1. The problem is to understand the probability model given by $$Pr(Export_i=1) = \Phi(\beta_0 + \beta_1 TransportObstacle_i + \beta_2 SkillsObstacle_i + \beta_3 X_i + \beta_4 Year2023_i)$$. 2. This is a Probit model where $\Phi$ is the cumulative distribution function (CDF) of the standard normal distribution. 3. The formula means the probability that the binary variable $Export_i$ equals 1 depends on a linear combination of explanatory variables: $TransportObstacle_i$, $SkillsObstacle_i$, $X_i$, and $Year2023_i$, each multiplied by their respective coefficients $\beta_j$. 4. The function $\Phi$ transforms the linear predictor into a probability between 0 and 1. 5. To interpret or estimate this model, one would typically use maximum likelihood estimation to find the $\beta$ coefficients. 6. The model captures how obstacles and other factors influence the likelihood of exporting. Final answer: The probability is modeled as $$Pr(Export_i=1) = \Phi(\beta_0 + \beta_1 TransportObstacle_i + \beta_2 SkillsObstacle_i + \beta_3 X_i + \beta_4 Year2023_i)$$ where $\Phi$ is the standard normal CDF.