|Observations|3709|3709|2792|1153|3709|3709|
|---|---|---|---|---|---|---|
|**C.**<br>**Upazilas not flood**|**ed in 1998, CRU based C**|**V of Rain**|||||
|CV Rain CRU|**-4.365***|**-4.569***|**-4.906***|**-12.06*****|**-9.030*****|**-11.87*****|
||(-1.739)|(-1.818)|(-1.669)|(-3.468)|(-2.648)|(-3.209)|
|Observations|1929|1929|1532|815|1929|1929|
|**D.**<br>**Upazilas flooded**|**in 1998, CRU based CV**|**of Rain**|||||
|CV Rain CRU|4.733|4.906|**12.25*****|-3.293|-1.062|3.710|
||(1.486)|(1.511)|(2.683)|(-0.754)|(-0.332)|(1.064)|
|Observations|1780|1780|1260|738|1780|1780|

Robust z-statistics in parentheses based on PSU level clusters *** p<0.01, ** p<0.05, * p<0.1 Note: The dependent variables for each column are (1) “Same Occupation as Head,” (2) “Same Sector as Head,” (3) “Both in Agriculture”, (4) “Both self-employed,” (5) “Both in the same sector and self employed,” and (6) “Both self employed in agriculture” respectively. Full estimation results are in the annex Table A3, Table A4, Table A5, and Table A6.

# **_Policy interactions_**

The three policy action variables, access to credit, safety net, and market are expected to reduce occupational choice constraints. That is, if households have access to credit, safety net, or market, households may cope with floods or local rainfall variability without resorting to diversifying to those occupations with lower but less risky income. We expect the coefficients of these indicators to be negative and find that to be true where significant.

To test the hypotheses that policy action variables, access to credit, safety-net, and market may help mitigate the effects of flood and rainfall variability risks on occupational choices, we estimate the equation (2) for the households in the non-flood prone Upazilas. Table 3 presents the relevant coefficients, their interactions, and the test of the sum to be 0 for the selected equation (2).

Access to credit and safety net have some dampening effects on some of the measures of sectoral and employment diversity attributable to flood and local rainfall variability risks. For example, in the flood prone Upazilas members of the households with access to credit or safety net are more likely to be in the same occupation as compared with households in flood prone Upazilas with no access to credit or safety net (Table 3, columns 1 and 2). Thus, access to credit or safety net is helpful to some extent to the households in the flood prone Upazilas in reducing the need to diversify into different occupation.

On the other hand, access to market may completely obliterate the households’ need to diversify into different occupations if they live in flood prone Upazilas (Table 3, column 3). The interaction between access to market and flood prone Upazilas is positive and significant and almost as big in magnitude as the coefficient of the flood prone Upazila by itself. This implies the strong positive effects of the interaction between market and flood completely cancels out the negative effects of flood on need to diversify into different occupations.

The effects of the policy action variables are similar when we consider local rainfall variability in the nonflood prone Upazilas. We have already established that local rainfall variability is only important to the households who live non-flood prone Upazilas. Thus, the analysis of policy interaction with local rainfall variability is only relevant for households in non-flood prone Upazilas.

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