# What we should do when the result is only significant to one country but the whole sample?

In a staggered Difference-in-Differences setting, Dasgupta, 2019 has a formula for such a static setting is

$$Y_{it}$$ = $$\alpha$$ + $$\beta$$ $$pt_{kt}$$ + $$\deltaX_{ikt}$$ + $$\theta_t$$ + $$\gamma_i$$ +$$\epsilon_{it}$$ (1)

where $$i$$, $$k$$, and $$t$$ index firms, countries, and years respectively. $$X_{ikt}$$ is a vector of the different firm, country, and industry control, while $$\gamma$$ and $$\theta$$ are firm and year fixed effects.

When replicating table 3 in his paper, I focus on columns (2) and (6). In these two columns, I have the same control variables sets, fixed effects sets, but only difference in sample. While columns (2) controlled for the whole sample but column (6) controlled for non-US sample.

As we can see from the Table, the coefficients of $$pt_{kt}$$ tell us the effect of laws stronger in US countries but weaker in non-US countries. The number of observations for spec (2) is 194,000 and 154,000 for spec (6). So, I need to address this issue

What I tried is that I add US_dummy equalling 1 to specification (2) if this firm is in the US to the equation. But I faced the omitted issue because the information in US_dummy is carried by the year fixed effect already.

That said, I simply cannot solve the weakened results in non-US sample problem by using the US dummy in all regressions. Can you suggest me any other way to deal with this issue?