business services - FINANCE; Personal<sup>and other services-</sup> PRIVATE. Agriculture, forestry and fishing is<sup>included in the constant term.</sup> Public services sector was not included<sup>in this analysis: see Neuman</sup> and Ziderman p.232.

11.

12.

13.

14.

15.

Two other specifications of wife's education which were tried yielded similar results: the variables<sup>representing wife's education</sup> were significant only in the primary<sup>sector, in both cases. First,</sup> wife's education was entered as a continuous<sup>variable, measured by</sup> years of schooling; the coefficient was<sup>0.0220 (t-3.38), indicating</sup> that each additional year of wife's<sup>schooling enhances husbands</sup> earnings (in the primary sector) by over<sup>2 per cent annually.</sup> Alternatively, two dummy variables<sup>for wife with 9-11 years and at</sup> least 12 years of schooling, respectively,<sup>were used (with 0-8 years</sup> of schooling as the reference group).<sup>The coefficients were, in</sup> turn, 0.0873 (t=l 19) and 0.1540 (t=2.90).

See Becker and also Finis Welch,<sup>"Comment," Journal of Political</sup> Economy (March-April 1974).

The standard deviations are similar:<sup>3.7 and 3.8. The wives of</sup> primary sector workers, too, are more<sup>educated-a mean of 9.8 years</sup> (standard deviation 3.7), compared with<sup>6.2 (3.9) for wives of</sup> secondary sector workers. The correlation<sup>coefficient between</sup> spouses' education attainment is higher<sup>in the primary sector</sup> (0.673) than the secondary (0.516).

To confirm that there is a significant<sup>difference between the</sup> WSCH.MARR term for the two segments,<sup>an earnings equation similar to</sup> regression model (2) was run jointly<sup>for the entire sample. A dummy</sup> variable PRIM was added (=1 if<sup>worker is attached to the primary</sup> sector), as well as interaction terms<sup>of PRIM with each of the</sup> worker's (own) human capital variables<sup>as well as with the</sup> characteristics of marriage variables. The coefficient<sup>on the</sup> variable PRIM.WSCH.MARR is positive (b=0.0058) and significant,<sup>at</sup> the 10 per cent level. Following<sup>the suggestion</sup><sup>**of**an anonymous</sup> referee, the composite regression was<sup>rerun without the full range</sup> of interactions between PRIM and husband's own characteristics leaving interactions with the characteristics of marriage variables only. The effect is to increase both<sup>the size of the PRIM.WSCH.MARR</sup> coefficient and its level of significance<sup>(b-0.0073, t=l.99).</sup> Regression model (2) was estimated<sup>with the alternative</sup> specifications of wife's schooling, given in note 10.<sup>Insignificant</sup> coefficients on the interaction term<sup>of wife's schooling and</sup> marriage duration were found for both<sup>the continuous WSCH variable</sup> case and for the middle level schooling<sup>interaction dummy (9-11</sup> years schooling and MARR), suggesting<sup>that it is only when wives are</sup> fairly well educated that they are able<sup>to exert a positive cross</sup> productivity effect in raising husbands'<sup>income.</sup> 14