fe|male|M|ale|
||Mean|S.D.|Mean|S.D.|Mean|S.D.|Mean|FS.D.|
|Share of housework in individual's workr|0.679|0.29|0.202|0.251|0.720|0.35|_0.353_|0.41|
|-=I if individual does housework<br>,,|1.000|--||--||-|||
|Share of self-employment in individual's work|0.279|0.28|0.561|0.393|0.157|0.28|0.311|0.41|
|=1 if individual works in self-employment<br>_|0.600|-|0.709|-|0.289|-|0.374|-|
|Share of wage work in individual's work|0.004||0.237||0.123||0.336||
|=1 ifindividualdoes wgework|0.075|-*|0.317|--|0.172|--|0.356|--|
|All work (housework, self- & we<br>e|58.12|28.31|48.59|24.34|49.712|28.63|42.889|30.83|
|=1 if individual is household head|0.091|-|0.702|-|0.132|--|0.589|--|
|Age in 1994'' (in years)<br>B_r__|38.946|16.73|38.47|16.12|39.33|17.15|40.088|18.17|
|=1 if individual is single|0.191|-|0.277|--|0.333|-|0.382|--|
|=1 if household is of native origin|0.369|--|0.382|--|0.107|--|0.101||
|Education (Years)<br>___________|4.116|3.67|5.853|3.622|8.164|4.17|8.889|3.73|
|Value of durable assets (real value)|824.41|1654.6|850.91|<br>1862.7|4616.2|9025.5|4450.1|7505.0|
|Value of agricultural assets (real value)|8649.2|18700|9923.9|23895|-|-|--|--|
|Value of urbanassets (realvalue<br>_<br>_<br>,-|--|||-,|2463.7|16410|1460.7|7934.8|
|Number of children under 4 years|0.713|0.86|0.693|0.848|0.394|0.661|0.406|0.67|
|Number of adolescent females between 5 & 14|0.75|0.92|0.699|0.898|0.528|0.79|0.525|0.79|
|Number of adult females between 15 & 60|1.59|0.94|1.36|0.822|2.102|1.23|1.763|1.09|
|Number of elderly females, over 60<br>|0.221|0.42|0.162|0.368|0.257|0.45|0.197|0.40|
|Wages (cluster median)<br>|52.41|29.40|51.47|28.29|94.49|83.00|126.88|89.84|
|Female unemployment rate<sup>26 </sup>(cluster mean)<br>|0.942|0.08|0.944|0.076|-|--|--|--|
|Male unemployment rate (cluster meanj|0.678|0.20|0.677|0.205|--||||
|=1if child was sick in household|0.242|-|0.242|--|0.130||0.134||
|=1 ifadult was sickinhousehold<sup>2 7</sup><br>|0.392||0.355||0.407||0.415||
|r=<br>if household source of energy is fuelwood/coal|0.780||0.779||0.079||0.084||
|<br>Duration of public water supply (minutes per day)|508.3|641.3|541.92|650.64|741.02|559.13|717.70|562.99|
|<br>=1 if water source is outside the household.<br>\|0.490|-|0.466|--|0.035|--|0.038|--|
|=1 if department is Lima|0.000|-|0.000||0.407||0.423||
|=1if department is Costa|0.229||0.211||0.251||0.218||
|=1if department is Selva|0.234|.,|0.232||0.153|_|0.174||
|=1 if department is Sierra|0.537||0.557||0.189||0.185||

~~~~~~~~~~~~~~~~~~~~~~~_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ I _ _ _ _ _ _ _ __ _ _ _ _ _ _ _ _ _ _

> 23 In reference to 7 days before the survey.

> **24** Hours per week.

> 25 The sample is restricted to prime age adults (between 15 and 60 years of age).

> 26 The proportion of those working less than 20 hours per week in all prime age adults of that sex in the cluster. 27 In reference to 30 days before the survey

31
Policy Research Working Paper 6935

# **Abstract**

The relationship between income inequality and crime has attracted the interest of many researchers, but little convincing evidence exists on the causal effect of inequality on crime in developing countries. This paper estimates this effect in a unique context: Mexico’s Drug War. The analysis takes advantage of a unique data set containing inequality and crime statistics for more than 2,000 Mexican municipalities covering a period of 20 years. Using an instrumental variable for inequality that tackles problems of reverse causality and omitted variable bias, this paper finds that an increment of one point in the Gini coefficient translates into an increase of more than 10 drug-related homicides per 100,000 inhabitants between 2006 and 2010. There are no significant effects before 2005. The fact that the effect was found during Mexico’s Drug War and not before is likely because the cost of crime decreased with the proliferation of gangs (facilitating access to knowledge and logistics, lowering the marginal cost of criminal behavior), which, combined with rising inequality, increased the expected net benefit from criminal acts after 2005.

This paper is a product of the Poverty Reduction and Economic Management Unit, Latin America and the Caribbean Region. 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 author may be contacted at crodriguezc@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
- World Bank. 2006. _World Development Report 2006: Equity and Development_ . Washington, DC: World Bank and Oxford University Press.

- World Bank, 2012. _World Development Indicators_ . Available at: <u>http://data.worldbank.org/datacatalog/world-development-indicators</u>

- World Bank, 2013. _World Development Indicators_ . Available at: <u>http://data.worldbank.org/datacatalog/world-development-indicators</u>

- World Bank, 2013. _ASPIRE. The 2011 Atlas of Social Protection: Indicators of Resilience and Equity_ , Available at <u>http://go.worldbank.org/PG2N7P0Z80.</u>

19
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substantially smaller.

# **5 Concluding Remarks**

This paper examines whether the nature of the spatial transformation affects the rate of poverty reduction, using cross-country panel data for developing countries (including countries in Sub-Saharan Africa). It is found that agglomeration in mega cities is on average associated with faster growth and higher income inequality, while diversification into rural nonfarm and secondary town activities typically facilitates a more inclusive but slower, growth process. Secondary towns are also where most of the urban poor live and where access to basic infrastructure services is lowest (compared with the metropoles) (Ferré, Ferreira and Lanjouw, 2012). Growth promoting interventions that enable poor people to access this growth and basic infrastructure services more directly are thus also more likely to lift more of them out of poverty, than when the benefits of growth have to spatially trickle down from the metropoles.

Joint evaluation of the trade-offs between these two counteracting forces (higher/lower average income growth and more unequal/equal income distribution) suggests indeed that migration out of agriculture into the rural economy and secondary towns is substantially more poverty reducing than rapid metropolitization. In effect, no statistical association could be established between metropolitization and poverty reduction. The empirical regularities suggest that the nature of the spatial transformation matters for the rate of economic growth and poverty reduction observed during the spatial and structural transformation. When fostering overall economic growth is taken as key target, the balance of public investment and policy choice should be shifted in favor of more rapid urbanization and mega city development. However, when rapid poverty reduction is the primary objective, more attention should be given to fostering rural diversification and secondary town development.

While more investigation is clearly needed to firmly establish causality, the empirical regularity with which these relations have been observed in this dataset, and their robustness against a series of alternative hypotheses, measurement issues and non-linearity, underscore 23
Table C.3 Finmncial Situation, St. Alphonse 1988-1989 Nominal Zaires <!-- Start of picture text --> 1988 1989 l)<br>. ........ ...................... I--------...............<br>X of Total Exp.1 X of Total Exp.<br>OPERATING REVENUE ...................... I----------------------<br>Insurance premium 88,090 4.2K1 77,625 2.2X<br>Member episodes 279,300 13.3X1 303,225 8.7X<br>Non-mesbers firat vfait 1,158,950 55.1X12,017,080 57.6X<br>Non-m mbers other visits 823,860 39.2X11,470,525 42.0%<br>CPN & CPS 110,600 5.3X1 469,350 13.4X<br>Sale of drugs C*) 544.080 25.9K1 171,675 4.9%<br>Laboratory (+) 265,960 12.6X1 579.150 16.5X<br>Sale of foodstuffs 163,580 7.8X1 195,150 5.6X<br>Other 33,710 1.6K1 33,000 0.9X<br>TOTAL: 3,468,130 164.9%15,316,780 151.7X<br>OPERATING EXPENSES |<br>Personnel 832,426 39.6X 11,962,990 56.0X<br>Drugs and med. supplies 977,856 46.5X 11,215,200 34.7%<br>Supplies, util., mint. 177,121 8.4X 322,043 9.2X<br>Purchase foodstuffs 34,475 1 6K 4 500 0.1X<br>Other 81,785 3.9X I 0 0.0C<br>---------....--- 1-------------<br>TOTAL: 2,103,663 100.0% 13,504.732 100.0X<br>BALANCE: 1,364,467 64.9K11,812,048 51.7X<br><!-- End of picture text -->

- 1989 fIgures extrapolated based on 8 months of actual data A* fter Nov. 1988 **reverse** from sales of drugs drops sharply. This my Indicate a chnge in pa wAnt system

- (more drugs included per episode) Laboratory receIpts recorded only for May through Dec. in 1988

- **Exchange** rates whd Inflation rates for each year are found in Amex III

large amount to the total revenue of the health center in either year Examining revenue as a percentage of total expenses, premiums covered 4.2% of expenses in 1988, and only 2.2% in 1989. Member co-payments covered another 13.3% of expenses in 1988 (8.7% in 1989). In both years, nearly all of health center expenses were covered by consultation fees paid by nonmembers. Other services (sale of drugs, laboratory, preventive care, etc.) contributed to the positive margin.

# 4. Udlization

Table C.2 showed member and non-member utilization for curative care cases, and combined utilization for preventive care. Member episodes accounted for 13.7% of all new cases of illness in 1988. This share dropped to 10.3% in 1989.

Unfortunately, follow-up visits for members are not recorded by the health center, so it is not possible to evaluate from its utilization data whether payment by episode encourages members to come back to the health center more frequently than non-members. For nonmembers, from 1988 to 1989 the number of visits per episode of illness increased dramatically from 2.9 visits per episode to 3.6. Changes in the relative price of follow-up visits compared to the initial visit may explain some of this difference (the relative price of follow-up visits 39
_Rural-urban gradient in Latin America and the Caribbean_ _page_ 11

# **Table 2.  Proportion of total population, by population density and remoteness**

||**P**|**opulati**|**on den**|**sity pe**|**r squar**|**e**|||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||||**kilom**|**eter**|||||||||
||**< 20**||**< 50**||**< 100**||**< 150**||**< 200**||**< 500**||
||**All**|**Remote**|**All**|**Remote**|**All**|**Remote**|**All**|**Remote**|**All**|**Remote**|**All**|**Remote**|
|Argentina|26%|<br>25%|37%|36%|42%|41%|46%|44%|<br>48%|<br>45%|54%|48%|
|Bolivia|33%|<br>33%|43%|42%|54%|51%|56%|53%|<br>58%|<br>55%|93%|80%|
|Brazil|18%|<br>17%|34%|33%|47%|44%|54%|49%|<br>57%|<br>51%|68%|56%|
|Chile|15%|<br>14%|27%|25%|39%|34%|43%|37%|<br>47%|<br>38%|52%|41%|
|Colombia|7%|<br>7%|21%|20%|35%|32%|43%|39%|<br>46%|<br>41%|59%|47%|
|Costa Rica|4%|<br>4%|23%|23%|44%|43%|47%|44%|<br>50%|<br>47%|60%|50%|
|Cuba|1%|<br>1%|11%|10%|36%|29%|54%|41%|<br>59%|<br>44%|80%|50%|
|Ecuador|7%|<br>7%|20%|18%|35%|31%|43%|36%|<br>47%|<br>38%|61%|42%|
|Guatemala|3%|<br>3%|7%|7%|21%|20%|35%|33%|<br>47%|<br>43%|74%|61%|
|Guyana|46%|<br>46%|58%|56%|59%|58%|62%|60%|<br>67%|<br>65%|100%|95%|
|Honduras|5%|<br>5%|22%|21%|41%|39%|52%|48%|<br>56%|<br>51%|64%|57%|
|Mexico|8%|<br>8%|19%|18%|34%|31%|43%|38%|<br>49%|<br>42%|62%|49%|
|Nicaragua|12%|<br>12%|26%|26%|47%|44%|56%|51%|<br>62%|<br>56%|75%|60%|
|Paraguay|20%|<br>20%|35%|35%|42%|42%|47%|46%|<br>51%|<br>50%|57%|53%|
|<br>Peru|17%|<br>17%|32%|32%|43%|42%|48%|46%|<br>52%|<br>48%|59%|52%|
|Panama|14%|<br>14%|32%|32%|44%|44%|55%|54%|<br>60%|<br>59%|84%|73%|
|Uruguay|40%|<br>40%|45%|45%|46%|46%|59%|58%|<br>59%|<br>58%|60%|58%|
|Venezuela|11%|<br>10%|23%|22%|34%|31%|39%|34%|<br>44%|<br>37%|62%|45%|
|_LCR Totals_|_13%_|<br>_13%_|_26%_|_25%_|_39%_|_36%_|_46%_|_42%_|<br>_50%_|<br>_45%_|_64%_|_52%_|

Source: Population and density are from CIESIN Gridded Population of the World 3. Notes: 1) LCR totals include all Latin American and Caribbean countries, including those that do not appear in the table.

2) There were a small number of coastal gridcells that were missing country codes, and were therefore omitted from the above table.

3) The denominator for the percentage was total population as given by the tabulated cells, and excluding those few cells with omitted country codes.

- 4)  "Remote" means that the gridcell is located an hour or greater away from a city of 100,000 people.