rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 8A. Second stage regressions instrumenting the likelihood that the household receives remittances**

This table shows marginal effects from the estimations of a probit using instrumental variables. The variable being instrumented is the dummy for whether households receive remittances. The instruments used are _Real GDP per capita in US states_ (columns (1), (3), (4), (6), (7), and (9)) and _Poverty in US states_ (columns (2), (3), (5), (6), (8), and (9)) _._ The dependent variables are dummy variables for whether the household has a deposit account (columns (1)-(3)), received a loan (columns (4)-(6)) and requested a loan (columns (7)-(9)). All independent variables and instruments are defined in Table 1. Department fixed effects are estimated but not reported. Robust z-statistics are shown in brackets. *,**, and *** denote significance at 10, 5 and 1 percent, respectively.

|Variables||Deposit accoun|t||Loan receive|d||Loan reques|ted|
|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|Remittances|0.779|0.778|0.778|0.003|-0.053|-0.048|-0.030|-0.072|-0.068|
||[38.88]***|[38.47]***|[39.18]***|[0.03]|[-1.01]|[-0.85]|[-0.40]|[-1.34]|[-1.20]|
|Real per capita income|0.015|0.015|0.015|0.001|0.001|0.001|0.001|0.001|0.001|
||[4.33]***|[4.37]***|[4.36]***|[0.62]|[0.41]|[0.44]|[0.75]|[0.58]|[0.60]|
|House and land ownership|-0.004|-0.004|-0.004|0.020|0.025|0.025|0.020|0.025|0.024|
||[-0.20]|[-0.17]|[-0.17]|[1.86]*|[2.03]**|[1.98]**|[1.64]|[1.83]*|[1.79]*|
|Adults average education|0.020|0.021|0.021|0.006|0.006|0.006|0.006|0.006|0.006|
||[4.93]***|[5.12]***|[5.12]***|[3.09]***|[2.96]***|[2.97]***|[2.87]***|<br>[2.77]***|<br>[2.78]***|
|Agriculture|-0.015|-0.015|-0.015|0.012|0.013|0.013|0.015|0.016|0.015|
||[-0.57]|[-0.57]|[-0.56]|[1.07]|[1.05]|[1.05]|[1.20]|[1.18]|[1.18]|
|Electricity|0.026|0.028|0.028|0.019|0.027|0.026|0.027|0.035|0.034|
||[0.95]|[1.08]|[1.08]|[1.42]|[1.87]*|[1.78]*|[1.87]*|[2.20]**|[2.14]**|
|Returned migrant|-0.142|-0.139|-0.139|0.024|0.063|0.058|0.046|0.083|0.079|
||[-3.25]***|[-3.30]***|[-3.32]***|[0.46]|[0.97]|[0.90]|[0.73]|[1.15]|[1.08]|
|Instrument: Real GDP per capita in US states|Yes|No|Yes|Yes|No|Yes|Yes|No|Yes|
|Instrument: Poverty in US states|No|Yes|Yes|No|Yes|Yes|No|Yes|Yes|
|Observations|2315|2315|2315|2287|2287|2287|2293|2293|2293|
|Wald test chi<sup>2</sup>|1353.0|1332.0|1339.0|57.3|62.8|61.6|64.9|73.6|72.1|
|Wald test p-value|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|
|<br>Log likelihood|-1866|-1857|-1857|-1452|-1447|-1447|-1498|-1492|-1492|
|Over-identification test chi<sup>2</sup>|-|-|0.04|-|-|2.08|-|-|1.59|
|Over-identification testp-value|-|-|0.85|-|-|0.15|-|-|0.21|

28
||Commerce or management (same)|7.6%*|
|---|---|---|
||Commerce or management<br>|13.1%*|
||(different)||
|Panel D. Course|timing - section 5.04||
|Night<br>6.0%|Night|14.1%|
|Day<br>14.3%*|Day|-4.0%*|
|Panel E. Indivduals’ educ<br>|ational level - section 5.05 2||
|Stopped at upper secondary<br> <br>15.0%|||
|education level|||
|Stopped at tertiaryeducation level<br>-10%*|||
|<br>.<br>Panel F. Working c<br> <br>|onditions - section5.06<br>||
|Military<br>13.4%1|Military|-17.2%1|
|Without formal employment<br>agreement<br>2.5%|Without formal employment<br>agreement|8.3%*|
|With formal employment<br>agreement<br>11.5%|With formal employment<br>agreement|0.8%|
|Self-employed<br>0.1%|Self-employed|-0.6%|
|Employer<br>-4.2%|Employer|-12.4%|
|Panel G. Gen|der - section 5.07||
|Female<br>1.8%1|Female|-2.1%|
|Male<br>17.8%*|Male|5.8%*|
|Panel H. Age (in|years) - section 5.08||
|Under 20 years old<br>19.5%|Under 20 years old|5.8%|
|Between 20 and 30 years old<br>17.3%|Between 20 and 30 years old|4.3%|
|More than 30years old<br>73%*|More than 30years old|13%*|
|<br>.<br>Panel I. Housing|<br>area - section 5.09|.|
|Non-metropolitan<br>8.3%|Non-metropolitan|7.9%|
|Metropolitan<br>11.6%|Metropolitan|-4.7%*|
|<br> <br>Panel J. Geographi|<br>c region - section 5.10||
|North<br>14.6%|North|5.1%|
|Northeast<br>15.6%|Northeast|6.9%|
|Central-West<br>29.6%*|Central-West|-2.9%*|
|Southeast<br>4.8%*|Southeast|-0.3%*|
|South<br>5.2%*|South|3.9%|
|No. of observations<br>14,410|No. of observations|63,564|

Note: Underlined results represent omitted groups in the regression models. * Results statistically different from the omitted group (underlined) at the significance level of 10%. 1 Statistically non-significant at 10%. We used weighted LS to estimate the parameters of interest. 2 No. of observations is smaller compared to the other results because we considered only individuals who completed a upper secondary education/tertiary education. 2 Furthermore we did not consider those who were attending these courses in 2007.

5.1. Institution Type 27
Marulanda, Beatriz; Lizbeth Fajury. 1993 June. La Prevision Social para los Empleados del Sector Publico. Tomo I: analisis del censo de prevision social y propuesta de reglamentacion del proyecto de ley en lo referente a empleados publicos. Santafe de Bogota, Colombia.

Mauritius, Government _of._ 1987. _Statistical Yearbook of Mauritius._ Mazumdar Dipak. 1994. "Wages in Africa." World Bank, Africa Regional Office, Office of the Chief Economist, Washington, D.C.

McAuley. 1994. Alastair. "Social Welfare in Transition: What Happened to Russia". Transition Economics. PRD. World Bank.

Mesa-Lago, Carmelo; Maria Cruz-Saco, and Lorena Zamalloa. 1990. "Determinantes de los Costos y la Cobertura del Seguro-Seguridad Social. Una comparacion intemacional enfocada en la America Latina." _El Trimestre Economico 57(225): 27-57._ Mesa-Lago, Carmelo. 1991. _Social Security and Prospects for Equity in LatinAmerica._ Washington, D.C.: World Bank. (World Bank Discussion Papers).

-. 1991 a. _Portfolio Performance of Selected Social Security Institutes in Latin America._ Discussion Paper 139. World Bank, Washington D.C.

Min, Jae-Sung (Korea Development Institute). 1988. "The National Pension Scheme and its Economic Effects." Unpublished (October).

Ministerio da Previdencia Social. 1993. Brazil. Anuario Estatistico da Previdencia Social. AEPS 92. Brasilia. Brazil.: Ministerio da Previdencia Social. Empresa de Processamento de Dados da Previdencia Social. Dataprev.

Ministerio de Hacienda. 1991 March. Venezuela: los costos de seguridad social en el sector publico descentralizado. Venezuela: Direccion General. Ministerio de Hacienda.

Ministerio de Trabajo y Seguridad Social. Republica de Colombia. Memoria al Congreso Nacional. Colombia; 1988-1989 and 1989-1990.

Ministerio de Trabajo y Seguridad Social. 1989. Espafia. Presupuestos de la Seguridad Social. Cifras y datos ejercicio 1989. Madrid, Spain: Centro de Publicaciones MTSS.

Ministry of Economic Affairs and Development, Institute of Macroeconomic Analysis. July 1993.

Ministry of Labour. Republic of Kenya. Report of the National Social Security Fund and accounts for the period ended 31st December, 1971, 1974, 1975. Kenya; 1972, 1975, 1976.

Ministry of Social Affairs and Employment. Social Security in the Netherlands. 1990. Deventer and Boston: Kluwer Law and Taxation Publishers.

134
**Figure 2: Employment growth and informal competition relationship**

<!-- Start of picture text --> Figure 2A<br>0.6<br>0.47<br>0.4<br>0.2<br>0<br>Low informal competition High informal competition<br>-0.2 (residuals; roughly below (residuals; roughly above<br>median) median)<br>-0.4<br>-0.6<br>-0.8 -0.7<br>Figure 2B<br>0.6<br>0.41<br>0.4<br>0.2<br>0<br>-0.2 Low informal competition High informal competition<br>(residuals; roughly below 75th (residuals; roughly above 75th<br>-0.4 percentile values) percentile values)<br>-0.6<br>-0.8<br>-1<br>-1.2<br>-1.17<br>-1.4<br>group mean values)<br>Employment growth rate (residuals,<br>group mean values)<br>Employment growth rate (residuals,<br><!-- End of picture text --> Source: Author’s own calculations based on Enterprise Surveys data. Note: 1) Employment growth rate is calculated as the difference in the level of employment (final year employment minus initial year employment) divided by the simple average (mean) of employment in the final and initial year. The ratio is then divided by the number of years between the final and initial year; the figure is multiplied by 100 to convert to percentage terms (Haltiwanger method). 2) The figures are a partial scatter plot of the residuals of employment growth rate (Y axis) plotted against residuals of informal competition (X-axis). The residuals are obtained by regressing the employment and informal competition (separately) on industry fixed effects, year fixed effects, initial level of employment (logs) and (log of) GDP per capita. Informal competition variable equals the proportion of firms in the country (excluding own firm) that report competing against informal firms. 3) Group averages (mean values) of the residuals are shown. Close to the median value of the informal competition residuals is used to categorize the two groups in Figure 2A. The cut-off value used for the two groups in Figure 2B is close to the 75<sup>th</sup> percentile value of informal competition residuals.

35
# **Introduction**

The integration of the Middle East and North Africa (MENA) region’s economy into global economy through trade and investment flows is essential for the welfare, growth, and job creation prospects of the region. Triggered in part by the collapse of oil prices in the 1980’s, the share of MENA in global trade has declined sharply from about 8% in 1981 to some 2.5% in recent years, thereby becoming one of the least integrated regions of the world (figure 1). This contrasts with the positive acceleration in the share of trade experienced by other regions. For instance, in 1985, MENA’s share in world trade (4.7%) was higher than East Asia Pacific (4.2%), however, by 2003, East Asia Pacific’s share in world exports was about four times higher than that of MENA.

**Figure 1: MENA has experienced significant declines in share of world exports....**

<!-- Start of picture text --> 10<br>9<br>8<br>7<br>6<br>5<br>4<br>3<br>2<br>1<br>0<br>1960 1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002<br>MNA SSA SAS LAC EAP ECA<br><!-- End of picture text --> The dismal trade performance points to a lost opportunity in benefiting from integration with the rest of the global economy. Based on a constant market share analysis, if MENA maintained its 1985 share in world exports (which was already relatively low), it would have received some US$2 trillion in extra export revenues over the 1986-2003 period, or an annual average of some US$110 billion in export revenues. Compared to current 2
<u>Table 3: Attrition table.</u>

|||Original H|ouseholds||New Ho|useholds|
|---|---|---|---|---|---|---|
||1997 dis|tribution|2017 dis|tribution|2017 dis|tribution|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Loc. marg index|0.099|-0.052|-0.001|0.053|-0.021|0.155|
|<br>|(0.071)|(0.126)|(0.076)|(0.141)|(0.061)|(0.123)|
|Female head<sup>_†_</sup>|0.033|0.017|0.083|0.030|0.179|0.521***|
||(0.085)|(0.144)|(0.092)|(0.164)|(0.112)|(0.192)|
|Head’s age|0.020***|0.023***|0.022***|0.025***|0.008***|0.015***|
||(0.002)|(0.003)|(0.002)|(0.003)|(0.003)|(0.004)|
|Head’s yrs of school|-0.006|0.007|-0.005|0.005|0.002|0.001|
||(0.011)|(0.017)|(0.010)|(0.016)|(0.012)|(0.021)|
|Head indigenous<sup>_†_</sup>|-0.085|0.041|0.206**|0.120|0.125|-0.206|
||(0.087)|(0.158)|(0.086)|(0.157)|(0.084)|(0.169)|
|Household size|-0.31***|-0.31***|-0.37***|-0.37***|-0.02|-0.01|
||(0.016)|(0.027)|(0.020)|(0.035)|(0.017)|(0.026)|
|Assets PCA|-0.044*|-0.054|-0.011|0.020|0.015|-0.001|
||(0.024)|(0.042)|(0.022)|(0.039)|(0.022)|(0.038)|
|Dirt foor<sup>_†_</sup>|0.061|0.078|0.035|0.184|0.092|0.012|
||(0.070)|(0.126)|(0.068)|(0.118)|(0.070)|(0.120)|
|Owns Productive land<sup>_†_</sup>|0.032|-0.121|0.013|-0.165|0.109|0.011|
||(0.064)|(0.110)|(0.057)|(0.104)|(0.069)|(0.125)|
|Num children 8-17|0.056***|0.084***|0.068***|0.100***|0.042*|0.039|
||(0.019)|(0.030)|(0.022)|(0.036)|(0.024)|(0.038)|
|Treatment<sup>_†_</sup>|0.005<br>|0.138<br>|-0.032<br>|0.422<br>|0.021<br>|0.348<br>|
||(0.098)|(0.284)|(0.089)|(0.298)|(0.080)|(0.340)|
|T x Loc. marg index||0.236||-0.078||-0.245*|
|||(0.156)||(0.164)||(0.141)|
|T x Female head||0.017||0.084||-0.534**|
|||(0.179)||(0.198)||(0.234)|
|T x Head’s age||-0.005||-0.006||-0.010*|
|||(0.004)||(0.004)||(0.006)|
|T x Head’s yrs of school||-0.020||-0.016||-0.000|
|||(0.022)||(0.021)||(0.025)|
|T x Head indigenous||-0.180||0.122||0.468**|
|||(0.192)||(0.186)||(0.192)|
|T x Household size||-0.001||-0.013||-0.012|
|||(0.034)||(0.042)||(0.034)|
|T x Assets PCA||0.015||-0.050||0.019|
|||(0.052)||(0.047)||(0.046)|
|T x Dirt foor||-0.025||-0.221||0.116|
|||(0.152)||(0.143)||(0.147)|
|T x Owns productive land||0.250*||0.281**||0.144|
|||(0.134)||(0.123)||(0.149)|
|T x Num children 8-17||-0.042||-0.051||0.011|
|||(0.039)||(0.045)||(0.048)|
|Constant|1.408***|1.329***|1.268***|0.976***|-0.101|-0.286|
||(0.150)|(0.223)|(0.139)|(0.240)|(0.173)|(0.270)|
|T x interactions (p-value)||0.678||0.331||0.075|
|Observations|12,163|12,163|9,635|9,635|6,905|6,905|
|Mean|0.641|0.641|0.541|0.541|0.607|0.607|

This table shows the relationship between attrition, based on the capability to find the original households from the 1997 ENCASEH sample, and household and locality characteristics measured at baseline with 1997 ENCASEH data (columns 1 to 4). Columns 3 and 4 only include households in localities sampled in 2017 ENCEL. In columns 5 and 6 attrition is defined based on the capability to find young members who formed their own household. Standard errors clustered at the locality level.<sup>_†_</sup> indicates a dummy variable. Asterisks indicate significance at the ***1%, **5%, and *10% level.

13
were the result of sampling variation in the original randomization rather than differential sample attrition.<sup>17</sup> The attrition rate from the experiment is 17%. Of 22 participants that dropped out of the sample, 10 were not found, 9 died, and 3 of those who were found refused to be interviewed.<sup>18</sup> In addition, treatment status is not a significant predictor of the overall probability of attrition or for any of the reasons for attrition. And, with just 4 exceptions out of 57, the means of individual variables are not significantly different between the group that dropped out and the group that stayed in the sample, even when we stratify by treatment and control.<sup>19</sup> Hence, in terms of measured variables, there appears to be no selective attrition and the remaining sample is representative of the original sample.

# **3.2 Non-Stunted Comparison Sample**

We found and interviewed 65 children out of the 84 children originally surveyed with an implied attrition rate of 23%, which is slightly higher than that for the experimental sample. In the baseline samples, 9 out of 19 characteristics are statistically significantly different between stunted and non-stunted. As expected, the non-stunted were less disadvantaged.<sup>20</sup> Non-stunted children have taller mothers with higher Picture Peabody Vocabulary Test (PPVT) scores and have higher birth weight, larger head circumference, and higher initial developmental scores.

Attrition from the non-stunted sample appears to be selective.<sup>21</sup> Mothers in the attrition group are older, better educated, and perform better on the PPVT than mothers who do not attrite. In addition, children in the attrition group lived in homes with more verbal

> 17See Table 15.

> 18See Table 16.

> 19See Table 17.

> 20Table 19 in the Appendix shows the differences in baseline and 7 years characteristics for the stunted and non-stunted samples. The top panel only includes the baseline variables, where the non-stunted group only consists of 32 people, while the bottom panel also includes variables at 7 years, when the additional 52 non-stunted children were added.

> 21See Table 18.

8
25

# **Control Variables**

We included a set of control variables to avoid bias due to omitted variables.<sup>26</sup> Most importantly, we include information on the democracy level of the country in the year before observation. As shown by Gurr (1974), Sanhueza (1999), and Gates et al. (2006), countries with intermediate levels of democracy are most likely to experience regime changes in any direction. As shown below, there is also a tendency for interventions to occur in these unstable regimes. To account for this, we include _Polity2 lagged_ , the Polity2 level for the year before the year of observation, and _Polity2 lagged squared_ to allow modeling a curvilinear relationship between the initial democracy level and the odds of democratization.

There is potential autocorrelation between instances of (incremental) democratization. To account for this statistical dependence, a variable called _Proximity of democratization_ was included in the model. The variable is a decaying function of the number of years since the previous democratization in the country; _prc=2_<sup>_(-years/2_)</sup> where 2 is the half-life measured in years (see Hegre et al., 2001).

Modernization theory suggests that a high level of development is a prerequisite for democracy (Lipset; 1959). Economic indicators of development have two important shortcomings; they do not account for possible internal inequalities and they do not capture non-economic aspects of development that influence peoples’ quality of life. Hence, we follow Urdal (2005: 424f.) in using as our measure of development the Infant Morality Rate (IMR) – the share of live-born children that die before the age of one year. GDP per capita and IMR are highly correlated, with a low value on IMR indicating a high level of development. The IMR data were collected by Urdal from the _World Population Prospects_ (UN, 1999) and the _Demographic Yearbook_ (UN, annual). The variable was log transformed in order to reduce its skewness.

Social capital has been put forward as a condition for democratization (Putnam, 1993). We follow Paxton (2002) and Wiik (2002) in operationalizing social capital as the number of memberships in international non-governmental organizations. Ideally, we would have preferred to use data on memberships in

> 26 Religion has also been found to influence democratization (Hadenius, 1992; Wiik, 2002). We originally included religion in our model, using data from _The World Factbook_ (CIA, 2002) and Hadenius (1992), failed find any strong relationship, and subsequently excluded it.
_The Global Information and Communications Technology Industry: Where Vietnam Fits in GVCs_ late 1990s and early 2000s (see below for description of key pieces of legislation). From 2009 to 2013, exports grew at an annual rate of 89 percent, with exports accounting for 80% of ICT hardware production. Export of final ICT goods grew most rapidly, overtaking intermediate goods in 2010. On the import side, final and intermediate goods imports grew at rates of 35 percent and 47 percent from 2009 to 2013. Vietnam’s trade deficit in intermediate goods has grown considerably in recent years, reaching $9.8 billion in 2013 (see Table 5), reflecting Vietnam’s emerging role in the ICT GVC as a hardware assembly hub. This role is also reflected in soaring final goods exports, which reached $15.7 billion surplus in 2013.

**Table 5. Vietnam Hardware Trade Balance, 2009-2013, USD millions**

||2009|2010|2011|2012|2013|
|---|---|---|---|---|---|
|**Intermediate Goods**|-$1,416|-$1,230|-$2,950|-$6,052|-$9,839|
|**Final Goods**|-$2,262|-$982|$2,853|$8,185|$15,746|
|**Total**|-$3,678|-$2,211|-$97|$2,133|$5,907|

_Source:_ UN Comtrade A closer look at the trade statistics reveals that Vietnam’s hardware trade is concentrated in a few narrow product groups (See Figure 2). Eighty-one percent of imports are in intermediate electronic components such as integrated circuits and LEDs, while final goods imports are concentrated in communications equipment such the base stations needed for rapid expansion of Vietnam’s mobile communications infrastructure (China’s Huawei, a leader in low cost mobile telephony infrastructure for developing markets, is a major vendor). Exports are even more concentrated, with 75% of exports in communications equipment, mainly mobile handsets, which account for 88% of communications equipment exports. But Vietnam’s ICT hardware exports are even more concentrated than these figures suggest. According to Vietnam’s General Statistics Office, Samsung Electronics Vietnam (SEV) is responsible for 98 percent of the country’s mobile handset and component exports. Other product groups that are important in Vietnam’s ICT goods export basket are computers and storage devices (12 percent ICT hardware exports) and automotive electronics (6 percent). Vietnam is also an important producer and exporter of automotive wire harnesses, with $1.9 million in exports in 2013.

Trade statistics also indicate that Vietnam is well integrated into regional value chains, with most intermediate goods, particularly electronic components, imported from neighboring China, Korea, Singapore, Japan, Chinese Taipei, Hong Kong SAR, China, and Thailand. In fact, 72 percent of intermediate ICT goods come from these seven countries (see Tale 6). While the top ten export destinations show that most final goods are exported to advanced market countries (USA, Europe, and Japan), export destinations are not as concentrated as sources of intermediate goods imports; the top ten destinations account for approximately 60 percent of final ICT goods exports.

12
One of these factors is actual exposure and inhalation. This element is important since indoor air quality differs greatly from outdoor air quality, and an individual's behavior determines what he/she actually inhales. In epidemiological studies (see meta-analysis in section 5), however, it is assumed that exposure is proportional to measured air quality at a specific outdoor air quality measurement station. Actual exposure will, however, differ from measured concentrations, depending on human activity patterns. Exposure-response functions incorporate the behavior of people in the particular study area and we need to assume that the behavior of people in Mexico City is similar to that of the people in the study areas where the epidemiological studies are performed. Also other factors, such as state of health, age, diet, and so forth may lead to differences in the exposure-response functions in different locations. Due to the lack of information and knowledge as to how these characteristics influence the estimated functions we have to assume there are no differences between the characteristics of people represented in the epidemiological studies and those living in Mexico City.

For exposure to PMIo, the metric we use is the annual average of the 24-hour average concentration in an area. For exposure to ozone, the metric we use is the annual average of the daily 1-hour maximum concentration in an area (see Cesar and others 2000 for more details.) Figure 4.2 summarizes population exposure in the 1995-99 reference air quality scenario we developed in the previous section. The baseline scenario for exposure can be summarized by computing population-weighted exposure for each pollutant. For PM 1o and ozone this is 64.06 pLg/m<sup>3</sup> /person and 0.114 ppm/person, respectively. The 10 percent reduction scenario would lead to a reduction of 6.41 ,ug/m<sup>3</sup> PM,W/person and 0.0114 ppm ozone/person, respectively. A 20 percent reduction would double this figure. The AQSl scenario would result in reduced exposures of 14.06 p.g/m<sup>3</sup> /person and 0.0702 ppm/person for PM1o and ozone, respectively. The AQS2 scenario would result in the reduced exposures of 29.99 jIg/m<sup>3</sup> /person and 0.0778 ppm/person for PM1 o and ozone, respectively. These results are summarized in table 4.1.

This table indicates that for PM1o, a compliance strategy aimed at achieving the air quality standard in each area and no further air quality improvement would have only a slightly higher benefit than a uniform 20 percent reduction in the annual average. Hence, an emission abatement strategy should target sources in highly polluted and populated areas of the metropolitan area.

_14_