Chapter II: micro determinants of poverty
Table 2.6: Reduction in poverty from an increase in employment without a decrease in wages
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- Controlling for household characteristics, geographic location also has an impact on income.
- Table 2.7: Marginal percentage increase in per capita income due to geographic location
- The importance of geographic location is confirmed by wage and labor force participation regressions
- Table 2.8: Marginal impact of location on labor force participation and earnings for adult men
- Table 2.9: Variance in department wages and labor force participation: area vs individual effects
- Whole department (urban+rural)
- Finally, even after controlling for the impact of geographic location and observable household characteristics, migration is still likely to raise per capita income.
- Table 2.10: Marginal percentage increase in per capita income due to migration
Table 2.6: Reduction in poverty from an increase in employment without a decrease in wages
Source: World Bank staff using March 1999 EHPHM. 2.15. Controlling for household characteristics, geographic location also has an impact on income. Differences in per capita income remain between departments even after controlling for a wide range of household characteristics. In the regressions, the impact of geography is measured with dummy variables for all departments except Atlantida which is the reference department. In table 2.7, the coefficient –0.21 for urban areas in Copan in March 1998 means that an urban household in Copan has an expected per capita income 21 percent below an otherwise similar urban household in Atlantida. By contrast, since the coefficient for rural areas in Copan is not significant for that survey, a rural household in Copan has the same expected per capita income as an otherwise similar rural household in Atlantida. The coefficients are thus measures of how various departments fare versus Atlantida. Many of the results are as expected. Apart from Atlantida, Cortes (where San Pedro Sula is located) does well. Departments such as Comayagua, Choluteca, Intibuca, Lempira, and Yoro tend to be poorer. There are a few surprises, such as the low performance of Francisco Morazan where the capital Tegucigalpa is located. This may be due to the lack of representativity of the EPHPM data at the departmental level within urban and rural areas. This lack of representativity suggests caution in interpreting the results department by department. But the message that geography does matter even after controlling for observable household characteristics remains valid and important. It also gives a rationale for so-called poor areas policies (e.g., investments in infrastructure), because if geographic effects matter for poverty reduction, the characteristics of the areas in which households live must be improved alongside the characteristics of the households themselves. More work is needed, however, to assess exactly which types of poor areas policies to adopt. Table 2.7: Marginal percentage increase in per capita income due to geographic location [The excluded reference category is the department of Atlantida]
Source: World Bank staff using EPHPM. NS means not statistically different from zero at the 10% level. Coefficients underlined are significant at the 10% level. Coefficients not underlined are significant at the 5% level. 2.16. The importance of geographic location is confirmed by wage and labor force participation regressions. To provide an additional test for the impact of geography on standards of living, we ran Heckman regressions (see annex 2, section MA.5) with a full set of geographic dummies in both the log wage and the labor force participation regressions. This was done for men aged 15 to 65 in the EPHPM surveys from March 1998, September 1998 and March 1999. Labor income includes not only wages from a principal occupation, but also earnings from a secondary occupation and from self-employment. Table 2.8 gives the geographic effect when the full sample is used (i.e., not separating urban and rural areas). There is no excluded department in the table, so that the coefficients measure the performance of a department versus the national mean (as opposed to a comparison with a reference department). Several findings stand out. First, and not surprisingly, the direction and magnitude of many of the marginal effects for individual level earnings in table 2.8 is similar to what was observed in table 2.7 for per capita household income. Moreover, some of the “surprises” observed in table 2.7 vanish in table 2.8; this is the case for Francisco Morazan for example, where expected earnings are higher than nationally. Second, in many instances, the impact of location on labor force participation has a sign opposed to the impact of location on earnings. This suggests that labor force participation is not much of a choice: in poorer departments, controlling for individual level characteristics, labor force participation is higher out of necessity6. Table 2.8: Marginal impact of location on labor force participation and earnings for adult men [There is no excluded dummy; the coefficients are estimates of differences versus the national mean]
Source: World Bank staff using EPHPM. NS means not statistically different from zero at the 10% level. Coefficients underlined are significant at the 10% level. Coefficients not underlined are significant at the 5% level. 2.17. The differences in labor force participation and wages between departments are due more to differences in the characteristics of the departments than to differences in the characteristics of the households living in the various departments. Using a methodology outlined in annex 2 (section MA.5), we tested whether differences in labor force participation and labor income between departments are due to differences in the characteristics of the individuals living in the various departments (such as education, experience, and demographics), or to differences in the characteristics of the departments themselves (which are captured by departmental dummy coefficients). Summary results in the form of the variance between departments in labor force participation and wages under various simulations are presented in table 2.9. In the March 1998 survey for example, using the full sample, the variance in labor force participation between departments when only differences in individual characteristics are taken into account is 0.73, which is much smaller than the variance of 2.73 when only differences in area characteristics are taken into account. This means that differences in area characteristics are more important than differences in individual characteristics in explaining labor force participation differentials between departments. The same holds for wages, and the results are robust to the choice of survey. This confirms the importance of geography in determining labor income, and it helps in justifying poor area policies. Note also that when both the individual and area effects are taken into account, the variance in labor force participation and earnings is even larger. This shows that as expected, the departments with good characteristics are also those whose inhabitants have good characteristics (e.g., a better education). Table 2.9: Variance in department wages and labor force participation: area vs individual effects
Source: World Bank staff using EPHPM. The numbers shown in the table are variances of differences in expected earnings and labor force participation between departments under different scenarios. The individual (area) effects scenario takes into account only the impact of differences in individual (area) characteristics between departments. The scenario with both effects takes into account both types of impacts when computing variances. See annex 2. 2.18. Finally, even after controlling for the impact of geographic location and observable household characteristics, migration is still likely to raise per capita income. The last set of variables used for the regressions for per capita income relates to migration (table 2.10). Individuals living in households where the head has migrated since his/her birth have a level of per capita income about 5 to 15 percent higher than other households. There is also an indication that migration over the last five years increases income. This is because the fact that the coefficients are not statistically significant indicates that at the place of destination, those who have migrated in the recent past do as well as those who have lived there for more than five years. Since migration typically takes place from poorer to richer areas, this suggests that the migrants are likely to do better at their place of destination than at their place of origin. More work would be needed, however, to compute the wage gains that can be expected from migration. Table 2.10: Marginal percentage increase in per capita income due to migration [The excluded reference categories are no migration since birth, or over the last five years]
Source: World Bank staff using EPHPM. NS means not statistically different from zero at the 10% level. Coefficients underlined are significant at the 10% level. Coefficients not underlined are significant at the 5% level. Directory: archive archive -> Tr-41. 4-03-05-024 Telecommunications archive -> Gary C. Kessler archive -> Tossups center of the known universe open 1998 Combined packet of Tennessee I and utc blue archive -> PriceWaterhouseCoopers Archive 148 linear feet in 98 record cartons and 4 flat boxes. Biography/History archive -> In this issue archive -> United states archive -> - archive -> The Tropical Rainfall Potential (TRaP) Technique. Part 1: Description and Examples Abstract archive -> United states archive -> Prediction from Weeks to Decades Download 281.6 Kb. Share with your friends:
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