Traducción y gramática: fenómenos de cohesión

Table 5.1 presents the descriptive statistics of predictors by expenditure quartiles. It shows that weighted averages of the variables Marital and Chinese increase over the quartiles, while

Child15, Madult, Fadult, Elderly, Migrant, Malays, Western, Northern and Eastern region

fall with increasing household expenditure per capita. As household size decreases with the increasing household expenditure per capita, the total years of education of household

members fall over the quartiles.6 Further analysis on the types of dwelling indicates that urban dwellers tend to move from living in squatters or rented homes to owning houses over the quartiles. No dwellers are reported to be living in squatter areas at the top mean expenditure group. These distributions meet a priori expectations. In addition, the decreasing number of children, male adult, female adult and elderly households with increasing household

expenditure per capita reflects the emergence of the nuclear family in higher income households in urban areas of Malaysia.

Table 5.1 Descriptive data by expenditure quartiles (Weighted mean) Variables Bottom mean Lower middle

mean Upper middle mean Top mean Age_hh 46.24 (12.48) 44.46 (13.42) 44.83 (13.47) 42.78 (14.97) Household size 5.76 (2.02) 4.38 (1.87) 3.68 (1.68) 2.80 (1.68) Sex 0.12 (0.32) 0.14 (0.35) 0.17 (0.38) 0.15 (0.36) Marital 0.12 (0.33) 0.17 (0.38) 0.19 (0.39) 0.35 (0.48) Educ 25.77 (15.81) 27.52 (16.17) 26.46 (14.95) 24.40 (13.98) Industry 0.34 (0.47) 0.32 (0.47) 0.32 (0.47) 0.35 (0.48) Ownership 0.41 (0.49) 0.44 (0.49) 0.34 (0.47) 0.43 (0.49) Child15 2.52 (1.74) 1.43 (1.41) 1.00 (1.13) 0.55 (0.98) Madult 1.36 (1.08) 1.20 (0.92) 1.11 (0.89) 0.94 (0.70) Fadult 1.38 (0.81) 1.29 (0.96) 1.16 (0.85) 0.92 (0.83) Elderly 0.50 (0.76) 0.46 (0.76) 0.41 (0.71) 0.39 (0.71) Migrant 0.05 (0.23) 0.03 (0.18) 0.03 (0.17) 0.00 (0.06) Malays 0.55 (0.49) 0.57 (0.49) 0.48 (0.50) 0.41 (0.49) Chinese 0.19 (0.39) 0.22 (0.42) 0.35 (0.48) 0.49 (0.50) Indians 0.10 (0.29) 0.12 (0.33) 0.10 (0.30) 0.07 (0.25) Western 0.14 (0.35) 0.11 (0.31) 0.07 (0.25) 0.04 (0.19) Northern 0.20 (0.40) 0.25 (0.44) 0.19 (0.39) 0.17 (0.38) Eastern 0.23 (0.42) 0.13 (0.33) 0.11 (0.31) 0.10 (0.29) Squatter 0.04 (0.19) 0.01 (0.12) 0.01 (0.08) 0.00 (0.04)

Note. Standard deviation is in parenthesis.

6 _{Household head education levels on the average are 7.46, 9.08, 9.69 and 10.95 years for the respective bottom,} lower, upper middle and top mean household expenditure per capita. This proves that fewer higher educated

5.3.2 Determinants of poverty

The estimates of the logistic regression are shown in Table 5.2. In general, the logit model fitted the data well. The chi-square test strongly rejects the hypothesis of no explanatory power and the model correctly predicted 96.5 percent of the observations. Furthermore, Educ,

Child15, Madults, Fadults, Elderly, Migrant, Malay, Western, and Eastern are statistically

significant and the signs on the parameter estimates support expectations. Chinese urban dwellers experience a low probability of falling into poverty, which is well supported by the observations in Table 5.1. However, this variable is found not to be significant at the tested level of significance and poverty line.

Table 5.2 Logistic model (Poverty Line RM 143.80)

Variables Estimated coefficient Marginal effect

Constant** _{-4.719 (1.67) -} Age* -0.126 (0.073) -0.001 (0.000) Sqage* 0.001 (0.000) 0.0000 (0.000) Educ** -0.092 (0.015) -0.0009 (0.000) Sex 0.223 (0.682) 0.0023 (0.008) Child15** 0.641 (0.07) 0.006 (0.001) Madults** 1.359 (0.169) 0.013 (0.003) Fadults** 0.988 (0.197) 0.009 (0.002) Elderly** _{0.943 (0.267) 0.009 (0.003)} Ownership -0.188 (0.30) -0.002 (0.003) Squatter 0.744 (0.542) 0.01 (0.010) Marital -0.746 (0.622) -0.006 (0.004) Industry -0.061 (0.271) -0.0006 (0.002) Migrant** 2.061 (0.525) 0.055 (0.030) Malay** 0.928 (0.468) 0.0089 (0.005) Chinese -0.556 (0.598) -0.005 (0.004) Indian 0.196 (0.874) 0.002 (0.009) Western** 1.266 (0.421) 0.0192 (0.009) Northern 0.634 (0.479) 0.0071 (0.006) Eastern** 0.964 (0.450) 0.011 (0.007) No. of observations 2,403 LR statistic (χ2_{) 267.52} Degrees of freedom 19 Log likelihood -256.63 McFadden R2 _0.343 % Predicted right 0.96

Note. Marginal effect is evaluated at the mean value of predictor variables. For dummy variables, marginal effect

is P|1-P|0. Standard errors are in parentheses. * _{p< .10.} ** _{p< .05}

The results show education is an important determinant, which supports the findings of most previous research (see Datt & Jolliffe, 2005; Grootaert, 1997; Johari & Kiong, 1991; Onn, 1989; Rodriguez & Smith, 1994; Serumaga-Zake & Naude, 2002; Thompson & McDowell, 1994). Additional insight can be obtained through analysis of the marginal effects calculated as the partial derivatives of the non-linear probability function, evaluated at each variable’s sample mean (Greene, 2003). An increase of a year of formal education of the members of the

household reduces the probability of a household falling into poverty by 0.09 percent. The results also show that a higher number of children under 15 years of age in the household increase the probability of the household falling into poverty. A large number of children in the household are generally found to be associated with poverty in most studies across the developing world.

A high number of adults in the household is expected to increase the income of the household through paid employment, thus reducing the incidence of poverty. On the contrary, the results suggest that the numbers of female and male adults in the household increase the chances of the household being poor. Table 5.2 shows that an increase of one male adult in an urban household increases by 1.3 percent the chances of the household falling into poverty. The marginal effect of a male adult is much higher than a female adult. This phenomenon could best be explained by the relatively low number of earners in the household and large number of years of education of the adults. The average number of earners and household adults’ years of education from the survey is 1.5 person and 9.5 years, respectively. This indicates that the average adults in the household (above 15 years old) are mainly seeking higher education and not generating income. Thus, this increases the family dependency ratio in urban areas. With the inclusion of number of children and elderly in households, this trend suggests that the urban households’ incomes are insufficient to support the extended families. Hence, this would create a ‘temporary’ poverty until the adults seek employment.

The gender of household head is statistically insignificant, but the positive effect shows that both genders (almost) equally increase the probability of being poor thus indicating a low level of gender discrimination in urban Malaysia. This could be the result of rising gender equality awareness through public education and the work of non-government organisations (NGOs) towards female-empowerment. In line with the campaign, local governments have initiated programs to provide childcare assistance to civil servants in order to encourage women to work.

Table 5.2 shows that there is a pronounced life-cycle effect on poverty. The probability to be poor continued to decline with age for urban households. The variable Migrant displays the highest marginal effect, 5.5 percent. This supports the a priori expectation based on the observation that most migrants do not receive social benefits and are not protected by labour laws. In addition, this finding corroborates the observation by Ruppert (1999) that foreign workers in Malaysia earn less than their Malaysian counterparts. Thus, the existence of market segmentation and discrimination in the job market has increased the risk of foreign workers falling into poverty. Caution should be practised in interpreting this result. Most of

the local employers provide food and accommodation to their foreign workers. Thus, the tested poverty line which includes the minimum expenditure of food and housing might not be suitable for such analysis as the foreign workers may not need the estimated amount on food and accommodation. However, consistent positive results are shown in Table 5.3 which uses a range of poverty lines in relation to the initial poverty line.

Amongst the major races in the country, the Malay seem to face a higher risk of falling into poverty in the urban areas. The marginal effect is 0.9 percent and statistically significant. The

Chinese have a negative while the Indians display a positive relationship with poverty but

both coefficients are insignificant at the tested poverty line. Further analysis on the types of employment by household head shows that 72.6 and 73 percent of Malay and Indian, respectively are engaged as employees. The Chinese employees make up 51.6 percent while 20.6 percent of them are self-employed. The percentage of self-employed Malay and Indian are much less prominent with 10.3 and 11.4 percent, respectively. Refer to Appendix 16. Urban households living in the Western and Eastern regions are found to be at higher risk of being poor compared to other regions. Milanovic (2001) found that Penang in the Northern and Central (used as point of reference) regions displayed the highest average earnings and growth rates between 1983 and 1997 compared to other regions. This research corroborates these prior research findings. Approximately 21 percent of the urban workers are engaged in self-employment in the Northern region. The Western and Eastern regions showed a relatively lower involvement of 19 and 14 percent, respectively. Refer to Appendix 17. Therefore, with the low average earnings, the urban poor in the Western and Eastern regions would certainly face hardship, especially with the rising cost of living.

Contrary to expectation, industry status is negatively correlated with poverty, though

statistically insignificant. This possibly indicates the importance of labour-intensive activities in helping the relatively poor escape from absolute poverty. Interestingly, the results show that owning a house does not significantly reduce the probability of being poor at the initial tested poverty line. This became important only at the highest poverty lines re-estimated from the model. The results are shown in Table 5.3. Owning a house in a squatter area may have a significant impact on poverty. Further analysis of urban dwellers living in squatter areas show insignificant results at the initial poverty line but it was found to be positively significant at all the poverty lines above the initial poverty line. Lower poverty lines which encompass the minimum expenditures of housing by the poor might have under-represented the housing expenditure in the urban areas. Thus, when the poverty lines are raised, the effect of this variable has become apparent.